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The Pennsylvania State University
The Graduate School
FACTORS AFFECTING AIRFIELD PAVEMENT PERFORMANCE IN THE UNITED
STATES AIR FORCE ENTERPRISE WIDE
A Thesis in
Civil Engineering
by
Matthew Bennett
2019 Matthew Bennett
Submitted in Partial Fulfillment of the Requirements
for the Degree of
Master of Science
December 2019
The thesis of Matthew Bennett was reviewed and approved by the following:
Shelley Stoffels Professor of Civil Engineering Thesis Advisor
Sukran Ilgin Guler Assistant Professor of Civil Engineering
Shihui Shen Associate Professor of Rail Transportation
Patrick Fox John A and Harriette K Shaw Professor Head of the Department of Civil and Environmental Engineering
iii
ABSTRACT
The United States Air Force is responsible for 1.7 billion square feet of concrete and
asphalt airfield pavement which requires millions of dollars to maintain and repair each year. As
funding constraints become more stringent, Air Force engineers must ensure the proper strategic
approach is taken to manage airfield pavement maintenance and repair activities. The United
States Air Force’s strategic approach is to use pavement asset management principles to prolong
the life of the airfield pavement assets and to maintain the desirable operational mission’s level of
service. One step in pavement asset management, which is often overlooked or not routinely
performed, is to provide feedback on the effectiveness of the total pavement management system
and alignment of design methods, specifications and policies with an agency’s goals. This
research provides feedback to the United States Air Force regarding its current pavement
management policies by conducting analysis on pavement distresses. Pavement distresses are a
key variable collected to assess a pavement’s condition. To assist in providing feedback, analysis
was performed to determine which airfield pavement distresses are causing the largest cumulative
reduction in pavement conditions across the entire United States Air Force enterprise. Linear
cracking, joint seal damage, large patches, shattered slabs, joint spalling, small patches, and alkali
silica reactivity are the portland cement concrete airfield pavement distresses causing the largest
summative reduction in pavement condition. Longitudinal and transverse cracking, weathering,
block cracking, and alligator cracking are the asphalt concrete airfield pavement distresses
causing the largest cumulative reduction in pavement condition. Each distress was statistically
analyzed to determine if pavement structure or climatic variables are influencing the likelihood of
each distress occurring under current policies. The distresses were analyzed independently and
the results suggest the United States Air Force’s current design and management policies are not
fully compensating for the impacts of pavement structural and climatic factors.
iv
TABLE OF CONTENTS
LIST OF FIGURES ................................................................................................................. v
LIST OF TABLES ................................................................................................................... viii
ACKNOWLEDGEMENTS ..................................................................................................... ix
CHAPTER 1 INTRODUCTION AND BACKGROUND .................................................... 1
1.1 BACKGROUND ....................................................................................................... 1 1.2 PROBLEM STATEMENT ........................................................................................ 2 1.3 RESEARCH OBJECTIVE......................................................................................... 5
CHAPTER 2 LITERATURE REVIEW ................................................................................. 6
2.1 PAVEMENT ASSET MANAGEMENT ................................................................... 6 2.2 PAVEMENT MANAGEMENT PROCESS .............................................................. 7 2.3 FACTORS AFFECTING PAVEMENT PERFORMANCE ...................................... 26 2.4 RESEARCH ON USAF AIRFIELD PAVEMENT DISTRESSES ........................... 32
CHAPTER 3 DATA COLLECTION AND ORGANIZATION ............................................ 38
CHAPTER 4 RESEARCH METHODOLOGY ..................................................................... 44
4.1 ANALYSIS OF AGGREGATED DATA ................................................................. 44 4.2 STATISTICAL ANALYSIS ...................................................................................... 45
CHAPTER 5 RESULTS AND DISCUSSION ....................................................................... 51 5.1 AGGREGATED DATA RESULTS .......................................................................... 51 5.2 STATISTCAL RESULTS ......................................................................................... 59
5.2.1 PORTLAND CEMENT CONCRETE PAVEMENT DISTRESSES ............. 61 5.2.2 ASPHALT CONCRETE PAVEMENT DISTRESSES .................................. 82
CHAPTER 6 SUMMARY AND CONCLUSIONS ............................................................... 95
6.1 FINDINGS AND RECOMMENDED INVESTIGATIONS FOR PORTLAND CEMENT CONCRETE PAVEMENTS .................................................................. 96
6.2 FINDINGS AND RECOMMENDED INVESTIGATIONS FOR ASPHALT CONCRETE PAVEMENTS .................................................................................... 100
6.3 LIMITATIONS .......................................................................................................... 103 6.4 RECOMMENDATIONS FOR FUTURE RESEARCH ............................................ 105
REFERENCES ........................................................................................................................ 107
APPENDIX A DETAILED STATISTICAL RESULTS ......................................................... 111
APPENDIX B USAF LOCALIZED MAINTENANCE ACTIONS ....................................... 182
APPENDIX C ACCROYNM LIST ......................................................................................... 184
v
LIST OF FIGURES
Figure 1-1 Generic Asset Management System Components .................................................. 3
Figure 2-1 Conceptual illustration of a pavement condition life cycle (Colorado State University, 2019) ............................................................................................................. 7
Figure 2-2 Standard Notation for Branch Identification (AFI 32-1041, 2017) ........................ 9
Figure 2-3 Standard Notation for Section Identification (AFI 32-1041, 2017) ....................... 9
Figure 2-4 PCN Subgrade Strength Categories (UFC 3-260-03, 2001) .................................. 12
Figure 2-5 Tire Pressure Limitation Code (UFC 3-260-03, 2001) .......................................... 12
Figure 2-6 Summary of PCN Code .......................................................................................... 13
Figure 2-7: Alligator Cracking Distress Severity Definitions (US Army Corps of Engineers, 2009) .............................................................................................................. 16
Figure 2-8: Example of Distress 41 Alligator Cracking (US Army Corps of Engineers, 2009) ................................................................................................................................ 16
Figure 2-9 PCI Deduct Curve for Distress 41: Alligator Cracking (ASTM D5340-12, 2012) ................................................................................................................................ 17
Figure 2-10 Initial Descriptive Rating Scale (Shahin, Darter, & Kohn, 1977) ........................ 19
Figure 2-11 Iterative Procedure to Determine Realistic Distress Deduct Values and Distress Definitions Using a Subjective Approach (Shahin, Darter, & Kohn, 1977) ...... 19
Figure 2-12 Example of a Flexible Pavement Condition Survey Data Sheet (ASTM D5340-12, 2012) .............................................................................................................. 20
Figure 2-13 Corrected Deduct Values for Flexible Airfield Pavement (ASTM D5340-12, 2012) ................................................................................................................................ 21
Figure 2-14 Calculation of Corrected PCI Value Example (ASTM D5340-12, 2012) ............ 22
Figure 2-15 Medium Severity Deduct Curve Example for PCC (Shahin, Darter, & Kohn, 1977) ................................................................................................................................ 23
Figure 2-16: Definition of Standard PCI Ratings (AFI 32-1041, 2017) .................................. 24
Figure 2-17: Standard PCI Rating Scale (Vansteenburg, 2019) .............................................. 25
Figure 2-18: PCI Color Scale Plotted on Example Airfield (Vansteenburg, 2019) ................. 25
vi
Figure 2-19 Factors Affecting Pavement Performance (Haas, 2001) ...................................... 27
Figure 2-20 Curling Stresses in a Typical PCC Slab (Pavement Interactive, 2019) ................ 29
Figure 2-21: Climate Zone Map for the US based on 2013 study (Meihaus, 2013) ................ 34
Figure 2-22: Overall Climate Zone Average Rates of Deterioration - PCC (Meihaus, 2013) ................................................................................................................................ 34
Figure 2-23: Overall Climate Zone Average Rates of Deterioration – AC (Meihaus, 2013) .. 35
Figure 2-24: AC Runway Model Based on Average Distress Behavior (Sahagun, 2014) ...... 36
Figure 2-25: PCC Runway Model Based on Average Distress Behavior (Sahagun, 2014) .... 36
Figure 5-1 ANOVA Table Example ........................................................................................ 59
Figure 5-2 Example Odds Ratios for Continuous Predictors ................................................... 60
Figure 5-3 Example Odds Ratio for Categorical Predictors .................................................... 60
Figure 5-4 Example Factorial Plot ........................................................................................... 61
Figure 5-5 Linear Cracking (US Army Corps of Engineers, 2009) ......................................... 67
Figure 5-6 Summary Statistics for Distress 63 - Linear Cracking ........................................... 69
Figure 5-7 Joint Seal Damage (US Army Corps of Engineers, 2009) ..................................... 70
Figure 5-8 Summary Statistics for Distress 67 - Joint Seal Damage ....................................... 71
Figure 5-9 Large Patch/Utility Cut (US Army Corps of Engineers, 2009) ............................. 72
Figure 5-10 Summary Statistics for Distress 67 - Large Patch/Utility Cut .............................. 73
Figure 5-11 Shattered Slab (US Army Corps of Engineers, 2009) .......................................... 74
Figure 5-12 Summary Statistics for Distress 72 - Shattered Slabs .......................................... 75
Figure 5-13 Joint Spalling (US Army Corps of Engineers, 2009) ........................................... 76
Figure 5-14 Summary Statistics for Distress 74 - Joint Spalling ............................................. 77
Figure 5-15 Small Patch (US Army Corps of Engineers, 2009) .............................................. 78
Figure 5-16 Summary Statistics for Distress 66 - Small Patches............................................. 79
Figure 5-17 Alkali Silica Reactivity (US Army Corps of Engineers, 2009) ........................... 80
vii
Figure 5-18 Summary Statistics for Distress 76 - Alkali Silica Reactivity .............................. 81
Figure 5-19 Longitudinal and Transverse Cracking (US Army Corps of Engineers, 2009).... 87
Figure 5-20 Summary Statistics for Distress 48 - Longitudinal and Transverse Cracking ...... 88
Figure 5-21 Weathering (US Army Corps of Engineers, 2009) .............................................. 89
Figure 5-22 Summary Statistics for Distress 57 - Weathering ................................................ 90
Figure 5-23 Block Cracking (US Army Corps of Engineers, 2009) ........................................ 91
Figure 5-24 Summary Statistics for Distress 43 - Block Cracking .......................................... 92
Figure 5-25 Alligator Cracking (US Army Corps of Engineers, 2009) ................................... 93
Figure 5-26 Summary Statistics for Distress 41 - Alligator Cracking ..................................... 94
viii
LIST OF TABLES
Table 2-1: Flexible Pavement Distress Types (US Army Corps of Engineers, 2009) ............. 14
Table 2-2: Rigid Pavement Distress Types (US Army Corps of Engineers, 2009) ................. 15
Table 3-1: Fields Used from PAVER Database ....................................................................... 39
Table 3-2: Climate Parameters Collected ................................................................................ 41
Table 4-1 Pavement Related Factors Used in Statistical Analysis .......................................... 47
Table 4-2 Climatic Factors Used in Statistical Analysis (U.S. Department of Transportation FHWA, 2018) .......................................................................................... 48
Table 5-1: Air Force Pavement Distresses Ranked by Cumulative PCI Deduct Values ......... 53
Table 5-2: Apron Pavement Distresses Ranked by Cumulative PCI Deduct Values .............. 54
Table 5-3: Runway Pavement Distresses Ranked by Cumulative PCI Deduct Values ........... 55
Table 5-4 Taxiway Pavement Distresses Ranked by Cumulative PCI Deduct Values ............ 56
Table 5-5 Combined Apron, Runway, and Taxiway Pavement PCI Deduct Value Sums ...... 57
Table 5-6 Combined Apron, Runway, and Taxiway Pavement PCI Deduct Sums Continued ......................................................................................................................... 58
Table 5-7 Portland Cement Concrete Distresses Analyzed ..................................................... 62
Table 5-8 Common Significant Factors in PCC Distresses ..................................................... 63
Table 5-9 Asphalt Concrete Distresses Analyzed .................................................................... 82
Table 5-10 Common Significant Factors in AC Distresses ..................................................... 83
Table 6-1 Average Years Since Major Work Analysis for PCC Distresses ............................ 97
Table 6-2 Average Years Since Major Work Analysis for AC Distresses .............................. 102
ix
ACKNOWLEDGEMENTS
I would first like to thank my fiancée and family throughout this process. They were all
great supporters and I would not have been able to complete my master’s program or thesis
without them. I would also like to thank Dr. Shelley Stoffels for her mentorship and guidance
throughout my graduate career and research effort. Dr. Shelley Stoffels made a conscious effort to
ensure I met the United States Air Force’s requirement to graduate on time while also ensuring I
gained all the knowledge required to be a successful pavement engineer. I would like to give a
special thanks Dr. Sukran Ilgin Guler and Dr. Shihui Shen for serving as members on my thesis
committee and taking their time to assist with my thesis. I understand being a committee member
is a large undertaking and the effort does not go unrecognized. I am thankful for Dr. Craig
Rutland from the Air Force Civil Engineer Center helping me identifying USAF research needs
and for providing me with this research topic. I would like to express my gratitude to George
Vansteenburg from the USACE Transportation System Center for providing me the United States
Air Force PAVER data and the training required to use the PAVER database. George
Vansteenburg’s USAF experience and pavement expertise was invaluable in the completion of
my thesis. Additionally, Lizhao Ge’s, from Penn State Library Research Data Services Statistical
Consultants, continued statistical assistance was an abundance of help and I am very grateful for
the time she spent working with me.
CHAPTER 1
INTRODUCTION AND BACKGROUND
1.1 BACKGROUND
The United States Air Force (USAF) is comprised of 11 Major Commands with a total of
183 bases worldwide, valued at more than $297 billion (Allen, 2018). With 1.7 billion square feet
of concrete and asphalt airfield pavement in the USAF inventory, the USAF requires millions of
dollars a year to maintain and repair airfield pavements (Allen, 2018). As funding constraints
become more stringent, Air Force engineers must ensure the proper strategic approach is taken to
manage airfield pavements maintenance and repair activities.
The Air Force Civil Engineer Center (AFCEC) is responsible for providing responsive,
flexible full-spectrum installation engineering services. The AFCEC's missions include facility
investment planning, design and construction, operations support, real property management,
energy support, environmental compliance and restoration, and audit assertions, acquisition and
program management (Air Force Civil Engineer Center Fact Sheets, 2013). As part of AFCEC’s
mission, the center is charged with ensuring USAF airfields are always in operational condition.
AFCEC created the Air Force Pavement Evaluation Program as one method to ensure all airfields
are capable of serving the Air Force’s mission (AFI 32-1041, 2017). The Air Force Pavement
Evaluation Program “obtains, compiles, and reports pavement strength, condition, and
performance data, including data on structural, friction, and anchor capability on all airfields with
present or potential missions” (AFI 32-1041, 2017). The data gathered by the Air Force Pavement
Evaluation Program is used by engineers to assist in making proper asset management decisions
to combat funding constraints and the requirement to keep airfield pavements operational.
2
1.2 PROBLEM STATEMENT
The USAF has over one hundred installations to maintain and keep operational. At each
of these installations there are airfield pavements that are deteriorating and require Maintenance
and Repair (M&R) activities to remain operational. With a limited set of resources, the Air Force
is required to use asset management principles as a part of the decision-making process on how to
prioritize the M&R activities to optimize the life of pavement assets. At the heart of the Air
Force’s pavement asset management system is the pavement condition data. The distresses on a
pavement section are a large factor in the calculation of a pavement’s condition. A further
understanding of pavement distresses will allow the USAF to more efficiently allocate resources
and will assist in prolonging pavements operational life. This research hopes to obtain a better
understanding of the current USAF airfield pavement distresses to assist the Air Force Civil
Engineer Center in optimizing their resources, assisting in potential updates to current design and
maintenance strategies, and focus the USAF’s M&R activities on the most predominant
distresses.
An asset management system is built around several key system components. Figure 1-1
is a flow chart displaying the system components of a generic asset management system. The
final system component is to provide performance feedback to adjust policies to ensure the
customer’s goals are met. The purpose of this research is to provide feedback to the USAF to
assist in updating or identifying areas of improvement in the current USAF policies. The
feedback is provided based on the USAF’s current PAVER data. The red arrow in Figure 1-1 is a
visual interpretation of the purpose of this research.
3
Figure 1-1 Generic Asset Management System Components (US Department of Transportation
FHWA, 1999)
4
Providing feedback to ensure policy and goals align with an assets current performance is
sometimes overlooked and is not performed. Often agencies have a difficult time determining
whether their current policies are generating the performance desired. There are often additional
constraints that affect an agency’s ability to practice proper asset management principles to
include: budget constraints, manpower capabilities, lack of resources. This research presents a
relatively simple methodology that can be adjusted to different infrastructure assets and can be
tailored to agencies other than the USAF.
This research was conducted on 102 USAF installations worldwide. The scope of the
study includes both portland cement concrete (PCC) and asphalt concrete (AC) airfield pavement
sections. Included in AC airfield pavement sections is asphalt-over-asphalt concrete (AAC) and
asphalt-over-portland-cement concrete (APC). Airfield pavement sections are the primary focus
of this study to include runways, taxiways, and aprons. The pavement section data is based on the
most recent pavement inspection per installation from the USAF’s June 2019 Roll Up PAVER
Database.
5
1.3 RESEARCH OBJECTIVE
The objective of this research is to advise the USAF on areas of improvement to their
current pavement management and design policies to meet their desired level of service. This is
accomplished by determining which airfield pavement distresses are cumulatively causing the
most negative impact on pavement condition in the USAF globally. In addition, the pavement
distresses are analyzed to determine which factors that affect pavement performance are assisting
in the occurrence of the distresses. The objective will be accomplished by analyzing the following
questions:
1. Which pavement distresses are causing the largest cumulative reduction in pavement
condition the USAF worldwide?
2. Under current policy, what climatic parameters remain correlated to pavement distress
occurrences in the USAF enterprise wide?
3. Under current policy, what pavement structural parameters remain correlated to
pavement distress occurrences in the USAF enterprise wide?
4. What improvements can be made to current USAF pavement management and design
based on the parameters that remain correlated under current USAF policies?
6
CHAPTER 2
LITERATURE REVIEW
The first portion of the literature provides a background of pavement asset management
and specifically discusses the practices the USAF follows to manage their pavement
infrastructure. After the background of pavement asset management is discussed, a detailed
description of the process and systems used to achieve asset management principles in the USAF
is presented. Following the pavement asset management section, factors that affect pavement
performance are reviewed. At the conclusion of this chapter, the existing literature associated
with USAF pavement condition data is summarized.
2.1 PAVEMENT ASSET MANAGEMENT
To assist in maintaining airfield pavements and overcoming funding constraints, the
USAF adopted an asset management policy to maintain their pavement infrastructure.
Asset management is a systematic process of maintaining, upgrading, and operating
physical assets cost-effectively. It combines engineering principles with sound business
practices and economic theory, and it provides tools to facilitate a more organized,
logical approach to decision-making Thus, asset management provides a framework for
handling both short- and long-range planning. (US Department of Transportation FHWA,
1999)
The USAF uses computer-based pavement management systems, to manage their pavement
assets efficiently. The pavement management system uses a “systematic, consistent method for
selecting M&R needs and determining priorities and the optimal time of repair by predicting
future pavement condition” (Shahin, 2005). Figure 2-1 is an image of a typical pavement asset’s
7
life-cycle. A newly constructed pavement asset starts in excellent condition and over time the
asset starts to deteriorate to a poorer condition. A point in a pavement’s life cycle is known as the
critical condition in which the pavement asset starts to deteriorate at a faster rate and the asset
becomes more expensive to rehabilitate. A pavement management system is helpful in identifying
and predicting the critical condition in a pavement’s life cycle and recommending M&R activities
that will help prevent the asset from deteriorating past its critical condition (Shahin, 2005).
Figure 2-1 Conceptual illustration of a pavement condition life cycle (Colorado State University,
2019)
2.2 PAVEMENT MANAGEMENT PROCESS
The USAF uses a pavement management system called PAVER. In addition to the
USAF, PAVER is supported by the United States Army, United States Navy, Federal Aviation
Administration, and Federal Highway Administration (Colorado State University, 2019). PAVER
8
is made up of five main steps for pavement management: pavement inventory, pavement
inspection, pavement condition prediction modeling and analysis, M&R family models, and
M&R work planning. PAVER organizes and documents an airfield’s pavement inventory and
work history and relates it to an organization’s real property data. Based on airfield inspection
data, PAVER automates the PCI computation process to determine an asset’s PCI. PAVER is
then able to develop deterioration models used to predict future PCI. Family models can be
generated to define M&R work plan parameters and costs. Finally, an M&R work plan and
budget can be created to keep pavements above the critical condition (US Army Corps of
Engineers, 2015).
In a pavement management system, pavement assets are defined as networks, branches,
and sections. The USAF typically defines a pavement network as one USAF installation that has
an airfield. An airfield pavement network inventory is broken down into branches and sections. In
airfield pavements, branches include the runways, overruns, taxiways, aprons, and shoulders.
Each branch is broken down into sections based on construction, condition, and traffic. A section
must be the same pavement type. For example, if an apron is made up of asphalt and concrete, the
apron would have to be broken into at least two sections one asphalt and one concrete. “A section
should be viewed as the smallest management unit when considering the application and selection
of M&R treatments” (Shahin, 2005). When dividing a branch into sections, there are seven
primary factors to consider: pavement structure, construction history, traffic, pavement functional
classification, drainage facilities and shoulders, conditions, and section size. When dividing a
branch into sections, the goal is to keep sections consistent in terms of pavement structure,
construction history, volume and load intensity of traffic, the rank of a branch (i.e. primary or
secondary runways), similar pavement design in terms of drainage and shoulders, pavements that
reflect similar conditions, and consistent section sizes throughout the pavement network. Each
branch and section within a pavement network have a unique identification number. Figure 2-2
9
displays standard Branch IDs for a given airfield network. For example, the Branch ID for
runway 18-36 would be RW1836. Figure 2-3 describes how Section IDs for a given branch are
established. For example, Branch ID RW1836 may have a section with a Section ID R01A1.
Figure 2-2 Standard Notation for Branch Identification (AFI 32-1041, 2017)
Figure 2-3 Standard Notation for Section Identification (AFI 32-1041, 2017)
10
For pavement inspection purposes, pavement sections are further divided into sample
units in accordance with ASTM D5340. Asphalt airfield pavement sections are typically broken
into 5,000 square foot sample units and concrete airfield pavements are typically broken into
sample units of 20 contiguous slabs (ASTM D5340-12, 2012). PAVER automates this process
and provides a recommendation for sample units to be surveyed. Verification by a pavement
expert is required to ensure the recommended sample units will develop an accurate PCI.
Inspecting every section or sample unit in a pavement branch can be a considerable effort, so a
select number of random sample units are inspected in accordance with ASTM D5340. Inspecting
pavements in accordance with ASTM D5340 provides an overall PCI with 95 percent confidence.
After sample units have been established in accordance with ASTM D5340, pavement inspection
can begin.
There are three major pavement evaluations the USAF performs on their airfield
pavements: structural, friction and PCI evaluations (AFI 32-1041, 2017). A structural inspection
uses destructive and non-nondestructive testing methods to determine the structural condition of
the pavement structure. A structural inspection includes a friction evaluation that determines the
roughness and skid resistance of a pavement surface. The USAF performs airfield structural
inspections every 8 years (AFI 32-1041, 2017). The USAF performs PCI evaluations by
conducting visual assessments of the pavement surface in accordance with ASTM standards and
they are performed on airfield pavements every 4 years (AFI 32-1041, 2017).
During a structural evaluation, the existing pavement structure is defined in terms of
materials and layer thicknesses. The USAF follows the guidance published in Air Force
Instruction (AFI) 32-1041 and ASTM standards to determine pavement structural properties.
Based on AFI 32-1041 and ASTM standards, one way the pavement structure properties are
determined is by taking core samples to determine thickness. The cores are also used to determine
the flexural strength of concrete using split-tensile tests. When core sampling is not ideal or
11
practical, non-destructive testing methods are used determine material properties. To determine
the material properties and thicknesses of the layers beneath the pavement layer, the USAF
determines the California Bearing Ratio (CBR) for flexible pavements or modulus of subgrade
reaction (k) value for rigid pavements based on destructive test methods such as dynamic cone
penetrometer (DCP). Other destructive and nondestructive tests are accomplished to determine
additional pavement properties such as moisture content, density of subgrade soils, density of
base course materials, classification of soil based on the Unified Soil Classification System, and
the quality of subgrade, subbase, and base courses (AFI 32-1041, 2017). This information is
stored in a USAF database and used to determine the airfield’s allowable gross loads (AGL) and
pavement classification numbers (PCN). Airfield AGL are not the primary focus of this research
and will not be discussed in detail. Results from the structural inspection are presented in a final
pavement evaluation report and stored in a USAF database to assist in asset management
decisions (AFI 32-1041, 2017).
The pavement classification is a result of structural inspections and will used be in this
research.
The PCN is a reporting method for weight-bearing capacity and not an evaluation
procedure. The National Imagery and Mapping Agency publishes weight bearing limits
in terms of PCN in a Flight Information Publication for civil and international use. The
intent is to provide planning information for individual flights or multiflight missions
which will avoid either overloading of pavement facilities or refused landing permission.
(UFC 3-260-03, 2001)
PCN is a number that expresses the relative load-carrying capacity of a pavement in terms of a
standard single-wheel load” (UFC 3-260-03, 2001). A PCN code is comprised of five-part code.
An example of a PCN code is 39/F/C/X/T. The first value (39) represents the PCN numerical
value which indicates the load-carrying capacity of the pavement. The second part of the code
12
could be either R or F which represents the pavement material is a rigid or flexible pavement. The
third part of the code represents the strength of the subgrade beneath the pavement section
evaluated. Figure 2-4 displays the four possible subgrade strength codes (A, B, C and D) and how
they are categorized. The fourth part represents the maximum tire pressure the pavement can
support. Figure 2-5 displays the four possible tire pressure classifications (W, X, Y, and Z) and
how they are categorized. The fifth and final part of the PCN code represents the evaluation
method used to determine the PCN number. The two codes for the evaluation method are T for
“technical evaluation” and U for “using aircraft” (UFC 3-260-03, 2001). Figure 2-6 is a visual
summary of the PCN code.
Figure 2-4 PCN Subgrade Strength Categories (UFC 3-260-03, 2001)
Figure 2-5 Tire Pressure Limitation Code (UFC 3-260-03, 2001)
13
Figure 2-6 Summary of PCN Code
Subgrade strength is based on the California Bearing Ratio (CBR) of the subgrade for
flexible pavements. Subgrade strength is based on the moduli of soil reaction, k, of the subgrade
for rigid pavements. The subgrade CBR and k values are then used to determine the PCN
numerical value.
As previously discussed, PCI evaluations are conducted by the USAF via visual
inspections. There are both manual and automated visual inspection methods. A manual visual
inspection is conducted by a technician physically walking on the airfield. Automated visual
inspection methods use vehicles to capture images of the pavement surface which is later
analyzed by a technician. The USAF does not currently use automated inspection methods, but
this research is applicable to automated inspection methods as well.
The USAF has a team centralized at Tyndall AFB, Florida called the Airfield Pavement
Evaluation (APE) team that performs the majority of the USAF pavement inspections. The APE
team follows the pavement inspection procedures established in ASTM D5340, which presents
the procedures to complete the PCI survey completely manually. Since the USAF uses PAVER,
part of the PCI survey is automated within the PAVER system.
14
One of the main goals of a pavement condition inspection is to determine a pavement
section’s PCI. PCI is a distress index widely used to portray a pavement’s condition (Shahin,
2005). A section’s PCI is based on distress type, distress severity, and distress quantity. The first
factor, distress type, is based on whether the pavement surface is asphalt concrete (AC) or
portland cement concrete (PCC). Table 2-1 and Table 2-2 depict the pavement distresses for both
AC and PCC pavement sections and the typical cause of such distress. Each distress has a number
associated with it, which is input into PAVER. For example, using Table 2-1, if the APE team
inspector came across an alligator crack in the AC pavement section, they would input a distress
code of 41 into PAVER.
Table 2-1: Flexible Pavement Distress Types (US Army Corps of Engineers, 2009)
Distress Name Distress Code Cause Alligator or Fatigue Cracking 41 Load Bleeding 42 Other Block Cracking 43 Climate Corrugation 44 Other Depression 45 Other Jet Blast Erosion 46 Other Joint Reflection Cracking 47 Climate Longitudinal and Transverse Cracking 48 Climate
Oil Spillage 49 Other Patching and Utility Cut Patch 50 Other Polished Aggregate 51 Other Raveling 52 Climate Rutting 53 Load Shoving 54 Other Slippage Cracking 55 Other Swell 56 Other Weathering 57 Climate
15
Table 2-2: Rigid Pavement Distress Types (US Army Corps of Engineers, 2009)
Distress Name Distress Code Cause Blowup 61 Climate Corner Break 62 Load Linear Cracks (Longitudinal, Transverse, and Diagonal)
63 Load
Durability (“D”) Cracking 64 Climate Joint Seal Damage 65 Climate Patching, Small 66 Other Patching, Large 67 Other Popouts 68 Other Pumping 69 Other Scaling 70 Other Settlement or Faulting 71 Other Shattered Slab 72 Load Shrinkage Crack 73 Other Spalling (Joint) 74 Other Spalling (Corner) 75 Other Alkali Silica Reaction 76 Other
The second factor considered in determining PCI is distress severity. Each distress for
both rigid and flexible pavement has definitions for three severity levels: low, medium, and high.
The US Army Corps of Engineers created a detailed, standardized manual and definitions for
determining a distress severity level to make the process as objective as possible. Figure 2-7
shows an example of the definitions of severity levels for the alligator cracking distress. Once the
distress severity is determined, it is also input into PAVER.
16
Figure 2-7: Alligator Cracking Distress Severity Definitions (US Army Corps of Engineers,
2009)
The final factor required to determine a pavement section PCI is distress quantity.
Depending on the distress type, the distress may be measured as length, surface area, depth, etc.
The manual created by the US Army Corps of Engineers states how to measure the quantity of
each distress. For example, alligator cracking is measured in square feet of surface area as seen in
Figure 2-8.
Figure 2-8: Example of Distress 41 Alligator Cracking (US Army Corps of Engineers, 2009)
17
After the distress quantity is collected, the distress quantity is converted to distress
density. The distress density is determined by dividing the distress quantity at each severity level
by the total area of the sample unit (Shahin, 2005). The result is then multiplied by 100 to convert
the density to a percent per sample unit for each distress type and severity (Shahin, 2005). The
distress type, distress severity, and distress density are combined to determine the PCI deduct
value required to calculate a pavement section’s PCI. Figure 2-9 provides an example of how a
PCI deduct value is determined. Each distress type has a PCI deduct curve with the three severity
levels plotted on the graph. The PCI deduct curves were created by the Army’s Construction
Engineering Research Lab from 1974 to 1976 for the Department of Defense (DoD) (Shahin,
Darter, & Kohn, 1977).
Figure 2-9 PCI Deduct Curve for Distress 41: Alligator Cracking (ASTM D5340-12, 2012)
The determination of the PCI deduct value was “the most difficult part of PCI
development” (Shahin, Darter, & Kohn, 1977). The PCI deduct curves are based on the measured
impact that a distress type has on a pavement’s “structural integrity and operational condition”
18
(Shahin, Darter, & Kohn, 1977). Although an analytical or theoretical determination of PCI
deduct curves is ideal, due to the complexity and large research effort required to generate
analytical and theoretical methods, the development of PCI deduct curves were created based on
a subjective approach based on experienced pavement engineers (Shahin, Darter, & Kohn, 1977).
The engineers developed initial distress, severity, and deduct value definitions and a rating scale
based on their experience. Figure 2-10 was the rating scale developed to be used by the
experienced engineers tasked in development of the PCI deduct. “The scale provides the
descriptive index needed to permit a rational subjective rating of the impact of a given distress.
For example, several experienced pavement engineers could independently rate a jointed concrete
pavement having 30 percent of its slabs containing transverse cracks which are working (i.e.,
moderately spalled) according to the scale based on their experience as to the impact of this
distress type, density or amount, and severity. If the mean of their ratings was 65, which is a
“good” condition, the deduct value for this situation would be 35 points (100-65=35) (Shahin,
Darter, & Kohn, 1977).” This process was repeated over multiple airfield, climates, pavement
designs, materials, and distress conditions. As the process was repeated, improvements were
made to the definitions and deduct values. Figure 2-11 depicts the iterative loop the experienced
pavement engineers used to determine realistic distress values and distress definitions. After
several iterations of this process, distress definitions and PCI deduction curves were published for
DoD use (Shahin, Darter, & Kohn, 1977).
19
Figure 2-10 Initial Descriptive Rating Scale (Shahin, Darter, & Kohn, 1977)
Figure 2-11 Iterative Procedure to Determine Realistic Distress Deduct Values and Distress
Definitions Using a Subjective Approach (Shahin, Darter, & Kohn, 1977)
After the PCI deduct value for each distress in a sample is known, a maximum corrected
deduct value (CDV) is determined. According to ASTM D5340-12 (2018), “if none or only one
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deduct value is greater than five, use the total deduct value in place of the CDV in determining
PCI; otherwise use the procedures to determine the CDV” (2012). Figure 2-12 is an example
presented in ASTM D5340-12 (2018) to assist in explaining how to determine the maximum
CDV.
Figure 2-12 Example of a Flexible Pavement Condition Survey Data Sheet (ASTM D5340-12,
2012)
To determine the maximum allowable number of distresses, the following equation is used where
HDV is the highest individual deduct value:
𝑚𝑚 = 1 + �9
95� ∗ (100 −𝐻𝐻𝐻𝐻𝐻𝐻) ≤ 10
The next step is to enter the m largest deduct values on Line 1 of the Figure 2-14 including the
fraction obtained by multiplying the last deduct value by the fractional portion of m. If less than m
21
deduct values are available, enter all the deduct values. The next step is to complete the total and
q column. The total column is the sum of all the deduct values in that row. The q column
represents the number of deduct values greater than five in a row. The reason for only counting
deduct values greater than 5 is that the data indicate that smaller deducts have little effect on
pavement condition (Shahin, Darter, & Kohn, 1977). Once the total and q columns are completed
for the row, the appropriate correction curve (AC or PCC), such as Figure 2-13, is used to
determine the CDV for that row. This process is repeated until q is equal to 1 by changing the
smallest deduct value greater than five to five. An example of this process can be seen in
Figure 2-14. The maximum CDV is used to determine the PCI for a given section of pavement.
Figure 2-13 Corrected Deduct Values for Flexible Airfield Pavement (ASTM D5340-12, 2012)
22
Figure 2-14 Calculation of Corrected PCI Value Example (ASTM D5340-12, 2012)
The CDV was created in 1977 during the development of the PCI. An assumption made
during the development of the deduct curves was that only one distress type at a given level of
severity exists in a pavement section (Shahin, Darter, & Kohn, 1977). The CDV was developed
for pavement sections that have more than one distress type. “The deduct values are not linearly
additive, because as additional distress types and/or severity levels occur in a given pavement
section, the resulting impact of those distress become smaller” (Shahin, Darter, & Kohn, 1977).
For that reason, the CDV curves, like Figure 2-13, were created.
Figure 2-15 is an example of several deduct curves plotted on a single plot. Figure 2-15
shows the different distress types on deduct values. “Most of the curves have similar shapes, but
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their effects on the PCI differ greatly. For example, intersecting cracks have a much larger deduct
value than does shrinkage cracking” (Shahin, Darter, & Kohn, 1977).
Figure 2-15 Medium Severity Deduct Curve Example for PCC (Shahin, Darter, & Kohn, 1977)
The USAF still uses the distress definitions and distress deduct values developed in 1977.
Although pavement distresses and their causes have not changed drastically, there are some
limitations or disadvantages to continuing to use the distress deduct values developed in 1977.
Since 1977, technology and data collection have evolved immensely. Current technology allows
for more objective determinations of pavement condition and deterioration rates, however, as the
USAF policy still mandates the use of the distress deduct values developed in 1977, they were
used for this research.
24
Although developed using the subjective analysis, PAVER automates the PCI calculation
process using equations of deduct and CDV curves. After all three of the factors are collected and
input into PAVER, it automatically calculates the PCI for that section. Figure 2-17 is the standard
PCI rating scale from 0 to 100. The scale is color coded with a PCI of 86 to 100 being green for
“good” and 0 to 10 as grey for “failed.” This scale is used to assist in visually portraying the
airfield pavement condition on a map or chart. Figure 2-18 is an example of how the PCI rating
scale color coding is used to present the pavement condition visually. Plots similar to Figure 2-18
are used by engineers to assist in asset management M&R decision making and advocating for
resources from decision makers.
Figure 2-16: Definition of Standard PCI Ratings (AFI 32-1041, 2017)
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Figure 2-17: Standard PCI Rating Scale (Vansteenburg, 2019)
Figure 2-18: PCI Color Scale Plotted on Example Airfield (Vansteenburg, 2019)
After the condition is determined for all sections and branches in an airfield, the next step
is to predict the future condition and perform condition analysis. Predicting future conditions is
important in the decision-making process to ensure the best M&R decision is made and it allows
for analysis of the consequences of not performing M&R due to budget or resource constraints.
PAVER has a prediction modeling function that uses pavement condition historical data to build a
model that predicts future performance (Colorado State University, 2019). After prediction
26
models are completed, condition analysis can be performed to compare current, future, and past
conditions. Assessing current, future, and past conditions allows engineers the ability to
determine the consequences associated with not receiving required resources to prepare necessary
M&R activities (Shahin, 2005).
With the condition analysis complete, engineers can begin to develop an M&R work
plan. The PAVER Work Planner function takes the data collected and provides a suggested M&R
plan, schedule, budget, and alternative M&R options. In this step, a budget for M&R activities is
usually generated and presented to decision makers. Work planning allows engineers to analyze
different alternative and budget options available to meet the specified management objective.
For example, “a typical management objective includes maintaining current network condition,
reaching a certain condition in x years, or eliminating all backlog of major M&R in x years”
(Shahin, 2005). The work plan allows engineers to analyze whether they are meeting the
management objective and advocate for the required resources to meet the management objective.
Once the work plan is approved, project planning can begin.
2.3 FACTORS AFFECTING PAVEMENT PERFORMANCE
There are several factors that affect pavement performance. These factors are presented
in Figure 2-19. The factors that affect pavement performance include the environment the
pavement is located in, the pavement structure, the construction of the pavement, the maintenance
performed on the pavement, and the traffic travelled on the pavement (Haas, 2001). These factors
can negatively affect pavement performance individually or from a combination of more than one
factor.
27
Figure 2-19 Factors Affecting Pavement Performance (Haas, 2001)
To perform a worldwide USAF pavement study, it is challenging to accurately collect
data on factors such as construction, maintenance, and traffic. Although the USAF has a
construction standard that must be met by contractors, the type and quality of craftsmanship to
construction airfields varies based on location. Additionally, although the USAF has pavement
maintenance standards and schedules, not all USAF bases have the resources to meet the
standards and do not accurately document M&R activities. The traffic data at each USAF location
was not made available for this study. Therefore, this study primarily focuses on the climatic and
pavement structure factors.
Although traffic loads have a significant role in pavement deterioration, climatic factors
can accelerate traffic-related deterioration and can lead to early M&R activities (Titus-Glover,
Darter, & Von Quintus, 2019). Climatic factors affecting pavement performance typically include
temperature, precipitation, freeze-thaw cycles, wind speed, and solar radiation factors (Qiao,
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Flintsch, Dawson, & Parry, 2013; Haas, 2001; Titus-Glover, Darter, & Von Quintus, 2019; Cetin,
Forman, Schwartz, & Ruppelt, 2018; Thompson, Dempsey, Hill, & Vogel, 1987). Freeze-thaw
cycles are a result of a combination of temperature, precipitation and pavement structure. Wind
speed and solar radiation have an effect on the pavement structure temperature through
convection and conduction, so they must be considered as part the climatic factors affecting
pavement performance (Jeong & Zollinger, 2005). According to Qiao, temperature is the most
influential climatic factor and has a significant impact on pavement distresses (Qiao, Flintsch,
Dawson, & Parry, 2013; Cetin, Forman, Schwartz, & Ruppelt, 2018). Temperature is a significant
factor in performance of AC because as temperature increases, AC becomes less stiff which can
lead to distresses such as rutting. When temperature decreases, AC becomes brittle and becomes
susceptible to surface cracking (Maadani & Abd El Halim, 2017). Variance in temperature also
affects PCC pavement. Climates with temperature variances can lead to temperature differences
between the surface and the bottom of a PCC slab which can then lead to curling stresses and
pavement distresses. Combined with loading, pavement curling stresses can lead to additional and
more severe pavement distresses (Titus-Glover, Darter, & Von Quintus, 2019). Figure 2-20
depicts curling stresses in a PCC slab with a temperature variance. Temperature can also impact
the depth of frost in the pavement structure which can combine with other climatic factors lead to
poor pavement performance.
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Figure 2-20 Curling Stresses in a Typical PCC Slab (Pavement Interactive, 2019)
In addition to temperature, precipitation is a common factor that is considered to affect
pavement performance. Precipitation alone may not directly have a negative effect on pavement
performance, but combined with other factors such as temperature, traffic, loading, and pavement
structure, precipitation is a key factor in pavement performance (Qiao, Flintsch, Dawson, &
Parry, 2013).
30
When combining temperature and precipitation, one resulting climatic factor affecting
pavement performance is freeze-thaw cycles. Freeze-thaw cycles have the greatest effect on PCC
pavement durability (Ovik, Birgisson, & Newcomb, 2000). One reason presented is due to the
repeated expansion and contraction of PCC which can lead to several pavement distresses (Titus-
Glover, Darter, & Von Quintus, 2019). Freeze-thaw cycles weaken pavement structure layers
which can lead to a lower pavement strength. Pavements with lower strength due to freeze-thaw
cycles under normal traffic loading can cause distresses if pavement structures are not designed
for freeze-thaw cycles. PCC with more frequent freeze-thaw cycles have a higher loss of strength
as compared to PCC pavements that are subjected to less frequent freeze-thaw cycles (Thompson,
Dempsey, Hill, & Vogel, 1987). Freeze-thaw cycles have a similar effect on AC pavements.
As previously stated, temperature can be detrimental to pavement performance by
affecting the stiffness of AC and inducing stresses on PCC. Wind speed and solar radiation is an
additional factor that is part of pavement temperature. “Radiation and convection play a dominate
role in transferring heat between the slab surface and its immediate surroundings, while
conduction plays a separate role in transferring heat within the concrete slab” (Jeong & Zollinger,
2005). Solar radiation can cause hardening of asphalt binders that can lead to distresses similar to
high temperature AC distresses (Titus-Glover, Darter, & Von Quintus, 2019).
Pavement structure is also a key factor affecting pavement performance. Factors that are
part of a pavement structure are layer thicknesses, layer material properties, subgrade type, and
subgrade properties. The USAF has requirements and specifications for material properties, but
difference in geographic regions worldwide plays a role in overall pavement structure properties.
For example, aggregate sources vary between the eastern US and the western US which could
potentially affect pavement performance. The subgrade type and properties change around the
world as well. Pavement structure materials are hard to accurately collect, but pavement subgrade
31
can be collected accurately. Additionally, it is assumed that engineers design pavement structures
to be able to withstand their design loads in the design locations.
The subgrade of a pavement structure can have serious implications on pavement
performance.
The mechanical behaviors of subgrades are affected by the presence of excessive
moisture with increasing or decreasing moisture content, resulting in significant loss of
strength and modulus. Wet unbound materials and subgrades are more likely to
experience shear failure when subjected to traffic loads, and materials that contain
significant amounts of fines are more likely to pump water when subjected to the
combined effects of excessive moisture and traffic loading. (Titus-Glover, Darter, & Von
Quintus, 2019)
In terms of environmental factors, environmental data was collected using the U.S.
Department of Transportation Federal Highway Administration Long Term Pavement
Performance (LTPP) Climate Tool. The LTPP Climate Tool “provides convenient access to the
National Aeronautics and Space Administration (NASA) Modern Era Retrospective Analysis for
Research and Applications (MERRA) climatic data” (FHWA, 2017). There were multiple
climatic data considered for this research such as weather station database provided by AASHTO,
automated surface observing station data collected by the National Oceanic and Atmospheric
Administration (NOAA), USAF ground-based weather stations, and NASA MERRA data.
“MERRA is a physics-based reanalysis model that combines computed model fields (e.g.,
atmospheric temperatures) with ground-, ocean-, atmospheric-, and satellite-based
observations that are distributed irregularly in space and time” (Schwartz, et al., 2015).
Research shows that MERRA climate data are as good and, in many cases, substantially better
than equivalent climate data” (Cetin, Forman, Schwartz, & Ruppelt, 2018; Schwartz, et al., 2015;
Schwartz, Forman, & Leininger, 2015).
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Due to the convenience associated with the LTPP Climate Tool, MERRA data was used
from the LTPP Climate Tool to collect climatic data for the analysis. The LTPP Climate Tool
search location bar was used to search for each USAF base analyzed and then visually verified
the location on the map after the search. The data collected from the LTPP Climate Tool includes
Basic MERRA Data, Annual Precipitation, Annual Temperature, Annual Wind, Annual
Humidity, and Annual Solar.
Pavement thickness, pavement surface type, and subgrade strength are the three factors
used to represent the pavement structure. The pavement surface type and thickness data were
collected from the USAF PAVER database. The subgrade strength data were collected from the
PCN code data. Part three of the PCN code is the subgrade strength as previously discussed. This
part of the code was extracted to represent the strength of the subgrade for each section of
pavement in the Air Force inventory.
2.4 RESEARCH ON USAF AIRFIELD PAVEMENT DISTRESSES
The PCI is the heart of pavement asset management. Rarely, are pavement management
decisions made without the PCI being considered. As previously stated, a PCI is based on three
factors: distress type, distress severity, and distress quantity. It can be concluded that
understanding and evaluating pavement distresses can be considered the most important aspect of
pavement management. The focus of this research is to gain a greater understanding of pavement
distresses in the USAF airfield pavements to help the USAF decision makers make the best
pavement management decisions. A better understanding for the USAF pavement distresses will
be accomplished by analyzing the USAF pavement distress data stored in the PAVER database.
The USAF has had research performed on pavement performance in comparison to
climate in the past. There have been three predominant studies using the USAF PAVER database
33
to analyze USAF pavement assets and environmental factors affecting pavement performance.
The three studies focused on USAF pavement assets within the United States and did not consider
USAF installations in foreign countries.
The first study was performed in 2013 and generated pavement deterioration models for
every pavement family for all the bases in four distinct climate regions within in the United
States. The researchers of the study used the literature to present precipitation, temperature,
subsurface moisture, and freeze-thaw cycles as four predominant factors that have a significant
influence on pavement performance. The climate model was built using precipitation and freezing
degree-days data collected from WeatherBank Inc. “WeatherBank continuously collects data
from approximately 1,700 National Oceanic and Atmospheric Administration (NOAA), National
Weather Service (NWS), and Federal Aviation Administration (FAA) stations scattered across the
United States” (Meihaus, 2013). The four climate zones were defined as:
• No Freeze-Wet: Precipitation > 25” and FDD < 750
• No Freeze-Dry: Precipitation < 25” and FDD < 750
• Freeze-Wet: Precipitation > 25” and FDD > 750
• Freeze-Dry: Precipitation < 25” and FDD > 750
The Kriging geospatial interpolation technique was used to interpolate between locations
to develop a climate region map. Figure 2-21 depicts the climate zone region map used in the
2013 study. The study found that their climate model may have been oversimplified for the
climate regions that exist in the United States.
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Figure 2-21: Climate Zone Map for the US based on 2013 study (Meihaus, 2013)
The climate model was used to “determine if a statistical difference in the region exists
between the regional climate regions average rate of deterioration for each family of pavements”
(Meihaus, 2013). They found that the climate region deterioration rates for both PCC and AC
were consistent with expected average deterioration rates for airfield pavements. The
deterioration rates for both AC and PCC pavements can be found in Figure 2-22 and Figure 2-23.
Figure 2-22: Overall Climate Zone Average Rates of Deterioration - PCC (Meihaus, 2013)
35
Figure 2-23: Overall Climate Zone Average Rates of Deterioration – AC (Meihaus, 2013)
In 2014, Lauren Sahagun conducted her master’s theses on modeling pavement distress
rates within USAF airfields. While Meihaus performed analysis on airfield taxiways, aprons, and
runways, Sahagun focused her analysis on USAF runways. The research set out to investigate
distress patterns within the four proposed climate regions and determine which distress types are
most prevalent in each climate zone.
Sahagun identified potential doubt in the climate regions presented by Meihuas, so
Sahagun set out to improve the model. Sahagun developed a model that included pavement type,
distress, and geographic location. Her model suggested that there are only two climate regions in
the US: western and eastern. An example of her model is presented in Figure 2-24 and Figure 2-
25. Sahagun found that some distresses were displaying a geographic pattern but could not find
correlation based solely on climate. The research could not confirm the hypothesis that climate is
the predominant contributing factor without performing additional research that considered
additional deterioration factors such as traffic, maintenance, structure, and construction history
(Sahagun, 2014).
36
Figure 2-24: AC Runway Model Based on Average Distress Behavior (Sahagun, 2014)
Figure 2-25: PCC Runway Model Based on Average Distress Behavior (Sahagun, 2014)
The third study by Parsons and Pullen also investigated the relationship between
pavement distress and climate factors. Parsons and Pullen’s hypothesis “was that certain types of
distresses would be more likely to occur, or occurs at a higher density when exposed to certain
climate factors” (Parsons & Pullen, 2016). Parsons and Pullen used Meihaus’ climate regions to
categorize the USAF pavement data and perform analysis. Installations outside of the United
States were outside the scope of the Meihaus research and were also not considered in Parsons
37
and Pullen’s research. Parsons and Pullen concluded that the following distresses were affected
by climate with significance α=0.05: alligator cracking, block cracking, joint reflection cracking,
raveling, blow-ups, D-cracking, popouts, and scaling. Six additional distresses were determined
to be affected by climate with a significance of α=0.10 to include: bleeding, rutting, swelling,
raveling, corner breaks, and ASR. They were also able to conclude that PCC pavements were
more affected by climate than AC pavements and AC pavements were more affected by moisture
than PCC.
38
CHAPTER 3
DATA COLLECTION AND ORGANIZATION
There are 3 sets of data used in this research. One of the sources of data was provided by
the Air Force Civil Engineer Center as a PAVER database E70 files. This dataset is the heart of
the research. and provides pavement inventory and distress data based on the most current
inspection for 102 USAF locations. This data was collected over the past years by the USAF
Pavement Evaluation Team in accordance with ASTM D5340-12. A PAVER database that
included current and previous inspection data was requested but was unable to be provided. The
information collected from the dataset for each location in the USAF is displayed in Table 3-1..
The User Defined Report option in PAVER was used to extract the pavement data. The
initial User Defined Report included 94 columns of data and 590,345 rows of data. After further
analyzing the data, it was evident that the data was categorized by Sample Unit instead of by
pavement SectionID. Being categorized by Sample Unit resulted in duplicate rows and after they
were removed from the User Defined Report, there were 112,059 rows of data remaining. After
further analyzing the data, additional duplicates and errors were found in the data, therefore
additional categories were removed from the User Defined Report. Such categories included
“Work Code”, “Material”, “Material Type”, and “Comments.”
39
Table 3-1: Fields Used from PAVER Database
Fields Used from PAVER Database Major Command Network Name NetworkID BranchID Branch Name Branch Use Branch Area Branch Area Units UID_SUniqueID Last Inspection Date Length Width Section Linear Units Section Rank SectionID Section True Area Section Area Units Slab Length Slab Width Slabs Years Since Major Work Years Since Inspection Surface Type - Current Thickness Thickness Units Sample Type Density Distress Code Distress Description Distress Mechanism Distress Quantity SYS_QuantityUnits Distress Quantity Units PCI Deduct Severity PCI PCI Category
40
There are two sample types in a pavement evaluation. The two sample types are Random
(R) and Additional (A) (US Army Corps of Engineers, 2015). For the purpose of this study, the
author only analyzed pavement sections that were Random sample types. This was accomplished
to ensure the pavement sections used for analysis were randomly selected to be representative of
the pavement section. When the pavement distress is collected, the distress has a severity of low,
medium or high associated with the distress quantity. To account for distress severity, the PCI
deduct values were used. Distress severity is one of the three components used to calculated PCI
deduct values. If one pavement section had two of the same distresses, but with different levels of
severity, the PCI deducts were summed to one distress per section. For example, if there is a
pavement section, A01A, with distress code 41 low severity with a PCI deduct of 5 and a distress
code 41 high severity with a PCI deduct of 10, in this research, section A01A would appear as
distress code 41 with a PCI deduct of 15. The final PAVER dataset used was left with the 37
columns displayed previously in Table 3-1 and 60,771 rows of data.
The second set of data is the climate data for each USAF location. The climate data was
manually extracted using the LTPP InfoPAVE: LTPP Climate Tool. The LTPP Climate Tool
search location bar was used to search for each USAF base analyzed and then visually verified
the location on the map after the search. These locations are very small islands that MERRA did
not have climatic data for and therefore they were removed from the research.
The date range this data was collected from is from 1980 to 2017 which was the
maximum date range at the time the data was collected. The climate data was collected for 99
locations and saved as individual Microsoft Excel© Files. The types of climate data collected for
each location is displayed in Table 3-2.
41
Table 3-2: Climate Parameters Collected
Climate Parameters Collected NetworkID MERRA_ID ELEVATION LATITUDE LONGITUDE RECORD_STATUS YEAR PRECIPITATION TEMP_MEAN_AVG FREEZE_INDEX FREEZE_THAW TEMP_MAX TEMP_MIN DAYS_ABOVE_32_C DAYS_BELOW_0_C
When the data was extracted from the LTPP Climate Tool, the data was spread through
an Microsoft Excel© Workbook on multiple Microsoft Excel© Sheets. The data for each location
was then merged into one Microsoft Excel© Sheet within each Microsoft Excel© Workbook. The
data collected over the 37-year range was then combined into a single row in Microsoft Excel©
by calculating the average, median, minimum, maximum, and standard deviation for each
parameter collected from the LTPP Climate Tool to be representative of the 37-year period. This
was accomplished for each of the 99 locations and then the data was imported into a single table
in Microsoft Access© named “Climate Data USED.”
The third dataset collected was the PCN data. The USAF has a SharePoint with each of
the past USAF pavement structural inspection documents. Similar to the PAVER dataset, the
PCN data was generated over the years by the USAF Pavement Evaluation Team. The author
manually accessed the SharePoint and downloaded the most recent pavement structural report
uploaded to the SharePoint. PCN data was not available for nine bases. These locations were not
42
included in the PCN aspect of the research. The oldest structural report is from 2005 and the
newest structural report is from 2017. There may be newer reports that are not uploaded to the
SharePoint, but the author only had access to the reports that are on the SharePoint. From the 93
PCN and soil data PDF files, 6,225 PCN codes were manually exported to Microsoft Excel©
files. The individual Microsoft Excel© files were then combined into one PCN Microsoft
Access© table.
After data collection, the next step was to manipulate the three datasets in preparation for
analysis. The method used to organize the three datasets was by Microsoft Access©. The overall
database was built used using Microsoft Access©’s query tool. In the table, a row represents an
independent pavement section at a specified location and the corresponding quantity of the
specified distress code. In the distress code 63 example, each row represents a pavement section
and the quantity of distress code 63 present on that individual pavement section. There may be
sections that do not have distress code 63 and will be signified with a PCI deduct of zero. If
distress code 63 is present on a pavement section, the PCI deduct will be greater than zero. It is
important to include the values of zero in the analysis to ensure false positive conclusions are not
drawn.
Once the data was separated by each distress code, it became apparent that there is not a
significant amount of airfield pavement overrun, helipad, and shoulder data. This data was
removed prior to statistical analysis and this research only focused on taxiway, runway, and apron
data. Similarly, it was apparent there were not a sufficient number of subgrade strength “D”
values to perform statistical analysis. For the pavement sections that did have subgrade strength
“D,” the majority of them had a distress which suggest the USAF might want to limit the use of
subgrades that weak.
The remaining data included 90 bases. After the combination and manipulation of data,
the final data set was reduced from 590,345 total rows of data to 2,337 rows of PCC data and
43
1064 rows of AC data. The 2,337 rows of PCC data represent the number of PCC pavement
sections used for this research effort. Likewise, the 1,064 rows of AC data represent the number
of AC pavement sections used in this study. Each distress analyzed received their own respective
database. This means that each PCC distress analyzed has its own table with 2,337 rows and each
AC distress analyzed has its own table with 1,064 rows. The USAF has not authorized the
publication or distribution of this data, so it is not able to be presented in this study.
44
CHAPTER 4
RESEARCH METHODOLOGY
This section outlines the two methodologies used in this research effort: analysis of
aggregated data and statistical analysis. The methodology was performed not to generate a
pavement performance or prediction model, but to provide feedback to the USAF on the
performance of their current pavement management and design policies. The first approach to
achieve that was accomplished by aggregating the data. The aggregated data was ranked by the
distresses that are causing the largest cumulative PCI deduct values across the entire USAF. This
was achieved to focus the research on the distresses that are causing the greatest summative
reduction in pavement condition. Statistical analysis was performed on the eleven pavement
distresses that are causing the most reduction in pavement condition. The purpose of this
statistical analysis was to determine which of the factors that typically affect pavement
performance are statistically significant despite the USAF policies and designs.
4.1 ANALYSIS OF AGGREGATED DATA
The first part of the data analysis was to aggregate and summarize the PAVER dataset.
The PAVER dataset was used in Microsoft Excel© and coded to count the number of occurrences
and sum the PCI deduct values for each distress at each individual location. An example of this
can be found in Chapter 5 of this research. After each individual location’s values are summed,
the total distress count of occurrences and sum PCI deduct values per distress code were
represented for the entire USAF. This analysis presents which pavement distress codes are
causing the most reduction in pavement condition globally in the USAF.
45
Similar analysis was completed on each pavement feature instead of pavement location.
Again, using the PAVER dataset, the data was coded to count the number of occurrences and sum
the PCI deduct values for each distress value for aprons, taxiways, and runways. After this was
completed, the pavement distresses causing the largest cumulative PCI deduct values for each
feature were determined.
4.2 STATISTICAL ANALYSIS
The next approach used was a statistical approach using Minitab© statistical software.
The first statistical approach used was Analysis of Variance (ANOVA) using the General Linear
Model. This model allowed for One-Way and Two-Way ANOVA capabilities. In the early stages
of the analysis, it was apparent that the dependent variable, PCI Deduct, does not follow a normal
distribution and is a right skewed distribution. The data were not successfully transformed to a
normal distribution and the residuals were also not normally distributed, so a General Linear
Model ANOVA was not used.
The second statistical tool used to analyze the data was Binary Logistic Regression.
Binary Logistic Regression is typically used to describe the relationship between a set of
predictors and a binary response (Minitab, 2019). For the purposes of this research, Binary
Logistic Regression was used to assist in describing the relationship between factors that typically
affect pavement performance with a response of a distress occurring or not. Binary Logistic
Regression does not assume normality and therefore was able to be used on the non-normal data.
PCI deduct is a continuous variable, so to use binary logistic regression, the data had to
be converted to dichotomous values. To change the PCI deduct value into dichotomous values,
the author defined pavement sections that had a PCI deduct value greater than zero a categorical
variable of “1” and pavement sections with a PCI deduct value equal to zero a categorical
46
variable of “0.” For distresses, such as linear and transverse cracking in AC, where a very small
quantity has minimal effect on pavement performance, analysis was accomplished using small
values greater than zero for the dichotomous value of “0.” The analysis showed that increasing
the distress limit from no distress quantity to a small quantity does not change the results, so the
dichotomous value of “0” remained defined as sections with no distress quantity for all distresses.
If a pavement has a pavement distress, then the section is assigned a value of “1” and if it does
not have a distress it is assigned a value of “0.” Analysis is performed by examining each
individual distress independently. For example, when analysis distress code 63 is present on a
pavement section, that section is assigned to the “1” category. Similarly, for the sections that
distress code 63 does not exist, that section is assigned to the “0” category.
The factors that typically affect pavement performance are used as predictors in the
statistical analysis These factors include the pavement structure and climatic data collected for
each location analyzed. Specifically, the factors selected to be used for statistical analysis can be
found in Table 4-1 and Table 4-2.Table 4-1 defines the pavement related factors and Table 4-2
defines the climatic variable used for analysis.
47
Table 4-1 Pavement Related Factors Used in Statistical Analysis
Factors Categorical or Continuous Definition
Years Since Last Major Repair Actual Continuous
This is the number of years since the last major repair was completed to the last inspection. A major repair is assumed to reset the pavement condition to near perfect.
Feature Categorical Apron, Taxiway, or Runway
Subgrade Strength Categorical
As defined in Figure 2-2. Four variables A, B, C, D with A being the strongest subgrade and D being the weakest. Subgrade strength D was removed from the study, so only A, B, C are studied.
Thickness Continuous Thickness of pavement surface layer in inches.
Surface Type - Current (AC Pavements Only) Categorical
Asphalt Concrete (AC) Asphalt Concrete Over Asphalt Concrete (AAC) Asphalt Concrete Over Portland Cement Concrete (APC)
48
Table 4-2 Climatic Factors Used in Statistical Analysis (U.S. Department of Transportation
FHWA, 2018)
Factors Categorical or Continuous Definition
Average PRECIPITATION Continuous
The average water equivalent of total surface precipitation over year time period from 1980 to 2017 for each location in millimeters.
Average TEMP_MEAN_AVG (deg C) Continuous
An average of the annual average of the monthly mean air temperatures 2 m above the MERRA centroid from 1980 to 2017 for each location in Celsius.
Average FREEZE_INDEX Continuous
The average of the annual summation of difference between 0 degrees Celsius and mean daily air temperature, when mean daily air temperature is less than 0 degrees Celsius for each location from 1980 to 2017 in Celsius degree days.
Average FREEZE_THAW Continuous
The average of the annual number of days in the year when the maximum air temperature is greater than 0 degrees Celsius and minimum air temperature is less than 0 degrees Celsius on the same day for each location from 1980 to 2017 in number of days.
Average TEMP_MAX Continuous
The average of the annual maximum air temperature 2 m above elevation of MERRA cell centroid for each location from 1980 to 2017 in Celsius.
Average TEMP_MIN Continuous
The average of the annual minimum air temperature 2 m above elevation of MERRA cell centroid for each location from 1980 to 2017 in Celsius.
Average DAYS_ABOVE_32_C Continuous
The average of the annual number of days in the year when the maximum air temperature is greater than 32.2 degrees Celsius for each location from 1980 to 2017 in number of days.
Average DAYS_BELOW_0_C Continuous
The average of the annual number of days in the year when the minimum air temperature is less than 0 degrees Celsius for each location from 1980 to 2017 in number of days.
49
The statistical analysis was performed by considering all of the factors in Table 4-1 and
Table 4-2 at once and performing backward stepwise elimination to determine which factors are
considered significant. The purpose of this research is not to create a predictive model, instead it
is to which factors may be significant. Therefore, the first statistical result was to determine if
there is any correlation between the factors used for analysis. The correlation between factors was
determined by the Variance Inflation Factors (VIF). The “rule of thumb” of the threshold of a
VIF less than 10 was used for this research. After the first iteration of backwards elimination, if a
remaining factor had a VIF greater than 10, it was removed from the analysis and the statistics
were performed again. The iterative process of analyzing VIFs and removing factors with a VIF
greater than 10 was accomplished until all remaining factors had a VIF less than 10 to ensure no
correlation between factors.
There are three main results interpreted in the research for Binary Logistic Regression.
The first is to determine if the association between the response and the term is statistically
different. It is determined if the response and term are statistically different by comparing the p-
value at a significance level of alpha equals .05 to the null hypothesis. If the p-value is equal to or
less than .05 the association is significantly different and it can be concluded that there is a
statistically significant association between the response variable and the term (Minitab, 2019).
On the contrary, if the p-value is greater than an alpha of .05 it can be concluded that the
association is not statistically significant (Minitab, 2019). There is one distress, distress 76 Alkali
Silica Reactivity, that had a p-value of .063 that was left in the analysis, but it is recognized that
the factor is not significant with 95 percent confidence.
The second result analyzed was the effects of the predictors in terms of an odds ratio. For
continuous predictors, when the odds ratio is greater than 1, the event is more likely to occur as a
predictor increases. When the odds ratio is less than 1, the event is less likely to occur as the
predictor increases (Minitab, 2019). For example, in this research if the odds ratio is 3 for a
50
continuous climate variable, that means a specified pavement distress is 3 times more likely to
occur as that continuous climate variable increases one increment. For categorical predictors, the
odds ratio compares the odds of the event occurring at 2 different levels of the predictor: Level A
and Level B. If the odds ratio is greater than 1, the event at Level A is more likely to occur. If the
odds ratio is less than 1, the event at Level A is less likely to occur (Minitab, 2019).
The third result investigated are the factorial plots created by Minitab©. The factorial
plots plot the slope of the coefficients to allow for visual interpretation of the factors causing the
distresses. The Y axis represents the probability of a distresses occurring. For continuous
variables the X axis of the factorial plot is the range of values associated with that factor. For
categorical variables the X axis is the categorical variables themselves. A factor can be deemed
statistically significant from the p-values from the Wald Test, but the impact of those significant
factors can be seen in the factorial plots.
From the aggregated data analysis, the pavement distresses that are causing the largest
cumulative reduction in pavement condition are known. Binary Logistic Regression was used to
attempt to find correlation between those pavement distresses and the factors presented in Table
4-1 and Table 4-2. Statistical results, such as R squared values and residual plots, are not
discussed in this research, because predictive models are not a goal of this research. The
statistical analysis was used to identify factors that are correlated to pavement distresses under
current USAF policies.
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CHAPTER 5
RESULTS AND DISCUSSION
There are two main results discussed in this chapter: aggregated data results and
statistical analysis results. The aggregated data results present the airfield pavement distresses
that are causing the largest cumulative PCI deduct values. The statistical analysis results are
presented in two different sections: PCC pavement distresses and AC pavement distresses. In
each of these sections, distresses are individually analyzed to determine which pavement structure
and climatic variables are significant despite current polices. Each section first presents the
summary statistical results and is followed by the detailed results.
5.1 AGGREGATED DATA RESULTS
To determine which pavement distress codes are causing the largest cumulative reduction
of pavement condition in the USAF, the data was aggregated together and ranked from largest to
smallest. Table 5-1 depicts the results according to the current USAF PAVER dataset. As shown
in Table 5-1, distress code 63, linear cracking, is the PCC pavement distress that has the highest
sum of PCI deduct values in the Air Force with a total of 46,705. Distress 63 is not the distress
that occurs the most in the Air Force though. Distress 66, small patch, occurs the most often with
a total number of occurrences of 4,461.
Table 5-2, Table 5-3, Table 5-4 present the distress codes that are causing the largest
cumulative PCI deduct values in the Air Force by each feature. Airfield features are often
maintained and prioritized differently. Typically, runways are the most important airfield feature,
followed by taxiways, and aprons. Each feature was analyzed separately to see there is a
difference in the rankings of distresses. Table 5-2, shows distress code 63, linear cracking, is the
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distress causing the highest summative PCI deduct values for aprons with a total sum of 27,888.
Table 5-3 presents the distresses causing the highest summative PCI deduct values for runways
with the highest distress code 48, longitudinal and transverse cracking, with a total PCI deduct
value of 7,361. The pavement distresses causing highest cumulative PCI deduct values for
taxiways is displayed in Table 5-4. As shown in Table 5-4, distress code 48, longitudinal and
transverse cracking, is the distress causing the highest summative PCI deduct values with a total
of 14,690.
53
Table 5-1: Air Force Pavement Distresses Ranked by Cumulative PCI Deduct Values
Code Distress Name Number of Distress
Occurrences Sum of PCI Deduct
Values 63 Linear Cracking 3452 46705 48 Longitudinal and Transverse Cracking 2346 39970 65 Joint Seal Damage 4735 36850 57 Weathering 2500 33089 43 Block Cracking 701 24968 67 Large Patch/Utility Cut 3070 23002 72 Shattered Slab 924 18675 74 Joint Spalling 4242 16009 66 Small Patch 4461 13074 41 Alligator Cracking 457 11889 76 Alkali Silica Reaction 500 11208 52 Ravelling 498 9514 50 Patching 850 9296 75 Corner Spalling 3263 7976 73 Shrinkage Cracking 2888 6066 70 Scaling/Crazing 1765 5860 62 Corner Break 1623 5603 47 Joint Reflection/Cracking 252 5182 64 Durability Cracking 440 4052 71 Faulting 549 2141 68 Popouts 274 1546 45 Depression 204 1464 53 Rutting 69 1439 56 Swelling 145 1145 42 Bleeding 123 1073 54 Shoving 83 580 61 Blow Up 12 523 69 Pumping 93 429 55 Slippage Cracking 45 387 49 Oil Spillage 68 209 44 Corrugation 6 177 46 Jet Blast 6 10 51 Polished Aggregate 0 0
54
Table 5-2: Apron Pavement Distresses Ranked by Cumulative PCI Deduct Values
Code Distress Name Number of Distress
Occurrences Sum of PCI Deduct
Values 63 Linear Cracking 1899 27888 65 Joint Seal Damage 2514 21100 72 Shattered Slab 635 13303 48 Longitudinal and Transverse Cracking 771 12858 67 Large Patch/Utility Cut 1643 12569 57 Weathering 855 12080 43 Block Cracking 319 12026 74 Joint Spalling 2157 8896 76 Alkali Silica Reaction 296 7009 66 Small Patch 2206 6727 41 Alligator Cracking 162 4807 50 Patching 351 4455 75 Corner Spalling 1663 4270 52 Ravelling 168 3803 62 Corner Break 949 3632 70 Scaling/Crazing 911 3273 73 Shrinkage Cracking 1450 2971 47 Joint Reflection/Cracking 101 2354 64 Durability Cracking 222 2261 71 Faulting 326 1327 45 Depression 94 790 42 Bleeding 57 753 68 Popouts 135 715 53 Rutting 31 622 61 Blow Up 10 509 56 Swelling 38 325 69 Pumping 59 278 54 Shoving 27 200 55 Slippage Cracking 18 161 49 Oil Spillage 44 130 44 Corrugation 1 61 46 Jet Blast 0 0 51 Polished Aggregate 0 0
55
Table 5-3: Runway Pavement Distresses Ranked by Cumulative PCI Deduct Values
Code Distress Name Number of Distress
Occurrences Sum of PCI Deduct
Values 48 Longitudinal and Transverse Cracking 405 7361 63 Linear Cracking 527 6922 65 Joint Seal Damage 778 5521 57 Weathering 421 5466 72 Shattered Slab 124 3128 67 Large Patch/Utility Cut 426 2733 43 Block Cracking 89 2687 74 Joint Spalling 697 2311 66 Small Patch 720 1924 41 Alligator Cracking 78 1866 50 Patching 122 1181 75 Corner Spalling 492 1177 52 Ravelling 68 1110 73 Shrinkage Cracking 471 1045 76 Alkali Silica Reaction 65 998 47 Joint Reflection/Cracking 45 979 70 Scaling/Crazing 260 765 62 Corner Break 199 552 64 Durability Cracking 70 445 71 Faulting 82 292 68 Popouts 35 155 53 Rutting 8 108 56 Swelling 26 84 54 Shoving 11 77 45 Depression 15 65 42 Bleeding 15 55 69 Pumping 12 55 49 Oil Spillage 12 43 55 Slippage Cracking 5 34 44 Corrugation 0 0 46 Jet Blast 0 0 51 Polished Aggregate 0 0 61 Blow Up 0 0
56
Table 5-4 Taxiway Pavement Distresses Ranked by Cumulative PCI Deduct Values
Code Distress Name Number of Distress
Occurrences Sum of PCI Deduct
Values 48 Longitudinal and Transverse Cracking 903 14690 63 Linear Cracking 1002 11686 57 Weathering 934 11531 65 Joint Seal Damage 1401 9952 67 Large Patch/Utility Cut 968 7447 43 Block Cracking 217 7422 74 Joint Spalling 1346 4653 66 Small Patch 1490 4303 41 Alligator Cracking 177 4128 52 Ravelling 198 3413 76 Alkali Silica Reaction 136 3170 50 Patching 262 2581 75 Corner Spalling 1080 2477 72 Shattered Slab 156 2009 73 Shrinkage Cracking 947 1997 47 Joint Reflection/Cracking 94 1708 70 Scaling/Crazing 571 1704 62 Corner Break 463 1379 64 Durability Cracking 145 1334 68 Popouts 102 638 53 Rutting 27 636 56 Swelling 56 534 71 Faulting 134 500 45 Depression 71 470 42 Bleeding 47 262 54 Shoving 33 219 55 Slippage Cracking 14 150 44 Corrugation 5 115 69 Pumping 21 95 49 Oil Spillage 12 36 61 Blow Up 2 15 46 Jet Blast 6 10 51 Polished Aggregate 0 0
57
Table 5-5 and Table 5-6 displays the differences in feature in terms of distress codes and
total PCI deduct values. It should be noted that the total number of sections per apron is 20,112,
total number of sections per runway is 6,278, and total number of sections per taxiway is 13,020.
At first glance it appears as though runways are in significantly better shape than aprons, but
there are more apron sections than runway sections. As such, this table depicts the difference in
distress codes that are causing high cumulative PCI deduct values by feature.
Table 5-5 Combined Apron, Runway, and Taxiway Pavement PCI Deduct Value Sums
Apron Runway Taxiway
Code Distress Name
Sum of PCI
deduct values
Code Distress Name
Sum of PCI
deduct values
Code Distress Name
Sum of PCI
deduct values
63 Linear Cracking 27888 48
Longitudinal and Transverse Cracking
7361 48 Longitudinal and Transverse Cracking
14690
65 Joint Seal Damage 21100 63 Linear
Cracking 6922 63 Linear Cracking 11686
72 Shattered Slab 13303 65 Joint Seal Damage 5521 57 Weathering 11531
48 Longitudinal and Transverse Cracking
12858 57 Weathering 5466 65 Joint Seal Damage 9952
67 Large Patch/Utility Cut
12569 72 Shattered Slab 3128 67 Large Patch/Utility Cut
7447
57 Weathering 12080 67 Large Patch/Utility Cut
2733 43 Block Cracking 7422
43 Block Cracking 12026 43 Block
Cracking 2687 74 Joint Spalling 4653
74 Joint Spalling 8896 74 Joint Spalling 2311 66 Small Patch 4303
76 Alkali Silica Reaction 7009 66 Small Patch 1924 41 Alligator
Cracking 4128
66 Small Patch 6727 41 Alligator Cracking 1866 52 Ravelling 3413
41 Alligator Cracking 4807 50 Patching 1181 76 Alkali Silica
Reaction 3170
50 Patching 4455 75 Corner Spalling 1177 50 Patching 2581
75 Corner Spalling 4270 52 Ravelling 1110 75 Corner
Spalling 2477
52 Ravelling 3803 73 Shrinkage Cracking 1045 72 Shattered Slab 2009
58
Table 5-6 Combined Apron, Runway, and Taxiway Pavement PCI Deduct Sums Continued
Apron Runway Taxiway
Code Distress Name
Sum of PCI
deduct values
Code Distress Name
Sum of PCI
deduct values
Code Distress Name
Sum of PCI
deduct values
62 Corner Break 3632 76 Alkali Silica Reaction 998 73 Shrinkage
Cracking 1997
70 Scaling/ Crazing 3273 47
Joint Reflection/ Cracking
979 47 Joint Reflection/ Cracking
1708
73 Shrinkage Cracking 2971 70 Scaling/
Crazing 765 70 Scaling/ Crazing 1704
47 Joint Reflection/Cracking
2354 62 Corner Break 552 62 Corner Break 1379
64 Durability Cracking 2261 64 Durability
Cracking 445 64 Durability Cracking 1334
71 Faulting 1327 71 Faulting 292 68 Popouts 638
45 Depression 790 68 Popouts 155 53 Rutting 636
42 Bleeding 753 53 Rutting 108 56 Swelling 534
68 Popouts 715 56 Swelling 84 71 Faulting 500
53 Rutting 622 54 Shoving 77 45 Depression 470
61 Blow Up 509 45 Depression 65 42 Bleeding 262
56 Swelling 325 42 Bleeding 55 54 Shoving 219
69 Pumping 278 69 Pumping 55 55 Slippage Cracking 150
54 Shoving 200 49 Oil Spillage 43 44 Corrugation 115
55 Slippage Cracking 161 55 Slippage
Cracking 34 69 Pumping 95
49 Oil Spillage 130 44 Corrugation 0 49 Oil Spillage 36
44 Corrugation 61 46 Jet Blast 0 61 Blow Up 15
46 Jet Blast 0 51 Polished Aggregate 0 46 Jet Blast 10
51 Polished Aggregate 0 61 Blow Up 0 51 Polished
Aggregate 0
59
5.2 STATISTCAL RESULTS
One aspect of this research is to examine the distresses causing the highest cumulative
reduction in pavement condition. To achieve that, distresses that had a total sum of PCI deduct
values greater than 10,000 were analyzed. Although, the rest of the distresses may still be critical,
they are not causing the largest cumulative reduction of PCI deduct values. Distresses with a sum
of PCI deduct values greater than 10,000 resulted in 11 distresses analyzed statistically. The
complete statistical analysis for each distress can be found in Appendix A. The three main results,
p-value, odds ratio, and factorial plots, are presented in this chapter. The p-value of the significant
factors are found in an ANOVA table similar to the one in Figure 5-1. In this example, the
interpretation would be that Years Since Major Work Actual, Thickness, Average Precipitation,
Average Freeze Index, Average Temp Max, Average Days Above 32 C, Feature, and Subgrade
Strength are factors that affect pavement performance of a specified distress.
Figure 5-1 ANOVA Table Example
Minitab© displays the odds ratios in two separate tables: Odds Ratios for Continuous
Predictors and Odds Ratios for Categorical Predictors. Examples of each table are presented in
Figure 5-2 and Figure 5-3. The odds ratio for the continuous predictors represent that per change
in 1 unit, the likelihood of a specified distress occurring changes by the odd ratio. For example,
Years Since Major Work Actual in Figure 5-2 has an odds ratio of 1.0586. This means that as a
60
pavement gets one year older, it is 1.0586 more times more likely to have the distress. A similar
interpretation is presented in Figure 5-3 for Subgrade Strength. A pavement section with
Subgrade Strength B is 1.1983 times more likely to have the distress than a pavement section
with Subgrade Strength A.
Figure 5-2 Example Odds Ratios for Continuous Predictors
Figure 5-3 Example Odds Ratio for Categorical Predictors
The factorial plots help to show the probability of a distress occurrence as a factor
changes. Figure 5-4 is an example of a factorial plot. The plot shows that as thickness increases,
the probability of a distress occurring decreases. As shown in Figure 5-2, subgrade strength was
deemed a statistically significant factor based on the alpha value. When analyzing the factorial
plot, probability of a distress occurring is almost the same for all subgrade strengths. For that
reason, it is important to consider both statistical significance and the factorial plots.
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Figure 5-4 Example Factorial Plot
This type of analysis was conducted on each distress examined. In the individual sections
each distress is not examined to the same level presented previously, but they will focus more on
the abnormal or informative results of the analyses. If factors are presumed to be consistent or
similar across several distresses, they will be discussed in the summary section of the distress
analysis.
After the statistics are discussed, possible causes of the distress occurrence are presented
based on the statistics and engineering judgement. The possible causations are not the reason the
distress occurred, but they are a possibility. Not all the possibilities are discussed, and it is
important to note that the explanation of the distress occurrence may be due to additional factors
not presented in this research.
5.2.1 PORTLAND CEMENT CONCRETE PAVEMENT DISTRESSES
The portland cement concrete airfield pavement distresses are evaluated in the
subsequent sections. The sections are ordered from the distresses causing highest summative
reduction of pavement condition to the lowest of the distresses analyzed. Each distress is first
analyzed by determining the factors that are statistically significant in the pavement’s
performance. Table 5-7 summarizes the ranks of each of the distresses in terms of which distress
62
is causing the highest reduction of pavement condition across the Air Force and summarizes the
factors that were found to be statistically significant.
Table 5-7 Portland Cement Concrete Distresses Analyzed
Distress Rank
Distress Code Distress Name
Typical Cause Significant Factors
1 63 Linear Cracking Load
Years Since Major Work Thickness Precipitation Freeze Index Maximum Temp # of Days Above 32 C Feature Subgrade Strength
3 65 Joint Seal Damage Climate
Years Since Major Work Freeze Index # of Freeze-Thaw Cycles Minimum Temp # of Days Above 32 C Feature
6 67 Large Patch/Utility Cut Other
Years Since Major Work Average Temp (deg C) Freeze Index # of Freeze-Thaw Cycles Maximum Temp Feature Subgrade Strength
7 72 Shattered Slab Load
Years Since Major Work Thickness # of Freeze-Thaw Cycles Maximum Temp # of Days Above 32 C # of Days Below 0 C Feature Subgrade Strength
8 74 Joint Spalling Other
Years Since Major Work Thickness Average Temp (deg C) # of Freeze-Thaw Cycles Maximum Temp Subgrade Strength
9 66 Small Patch Other
Years Since Major Work Thickness Precipitation # of Days Above 32 C Feature
11 76 Alkali Silica Reaction Other
Years Since Major Work Thickness Precipitation Average Temp (deg C) Freeze Index # of Freeze-Thaw Cycles Maximum Temp Subgrade Strengtha
ap-value of .063
63
5.2.1.1 COMMON SIGNIFICANT FACTORS
When analyzing Table 5-7, several distresses share significant factors. Table 5-8 shows
the commonalities among the seven distresses in terms of factors affecting pavement
performance. When comparing the distresses that share factors, some trends are noted and are
further discussed in this section. Distresses and factors that are unique to each distress will be
further elaborated in the individual distress sections.
Table 5-8 Common Significant Factors in PCC Distresses
Factors Common Among
Statistically Significant Factors
7 of the 7 Distresses Years Since Major Work
6 of the 7 Distresses None
5 of the 7 Distresses
Thickness Feature
Subgrade Freeze-thaw Cycles
4 of the 7 Distresses
Average Temp Max Average Days Above 32 C
Freeze Index 3 of the 7 Distresses
Average Precipitation Average Mean Temp
2 of the 7 Distresses None
1 of the 7 Distresses
Average Temp Min Average Days Below 0 C
0 of the 7 Distresses None
5.2.1.1.1 YEARS SINCE LAST MAJOR WORK
Observation: Years since last major work is significant in each distress and follows the
same positive trend in the factorial plots. In some distresses, the slopes are steeper and present a
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higher probability of a distress occurring, but overall the greater number of years since the last
major repair, the more likely a distress will occur.
Possible Cause: This result aligns with conventional wisdom that pavements deteriorate
with age.
5.2.1.1.2 PAVEMENT THICKNESS
Pavement thickness is a significant variable in five out of the seven PCC distresses. The
factorial plots show that as thickness increases, linear cracking, shattered slabs, joint spalling and
alkali silica reaction (ASR) are less likely to occur.
Observation: Thinner pavements are much more likely to result in linear cracking and
joint spalling than in shattered slabs and ASR based on probabilities from their factorial plots.
Linear cracking and joint spalling share similar factorial plots for subgrade and temperature
climate variables.
Possible Cause: The factorial plots for subgrade and temperature variables suggests that
curling and shrinkage stresses could be influencing the thinner pavements and resulting in distress
occurrence.
Observation: Small patches are the fifth distress and as pavements increased in thickness
the probability of a distress occurring increases.
Possible Cause: This phenomenon may be due to the cost to replace thick pavement
sections can be very expensive. Thicker pavements also tend to be in more critical areas of an
airfield that cannot be shut down for reconstruction. The effect of features on PCC distresses is
discussed in the upcoming section; the results show runways have the most small patches.
Runways tend to be the thickest pavement sections, and since runways have the most small
patches, it makes sense that the probability increases as thickness increases. The combination of
65
budget constraints and the airfield operation effects may be why thicker pavements are more
likely to have small patches than thinner pavement sections.
5.2.1.1.3 AIRFIELD FEATURE
Feature is also significant across five of the seven PCC distresses: linear cracking, joint
seal damage, large patch, shattered slab, and small patch.
Observation: For linear cracking, joint seal damage, large patch, and shattered slab, the
feature with the highest probability of causing a distress occurrence is apron, followed by
taxiway, and then runway. This means that runways have the lowest probability of a distress
occurring when analyzing those four distresses.
Possible Cause: Runways are the most important pavement asset on an airfield; the
USAF has good policies and procedures in place to make sure runways are the healthiest feature.
Another possible explanation for this trend is due to the difference in aircraft that travel on each
of the features. The factorial plots show that feature is an impactful factor in the probability of the
occurrence of linear cracking, joint seal damage, and large patches.
Observation: Feature is also a significant factor in small patch distresses. Small patches
have the opposite trend in that runways have the highest probability of the distress occurring, then
taxiways, and then aprons.
Possible Cause: This trend may be due to the same policy of a runway’s criticality to the
USAF. Large construction on a runway can deplete the USAF mission and may reduce
capabilities to an unacceptable level. Small patches on degraded pavement sections allow for
expedient repairs and limit the USAF operational effects.
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5.2.1.1.4 SUBGRADE STRENGTH
Subgrade strength is an additional factor that is significant in five out of the seven
distresses analyzed. Linear cracking, large patch, shattered slab, joint spalling, and ASR are the
distresses that have subgrade strength as significant.
Observation: In all five cases, the trend is that the weaker the subgrade strength, the
higher probability of a distress occurrence. From the factorial plots, the probability of a distress
occurrence is influenced at a high level by subgrade strength for linear cracking, large patches,
and joint spalling.
Possible Cause: Similar to thickness, these three distresses are coupled with an array of
temperature variables that suggest curling and shrinkage stresses may be prevalent in these
pavement sections.
5.2.1.2 DISTRESS 63 - LINEAR CRACKING (LONGITUDINAL, TRANSVERSE, DIAGONAL)
Longitudinal, transverse cracks are cracks that divide a slab into two or three pieces that
are conventionally caused by a combination of load repetition, curling stresses, and shrinkage
stresses (US Army Corps of Engineers, 2009). Distress 63 can also be caused in soft or expansive
sublayers and improper spacing of joints (Florida Department of Transportation Aviation Office,
2013). Figure 5-5 is an image of a typical linear crack.
Figure 5-5 Linear Cracking (US Army Corps of Engineers, 2009)
Observation: Figure 5-6 shows the results of the statistical analysis for linear cracking.
Linear cracking follows the trend that older pavements and thinner pavements are more likely to
have a distress occurrence. Additionally, it follows the trend that weaker subgrades are more
likely to have a distress occurrence. Aprons are more prone to the distress followed by taxiways
and then runways. Although precipitation is a significant factor in terms of p-values, the factorial
plot shows that the probability of the distress occurring does not change drastically with more
precipitation. The remaining factors are temperature climate factors: freeze index, maximum
temperature, and number of days above 32 degrees Celsius. As the freezing index increases, the
68
probability increases the likelihood of linear cracking occurring greatly. The maximum
temperature and number of days above 32 degrees Celsius also affect performance, but affect the
likelihood of a distress occurring in different trends. As maximum temperature increases, the
distress is more likely to occur, however, as the number of days above 32 degrees Celsius
increases the likelihood of a distress occurrence decreases.
Possible Cause: Although a direct explanation of this phenomenon is unknown without
further investigation, the combination of cold and warm temperature factors can assist in
understanding the distress occurrence. The fact that thinner pavements have a higher probability
of a distress occurrence and the combination of cold and warm temperature significant factors
suggest that curling and shrinkage stresses may have played a role in the causation of linear
cracking. Additionally, as linear cracking is known to be a load-related distress, loads coupled
with curling stresses could also lead to linear cracking.
69
Figure 5-6 Summary Statistics for Distress 63 - Linear Cracking
5.2.1.3 DISTRESS 65 - JOINT SEAL DAMAGE
“Joint seal is a pliable joint filler bonded to the edges of the slabs that protects the joints
from accumulation of materials and also prevents water from seeping down and softening the
foundation supporting the slab. Joint seal damage is any condition which enables soil or rocks to
accumulate in the joints or allows significant infiltration of water” (US Army Corps of Engineers,
2009). When rocks and incompressible soils accumulate between slabs, slabs will not be able to
properly expand which may result in additional distresses. Typical causes of joint seal damage are
improper joint width, wrong type of sealant used, incorrect application, and improper cleaning of
joint before application (Florida Department of Transportation Aviation Office, 2013). Figure 5-7
is an image of joint seal damage between two PCC slabs.
Figure 5-7 Joint Seal Damage (US Army Corps of Engineers, 2009)
Observation: Joint seal damage is most likely to occur on aprons, then taxiways,
followed by runways. Also, the longer it has been since a major repair has occurred on a
pavement section, the more likely a distress is to occur. The remaining four significant factors are
freeze index, freeze-thaw, minimum temperature, and number of days above 32 degrees Celsius.
As seen in the factorial plots, as the climate variable increases, they all decrease the likelihood of
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a distress occurring. As freeze index increases it signifies colder climates. As the minimum
temperature and days above 32 degrees Celsius increases, it signifies warmer climates. As freeze-
thaw cycles increase, it signifies a cyclical process between warm and cold climates.
Possible Cause: The lack of trend between climate variables suggests that joint seal
damage can happen in all climates and can be due to a combination of high and low temperatures.
Joint seal damage is a known climate-related distress and since the climate variables are still
significant in the likelihood of joint seal damage occurrence, it appears current joint seal design
could better account for all the climate variables.
Figure 5-8 Summary Statistics for Distress 67 - Joint Seal Damage
5.2.1.4 DISTRESS 67 - LARGE PATCH AND UTILITY CUT
Large patches are defined as an area greater than 5.5 square feet where an original
pavement has been removed and replaced by a filler material. Similarly, a utility cut is where an
original pavement has been removed due to the placement of an underground utility (US Army
Corps of Engineers, 2009). Large patches are used to improve a portion of a pavement section
that is poor in hopes to improve the pavement’s condition. Patches can be effective, less
expensive than full slab replacement, and an expedient method to improve critical pavement
sections. Figure 5-9 shows an image of a large patch/utility cut.
Figure 5-9 Large Patch/Utility Cut (US Army Corps of Engineers, 2009)
Observation: Years since major work, feature, and subgrade strength follow the trend
presented in the summary. Although significant variables, feature and subgrade both display a
probability of distress occurring of less than 50 percent as seen in the factorial plots in Figure 5-
10. The remaining significant variables are climate temperature factors to include average
temperature, freeze index, number of freeze-thaw cycles, and maximum temperature.
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Possible Cause: As freeze index increases, the probably of a distress occurring increases
greatly suggesting that large patches are more prevalent in cooler climates. The three additional
factors do not align with that interpretation, suggesting the distress occurs in both warm and cool
climates. The three additional distresses do not have a great impact on the probability of a distress
occurrence though, which leaves the possibility of large patching being more common in colder
climates.
Figure 5-10 Summary Statistics for Distress 67 - Large Patch/Utility Cut
5.2.1.5 DISTRESS 72 - SHATTERED SLAB/INTERSECTION CRACKS
Shattered slabs are when intersecting cracks break a slab into four or more pieces. The
shattered slabs are typically caused by overloading a pavement section and inadequate support
(US Army Corps of Engineers, 2009). Inadequate support could be a combination of several
factors to include weak or expansive sublayers, curling and shrinkage stresses or improper design
or spacing of joints. Figure 5-11 depicts an image of a shattered slab.
Figure 5-11 Shattered Slab (US Army Corps of Engineers, 2009)
Observations: The years since major work, thickness, feature, and subgrade strength all
follow the overall trend presented in the summary. The remaining significant variables are four
climatic variables: freeze-thaw cycles, maximum temperature, days above 32 degrees Celsius and
days below 0 degrees Celsius. Maximum temperature and days below 0 degrees Celsius share
similar positive slopes. The positive slopes suggest as maximum temperature increases and days
below 0 increase, the more likely shattered slabs will occur. As the number of freeze-thaw cycles
and days above 32 degrees Celsius increases, the likelihood of the distress occurring decreases.
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Possible Causes: The contradicting climate factorial plots suggest shattered slabs happen
in an array of climates. Shattered slabs are a known load-related distress, so the climate variables
coupled with the pavement structure variables may be correlated in the occurrence of the distress.
Similar to linear cracking, shattered slabs may be caused due to a combination of curling stresses
and heavy aircraft loads that exceed the design loads.
Figure 5-12 Summary Statistics for Distress 72 - Shattered Slabs
5.2.1.6 DISTRESS 74 - JOINT SPALLING
A joint spall is the degradation of a PCC slab within 2 feet of the side of the joint. A joint
spall does not usually extend vertically through the slab, but intersects the joint at an angle (US
Army Corps of Engineers, 2009). “Spalling results from excessive stresses at the joint or crack
caused by infiltration of incompressible materials or traffic loads. Weak concrete at the joint
(caused by overworking) combined with traffic loads also causes spalling” (US Army Corps of
Engineers, 2009). Figure 5-13 is an image of a joint spall.
Figure 5-13 Joint Spalling (US Army Corps of Engineers, 2009)
Observation: The years since major work, thickness, and subgrade strength all follow the
overall trend presented in the summary. The remaining variables are average temperature, freeze-
thaw cycles, and maximum temperature and the results can be found in Figure 5-14. Mean
temperature suggests as the mean temperature increases, the less likely the distress will occur,
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while the maximum temperature suggests that as the max temperature increases, the likelihood of
the distress increases. As the number of freeze-thaw cycles increase, joint spalling is less likely to
occur.
Possible Cause: Joint spalling is a known to be caused by excessive stress at the joints.
The array of climate variables and the significant pavement structure factors suggest that the
excess stress at the joints may be caused due to curling stresses and loading conditions.
Figure 5-14 Summary Statistics for Distress 74 - Joint Spalling
5.2.1.7 DISTRESS 66 - SMALL PATCH
A small patch is similar to a large patch, but a small patch has an area less than 5.5 square
feet (US Army Corps of Engineers, 2009) . Like a large patch, a small patch uses a filler material
to replace a degraded section of original pavement in hopes to increase the overall pavement
condition. Figure 5-15 shows an image of a typical small patch.
Figure 5-15 Small Patch (US Army Corps of Engineers, 2009)
Observation: The years since major work aligns with the summary findings. As stated in
the summary, small patches are odd in that as the pavement thickness increase, small patches are
more likely to occur. As seen in Figure 5-16, small patches are the only distress that have runway
as the most prominent pavement feature to have the distress. Outside of those three factors,
precipitation and days above 32 degrees Celsius are significant climate variables. As precipitation
increases, the likelihood of a distress occurring decreases. Similarly, as average days above 32
degrees Celsius increases the likelihood of the distress occurring decreases.
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Possible Causes: Runways are statistically most likely to have a small patch. Runway
construction presents airfield operational problems, so typically large M&R activities on runways
are avoided. Small patches are one way to improve runway pavement condition and have minimal
impact on airfield operations. When analyzing thickness and while considering the feature, it
makes sense that thicker pavements are more likely to have small patches because runways tend
to have the thickest pavements.
Figure 5-16 Summary Statistics for Distress 66 - Small Patches
5.2.1.8 DISTRESS 76 - ALKALI SILICA REACTIVITY
Alkali Silica Reactivity (ASR) is a distress that is caused by a reaction between silica in
aggregate and alkali in portland cement. This reaction forms an expansive white gel that induces
stresses within a concrete specimen. The reaction from ASR takes time and does not present itself
immediately, but can be accelerated by some pavement deicers. Figure 5-17 provides an image of
ASR in a pavement structure.
Figure 5-17 Alkali Silica Reactivity (US Army Corps of Engineers, 2009)
ASR is a heavily researched topic and it is well known that the distress is caused by a
reaction between silica in aggregates and alkali in the portland cement. The analysis in Figure 5-
18 was accomplished with the anticipation of not finding any major factors since ASR is well
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understood. Although there were several significant factors, the factorial plots depict that none of
the factors attribute greatly to the probability of a distress occurring.
Figure 5-18 Summary Statistics for Distress 76 - Alkali Silica Reactivity
5.2.2 ASPHALT CONCRETE PAVEMENT DISTRESSES
The asphalt concrete airfield pavement distresses will be evaluated in the following
sections. The sections are ordered from the distresses causing largest summative reduction of
pavement condition to the lowest of the distresses analyzed. Each distress is first analyzed by
determining the factors that are statistically significant in the pavement’s performance. Table 5-9
summarizes the ranks each of the distresses in terms of which is causing the highest reduction of
pavement condition across the Air Force and summarizes the factors that were found to be
statistically significant.
Table 5-9 Asphalt Concrete Distresses Analyzed
Distress Rank
Distress Code Distress Name
Typical Cause Significant Factors
2 48 Longitudinal
and Transverse Cracking
Climate
Years Since Major Work Thickness Average Freeze Index Average # of Freeze-Thaw Cycles Average Temp Min Surface Type
4 57 Weathering Climate
Thickness Average Precipitation Average Freeze Index Average # of Freeze-thaw Cycles Average Temp Min Average Days Above 32 C
5 43 Block Cracking Climate
Years Since Major Work Average Precipitation Average Temp Min Average Days Above 32 C Surface Type
10 41 Alligator Cracking Load
Years Since Major Work Average # of Freeze-Thaw Cycles Average Temp Max Average Days Above 32 C Subgrade Strength Surface Type
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5.2.2.1 COMMON SIGNIFICANT FACTORS
When analyzing Table 5-9, several distresses share significant factors. Table 5-10 shows
the commonalities the seven distresses have in terms of factors affecting pavement performance.
When comparing the distresses that share factors, some trends are noted and they are further
discussed in this section. Distresses and factors that are unique to each distress will be further
elaborated in the individual distress sections.
Table 5-10 Common Significant Factors in AC Distresses
Factors Common Among
Statistically Significant Factors
4 of the 4 Distresses None
3 of the 4 Distresses
Years Since Major Work Freeze-Thaw Cycles
Temp Min Surface Type
Average Days Above 32 C
2 of the 4 Distresses
Thickness Freeze Index
Average Precipitation
1 of the 4 Distresses
Average Temp Max Subgrade Strength
0 of the 4 Distresses
Feature Average Temp
Average Days Below 0 C
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5.2.2.1.1 YEARS SINCE LAST MAJOR WORK
Observation: Years since last major work is a significant predictor in three out of the four
distresses: longitudinal and transverse cracks, block cracking, and alligator cracking. Block
cracking and alligator cracking follow the anticipated trend of the more years since the last major
work, the more likely a distress is to occur. Longitudinal and transverse cracking do not follow
the same trend though.
Possible Cause: The factorial plots for longitudinal and transverse cracking suggest the
more years since the last major work the less like a distress is to occur. According to the response
information, approximately 87 percent of AC pavement sections have longitudinal and transverse
cracks. Although, typically we may anticipate the trend to be the more years since major repair
the more likely a distress to occur, the data set suggest almost all AC pavement sections have
longitudinal and transverse cracking which may be why this result occurred.
5.2.2.2.2 NUMBER OF FREEZE THAW CYCLES
Observation: Longitudinal and transverse cracking, weathering and alligator cracking all
contain the number of freeze-thaw cycles as a significant factor. Longitudinal and transverse
cracking and weathering suggest that as freeze-thaw cycles increase, the likelihood of a distress
occurrence decreases.
Possible Cause: This information suggests that the USAF currently designs and
maintains pavement sections well in areas with large numbers of freeze-thaw cycles. On the
contrary, alligator cracking suggests that as freeze-thaw cycles increase, the more likely a distress
is to occur. After reviewing the factorial plots, the slope is so small that it suggests freeze-thaw
cycles have a negligible impact on the occurrence of alligator cracking.
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5.2.2.2.3 AVERAGE MINIMUM TEMPERATURE
Observation: The average annual one-day minimum temperature is a significant factor in
longitudinal and transverse cracking, weathering, and block cracking. For all three distresses, the
trend is the same in that the colder the minimum temperature, the more likely the distress is to
occur.
Possible Cause: In all three cases the slopes are steep which suggests minimum
temperature plays a significant role in a distress occurring. Longitudinal and transverse cracking,
weathering, and block cracking are typically climate-related distresses. The observation aligns
with conventional wisdom which suggests current policies are not effectively accounting for low
temperatures in AC pavements.
5.2.2.2.4 DIFFERENT AC PAVEMENT SURFACES
Observation: The different AC pavement surfaces (AC, APC, and AAC) is a significant
factor among longitudinal and transverse cracking, block cracking, and alligator cracking.
Longitudinal and transverse cracks are more prevalent on AC pavements followed by APC
pavements, and then AAC pavements. Alligator cracking is also most prevalent on AC
pavements, but APC is next and then AAC. Block cracking follows a different trend in that AAC
pavements are most likely to have a distress occur, followed by AC, and then APC.
Possible Cause: Without further analysis, a possible cause could not be discovered due to
the lack of trends or patterns.
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5.2.2.2.5 NUMBER OF DAYS ABOVE 32 DEGREES CELSIUS
Observation: The number of days above 32 degrees Celsius per year is also a significant
factor in three out of four distresses. Weathering and alligator cracking suggest that the more days
above 32 degrees Celsius, the less likely a distress is to occur. The opposite can be said for block
cracking which also has the number of days above 32 degrees Celsius as a significant factor. The
more days above 32 degrees Celsius suggest block cracking is more likely to occur.
Possible Cause: Based on these observations, it appears that weathering and alligator
cracking are less likely to occur in hotter climates. Alternatively, the results suggest block
cracking is more likely to occur in hotter climates.
5.2.2.1 DISTRESS 48 - LONGITUDINAL AND TRANSVERSE CRACKING
Longitudinal cracks are parallel to the pavement centerline and transverse cracks extend
across a pavement section perpendicular to the centerline. They are caused by shrinkage of the
AC surface due to low temperatures or hardening of the asphalt or reflective cracks caused by the
layer beneath the surface layer. Longitudinal cracking may also be caused by a poorly constructed
paving line (US Army Corps of Engineers, 2009). These cracks are typically not load associated
and are usually climate-related. Figure 5-19 depicts a cracking in the longitudinal or transverse
direction.
Figure 5-19 Longitudinal and Transverse Cracking (US Army Corps of Engineers, 2009)
Observation: Longitudinal and transverse cracking is ranked second on the list of all
distresses analyzed in terms of the largest cumulative PCI deduct values in the USAF. It is also
the AC pavement distress causing the highest PCI deduct values. Figure 5-20 shows the summary
statistics for longitudinal and transverse cracking. According to Figure 5-20, approximately 87
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percent of all AC pavement sections have longitudinal and transverse cracking. This high
percentage of distress occurrences may be the reason for some of the unconventional statistical
results. Some unconventional results include: a decrease in likelihood of distress occurrence as
years since major repair increases, an increase in likelihood of distress occurrence as thickness
increases, a decrease in likelihood of distress occurrence as freeze index increases, and the
decrease in likelihood of distress occurrence as the number of freeze-thaw cycles increase. We
expect as average minimum temperature increases, longitudinal and transverse cracking
likelihood of occurrence decreases and that can be seen in the factorial plots.
Possible Cause: The combination of 87 percent of AC pavement sections have distress
48 and the minimum temperature factorial plot suggest current USAF policy could be improved
to mitigate or prevent longitudinal and transverse cracking.
Figure 5-20 Summary Statistics for Distress 48 - Longitudinal and Transverse Cracking
5.2.2.2 DISTRESS 57 - WEATHERING
Weathering is the “wearing away of the asphalt binder and fine aggregate matrix from the
pavement surface” (US Army Corps of Engineers, 2009). Weathering typically occurs in older
pavements or due to climatic weather conditions over time (Florida Department of Transportation
Aviation Office, 2013). Figure 5-21 is an image of wearing away of the asphalt binder and fine
aggregate as a result of weathering.
Figure 5-21 Weathering (US Army Corps of Engineers, 2009)
Observation: Weathering is ranked second on the list of AC pavement distress causing
the highest collective reduction in PCI deduct values. Similar to longitudinal and transverse
cracking, a large number of pavement sections, 94 percent, have a weathering issue. Due to the
large number of pavement sections with weathering, there are unconventional results in the
summary statistics in Figure 5-22. It is typically not expected for weathering to be more apparent
when pavement thickness increases, as freeze index decreases, and as freeze-thaw cycles
decrease. Average precipitation is a significant factor, but according to the factorial plots, almost
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any amount of precipitation has a high likelihood of weathering. Average minimum temperature
suggests colder climates have a higher likelihood of distress occurrence. Additionally, the less
days above 32 degrees Celsius, the more likely a distress is to occur.
Possible Cause: Similar to longitudinal and transverse cracking, the combination of 94
percent of pavement sections have weathering and the quantity of significant climate variables
suggest the USAF policy to mitigate or prevent weathering can use improvements.
Figure 5-22 Summary Statistics for Distress 57 - Weathering
5.2.2.3 DISTRESS 43 - BLOCK CRACKING
“Block cracks are interconnected cracks that divide the pavement into approximately
rectangular pieces” (US Army Corps of Engineers, 2009). Block cracking is caused mainly by
shrinkage of the AC, daily temperature cycling, and aging. It is not load-related and typically
indicates the asphalt has hardened significantly (US Army Corps of Engineers, 2009). Figure 5-
23 presents an image of block cracking.
Figure 5-23 Block Cracking (US Army Corps of Engineers, 2009)
Observation: As stated in the summary, the more years since the last major work there
are, the more likely a block cracking is to occur. Also, AAC pavements are more likely to have
block cracking, followed by AC, and then APC. The difference between AAC, AC, and APC is
minimal though and does not suggest one is drastically more likely to have block cracking than
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the other. As presented in Figure 5-24, precipitation, minimum temperature, and average days
above 32 degrees are also significant factors. Their impacts can be seen in the factorial plots.
Possible Cause: The climate variables do not depict a specific trend, but suggest that the
block cracking can occur in an array of climates ranging from cold climates, to hot climates, and
climates with a range of precipitation. Conventional wisdom suggests that block cracking
happens due to AC shrinkage, temperature cycling, and aging. These factors analyzed together
align with that conventional wisdom of block cracking and suggests AC is currently not designed
or maintained to prevent the factors from having a significant impact.
Figure 5-24 Summary Statistics for Distress 43 - Block Cracking
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5.2.2.4 DISTRESS 41 - ALLIGATOR CRACKING
“Alligator or fatigue cracking is a series of interconnecting cracks caused by fatigue
failure of the asphalt surface under repeated traffic loading. The cracking initiates at the bottom of
the asphalt surface (or stabilized base) where tensile stress and strain is highest under a wheel
load. The cracks propagate to the surface initially as a series of parallel cracks. After repeated
traffic loading, the cracks connect and form multi-sided, sharp-angled pieces that develop a
pattern resembling chicken wire or the skin of an alligator. Alligator cracking occurs only in areas
that are subjected to repeated traffic loadings, such as wheel paths. Alligator cracking is
considered a major structural distress” (US Army Corps of Engineers, 2009). Figure 5-25 is an
image of a typical alligator crack. Alligator cracking has a similar appearance to block cracking,
but alligator cracking is typically smaller and confined to areas with repeated loading.
Figure 5-25 Alligator Cracking (US Army Corps of Engineers, 2009)
Observation: Years since major work aligns with conventional wisdom that the more
years since major repair, the more likely alligator cracking will occur. Alligator cracking is the
only AC pavement distress that has subgrade as a significant factor and the factorial plots in
Figure 5-26 show that stronger subgrades are less likely to have alligator cracking than weaker
subgrades. Also, the factorial plot for surface type for alligator cracking suggests that AC is more
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likely to have the distress than AAC, and then APC. Subgrade strength and surface type together
suggests that stronger layers beneath the AC layer are less likely to have alligator cracking.
Weaker sub layers are less ideal for pavement design, but design procedures consider the
subgrade strength in the design.
Possible Cause: Since alligator cracking is a known load and structural related distress, a
possible cause could be that design has not fully accounted for subgrade strength or heavier loads
than the design loads are traversing on the AC pavement sections. There are also three climate
variables significant in alligator cracking to include freeze-thaw cycles, maximum temperature,
and number of days above 32 degrees Celsius. Freeze-thaw cycles and days above 32 degrees do
not have a very steep slope as they increase. Maximum temperature has a steep slope as it
increases which suggests areas with hot climates are more likely to have alligator cracking. These
climate variables may also impact the pavement layer and sublayer’s strength which could assist
in the development of alligator cracks.
Figure 5-26 Summary Statistics for Distress 41 - Alligator Cracking
CHAPTER 6
SUMMARY AND CONCLUSIONS
The objective of this research was to provide feedback to the USAF on the condition of
its airfield pavement assets worldwide. A step in a typical pavement asset management system is
to provide feedback on the effectiveness of the current policies ability to maintain the owner’s
desired level of service. This study sought to provide feedback to the USAF by analyzing the
pavement distresses that are currently causing the highest cumulative reduction of pavement
condition and determine the factors that are correlated to the distresses occurring under the
USAF’s current policies.
The airfield pavement distresses causing the largest summative reduction of pavement
condition were determined using the current USAF pavement inspections. Table 5-1 in the
previous chapter depicts the complete list of ranked pavement distresses causing the highest
cumulative PCI deduct values. From that list, the eleven distresses with the largest summative
PCI deduct values were analyzed with seven PCC distresses and four AC distresses. The seven
PCC distresses are linear cracking, joint seal damage, large patch/utility cut, shattered slab, joint
spalling, small patch, and alkali silica reactivity. The four AC distresses are longitudinal and
transverse cracking, weathering, block cracking, and alligator cracking.
Developing pavement management processes and policies to eliminate pavement distress
occurrences is very challenging and more likely impossible. The significant factors found in this
study identify possible areas of improvement to current USAF policy and their pavement
management system, although budget constraints and other priorities may be restricting the
available options. Many of the pavement sections may have been designed and maintained under
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historical policies. This research can also be used to ensure updates to the historical policies have
been made to account for the factors that are presented as statistically significant.
PCC and AC pavement sections have different design and management policies, because
PCC and AC pavement are known to perform differently. In the subsequent sections, PCC and
AC pavements are discussed independently to account for the difference in material performance.
After the discussion of PCC and AC airfield pavements, the limitations of this research are
presented. Throughout the process of this research, several potential future research projects were
identified.
6.1 FINDINGS AND RECOMMENDED INVESTIGATIONS FOR PORTLAND CEMENT CONCRETE PAVEMENTS
There were sixteen PCC airfield pavement distresses analyzed in this research. From the
aggregated data analysis, the seven PCC pavement distresses with the largest cumulative PCI
deduct values were used for statistical analysis. The seven PCC pavement distresses are linear
cracking, joint seal damage, large patch/utility cut, shattered slab, joint spalling, small patch, and
alkali silica reactivity. These distresses were statistically analyzed to determine if the distress
occurrence is correlated to pavement structure or climatic variables.
The difference in airfield features; runways, taxiways, and aprons, were determined to be
statistically significant in many of the PCC pavement distresses analyzed. According to the
statistical results, runways remain the feature in the best condition, followed by taxiways, and
then aprons. The USAF’s current M&R policies align with this finding as runways are the most
important pavement asset on an airfield. It is important to note that each feature has a different
probability of a pavement distress occurring, so each feature may be designed and maintained
separately. For example, the difference in wander of traffic for each feature needs to be
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considered. With all factors considered, the USAF has a successful policy in ensuring PCC
runways are the healthiest feature.
Years since major work was a significant factor in seven of the seven pavement
distresses. Table 6-1 below depicts the average years since major work for each PCC pavement
distress in terms of pavement sections without distress occurrence and sections with distress
occurrence. For example, in terms of linear cracking, it has been 19 years on average since a
major work activity was completed on a pavement section without a distress occurrence.
Conversely, a pavement section with a linear cracking occurrence has not had a major work
activity for 35 years on average. The remaining results suggest that for the distresses analyzed,
the average years since major work for pavement sections without distress occurrence is 22 years.
Although the USAF does not design for a pavement design life in years, this result suggests the
USAF has an average design life of 22 years. Organizations that use similar design procedures as
the USAF design for a life of 20 years, so on average the USAF’s pavement system exceeds the
typical 20-year design life. This result suggests the USAF pavement management and design
policies are successful in achieving a 20-year design life for PCC pavements sections.
Table 6-1 Average Years Since Major Work Analysis for PCC Distresses
Average Years Since Major Work (years)
Distress Code Distress Name No Distress
Occurrence Standard
Dev. Median Distress Occurrence
Standard Dev. Median
63 Linear Cracking 19 14 16 35 19 31 65 Joint Seal Damage 17 15 12 30 19 25
67 Large Patch/Utility Cut 21 16 16 35 19 31
72 Shattered Slab 25 18 21 45 18 45 74 Joint Spalling 24 18 20 29 19 23 66 Small Patch 22 17 16 30 19 25
76 Alkali Silica Reaction 27 19 21 36 21 30
Total Avg 22 17 17 34 19 30
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The two pavement structure factors, pavement layer thickness and subgrade, are
significant across several distresses. Typically, in this research, thinner PCC pavements are more
likely to have a distress occurrence. In a laboratory experiment, thinner pavements are weaker
and more likely to crack under a load. In a practical application, design policies should ideally
have considered all the PCC design parameters and thinner pavement should not inherently be
more prone to distress occurrence. Subgrade strength is consistently significant across the PCC
distresses. The weaker the subgrade, the more likely the pavement section is to have a distress
occurrence. A possible reason for the pavement structural factors to be significant is that current
policy and design could more effectively consider PCC thickness or subgrade strength when
designing PCC layers; alternatively, the costs of replacing or removing the subgrade or placing a
thicker pavement may have been found to outweigh the advantages of better life-cycle
performance. Or perhaps it is just not possible to fully accommodate a weak subgrade with a
thicker pavement. Another possible cause may be that aircrafts that are heavier than the PCC
pavement design loads are traversing on the pavement sections. When designing pavement
sections, the USAF might consider requiring a minimum subgrade strength for all traffic
conditions. It is not uncommon for aircrafts heavier than design loads to traverse on pavement
sections, so creating a minimum subgrade strength may assist in accounting for the possibility of
heavier aircrafts and could also potentially reduce pavement structural section thicknesses. A cost
benefit analysis could be performed to determine if it is cost effective to create the minimum
requirement. However, weaker subgrades are consistently more likely to have distress
occurrences, so enhancements made to subgrade strength may enhance overall pavement
performance.
Linear cracking, joint seal damage, large patches, shattered slabs, joint spalling, and
small patches are all in the top ten distresses causing the highest cumulative reduction in
pavement condition. Based on how these distresses occur typically, it is possible that these
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distresses might all be related. As one example, joint sealing could be a common factor. Joint seal
damage can lead to water infiltration into the pavement structure which can cause loss of support,
weak foundations, or other additional adverse effects to the pavement structure. Joint seal damage
can also allow incompressible rocks and debris to get in between the slab joints which can
prevent PCC expansion at the joints. The lack of expansion capability can lead to additional
stresses in the concrete or amplify curling stresses. These stresses combined with loads can cause
linear cracking and joint spalling. The linear cracks and joint spalls can be mitigated by small
patches or large patches. If the linear cracks are not proactively maintained, eventually the linear
cracks will intersect and shattered slabs will occur. This compounding effect is supported by the
ranking of each distress in terms of largest cumulative PCI deduct values. This phenomenon
suggests that a better understanding of joint seal damage and linear cracks, development or
adoption of improved sealant materials and techniques, or changes in joint seal policies could be
some avenues to explore for the reduction of joint spalling, small patches, large patches, and
shattered slabs. The recommended airfield pavement maintenance actions from AFI 32-1041 are
presented in Appendix B. The suggested low severity joint seal damage and low severity linear
cracking localized maintenance action is currently to do nothing. It is not until medium severity
joint seal damage and medium severity linear cracking that the localized maintenance plan is to
replace the joint seal or seal linear cracks. Water and debris can infiltrate the cracks at low
severities, so it may help prevent higher severity distresses and other distresses from occurring if
crack sealing and replacing joint seal with effective sealants is accomplished at low severities.
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6.2 FINDINGS AND RECOMMENDED INVESTIGATIONS FOR ASPHALT CONCRETE PAVEMENTS
There are approximately two times more PCC pavement sections in the USAF airfield
pavement inventory than AC pavement sections. Although there are more PCC pavement sections
than AC pavement sections, there are still four AC pavement distresses with cumulative PCI
deduct values in the top ten ranked distresses. The AC pavement distresses ranked second, fourth,
fifth and tenth on the list of distresses causing the largest cumulative PCI deduct values in the
USAF. Without any statistical analysis, this alone suggests the USAF could make improvements
on AC design and maintenance policies. If the AC pavement sections are performing better than
this data represented, it may also suggest that the deduct values or data collection procedures
could be altered to accurately depict the pavement condition for AC pavements.
Approximately, 87 percent of AC pavement sections have longitudinal and transverse
cracking and 94 percent of sections have weathering. It is possible that the USAF may be
focusing maintenance and repair activities on PCC airfield pavements and limited AC airfield
pavement sections to less critical airfields and features. If that is the case, this statistic might align
with the USAF strategic plans. If the USAF goal is to continue using AC airfield pavements,
however, additional research might be accomplished to better understand these two distresses and
updates to design and maintenance policy may be considered.
Longitudinal and transverse cracking is the top AC pavement distress and block cracking
is the number three AC pavement distress causing highest summative PCI deduct values. It is
likely that block cracking is a result of intersecting longitudinal and transverse cracks. There are
significantly less block cracks than linear and transverse cracks, so the USAF may have a M&R
policy to replace the cracks before they become block cracking. If that is the case, the USAF is
doing a good job in keeping the amount of block cracking down. Alternatively, since both
longitudinal and transverse cracks and block cracks are in the top five distresses causing the
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highest cumulative PCI deduct values in the entire USAF, this suggests the USAF may not be
fully accounting for the climatic variable that causes these distresses. If the USAF can better
understand the causes and prevention of longitudinal and transverse cracking under current
processes, it may be able to reduce the occurrence of block cracking.
Years since major work was significant in three out of the four distresses. The results for
all four distresses are presented in Table 6-2. The analysis on Table 6-2 was performed the same
way it was for Table 6-1 for the PCC distresses. The results in Table 6-2 offer differing
conclusions to the idea that the more years since major work, the more likely a distress is to
occur. For longitudinal and transverse cracking and weather, the trend is that pavement sections
without distress occurrences have an average of more years since major work than pavement
sections with a distress occurrence. One way to interpret that data is by recalling that 87 percent
of AC pavement sections have longitudinal and transverse cracking and 94 percent of AC
pavement sections have weathering. Due to such a large number of pavement sections with the
distresses occurring, these results can be skewed. Another way to look at the results is to analyze
the relationship between longitudinal and transverse cracking and block cracking. Block cracks
are intersecting longitudinal and transverse cracks, so eventually longitudinal and transverse
cracks often become block cracks. When longitudinal and transverse cracks intersect to become
block cracks, the current policy transitions the distress to only include block cracks, bringing the
extent of longitudinal and transverse cracks to zero. Additionally, there is not currently a
designation for the difference between longitudinal cracks that are climate-related or load-related.
Load-related cracks may sometimes first appear as a longitudinal or transverse crack. At a later
inspection, the load-related longitudinal or transverse crack may have developed into a load-
related distress such as alligator cracking. The loss of distress information through the transition
from longitudinal and transverse cracking to block cracking or alligator cracking suggests a
limitation and could assist in explaining Table 6-2. To assist in determining if the transition may
102
help in explaining Table 6-2, longitudinal and transverse cracking was combined with block
cracking to form one distress. Analysis on this combined distress present the average years since
major work for a section with no distress occurrence to be 15 years and a section with distress
occurrence to be 18 years. These results present a trend that is expected and suggest the transition
from longitudinal and transverse cracking to block cracking is a limitation.
Table 6-2 Average Years Since Major Work Analysis for AC Distresses
Average Years Since Major Work (years) Distress
Code Distress Name No Distress Occurrence
Standard Dev. Median Distress
Occurrence Standard
Dev. Median
48 Longitudinal and Transverse Cracking 22 17 16 16 11 13
57 Weathering 20 20 7 16 11 13 43 Block Cracking 14 10 12 25 14 22 41 Alligator Cracking 16 12 13 19 13 15 Total Avg 18 15 12 19 12 16
The three AC pavement distresses, longitudinal and transverse cracking, weathering, and
block cracking are typically climate-related distresses and not load associated. All three distresses
have predominantly statistically significant climate factors associated with the distress. The
climate variables do not indicate whether low temperatures or high temperatures decrease
pavement performance, but suggest all ranges of climate variables are not being fully addressed
by current procedures. For example, the performance grading (PG) specifications for the asphalt
binders may not be considering the actual pavement temperature range at each location. It could
also be that temperature maximums and minimums have evolved to be outside the initial PG
specifications. The climate variables may also be accelerating oxidation of the asphalt binder,
causing these distresses to be more prevalent.
Alligator cracking is the lone structural related AC pavement distress analyzed. Alligator
cracking is not causing the same summative reduction of pavement condition as the other three
AC pavement distresses, but there are improvements that could be made to reduce the amount of
103
alligator cracking could still be considered. Alligator cracking is the only AC distress that has
subgrade strength as a significant factor. If the design subgrade strength was known, it could be
useful to analyze whether the strengths have decreased since the original design since, as
discussed for portland cement concrete pavements, design procedures would be expected to
compensate for poor subgrades to the extent possible. Again, it may be an indication that it is not
cost-effectiveness to address or that loads have increased beyond what was intended at the time of
design, either in magnitudes or repetitions. As discussed for PCC pavements, when designing
pavement sections, the USAF might consider requiring a minimum subgrade strength. A cost
benefit analysis could be performed to determine if it is cost effective to create the minimum
requirement. It is not uncommon for aircrafts heavier than design loads to traverse on pavement
sections, so establishing a minimum subgrade strength may also assist in accounting for the
possibility of heavier aircrafts. The significant climate variables associated with this load-related
distress may also contribute to the reduction of the pavement structure strength.
6.3 LIMITATIONS
Part of this research was to determine which airfield pavement distresses are causing the
highest cumulative reduction of pavement condition and the factors correlated with that reduction
of pavement condition. The contributing factors considered for this research were pavement
structure and climate variables. There are other factors, such as loading factors, local and global
M&R activities, and quality of construction, that could also be correlated with the occurrence of a
distress. This type of data was not available to the author from the PAVER database, but could
potentially have assisted in determining which factors are most correlated to a distress
occurrence.
104
Of the total of 102 bases, there were three USAF bases for which MERRA climate data
was not available, nine locations without subgrade strength data, and additional sections that were
missing pavement layer thickness data. Prior to accounting for the missing data, there were 6,000
PCC pavement sections in the USAF inventory and 2,432 AC pavement sections. After removing
sections with missing data, the remaining number of pavement sections were 2,337 PCC
pavement sections and 1,064 AC pavement sections. The additional data points may have been
beneficial in broadening the range of this research to include more USAF locations and more
pavement sections.
The current PAVER database capabilities only allowed for the author to use data based
on the most recent pavement inspections. The author was interested in collecting pavement
performance data for each section over the course of several previous inspections to determine the
rate of deterioration for each section. Performing research considering rate of deterioration for
each pavement section instead of age may assist in better understanding the factors that are
causing pavement to deteriorate the fastest.
When using binary logistic regression dichotomous values of “0” and “1” are required as
the response variable. Values of “0” are defined as a pavement section with a PCI deduct value
equal to zero and values of “1” are defined as a pavement section with PCI deduct value greater
than zero. Defining any quantity PCI deduct value as a value of “1” presents a limitation because
it assumes a small quantity of a distress affects pavement performance the same as a large
quantity. For example, it suggests a section with a PCI deduct of five affects pavement
performance the same as a PCI deduct value of twenty. One way to mitigate this limitation is to
establish thresholds for each distress and adjust the defined values of “0” and “1” based on
experience and the owner’s input. A possible threshold could be to change the definition of “0” to
a pavement section with a PCI deduct value less than five. Alternatively, the definition of “1”
would become a pavement section with a PCI deduct greater than five. This step was not
105
accomplished in this study. This study presents a methodology of how to provide this type of
feedback to the USAF, and a first cut at providing recommendations that could be modified in
future work to consider the sensitivity of the findings to different thresholds.
In most research using field data, accuracy and precision of the data is a concern.
Although there is a standard methodology and clear definitions for collecting pavement distress
data, the data collection is nonetheless variable. Data collection can vary based on the inspector,
time of day, weather conditions, and several other factors. Similarly, the methodology the USAF
uses to determine PCI deduct values was developed in the late 1970s by pavement engineers’
expert opinions. Although this method of determining PCI deduct values has been the standard
for several decades, the potential subjectivity and possible outdated considerations present
limitations in the meaningfulness of the PCI calculations and thus in the data used for this study.
6.4 RECOMMENDATIONS FOR FUTURE RESEARCH
This research only analyzed the predominant pavement distress correlations climate and
pavement structure factors. Future research including consideration of loading factors, M&R
activities, designed material properties, construction conditions, pavement structure, and climatic
variables may assist greatly in providing feedback to the USAF. The reason all the additional
factors were not used in this research was because the information was not provided to the author
and because there is a lack of data for some of those factors.
For many distresses, feature, subgrade strength, and surface type are significant factorial
variables. Further research on these categorical variables may assist in the USAF feedback. A
better understanding of how each categorical variable relates to factors affecting pavement
performance may provide further information on the effects each categorical variable has on a
106
distress. This could be useful in adjusting maintenance and design policies to be tailored to
specific conditions.
Improvements or revalidation of the subjective methodology to determine PCI deduct
values is another potential research area. Technology and pavement distress understanding has
grown substantially since the development of PCI deduct calculations in the 1970s. Although it
may be a large research effort, future research to update PCI deduct values may present a more
objective way to determine pavement condition.
As previously presented, it appears that some of the distresses analyzed are compounding
each other. For example, in PCC pavement distress, joint seal damage and linear cracking seem to
play a role in the development of joint spalling, small patches, large patches, and shattered slabs.
A similar comparison was be made for the AC pavement distresses. Further research into this
phenomenon may help explain these distress occurrences and provide feedback to the USAF on
their pavement management policies.
107
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APPENDIX A
DETAILED STATISTICAL RESULTS
Distress 63 Linear Cracking
During the first iteration Average Precipitation*Average TEMP_MEAN_AVG (deg C) had a VIF of 25.51 and was removed from the predictor list. During the second iteration, 'Average DAYS_BELOW_0_C' had a VIF of 13.53 and was removed from the predictor list. The third iteration is below.
Method
Link function Logit Categorical predictor coding
(1, 0)
Rows used 2337 Backward Elimination of Terms
Candidate terms: Years Since Major Work Actual, Thickness, Average Precipitation, Average TEMP_MEAN_AVG (deg C), Average FREEZE_INDEX, Average FREEZE_THAW, Average TEMP_MAX, Average TEMP_MIN, Average DAYS_ABOVE_32_C, Feature, Subgrade Strength, Average Precipitation*Average TEMP_MEAN_AVG (deg C), Thickness*Subgrade Strength
------Step 1------ ------Step 2------ Coef P Coef P Constant -2.34 -2.34 Years Since Major Work Actual 0.05678 0.000 0.05678 0.000 Thickness -0.1352 0.006 -0.1353 0.006 Average Precipitation -
0.000452 0.335 -
0.000460 0.010
Average TEMP_MEAN_AVG (deg C) 0.0389 0.401 0.0382 0.197 Average FREEZE_INDEX 0.001423 0.000 0.001420 0.000 Average FREEZE_THAW 0.00290 0.367 0.00289 0.366 Average TEMP_MAX 0.0698 0.024 0.0698 0.024 Average TEMP_MIN 0.0033 0.866 0.0033 0.866 Average DAYS_ABOVE_32_C -0.00598 0.083 -0.00596 0.076 Feature -0.684 0.000 -0.684 0.000 Subgrade Strength -0.755 0.305 -0.755 0.306 Average Precipitation*Average TEMP_MEAN_AVG (deg C)
-0.000001
0.985
112
Thickness*Subgrade Strength 0.0688 0.374 0.0688 0.374 Deviance R-Sq 19.49% 19.49% Deviance R-Sq(adj) 18.99% 19.03% AIC 2631.78 2629.78 AICc 2632.04 2630.01 BIC 2729.64 2721.89 ------Step 3------ ------Step 4------ Coef P Coef P Constant -2.292 -3.042 Years Since Major Work Actual 0.05681 0.000 0.05673 0.000 Thickness -0.1350 0.006 -0.0761 0.000 Average Precipitation -
0.000469 0.006 -
0.000464 0.006
Average TEMP_MEAN_AVG (deg C) 0.0404 0.127 0.0404 0.122 Average FREEZE_INDEX 0.001377 0.000 0.001365 0.000 Average FREEZE_THAW 0.00262 0.344 0.00265 0.338 Average TEMP_MAX 0.0675 0.016 0.0663 0.018 Average TEMP_MIN Average DAYS_ABOVE_32_C -0.00597 0.076 -0.00588 0.079 Feature -0.684 0.000 -0.680 0.000 Subgrade Strength -0.756 0.306 0.435 0.030 Average Precipitation*Average TEMP_MEAN_AVG (deg C)
Thickness*Subgrade Strength 0.0688 0.375 Deviance R-Sq 19.49% 19.43% Deviance R-Sq(adj) 19.06% 19.06% AIC 2627.81 2625.79 AICc 2628.02 2625.95 BIC 2714.16 2700.63 ------Step 5------ ------Step 6------ Coef P Coef P Constant -3.151 -2.873 Years Since Major Work Actual 0.05683 0.000 0.05692 0.000 Thickness -0.0774 0.000 -0.0777 0.000 Average Precipitation -
0.000456 0.007 -
0.000365 0.018
Average TEMP_MEAN_AVG (deg C) 0.0213 0.208 Average FREEZE_INDEX 0.001326 0.000 0.001148 0.000 Average FREEZE_THAW Average TEMP_MAX 0.0827 0.000 0.0798 0.000 Average TEMP_MIN Average DAYS_ABOVE_32_C -0.00716 0.020 -0.00554 0.046 Feature -0.676 0.000 -0.666 0.000 Subgrade Strength 0.440 0.030 0.468 0.014
113
Average Precipitation*Average TEMP_MEAN_AVG (deg C)
Thickness*Subgrade Strength Deviance R-Sq 19.40% 19.35% Deviance R-Sq(adj) 19.06% 19.04% AIC 2624.71 2624.30 AICc 2624.84 2624.42 BIC 2693.79 2687.63
α to remove = 0.1 If a term has more than one coefficient, the largest in magnitude is shown.
Response Information
Variable Value Count Unhealthy vs Healthy
1 1256 (Event)
0 1081 Total 2337
Regression Equation
P(1) = exp(Y')/(1 + exp(Y'))
Feature Subgrade Strength
A A Y' = -2.873 + 0.05692 Years Since Major Work Actual - 0.07769 Thickness - 0.000365 Average Precipitation + 0.001148 Average FREEZE_INDEX + 0.07979 Average TEMP_MAX - 0.005542 Average DAYS_ABOVE_32_C
A B Y' = -2.692 + 0.05692 Years Since Major Work Actual
- 0.07769 Thickness - 0.000365 Average Precipitation + 0.001148 Average FREEZE_INDEX + 0.07979 Average TEMP_MAX - 0.005542 Average DAYS_ABOVE_32_C
A C Y' = -2.404 + 0.05692 Years Since Major Work Actual
- 0.07769 Thickness - 0.000365 Average Precipitation + 0.001148 Average FREEZE_INDEX + 0.07979 Average TEMP_MAX - 0.005542 Average DAYS_ABOVE_32_C
114
R A Y' = -3.539 + 0.05692 Years Since Major Work Actual - 0.07769 Thickness - 0.000365 Average Precipitation + 0.001148 Average FREEZE_INDEX + 0.07979 Average TEMP_MAX - 0.005542 Average DAYS_ABOVE_32_C
R B Y' = -3.358 + 0.05692 Years Since Major Work Actual
- 0.07769 Thickness - 0.000365 Average Precipitation + 0.001148 Average FREEZE_INDEX + 0.07979 Average TEMP_MAX - 0.005542 Average DAYS_ABOVE_32_C
R C Y' = -3.071 + 0.05692 Years Since Major Work Actual
- 0.07769 Thickness - 0.000365 Average Precipitation + 0.001148 Average FREEZE_INDEX + 0.07979 Average TEMP_MAX - 0.005542 Average DAYS_ABOVE_32_C
T A Y' = -3.101 + 0.05692 Years Since Major Work Actual
- 0.07769 Thickness - 0.000365 Average Precipitation + 0.001148 Average FREEZE_INDEX + 0.07979 Average TEMP_MAX - 0.005542 Average DAYS_ABOVE_32_C
T B Y' = -2.920 + 0.05692 Years Since Major Work Actual
- 0.07769 Thickness - 0.000365 Average Precipitation + 0.001148 Average FREEZE_INDEX + 0.07979 Average TEMP_MAX - 0.005542 Average DAYS_ABOVE_32_C
T C Y' = -2.632 + 0.05692 Years Since Major Work Actual
- 0.07769 Thickness - 0.000365 Average Precipitation + 0.001148 Average FREEZE_INDEX + 0.07979 Average TEMP_MAX - 0.005542 Average DAYS_ABOVE_32_C
Coefficients
Term Coef SE Coef VIF Constant -2.873 0.730 Years Since Major Work Actual
0.05692 0.00319 1.09
Thickness -0.0777 0.0131 1.12
115
Average Precipitation -0.000365
0.000154 1.45
Average FREEZE_INDEX 0.001148 0.000196 1.48 Average TEMP_MAX 0.0798 0.0220 4.61 Average DAYS_ABOVE_32_C
-0.00554 0.00278 4.79
Feature R -0.666 0.147 1.20 T -0.228 0.107 1.20 Subgrade Strength B 0.181 0.187 3.62 C 0.468 0.203 3.82
Odds Ratios for Continuous Predictors
Odds Ratio 95% CI Years Since Major Work Actual
1.0586 (1.0520, 1.0652)
Thickness 0.9253 (0.9017, 0.9494)
Average Precipitation 0.9996 (0.9993, 0.9999)
Average FREEZE_INDEX 1.0011 (1.0008, 1.0015)
Average TEMP_MAX 1.0831 (1.0374, 1.1307)
Average DAYS_ABOVE_32_C
0.9945 (0.9891, 0.9999)
Odds Ratios for Categorical Predictors
Level A Level B Odds Ratio 95% CI Feature R A 0.5136 (0.3850,
0.6851) T A 0.7962 (0.6452,
0.9825) T R 1.5501 (1.1609,
2.0698) Subgrade Strength
B A 1.1983 (0.8304, 1.7292)
C A 1.5970 (1.0724, 2.3781)
C B 1.3327 (1.0727, 1.6557)
Odds ratio for level A relative to level B
Model Summary
116
Deviance R-Sq
Deviance R-Sq(adj) AIC AICc BIC
19.35% 19.04% 2624.30 2624.42 2687.63 Goodness-of-Fit Tests
Test DF Chi-Square P-Value Deviance 2326 2602.30 0.000 Pearson 2326 2337.54 0.429 Hosmer-Lemeshow
8 19.43 0.013
Analysis of Variance
Wald Test Source DF Chi-Square P-Value Regression 10 430.29 0.000 Years Since Major Work Actual
1 318.82 0.000
Thickness 1 34.94 0.000 Average Precipitation 1 5.63 0.018 Average FREEZE_INDEX 1 34.47 0.000 Average TEMP_MAX 1 13.20 0.000 Average DAYS_ABOVE_32_C
1 3.97 0.046
Feature 2 20.82 0.000 Subgrade Strength 2 8.57 0.014
117
118
119
Distress 48 Linear and Transverse Cracking
During the first iteration, 'Average DAYS_BELOW_0_C' had a VIF of 111.82 and was removed from the predictor list. The second iteration Average TEMP_MEAN_AVG (deg C) had a VIF of 27.04 and was removed. The third iteration had all predictors with a VIF less than 10 and are below.
Method
Link function Logit Categorical predictor coding
(1, 0)
Rows used 1064 Backward Elimination of Terms
Candidate terms: Thickness, Average Precipitation, Average FREEZE_INDEX, Average FREEZE_THAW, Average TEMP_MAX, Average TEMP_MIN, Average DAYS_ABOVE_32_C, Subgrade Strength, Surface Type - Current, Thickness*Subgrade Strength, Years Since Major Work Actual, Feature
------Step 1----- ------Step 2----- ------Step 3----- Coef P Coef P Coef P Constant 1.17 1.18 1.15 Thickness 0.0987 0.088 0.1116 0.017 0.1123 0.017 Average Precipitation -
0.000068 0.841 -
0.000066 0.847
Average FREEZE_INDEX -0.000460
0.167 -0.000473
0.154 -0.000469
0.156
Average FREEZE_THAW -0.01460 0.002 -0.01461 0.002 -0.01424 0.001 Average TEMP_MAX 0.0146 0.736 0.0127 0.768 0.0105 0.800 Average TEMP_MIN -0.0515 0.013 -0.0517 0.012 -0.0512 0.012 Average DAYS_ABOVE_32_C
-0.00217 0.710 -0.00215 0.711 -0.00163 0.751
Subgrade Strength -0.431 0.702 0.183 0.526 0.182 0.533 Surface Type - Current 0.916 0.001 0.918 0.001 0.921 0.001 Thickness*Subgrade Strength
0.072 0.880
Years Since Major Work Actual
-0.02853 0.000 -0.02853 0.000 -0.02854 0.000
Feature 0.454 0.138 0.458 0.128 0.458 0.127 Deviance R-Sq 9.56% 9.53% 9.52% Deviance R-Sq(adj) 7.59% 7.81% 7.92% AIC 769.60 765.87 763.91 AICc 770.19 766.33 764.31 BIC 854.09 840.42 833.48 ------Step 4----- ------Step 5----- ------Step 6----- Coef P Coef P Coef P Constant 1.472 1.462 1.497
120
Thickness 0.1130 0.016 0.1121 0.016 0.1088 0.018 Average Precipitation Average FREEZE_INDEX -
0.000540 0.002 -
0.000527 0.001 -
0.000520 0.002
Average FREEZE_THAW -0.01432 0.001 -0.01408 0.001 -0.01402 0.001 Average TEMP_MAX Average TEMP_MIN -0.0535 0.003 -0.0526 0.003 -0.0527 0.003 Average DAYS_ABOVE_32_C
-0.00064 0.847
Subgrade Strength 0.188 0.535 0.177 0.544 Surface Type - Current 0.925 0.001 0.921 0.001 0.923 0.001 Thickness*Subgrade Strength
Years Since Major Work Actual
-0.02849 0.000 -0.02861 0.000 -0.02865 0.000
Feature 0.457 0.127 0.455 0.129 0.464 0.117 Deviance R-Sq 9.52% 9.51% 9.36% Deviance R-Sq(adj) 8.04% 8.16% 8.25% AIC 761.97 760.01 757.23 AICc 762.32 760.30 757.44 BIC 826.58 819.65 806.92 ------Step 7----- Coef P Constant 1.910 Thickness 0.1125 0.014 Average Precipitation Average FREEZE_INDEX -
0.000544 0.001
Average FREEZE_THAW -0.01466 0.000 Average TEMP_MAX Average TEMP_MIN -0.0534 0.002 Average DAYS_ABOVE_32_C
Subgrade Strength Surface Type - Current 0.899 0.001 Thickness*Subgrade Strength
Years Since Major Work Actual
-0.03185 0.000
Feature Deviance R-Sq 8.85% Deviance R-Sq(adj) 7.99% AIC 757.40 AICc 757.54 BIC 797.16
121
α to remove = 0.1 If a term has more than one coefficient, the largest in magnitude is shown.
Response Information
Variable Value Count Unhealthy vs Healthy
1 928 (Event)
0 136 Total 1064
Regression Equation
P(1) = exp(Y')/(1 + exp(Y'))
Surface Type - Current AAC Y' = 1.910 + 0.1125 Thickness
- 0.000544 Average FREEZE_INDEX - 0.01466 Average FREEZE_THAW - 0.05337 Average TEMP_MIN - 0.03185 Years Since Major Work Actual
AC Y' = 2.810 + 0.1125 Thickness
- 0.000544 Average FREEZE_INDEX - 0.01466 Average FREEZE_THAW - 0.05337 Average TEMP_MIN - 0.03185 Years Since Major Work Actual
APC Y' = 2.392 + 0.1125 Thickness
- 0.000544 Average FREEZE_INDEX - 0.01466 Average FREEZE_THAW - 0.05337 Average TEMP_MIN - 0.03185 Years Since Major Work Actual
Coefficients
Term Coef SE Coef VIF Constant 1.910 0.338 Thickness 0.1125 0.0456 1.10 Average FREEZE_INDEX -
0.000544 0.000162 4.45
Average FREEZE_THAW -0.01466 0.00404 2.84 Average TEMP_MIN -0.0534 0.0175 5.87 Surface Type - Current AC 0.899 0.246 1.65 APC 0.482 0.298 1.65 Years Since Major Work Actual
-0.03185 0.00706 1.15
Odds Ratios for Continuous Predictors
122
Odds Ratio 95% CI Thickness 1.1191 (1.0234,
1.2237) Average FREEZE_INDEX 0.9995 (0.9991,
0.9998) Average FREEZE_THAW 0.9855 (0.9777,
0.9933) Average TEMP_MIN 0.9480 (0.9161,
0.9811) Years Since Major Work Actual
0.9687 (0.9553, 0.9821)
Odds Ratios for Categorical Predictors
Level A Level B Odds Ratio 95% CI Surface Type - Current
AC AAC 2.4580 (1.5181, 3.9800)
APC AAC 1.6188 (0.9025, 2.9036)
APC AC 0.6586 (0.3986, 1.0880)
Odds ratio for level A relative to level B
Model Summary
Deviance R-Sq
Deviance R-Sq(adj) AIC AICc BIC
8.85% 7.99% 757.40 757.54 797.16 Goodness-of-Fit Tests
Test DF Chi-Square P-Value Deviance 1056 741.40 1.000 Pearson 1056 1096.58 0.188 Hosmer-Lemeshow
8 9.70 0.286
Analysis of Variance
Wald Test Source DF Chi-Square P-Value Regression 7 67.55 0.000 Thickness 1 6.09 0.014 Average FREEZE_INDEX 1 11.28 0.001 Average FREEZE_THAW 1 13.15 0.000 Average TEMP_MIN 1 9.32 0.002 Surface Type - Current 2 13.71 0.001
123
Years Since Major Work Actual
1 20.36 0.000
124
125
Distress 65 Joint Seal Damage
During the first iteration, Thickness*Subgrade Strength had a VIF of 74.24 and was removed from the predictor list. The second iteration 'Average DAYS_BELOW_0_C' had a VIF of 11.37 and was removed. The third iteration Average TEMP_MEAN_AVG (deg C) had a VIF of 10.30 and was removed. The fourth iteration had all predictors with a VIF less than 10 and are below.
Method
Link function Logit Categorical predictor coding
(1, 0)
Rows used 2337 Backward Elimination of Terms
Candidate terms: Years Since Major Work Actual, Thickness, Average Precipitation, Average FREEZE_INDEX, Average FREEZE_THAW, Average TEMP_MAX, Average TEMP_MIN, Average DAYS_ABOVE_32_C, Subgrade Strength, Feature
------Step 1------ ------Step 2------ ------Step 3------ Coef P Coef P Coef P Constant 1.127 1.134 1.710 Years Since Major Work Actual
0.05287 0.000 0.05288 0.000 0.05284 0.000
Thickness -0.0121 0.437 -0.0121 0.437 -0.0100 0.514 Average Precipitation 0.000009 0.961 Average FREEZE_INDEX -
0.000684 0.079 -
0.000690 0.064 -
0.000787 0.020
Average FREEZE_THAW -0.01358 0.000 -0.01364 0.000 -0.01351 0.000 Average TEMP_MAX 0.0200 0.532 0.0202 0.526 Average TEMP_MIN -0.0523 0.012 -0.0524 0.011 -0.0564 0.004 Average DAYS_ABOVE_32_C
-0.00998 0.015 -0.01006 0.007 -0.00802 0.000
Subgrade Strength -0.494 0.145 -0.494 0.145 -0.475 0.166 Feature -0.549 0.000 -0.549 0.000 -0.534 0.000 Deviance R-Sq 11.98% 11.98% 11.96% Deviance R-Sq(adj) 11.45% 11.49% 11.52% AIC 2016.02 2014.02 2012.42 AICc 2016.18 2014.16 2012.54 BIC 2090.86 2083.10 2075.75 ------Step 4------ ------Step 5------ Coef P Coef P Constant 1.584 1.249 Years Since Major Work Actual
0.05299 0.000 0.05196 0.000
Thickness
126
Average Precipitation Average FREEZE_INDEX -
0.000783 0.021 -
0.000766 0.024
Average FREEZE_THAW -0.01351 0.000 -0.01357 0.000 Average TEMP_MAX Average TEMP_MIN -0.0560 0.004 -0.0576 0.003 Average DAYS_ABOVE_32_C
-0.00810 0.000 -0.00848 0.000
Subgrade Strength -0.474 0.168 Feature -0.546 0.000 -0.538 0.000 Deviance R-Sq 11.94% 11.78% Deviance R-Sq(adj) 11.55% 11.47% AIC 2010.85 2010.54 AICc 2010.94 2010.61 BIC 2068.41 2056.60
α to remove = 0.1 If a term has more than one coefficient, the largest in magnitude is shown.
Response Information
Variable Value Count Unhealthy vs Healthy
1 1897 (Event)
0 440 Total 2337
Regression Equation
P(1) = exp(Y')/(1 + exp(Y'))
Feature A Y' = 1.249 + 0.05196 Years Since Major Work Actual
- 0.000766 Average FREEZE_INDEX - 0.01357 Average FREEZE_THAW - 0.05756 Average TEMP_MIN - 0.008481 Average DAYS_ABOVE_32_C
R Y' = 0.7109 + 0.05196 Years Since Major Work Actual
- 0.000766 Average FREEZE_INDEX - 0.01357 Average FREEZE_THAW - 0.05756 Average TEMP_MIN - 0.008481 Average DAYS_ABOVE_32_C
T Y' = 0.7219 + 0.05196 Years Since Major Work Actual
- 0.000766 Average FREEZE_INDEX - 0.01357 Average FREEZE_THAW - 0.05756 Average TEMP_MIN - 0.008481 Average DAYS_ABOVE_32_C
Coefficients
Term Coef SE Coef VIF
127
Constant 1.249 0.191 Years Since Major Work Actual
0.05196 0.00440 1.09
Average FREEZE_INDEX -0.000766
0.000339 4.20
Average FREEZE_THAW -0.01357 0.00330 4.90 Average TEMP_MIN -0.0576 0.0195 9.21 Average DAYS_ABOVE_32_C
-0.00848 0.00181 1.50
Feature R -0.538 0.157 1.29 T -0.527 0.128 1.27
Odds Ratios for Continuous Predictors
Odds Ratio 95% CI Years Since Major Work Actual
1.0533 (1.0443, 1.0624)
Average FREEZE_INDEX 0.9992 (0.9986, 0.9999)
Average FREEZE_THAW 0.9865 (0.9802, 0.9929)
Average TEMP_MIN 0.9441 (0.9087, 0.9808)
Average DAYS_ABOVE_32_C
0.9916 (0.9881, 0.9951)
Odds Ratios for Categorical Predictors
Level A Level B Odds Ratio 95% CI Feature R A 0.5841 (0.4296,
0.7940) T A 0.5905 (0.4597,
0.7585) T R 1.0110 (0.7507,
1.3616)
Odds ratio for level A relative to level B
Model Summary
Deviance R-Sq
Deviance R-Sq(adj) AIC AICc BIC
11.78% 11.47% 2010.54 2010.61 2056.60 Goodness-of-Fit Tests
Test DF Chi-Square P-Value Deviance 2329 1994.54 1.000 Pearson 2329 2636.20 0.000 Hosmer-Lemeshow
8 49.20 0.000
128
Analysis of Variance
Wald Test Source DF Chi-Square P-Value Regression 7 195.09 0.000 Years Since Major Work Actual
1 139.68 0.000
Average FREEZE_INDEX 1 5.11 0.024 Average FREEZE_THAW 1 16.94 0.000 Average TEMP_MIN 1 8.72 0.003 Average DAYS_ABOVE_32_C
1 22.05 0.000
Feature 2 20.19 0.000
129
130
Distress 57 Weathering
During the first iteration, Average TEMP_MEAN_AVG (deg C) had a VIF of 78.51 and was removed from the predictor list. The second iteration Average 'Average DAYS_BELOW_0_C' had a VIF of 11.02 and was removed. The third iteration had all predictors with a VIF less than 10 and are below.
Method
Link function Logit Categorical predictor coding
(1, 0)
Rows used 1064 Backward Elimination of Terms
Candidate terms: Years Since Major Work Actually, Thickness, Average Precipitation, Average FREEZE_INDEX, Average FREEZE_THAW, Average TEMP_MAX, Average TEMP_MIN, Average DAYS_ABOVE_32_C, Subgrade Strength, Surface Type - Current, Feature, Thickness*Subgrade Strength
------Step 1----- ------Step 2----- ------Step 3----- Coef P Coef P Coef P Constant 1.53 1.48 1.63 Years Since Major Work Actually
-0.0053 0.628 -0.0056 0.612 -0.0056 0.612
Thickness 0.179 0.099 0.2114 0.012 0.2058 0.013 Average Precipitation -
0.001478 0.004 -
0.001467 0.004 -
0.001461 0.004
Average FREEZE_INDEX -0.000543
0.284 -0.000544
0.280 -0.000570
0.253
Average FREEZE_THAW -0.01923 0.005 -0.01909 0.005 -0.01899 0.005 Average TEMP_MAX 0.0845 0.236 0.0825 0.244 0.0765 0.270 Average TEMP_MIN -0.0561 0.076 -0.0558 0.078 -0.0561 0.076 Average DAYS_ABOVE_32_C
-0.02014 0.022 -0.01969 0.024 -0.01935 0.025
Subgrade Strength -0.383 0.840 -0.296 0.798 Surface Type - Current 0.428 0.524 0.398 0.555 0.370 0.561 Feature 0.539 0.439 0.517 0.458 0.527 0.446 Thickness*Subgrade Strength 0.094 0.859 Deviance R-Sq 7.95% 7.89% 7.80% Deviance R-Sq(adj) 4.64% 4.99% 5.32% AIC 479.40 475.70 472.13 AICc 479.98 476.16 472.48 BIC 563.88 550.25 536.74 ------Step 4----- ------Step 5----- ------Step 6----- Coef P Coef P Coef P
131
Constant 1.44 1.70 3.909 Years Since Major Work Actually
Thickness 0.2093 0.012 0.2199 0.007 0.2299 0.005 Average Precipitation -
0.001478 0.004 -
0.001497 0.003 -
0.001317 0.005
Average FREEZE_INDEX -0.000600
0.227 -0.000650
0.183 -0.001146
0.000
Average FREEZE_THAW -0.01926 0.005 -0.01908 0.004 -0.01858 0.003 Average TEMP_MAX 0.0783 0.262 0.0788 0.253 Average TEMP_MIN -0.0599 0.051 -0.0597 0.046 -0.0739 0.005 Average DAYS_ABOVE_32_C
-0.01970 0.023 -0.01968 0.021 -0.01192 0.015
Subgrade Strength Surface Type - Current 0.357 0.581 Feature 0.608 0.283 0.630 0.249 0.642 0.229 Thickness*Subgrade Strength Deviance R-Sq 7.75% 7.53% 7.25% Deviance R-Sq(adj) 5.47% 5.67% 5.59% AIC 470.39 467.43 466.80 AICc 470.69 467.64 466.97 BIC 530.03 517.13 511.53 ------Step 7----- Coef P Constant 4.125 Years Since Major Work Actually
Thickness 0.2281 0.004 Average Precipitation -
0.001289 0.005
Average FREEZE_INDEX -0.001159
0.000
Average FREEZE_THAW -0.01910 0.002 Average TEMP_MAX Average TEMP_MIN -0.0749 0.004 Average DAYS_ABOVE_32_C
-0.01073 0.026
Subgrade Strength Surface Type - Current Feature Thickness*Subgrade Strength Deviance R-Sq 6.60% Deviance R-Sq(adj) 5.36% AIC 465.94 AICc 466.04
132
BIC 500.72
α to remove = 0.1 If a term has more than one coefficient, the largest in magnitude is shown.
Response Information
Variable Value Count Unhealthy vs Healthy
1 1000 (Event)
0 64 Total 1064
Regression Equation
P(1) = exp(Y')/(1 + exp(Y'))
Y' = 4.125 + 0.2281 Thickness - 0.001289 Average Precipitation - 0.001159 Average FREEZE_INDEX - 0.01910 Average FREEZE_THAW - 0.0749 Average TEMP_MIN - 0.01073 Average DAYS_ABOVE_32_C
Coefficients
Term Coef SE Coef VIF Constant 4.125 0.755 Thickness 0.2281 0.0798 1.11 Average Precipitation -
0.001289 0.000459 2.06
Average FREEZE_INDEX
-0.001159
0.000252 7.57
Average FREEZE_THAW
-0.01910 0.00628 3.75
Average TEMP_MIN -0.0749 0.0261 6.90 Average DAYS_ABOVE_32_C
-0.01073 0.00482 1.78
Odds Ratios for Continuous Predictors
Odds Ratio 95% CI Thickness 1.2562 (1.0743,
1.4690) Average Precipitation 0.9987 (0.9978,
0.9996) Average FREEZE_INDEX
0.9988 (0.9983, 0.9993)
Average FREEZE_THAW
0.9811 (0.9691, 0.9932)
Average TEMP_MIN 0.9278 (0.8815, 0.9765)
Average DAYS_ABOVE_32_C
0.9893 (0.9800, 0.9987)
Model Summary
133
Deviance R-Sq
Deviance R-Sq(adj) AIC AICc BIC
6.60% 5.36% 465.94 466.04 500.72 Goodness-of-Fit Tests
Test DF Chi-Square P-Value Deviance 1057 451.94 1.000 Pearson 1057 1033.23 0.694 Hosmer-Lemeshow
8 7.09 0.527
Analysis of Variance
Wald Test Source DF Chi-Square P-Value Regression 6 29.48 0.000 Thickness 1 8.17 0.004 Average Precipitation 1 7.87 0.005 Average FREEZE_INDEX
1 21.18 0.000
Average FREEZE_THAW
1 9.26 0.002
Average TEMP_MIN 1 8.23 0.004 Average DAYS_ABOVE_32_C
1 4.96 0.026
134
135
Distress 43 Block Cracking
The first iteration had all predictors with a VIF less than 10 and are below.
Method
Link function Logit Categorical predictor coding
(1, 0)
Rows used 1064 Backward Elimination of Terms
Candidate terms: Thickness, Average Precipitation, Average TEMP_MEAN_AVG (deg C), Average FREEZE_INDEX, Average FREEZE_THAW, Average TEMP_MAX, Average TEMP_MIN, Average DAYS_ABOVE_32_C, Average DAYS_BELOW_0_C, Feature, Subgrade Strength, Surface Type - Current, Average Precipitation*Average TEMP_MEAN_AVG (deg C), Thickness*Subgrade Strength, Years Since Major Work Actual
------Step 1------ ------Step 2----- Coef P Coef P Constant -3.46 -3.06 Thickness 0.0300 0.263 0.0293 0.274 Average Precipitation 0.001158 0.207 0.000785 0.031 Average TEMP_MEAN_AVG (deg C) 0.0610 0.458 0.0381 0.556 Average FREEZE_INDEX 0.000638 0.265 0.000593 0.293 Average FREEZE_THAW 0.0126 0.357 0.0135 0.323 Average TEMP_MAX -0.0447 0.525 -0.0467 0.507 Average TEMP_MIN -0.0487 0.241 -0.0530 0.194 Average DAYS_ABOVE_32_C 0.00930 0.056 0.00973 0.044 Average DAYS_BELOW_0_C -0.0076 0.553 -0.0088 0.489 Feature -0.344 0.418 -0.337 0.433 Subgrade Strength 0.857 0.414 0.852 0.421 Surface Type - Current -0.731 0.055 -0.714 0.059 Average Precipitation*Average TEMP_MEAN_AVG (deg C)
-0.000024 0.657
Thickness*Subgrade Strength -0.270 0.229 -0.266 0.239 Years Since Major Work Actual 0.07544 0.000 0.07501 0.000 Deviance R-Sq 17.89% 17.87% Deviance R-Sq(adj) 16.23% 16.30% AIC 980.84 979.03 AICc 981.65 979.76 BIC 1080.2
4 1073.4
6 ------Step 3----- ------Step 4----- Coef P Coef P
136
Constant -3.44 -3.929 Thickness 0.0293 0.274 0.0290 0.278 Average Precipitation 0.000882 0.007 0.000870 0.007 Average TEMP_MEAN_AVG (deg C) Average FREEZE_INDEX 0.000479 0.365 0.000436 0.396 Average FREEZE_THAW 0.0095 0.420 0.00700 0.452 Average TEMP_MAX -0.0164 0.730 Average TEMP_MIN -0.0350 0.191 -0.0284 0.127 Average DAYS_ABOVE_32_C 0.00928 0.053 0.00806 0.012 Average DAYS_BELOW_0_C -0.0058 0.614 -0.00310 0.714 Feature -0.348 0.405 -0.355 0.387 Subgrade Strength 0.877 0.403 0.858 0.415 Surface Type - Current -0.710 0.061 -0.699 0.063 Average Precipitation*Average TEMP_MEAN_AVG (deg C)
Thickness*Subgrade Strength -0.273 0.220 -0.273 0.218 Years Since Major Work Actual 0.07435 0.000 0.07428 0.000 Deviance R-Sq 17.84% 17.83% Deviance R-Sq(adj) 16.36% 16.44% AIC 977.39 975.50 AICc 978.04 976.09 BIC 1066.8
4 1059.99
------Step 5----- ------Step 6----- Coef P Coef P Constant -3.976 -4.199 Thickness 0.0290 0.279 0.0269 0.312 Average Precipitation 0.000896 0.004 0.000884 0.005 Average TEMP_MEAN_AVG (deg C) Average FREEZE_INDEX 0.000262 0.187 0.000260 0.187 Average FREEZE_THAW 0.00394 0.342 0.00429 0.298 Average TEMP_MAX Average TEMP_MIN -0.0266 0.139 -0.0273 0.126 Average DAYS_ABOVE_32_C 0.00843 0.006 0.00773 0.010 Average DAYS_BELOW_0_C Feature -0.350 0.396 Subgrade Strength 0.869 0.408 0.957 0.337 Surface Type - Current -0.698 0.063 -0.703 0.062 Average Precipitation*Average TEMP_MEAN_AVG (deg C)
Thickness*Subgrade Strength -0.276 0.212 -0.293 0.173 Years Since Major Work Actual 0.07439 0.000 0.07797 0.000 Deviance R-Sq 17.82% 17.66% Deviance R-Sq(adj) 16.51% 16.52% AIC 973.64 971.50
137
AICc 974.16 971.90 BIC 1053.1
6 1041.08
------Step 7----- ------Step 8----- Coef P Coef P Constant -3.909 -3.860 Thickness 0.0275 0.301 0.0264 0.320 Average Precipitation 0.000766 0.009 0.000709 0.013 Average TEMP_MEAN_AVG (deg C) Average FREEZE_INDEX 0.000105 0.417 Average FREEZE_THAW Average TEMP_MAX Average TEMP_MIN -0.0414 0.000 -0.04750 0.000 Average DAYS_ABOVE_32_C 0.00629 0.017 0.00592 0.024 Average DAYS_BELOW_0_C Feature Subgrade Strength 0.963 0.332 0.941 0.349 Surface Type - Current -0.731 0.046 -0.798 0.018 Average Precipitation*Average TEMP_MEAN_AVG (deg C)
Thickness*Subgrade Strength -0.290 0.172 -0.287 0.172 Years Since Major Work Actual 0.07813 0.000 0.07957 0.000 Deviance R-Sq 17.56% 17.51% Deviance R-Sq(adj) 16.52% 16.55% AIC 970.58 969.23 AICc 970.92 969.53 BIC 1035.1
8 1028.87
------Step 9----- -----Step 10----- Coef P Coef P Constant -3.884 -3.874 Thickness 0.0229 0.328 0.0217 0.355 Average Precipitation 0.000720 0.011 0.000723 0.010 Average TEMP_MEAN_AVG (deg C) Average FREEZE_INDEX Average FREEZE_THAW Average TEMP_MAX Average TEMP_MIN -0.04843 0.000 -0.04869 0.000 Average DAYS_ABOVE_32_C 0.00644 0.013 0.00670 0.007 Average DAYS_BELOW_0_C Feature Subgrade Strength -0.193 0.604 Surface Type - Current -0.798 0.015 -0.810 0.011 Average Precipitation*Average TEMP_MEAN_AVG (deg C)
Thickness*Subgrade Strength
138
Years Since Major Work Actual 0.08052 0.000 0.08045 0.000 Deviance R-Sq 17.10% 17.01% Deviance R-Sq(adj) 16.32% 16.40% AIC 969.87 966.90 AICc 970.08 967.03 BIC 1019.5
7 1006.66
-----Step 11----- Coef P Constant -3.757 Thickness Average Precipitation 0.000699 0.013 Average TEMP_MEAN_AVG (deg C) Average FREEZE_INDEX Average FREEZE_THAW Average TEMP_MAX Average TEMP_MIN -0.04882 0.000 Average DAYS_ABOVE_32_C 0.00673 0.007 Average DAYS_BELOW_0_C Feature Subgrade Strength Surface Type - Current -0.818 0.011 Average Precipitation*Average TEMP_MEAN_AVG (deg C)
Thickness*Subgrade Strength Years Since Major Work Actual 0.07966 0.000 Deviance R-Sq 16.94% Deviance R-Sq(adj) 16.42% AIC 965.72 AICc 965.82 BIC 1000.50
α to remove = 0.1 If a term has more than one coefficient, the largest in magnitude is shown.
Response Information
Variable Value Count Unhealthy vs Healthy
1 244 (Event)
0 820 Total 1064
Regression Equation
P(1) = exp(Y')/(1 + exp(Y'))
139
Surface Type - Current AAC Y' = -3.757 + 0.000699 Average Precipitation
- 0.04882 Average TEMP_MIN + 0.006727 Average DAYS_ABOVE_32_C + 0.07966 Years Since Major Work Actual
AC Y' = -4.189 + 0.000699 Average Precipitation
- 0.04882 Average TEMP_MIN + 0.006727 Average DAYS_ABOVE_32_C + 0.07966 Years Since Major Work Actual
APC Y' = -4.574 + 0.000699 Average Precipitation
- 0.04882 Average TEMP_MIN + 0.006727 Average DAYS_ABOVE_32_C + 0.07966 Years Since Major Work Actual
Coefficients
Term Coef SE Coef VIF Constant -3.757 0.454 Average Precipitation 0.000699 0.000280 1.54 Average TEMP_MIN -0.04882 0.00895 1.85 Average DAYS_ABOVE_32_C
0.00673 0.00250 1.51
Surface Type - Current AC -0.433 0.212 1.58 APC -0.818 0.275 1.58 Years Since Major Work Actual
0.07966 0.00684 1.06
Odds Ratios for Continuous Predictors
Odds Ratio 95% CI Average Precipitation 1.0007 (1.0001,
1.0012) Average TEMP_MIN 0.9524 (0.9358,
0.9692) Average DAYS_ABOVE_32_C
1.0067 (1.0018, 1.0117)
Years Since Major Work Actual
1.0829 (1.0685, 1.0975)
Odds Ratios for Categorical Predictors
Level A Level B Odds Ratio 95% CI Surface Type - Current
AC AAC 0.6488 (0.4282, 0.9830)
140
APC AAC 0.4414 (0.2574, 0.7568)
APC AC 0.6803 (0.4336, 1.0673)
Odds ratio for level A relative to level B
Model Summary
Deviance R-Sq
Deviance R-Sq(adj) AIC AICc BIC
16.94% 16.42% 965.72 965.82 1000.50 Goodness-of-Fit Tests
Test DF Chi-Square P-Value Deviance 1057 951.72 0.991 Pearson 1057 1026.21 0.746 Hosmer-Lemeshow
8 13.15 0.107
Analysis of Variance
Wald Test Source DF Chi-Square P-Value Regression 6 151.88 0.000 Average Precipitation 1 6.22 0.013 Average TEMP_MIN 1 29.75 0.000 Average DAYS_ABOVE_32_C
1 7.23 0.007
Surface Type - Current 2 8.99 0.011 Years Since Major Work Actual
1 135.55 0.000
141
142
143
Distress 67 Large Patch/Utility Cut
During the first iteration, Average Precipitation*Average TEMP_MEAN_AVG (deg C) had a VIF of 26.13 and was removed from the predictor list. The second iteration 'Average DAYS_BELOW_0_C' had a VIF of 13.24 and was removed. The third iteration had all predictors with a VIF less than 10 and are below.
Method
Link function Logit Categorical predictor coding
(1, 0)
Rows used 2337 Backward Elimination of Terms
Candidate terms: Years Since Major Work Actual, Thickness, Average Precipitation, Average TEMP_MEAN_AVG (deg C), Average FREEZE_INDEX, Average FREEZE_THAW, Average TEMP_MAX, Average TEMP_MIN, Average DAYS_ABOVE_32_C, Feature, Subgrade Strength, Thickness*Subgrade Strength
------Step 1------ ------Step 2------ ------Step 3----- Coef P Coef P Coef P Constant -2.029 -1.971 -2.092 Years Since Major Work Actual 0.04506 0.000 0.04502 0.000 0.04500 0.000 Thickness 0.0429 0.389 0.0433 0.384 0.0432 0.385 Average Precipitation -0.000066 0.698 -
0.000058 0.712
Average TEMP_MEAN_AVG (deg C)
0.0422 0.129 0.0423 0.128 0.0376 0.128
Average FREEZE_INDEX 0.000865 0.005 0.000865 0.005 0.000893 0.003 Average FREEZE_THAW 0.00600 0.050 0.00613 0.033 0.00612 0.033 Average TEMP_MAX -0.0306 0.293 -0.0331 0.095 -0.0288 0.072 Average TEMP_MIN 0.0131 0.478 0.0131 0.478 0.0153 0.382 Average DAYS_ABOVE_32_C -0.00038 0.905 Feature -0.334 0.048 -0.333 0.048 -0.334 0.048 Subgrade Strength 1.357 0.125 1.360 0.123 1.368 0.119 Thickness*Subgrade Strength -0.0561 0.534 -0.0564 0.531 -0.0572 0.518 Deviance R-Sq 12.69% 12.69% 12.68% Deviance R-Sq(adj) 12.23% 12.26% 12.28% AIC 2854.06 2852.0
7 2850.2
1 AICc 2854.29 2852.2
8 2850.3
9 BIC 2946.17 2938.4
2 2930.8
0 ------Step 4----- ------Step 5----- ------Step 6----- Coef P Coef P Coef P
144
Constant -1.558 -1.557 -1.430 Years Since Major Work Actual 0.04499 0.000 0.04498 0.000 0.04515 0.000 Thickness 0.0002 0.989 Average Precipitation Average TEMP_MEAN_AVG (deg C)
0.0404 0.101 0.0403 0.100 0.0476 0.039
Average FREEZE_INDEX 0.000912 0.002 0.000913 0.002 0.000731 0.000 Average FREEZE_THAW 0.00626 0.029 0.00626 0.029 0.00480 0.037 Average TEMP_MAX -0.0289 0.071 -0.0288 0.066 -0.0364 0.005 Average TEMP_MIN 0.0150 0.393 0.0150 0.392 Average DAYS_ABOVE_32_C Feature -0.340 0.043 -0.340 0.042 -0.340 0.042 Subgrade Strength 0.593 0.003 0.593 0.003 0.588 0.003 Thickness*Subgrade Strength Deviance R-Sq 12.64% 12.64% 12.62% Deviance R-Sq(adj) 12.30% 12.33% 12.34% AIC 2847.5
4 2845.5
4 2844.2
7 AICc 2847.6
7 2845.6
5 2844.3
7 BIC 2916.6
2 2908.8
6 2901.8
4
α to remove = 0.1 If a term has more than one coefficient, the largest in magnitude is shown.
Response Information
Variable Value Count Unhealthy vs Healthy
1 1102 (Event)
0 1235 Total 2337
Regression Equation
P(1) = exp(Y')/(1 + exp(Y'))
Feature Subgrade Strength
A A Y' = -1.430 + 0.04515 Years Since Major Work Actual + 0.04758 Average TEMP_MEAN_AVG (deg C) + 0.000731 Average FREEZE_INDEX + 0.004798 Average FREEZE_THAW - 0.03638 Average TEMP_MAX
A B Y' = -1.137 + 0.04515 Years Since Major Work Actual
+ 0.04758 Average TEMP_MEAN_AVG (deg C) + 0.000731 Average FREEZE_INDEX
145
+ 0.004798 Average FREEZE_THAW - 0.03638 Average TEMP_MAX
A C Y' = -0.8418 + 0.04515 Years Since Major Work Actual
+ 0.04758 Average TEMP_MEAN_AVG (deg C) + 0.000731 Average FREEZE_INDEX + 0.004798 Average FREEZE_THAW - 0.03638 Average TEMP_MAX
R A Y' = -1.770 + 0.04515 Years Since Major Work Actual
+ 0.04758 Average TEMP_MEAN_AVG (deg C) + 0.000731 Average FREEZE_INDEX + 0.004798 Average FREEZE_THAW - 0.03638 Average TEMP_MAX
R B Y' = -1.477 + 0.04515 Years Since Major Work Actual
+ 0.04758 Average TEMP_MEAN_AVG (deg C) + 0.000731 Average FREEZE_INDEX + 0.004798 Average FREEZE_THAW - 0.03638 Average TEMP_MAX
R C Y' = -1.182 + 0.04515 Years Since Major Work Actual
+ 0.04758 Average TEMP_MEAN_AVG (deg C) + 0.000731 Average FREEZE_INDEX + 0.004798 Average FREEZE_THAW - 0.03638 Average TEMP_MAX
T A Y' = -1.446 + 0.04515 Years Since Major Work Actual
+ 0.04758 Average TEMP_MEAN_AVG (deg C) + 0.000731 Average FREEZE_INDEX + 0.004798 Average FREEZE_THAW - 0.03638 Average TEMP_MAX
T B Y' = -1.152 + 0.04515 Years Since Major Work Actual
+ 0.04758 Average TEMP_MEAN_AVG (deg C) + 0.000731 Average FREEZE_INDEX + 0.004798 Average FREEZE_THAW - 0.03638 Average TEMP_MAX
T C Y' = -0.8576 + 0.04515 Years Since Major Work Actual
+ 0.04758 Average TEMP_MEAN_AVG (deg C) + 0.000731 Average FREEZE_INDEX + 0.004798 Average FREEZE_THAW - 0.03638 Average TEMP_MAX
Coefficients
Term Coef SE Coef VIF Constant -1.430 0.452 Years Since Major Work Actual 0.04515 0.00275 1.09
146
Average TEMP_MEAN_AVG (deg C)
0.0476 0.0230 7.17
Average FREEZE_INDEX 0.000731 0.000206 2.37 Average FREEZE_THAW 0.00480 0.00230 3.67 Average TEMP_MAX -0.0364 0.0130 1.84 Feature R -0.340 0.141 1.19 T -0.016 0.101 1.16 Subgrade Strength B 0.293 0.186 4.00 C 0.588 0.201 4.27
Odds Ratios for Continuous Predictors
Odds Ratio 95% CI Years Since Major Work Actual 1.0462 (1.0405,
1.0518) Average TEMP_MEAN_AVG (deg C)
1.0487 (1.0025, 1.0971)
Average FREEZE_INDEX 1.0007 (1.0003, 1.0011)
Average FREEZE_THAW 1.0048 (1.0003, 1.0093)
Average TEMP_MAX 0.9643 (0.9400, 0.9891)
Odds Ratios for Categorical Predictors
Level A Level B Odds Ratio 95% CI Feature R A 0.7115 (0.5393,
0.9388) T A 0.9843 (0.8072,
1.2004) T R 1.3834 (1.0462,
1.8293) Subgrade Strength
B A 1.3409 (0.9310, 1.9313)
C A 1.8004 (1.2143, 2.6696)
C B 1.3427 (1.0910, 1.6525)
Odds ratio for level A relative to level B
Model Summary
Deviance R-Sq
Deviance R-Sq(adj) AIC AICc BIC
147
12.62% 12.34% 2844.27 2844.37 2901.84 Goodness-of-Fit Tests
Test DF Chi-Square P-Value Deviance 2327 2824.27 0.000 Pearson 2327 2350.66 0.361 Hosmer-Lemeshow
8 28.58 0.000
Analysis of Variance
Wald Test Source DF Chi-Square P-Value Regression 9 332.45 0.000 Years Since Major Work Actual
1 268.65 0.000
Average TEMP_MEAN_AVG (deg C)
1 4.27 0.039
Average FREEZE_INDEX 1 12.54 0.000 Average FREEZE_THAW 1 4.36 0.037 Average TEMP_MAX 1 7.85 0.005 Feature 2 6.32 0.042 Subgrade Strength 2 11.67 0.003
148
149
Distress 72 Shattered Slab
During the first iteration had all predictors with a VIF less than 10 and are below.
Method
Link function Logit Categorical predictor coding
(1, 0)
Rows used 2337 Backward Elimination of Terms
Candidate terms: Years Since Major Work Actual, Thickness, Average Precipitation, Average TEMP_MEAN_AVG (deg C), Average FREEZE_INDEX, Average FREEZE_THAW, Average TEMP_MAX, Average TEMP_MIN, Average DAYS_ABOVE_32_C, Average DAYS_BELOW_0_C, Feature, Subgrade Strength, Average Precipitation*Average TEMP_MEAN_AVG (deg C), Thickness*Subgrade Strength
------Step 1------ ------Step 2------ Coef P Coef P Constant -10.59 -10.81 Years Since Major Work Actual 0.05172 0.000 0.05176 0.000 Thickness -0.1381 0.146 -0.1208 0.000 Average Precipitation -
0.001443 0.071 -
0.001451 0.069
Average TEMP_MEAN_AVG (deg C) -0.0257 0.712 -0.0239 0.731 Average FREEZE_INDEX -
0.001918 0.054 -
0.001905 0.055
Average FREEZE_THAW -0.0485 0.001 -0.0483 0.001 Average TEMP_MAX 0.2860 0.000 0.2851 0.000 Average TEMP_MIN -0.0141 0.658 -0.0145 0.649 Average DAYS_ABOVE_32_C -0.02486 0.000 -0.02482 0.000 Average DAYS_BELOW_0_C 0.0419 0.002 0.0418 0.002 Feature -1.627 0.000 -1.630 0.000 Subgrade Strength 0.46 0.506 0.594 0.028 Average Precipitation*Average TEMP_MEAN_AVG (deg C)
0.000063 0.200 0.000063 0.198
Thickness*Subgrade Strength 0.0248 0.911 Deviance R-Sq 24.50% 24.49% Deviance R-Sq(adj) 23.53% 23.64% AIC 1371.1
4 1367.33
AICc 1371.43
1367.56
BIC 1474.76
1459.43
150
------Step 3------ ------Step 4------ Coef P Coef P Constant -11.05 -11.85 Years Since Major Work Actual 0.05168 0.000 0.05197 0.000 Thickness -0.1213 0.000 -0.1194 0.000 Average Precipitation -
0.001335 0.074 -
0.000472 0.074
Average TEMP_MEAN_AVG (deg C) -0.0267 0.698 0.0405 0.320 Average FREEZE_INDEX -
0.001735 0.057 -
0.001353 0.110
Average FREEZE_THAW -0.0470 0.001 -0.0439 0.002 Average TEMP_MAX 0.2924 0.000 0.2870 0.000 Average TEMP_MIN Average DAYS_ABOVE_32_C -0.02477 0.000 -0.02605 0.000 Average DAYS_BELOW_0_C 0.0421 0.001 0.0395 0.002 Feature -1.626 0.000 -1.601 0.000 Subgrade Strength 0.598 0.027 0.592 0.032 Average Precipitation*Average TEMP_MEAN_AVG (deg C)
0.000058 0.217
Thickness*Subgrade Strength Deviance R-Sq 24.47% 24.39% Deviance R-Sq(adj) 23.68% 23.65% AIC 1365.5
4 1365.07
AICc 1365.74
1365.25
BIC 1451.89
1445.66
------Step 5------ ------Step 6------ Coef P Coef P Constant -11.35 -11.77 Years Since Major Work Actual 0.05149 0.000 0.05136 0.000 Thickness -0.1202 0.000 -0.1220 0.000 Average Precipitation -
0.000375 0.127
Average TEMP_MEAN_AVG (deg C) Average FREEZE_INDEX -
0.001361 0.104 -
0.001273 0.122
Average FREEZE_THAW -0.0431 0.002 -0.0426 0.002 Average TEMP_MAX 0.2928 0.000 0.2912 0.000 Average TEMP_MIN Average DAYS_ABOVE_32_C -0.02528 0.000 -0.02267 0.000 Average DAYS_BELOW_0_C 0.0362 0.003 0.0367 0.003 Feature -1.597 0.000 -1.582 0.000 Subgrade Strength 0.580 0.028 0.534 0.037
151
Average Precipitation*Average TEMP_MEAN_AVG (deg C)
Thickness*Subgrade Strength Deviance R-Sq 24.33% 24.20% Deviance R-Sq(adj) 23.65% 23.58% AIC 1364.0
6 1364.40
AICc 1364.21
1364.53
BIC 1438.89
1433.48
------Step 7----- Coef P Constant -11.12 Years Since Major Work Actual 0.05119 0.000 Thickness -0.1204 0.000 Average Precipitation Average TEMP_MEAN_AVG (deg C) Average FREEZE_INDEX Average FREEZE_THAW -0.02260 0.000 Average TEMP_MAX 0.2713 0.000 Average TEMP_MIN Average DAYS_ABOVE_32_C -0.02129 0.000 Average DAYS_BELOW_0_C 0.01833 0.000 Feature -1.588 0.000 Subgrade Strength 0.511 0.043 Average Precipitation*Average TEMP_MEAN_AVG (deg C)
Thickness*Subgrade Strength Deviance R-Sq 24.06% Deviance R-Sq(adj) 23.50% AIC 1364.78 AICc 1364.89 BIC 1428.10
α to remove = 0.1 If a term has more than one coefficient, the largest in magnitude is shown.
Response Information
Variable Value Count Unhealthy vs Healthy
1 294 (Event)
0 2043
152
Total 2337 Regression Equation
P(1) = exp(Y')/(1 + exp(Y'))
Feature Subgrade Strength
A A Y' = -11.12 + 0.05119 Years Since Major Work Actual - 0.1204 Thickness - 0.02260 Average FREEZE_THAW + 0.2713 Average TEMP_MAX - 0.02129 Average DAYS_ABOVE_32_C + 0.01833 Average DAYS_BELOW_0_C
A B Y' = -10.97 + 0.05119 Years Since Major Work Actual
- 0.1204 Thickness - 0.02260 Average FREEZE_THAW + 0.2713 Average TEMP_MAX - 0.02129 Average DAYS_ABOVE_32_C + 0.01833 Average DAYS_BELOW_0_C
A C Y' = -10.61 + 0.05119 Years Since Major Work Actual
- 0.1204 Thickness - 0.02260 Average FREEZE_THAW + 0.2713 Average TEMP_MAX - 0.02129 Average DAYS_ABOVE_32_C + 0.01833 Average DAYS_BELOW_0_C
R A Y' = -12.70 + 0.05119 Years Since Major Work Actual
- 0.1204 Thickness - 0.02260 Average FREEZE_THAW + 0.2713 Average TEMP_MAX - 0.02129 Average DAYS_ABOVE_32_C + 0.01833 Average DAYS_BELOW_0_C
R B Y' = -12.56 + 0.05119 Years Since Major Work Actual
- 0.1204 Thickness - 0.02260 Average FREEZE_THAW + 0.2713 Average TEMP_MAX - 0.02129 Average DAYS_ABOVE_32_C + 0.01833 Average DAYS_BELOW_0_C
R C Y' = -12.19 + 0.05119 Years Since Major Work Actual
- 0.1204 Thickness - 0.02260 Average FREEZE_THAW + 0.2713 Average TEMP_MAX - 0.02129 Average DAYS_ABOVE_32_C + 0.01833 Average DAYS_BELOW_0_C
153
T A Y' = -12.08 + 0.05119 Years Since Major Work Actual - 0.1204 Thickness - 0.02260 Average FREEZE_THAW + 0.2713 Average TEMP_MAX - 0.02129 Average DAYS_ABOVE_32_C + 0.01833 Average DAYS_BELOW_0_C
T B Y' = -11.93 + 0.05119 Years Since Major Work Actual
- 0.1204 Thickness - 0.02260 Average FREEZE_THAW + 0.2713 Average TEMP_MAX - 0.02129 Average DAYS_ABOVE_32_C + 0.01833 Average DAYS_BELOW_0_C
T C Y' = -11.56 + 0.05119 Years Since Major Work Actual
- 0.1204 Thickness - 0.02260 Average FREEZE_THAW + 0.2713 Average TEMP_MAX - 0.02129 Average DAYS_ABOVE_32_C + 0.01833 Average DAYS_BELOW_0_C
Coefficients
Term Coef SE Coef VIF Constant -11.12 1.33 Years Since Major Work Actual
0.05119 0.00392 1.12
Thickness -0.1204 0.0189 1.13 Average FREEZE_THAW -
0.02260 0.00506 7.27
Average TEMP_MAX 0.2713 0.0417 7.85 Average DAYS_ABOVE_32_C
-0.02129
0.00471 7.19
Average DAYS_BELOW_0_C
0.01833 0.00331 7.20
Feature R -1.588 0.314 1.06 T -0.959 0.169 1.08 Subgrade Strength B 0.142 0.347 5.99 C 0.511 0.355 6.27
Odds Ratios for Continuous Predictors
Odds Ratio 95% CI Years Since Major Work Actual
1.0525 (1.0445, 1.0606)
Thickness 0.8866 (0.8544, 0.9200)
Average FREEZE_THAW 0.9777 (0.9680, 0.9874)
154
Average TEMP_MAX 1.3116 (1.2087, 1.4233)
Average DAYS_ABOVE_32_C
0.9789 (0.9699, 0.9880)
Average DAYS_BELOW_0_C
1.0185 (1.0119, 1.0251)
Odds Ratios for Categorical Predictors
Level A Level B Odds Ratio 95% CI Feature R A 0.2044 (0.1105,
0.3781) T A 0.3833 (0.2750,
0.5342) T R 1.8752 (0.9840,
3.5737) Subgrade Strength
B A 1.1531 (0.5845, 2.2748)
C A 1.6676 (0.8311, 3.3459)
C B 1.4462 (1.0660, 1.9621)
Odds ratio for level A relative to level B
Model Summary
Deviance R-Sq
Deviance R-Sq(adj) AIC AICc BIC
24.06% 23.50% 1364.78 1364.89 1428.10 Goodness-of-Fit Tests
Test DF Chi-Square P-Value Deviance 2326 1342.78 1.000 Pearson 2326 2557.84 0.000 Hosmer-Lemeshow
8 13.04 0.111
Analysis of Variance
Wald Test Source DF Chi-Square P-Value Regression 10 298.14 0.000 Years Since Major Work Actual
1 170.67 0.000
Thickness 1 40.57 0.000 Average FREEZE_THAW 1 19.95 0.000 Average TEMP_MAX 1 42.35 0.000
155
Average DAYS_ABOVE_32_C
1 20.39 0.000
Average DAYS_BELOW_0_C
1 30.75 0.000
Feature 2 49.04 0.000 Subgrade Strength 2 6.29 0.043
156
157
Distress 74 Joint Spalling
The first iteration had all predictors with a VIF less than 10 and are below.
Method
Link function Logit Categorical predictor coding
(1, 0)
Rows used 2337 Backward Elimination of Terms
Candidate terms: Years Since Major Work Actual, Thickness, Average Precipitation, Average TEMP_MEAN_AVG (deg C), Average FREEZE_INDEX, Average FREEZE_THAW, Average TEMP_MAX, Average TEMP_MIN, Average DAYS_ABOVE_32_C, Average DAYS_BELOW_0_C, Feature, Subgrade Strength, Average Precipitation*Average TEMP_MEAN_AVG (deg C), Thickness*Subgrade Strength
------Step 1------ ------Step 2------ Coef P Coef P Constant -1.08 -1.357 Years Since Major Work Actual 0.01109 0.000 0.01107 0.000 Thickness -0.0611 0.204 -0.0393 0.003 Average Precipitation 0.000593 0.226 0.000601 0.220 Average TEMP_MEAN_AVG (deg C) -0.0588 0.216 -0.0603 0.203 Average FREEZE_INDEX -0.000179 0.797 -
0.000203 0.770
Average FREEZE_THAW -0.01586 0.108 -0.01617 0.100 Average TEMP_MAX 0.1089 0.002 0.1089 0.002 Average TEMP_MIN 0.0275 0.171 0.0278 0.166 Average DAYS_ABOVE_32_C -0.00323 0.377 -0.00316 0.387 Average DAYS_BELOW_0_C 0.00836 0.328 0.00860 0.313 Feature -0.146 0.309 -0.146 0.304 Subgrade Strength -0.165 0.852 0.468 0.006 Average Precipitation*Average TEMP_MEAN_AVG (deg C)
-0.000028 0.363 -0.000028
0.363
Thickness*Subgrade Strength 0.0303 0.839 Deviance R-Sq 3.05% 3.04% Deviance R-Sq(adj) 2.42% 2.48% AIC 2653.63 2649.9
8 AICc 2653.92 2650.2
2 BIC 2757.25 2742.0
9 ------Step 3------ ------Step 4------
158
Coef P Coef P Constant -1.361 -0.798 Years Since Major Work Actual 0.01106 0.000 0.01071 0.000 Thickness -0.0394 0.002 -0.0387 0.003 Average Precipitation 0.000654 0.149 0.000634 0.161 Average TEMP_MEAN_AVG (deg C) -0.0572 0.216 -0.0595 0.195 Average FREEZE_INDEX Average FREEZE_THAW -0.01360 0.002 -0.01202 0.002 Average TEMP_MAX 0.1072 0.002 0.0863 0.000 Average TEMP_MIN 0.0294 0.127 0.0278 0.144 Average DAYS_ABOVE_32_C -0.00299 0.406 Average DAYS_BELOW_0_C 0.00645 0.130 0.00583 0.164 Feature -0.146 0.305 -0.141 0.317 Subgrade Strength 0.466 0.006 0.465 0.005 Average Precipitation*Average TEMP_MEAN_AVG (deg C)
-0.000031 0.288 -0.000026
0.359
Thickness*Subgrade Strength Deviance R-Sq 3.03% 3.01% Deviance R-Sq(adj) 2.51% 2.53% AIC 2648.07 2646.7
5 AICc 2648.27 2646.9
4 BIC 2734.41 2727.3
5 ------Step 5----- ------Step 6----- Coef P Coef P Constant -0.508 -0.487 Years Since Major Work Actual 0.01059 0.000 0.01061 0.000 Thickness -0.0398 0.002 -0.0418 0.001 Average Precipitation 0.000249 0.136 0.000236 0.157 Average TEMP_MEAN_AVG (deg C) -0.0908 0.003 -0.0905 0.003 Average FREEZE_INDEX Average FREEZE_THAW -0.01127 0.003 -0.01139 0.003 Average TEMP_MAX 0.0920 0.000 0.0910 0.000 Average TEMP_MIN 0.0295 0.120 0.0292 0.123 Average DAYS_ABOVE_32_C Average DAYS_BELOW_0_C 0.00498 0.221 0.00509 0.211 Feature -0.142 0.319 Subgrade Strength 0.466 0.005 0.469 0.004 Average Precipitation*Average TEMP_MEAN_AVG (deg C)
Thickness*Subgrade Strength Deviance R-Sq 2.98% 2.89% Deviance R-Sq(adj) 2.53% 2.52%
159
AIC 2645.60
2643.87
AICc 2645.75
2643.99
BIC 2720.43
2707.20
------Step 7----- ------Step 8----- Coef P Coef P Constant 0.010 0.022 Years Since Major Work Actual 0.01080 0.000 0.01095 0.000 Thickness -0.0408 0.001 -0.0404 0.002 Average Precipitation 0.000187 0.244 0.000155 0.321 Average TEMP_MEAN_AVG (deg C) -0.0900 0.003 -0.0699 0.001 Average FREEZE_INDEX Average FREEZE_THAW -0.00782 0.002 -0.00828 0.001 Average TEMP_MAX 0.0778 0.000 0.0671 0.000 Average TEMP_MIN 0.0127 0.343 Average DAYS_ABOVE_32_C Average DAYS_BELOW_0_C Feature Subgrade Strength 0.462 0.003 0.458 0.003 Average Precipitation*Average TEMP_MEAN_AVG (deg C)
Thickness*Subgrade Strength Deviance R-Sq 2.83% 2.80% Deviance R-Sq(adj) 2.50% 2.50% AIC 2643.4
5 2642.3
5 AICc 2643.5
5 2642.4
3 BIC 2701.0
2 2694.1
6 ------Step 9----- Coef P Constant 0.261 Years Since Major Work Actual 0.01087 0.000 Thickness -0.0391 0.002 Average Precipitation Average TEMP_MEAN_AVG (deg C) -0.0603 0.002 Average FREEZE_INDEX Average FREEZE_THAW -0.00774 0.001 Average TEMP_MAX 0.0593 0.000 Average TEMP_MIN Average DAYS_ABOVE_32_C Average DAYS_BELOW_0_C Feature
160
Subgrade Strength 0.467 0.003 Average Precipitation*Average TEMP_MEAN_AVG (deg C)
Thickness*Subgrade Strength Deviance R-Sq 2.76% Deviance R-Sq(adj) 2.50% AIC 2641.34 AICc 2641.40 BIC 2687.39
α to remove = 0.1 If a term has more than one coefficient, the largest in magnitude is shown.
Response Information
Variable Value Count Unhealthy vs Healthy
1 1719 (Event)
0 618 Total 2337
Regression Equation
P(1) = exp(Y')/(1 + exp(Y'))
Subgrade Strength A Y' = 0.2611 + 0.01087 Years Since Major Work Actual - 0.03912 Thickness
- 0.06033 Average TEMP_MEAN_AVG (deg C) - 0.007736 Average FREEZE_THAW + 0.05930 Average TEMP_MAX
B Y' = 0.3477 + 0.01087 Years Since Major Work Actual - 0.03912 Thickness
- 0.06033 Average TEMP_MEAN_AVG (deg C) - 0.007736 Average FREEZE_THAW + 0.05930 Average TEMP_MAX
C Y' = 0.7279 + 0.01087 Years Since Major Work Actual - 0.03912 Thickness
- 0.06033 Average TEMP_MEAN_AVG (deg C) - 0.007736 Average FREEZE_THAW + 0.05930 Average TEMP_MAX
Coefficients
Term Coef SE Coef VIF Constant 0.261 0.417 Years Since Major Work Actual 0.01087 0.00276 1.06 Thickness -0.0391 0.0127 1.07 Average TEMP_MEAN_AVG (deg C)
-0.0603 0.0194 4.58
161
Average FREEZE_THAW -0.00774
0.00240 3.54
Average TEMP_MAX 0.0593 0.0143 1.95 Subgrade Strength B 0.087 0.181 3.23 C 0.467 0.202 3.44
Odds Ratios for Continuous Predictors
Odds Ratio 95% CI Years Since Major Work Actual 1.0109 (1.0055,
1.0164) Thickness 0.9616 (0.9380,
0.9859) Average TEMP_MEAN_AVG (deg C)
0.9415 (0.9064, 0.9779)
Average FREEZE_THAW 0.9923 (0.9876, 0.9970)
Average TEMP_MAX 1.0611 (1.0317, 1.0913)
Odds Ratios for Categorical Predictors
Level A Level B Odds Ratio 95% CI Subgrade Strength
B A 1.0906 (0.7646, 1.5555)
C A 1.5950 (1.0732, 2.3703)
C B 1.4625 (1.1647, 1.8365)
Odds ratio for level A relative to level B
Model Summary
Deviance R-Sq
Deviance R-Sq(adj) AIC AICc BIC
2.76% 2.50% 2641.34 2641.40 2687.39 Goodness-of-Fit Tests
Test DF Chi-Square P-Value Deviance 2329 2625.34 0.000 Pearson 2329 2337.99 0.444 Hosmer-Lemeshow
8 10.00 0.265
Analysis of Variance
Wald Test Source DF Chi-Square P-Value Regression 7 67.34 0.000
162
Years Since Major Work Actual
1 15.49 0.000
Thickness 1 9.48 0.002 Average TEMP_MEAN_AVG (deg C)
1 9.71 0.002
Average FREEZE_THAW 1 10.39 0.001 Average TEMP_MAX 1 17.14 0.000 Subgrade Strength 2 11.54 0.003
163
164
Distress 66 Small Patch
During the first iteration, Average Precipitation*Average TEMP_MEAN_AVG (deg C) had a VIF of 23.72 and was removed from the predictor list. The second iteration had all predictors with a VIF less than 10 and are below.
Method
Link function Logit Categorical predictor coding
(1, 0)
Rows used 2337 Backward Elimination of Terms
Candidate terms: Years Since Major Work Actual, Thickness, Average Precipitation, Average TEMP_MEAN_AVG (deg C), Average FREEZE_INDEX, Average FREEZE_THAW, Average TEMP_MAX, Average TEMP_MIN, Average DAYS_ABOVE_32_C, Average DAYS_BELOW_0_C, Feature, Subgrade Strength, Thickness*Subgrade Strength
------Step 1------ ------Step 2------ ------Step 3------ Coef P Coef P Coef P Constant 0.87 1.01 0.643 Years Since Major Work Actual 0.03129 0.000 0.03128 0.000 0.03130 0.000 Thickness 0.0578 0.309 0.0573 0.313 0.0551 0.332 Average Precipitation -
0.000513 0.005 -
0.000507 0.006 -
0.000498 0.007
Average TEMP_MEAN_AVG (deg C)
-0.0157 0.615 -0.0169 0.586 -0.0253 0.347
Average FREEZE_INDEX -0.000580
0.364 -0.000366
0.264 -0.000317
0.315
Average FREEZE_THAW -0.00567 0.559 -0.00213 0.526 -0.00261 0.422 Average TEMP_MAX -0.0139 0.684 -0.0175 0.592 Average TEMP_MIN -0.0113 0.569 -0.0117 0.555 -0.0073 0.686 Average DAYS_ABOVE_32_C -0.00444 0.214 -0.00414 0.235 -0.00552 0.018 Average DAYS_BELOW_0_C 0.00332 0.697 Feature 0.611 0.000 0.608 0.000 0.602 0.000 Subgrade Strength 0.334 0.269 -0.329 0.260 -0.340 0.254 Thickness*Subgrade Strength -0.0337 0.224 -0.0333 0.217 -0.0329 0.219 Deviance R-Sq 5.30% 5.30% 5.29% Deviance R-Sq(adj) 4.69% 4.72% 4.75% AIC 2507.81 2505.96 2504.24 AICc 2508.07 2506.19 2504.45 BIC 2605.67 2598.06 2590.59 ------Step 4------ ------Step 5------ ------Step 6------ Coef P Coef P Coef P Constant 0.657 0.405 0.288
165
Years Since Major Work Actual 0.03118 0.000 0.03126 0.000 0.03130 0.000 Thickness 0.0552 0.330 0.0535 0.346 0.0513 0.366 Average Precipitation -
0.000480 0.007 -
0.000491 0.006 -
0.000486 0.006
Average TEMP_MEAN_AVG (deg C)
-0.0282 0.279 -0.0142 0.419 -0.0061 0.671
Average FREEZE_INDEX -0.000228
0.317 -0.000166
0.427
Average FREEZE_THAW -0.00169 0.468 Average TEMP_MAX Average TEMP_MIN Average DAYS_ABOVE_32_C -0.00508 0.013 -0.00539 0.007 -0.00548 0.006 Average DAYS_BELOW_0_C Feature 0.605 0.000 0.591 0.000 0.592 0.000 Subgrade Strength -0.331 0.258 -0.374 0.235 -0.419 0.218 Thickness*Subgrade Strength -0.0329 0.224 -0.0319 0.208 -0.0298 0.198 Deviance R-Sq 5.28% 5.26% 5.23% Deviance R-Sq(adj) 4.78% 4.80% 4.81% AIC 2502.41 2500.93 2499.55 AICc 2502.59 2501.09 2499.69 BIC 2583.00 2575.77 2568.63 ------Step 7------ ------Step 8------ ------Step 9------ Coef P Coef P Coef P Constant 0.277 0.419 0.312 Years Since Major Work Actual 0.03128 0.000 0.03108 0.000 0.03085 0.000 Thickness 0.0512 0.367 0.0409 0.003 0.0410 0.002 Average Precipitation -
0.000529 0.000 -
0.000530 0.000 -
0.000542 0.000
Average TEMP_MEAN_AVG (deg C)
Average FREEZE_INDEX Average FREEZE_THAW Average TEMP_MAX Average TEMP_MIN Average DAYS_ABOVE_32_C -0.00607 0.000 -0.00612 0.000 -0.00631 0.000 Average DAYS_BELOW_0_C Feature 0.590 0.000 0.581 0.000 0.581 0.000 Subgrade Strength -0.446 0.196 -0.153 0.777 Thickness*Subgrade Strength -0.0296 0.186 Deviance R-Sq 5.23% 5.10% 5.08% Deviance R-Sq(adj) 4.85% 4.79% 4.85% AIC 2497.73 2497.13 2493.65 AICc 2497.84 2497.21 2493.70 BIC 2561.05 2548.94 2533.94
166
α to remove = 0.1 If a term has more than one coefficient, the largest in magnitude is shown.
Response Information
Variable Value Count Unhealthy vs Healthy
1 1760 (Event)
0 577 Total 2337
Regression Equation
P(1) = exp(Y')/(1 + exp(Y'))
Feature A Y' = 0.3123 + 0.03085 Years Since Major Work Actual
+ 0.04098 Thickness - 0.000542 Average Precipitation - 0.006313 Average DAYS_ABOVE_32_C
R Y' = 0.8929 + 0.03085 Years Since Major Work Actual
+ 0.04098 Thickness - 0.000542 Average Precipitation - 0.006313 Average DAYS_ABOVE_32_C
T Y' = 0.7222 + 0.03085 Years Since Major Work Actual
+ 0.04098 Thickness - 0.000542 Average Precipitation - 0.006313 Average DAYS_ABOVE_32_C
Coefficients
Term Coef SE Coef VIF Constant 0.312 0.252 Years Since Major Work Actual
0.03085 0.00316 1.12
Thickness 0.0410 0.0135 1.06 Average Precipitation -
0.000542 0.000144 1.19
Average DAYS_ABOVE_32_C
-0.00631 0.00142 1.25
Feature R 0.581 0.152 1.17 T 0.410 0.112 1.16
Odds Ratios for Continuous Predictors
Odds Ratio 95% CI Years Since Major Work Actual
1.0313 (1.0250, 1.0377)
Thickness 1.0418 (1.0146, 1.0698)
167
Average Precipitation 0.9995 (0.9992, 0.9997)
Average DAYS_ABOVE_32_C
0.9937 (0.9909, 0.9965)
Odds Ratios for Categorical Predictors
Level A Level B Odds Ratio 95% CI Feature R A 1.7872 (1.3264,
2.4079) T A 1.5067 (1.2098,
1.8763) T R 0.8431 (0.6210,
1.1445)
Odds ratio for level A relative to level B
Model Summary
Deviance R-Sq
Deviance R-Sq(adj) AIC AICc BIC
5.08% 4.85% 2493.65 2493.70 2533.94 Goodness-of-Fit Tests
Test DF Chi-Square P-Value Deviance 2330 2479.65 0.016 Pearson 2330 2333.85 0.474 Hosmer-Lemeshow
8 4.73 0.786
Analysis of Variance
Wald Test Source DF Chi-Square P-Value Regression 6 115.79 0.000 Years Since Major Work Actual
1 95.42 0.000
Thickness 1 9.17 0.002 Average Precipitation 1 14.21 0.000 Average DAYS_ABOVE_32_C
1 19.72 0.000
Feature 2 21.00 0.000
168
169
170
Distress 41 Alligator Cracking
During the first iteration, 'Average DAYS_BELOW_0_C' had a VIF of 126.65 and was removed from the predictor list. The second iteration Average Precipitation*Average TEMP_MEAN_AVG (deg C) had a VIF of 20.56 and was removed. The third iteration had all predictors with a VIF less than 10 and are below.
Method
Link function Logit Categorical predictor coding
(1, 0)
Rows used 1064 Backward Elimination of Terms
Candidate terms: Years Since Major Work Actual, Thickness, Average Precipitation, Average TEMP_MEAN_AVG (deg C), Average FREEZE_INDEX, Average FREEZE_THAW, Average TEMP_MAX, Average TEMP_MIN, Average DAYS_ABOVE_32_C, Feature, Subgrade Strength, Surface Type - Current, Thickness*Subgrade Strength
------Step 1----- ------Step 2----- ------Step 3----- Coef P Coef P Coef P Constant -9.53 -9.45 -9.41 Years Since Major Work Actual 0.02454 0.001 0.02554 0.000 0.02479 0.000 Thickness -0.0102 0.805 -0.0099 0.811 0.0174 0.526 Average Precipitation 0.000457 0.176 0.000363 0.247 0.000392 0.208 Average TEMP_MEAN_AVG (deg C)
-0.0423 0.462
Average FREEZE_INDEX 0.000896 0.027 0.000869 0.033 0.000859 0.034 Average FREEZE_THAW 0.00902 0.058 0.01016 0.024 0.01032 0.022 Average TEMP_MAX 0.2229 0.000 0.1983 0.000 0.1921 0.000 Average TEMP_MIN 0.0567 0.095 0.0387 0.099 0.0393 0.091 Average DAYS_ABOVE_32_C -0.00940 0.072 -0.00918 0.078 -0.00900 0.081 Feature -0.486 0.063 -0.468 0.074 -0.415 0.117 Subgrade Strength 0.268 0.861 0.257 0.868 0.932 0.001 Surface Type - Current -0.400 0.012 -0.411 0.012 -0.457 0.006 Thickness*Subgrade Strength 0.1335 0.293 0.1379 0.266 Deviance R-Sq 10.30% 10.25% 9.98% Deviance R-Sq(adj) 8.60% 8.64% 8.57% AIC 928.87 927.41 926.09 AICc 929.53 928.00 926.55 BIC 1018.3
3 1011.9
0 1000.6
3 -----Step 4----- -----Step 5----- -----Step 6----- Coef P Coef P Coef P
171
Constant -9.28 -8.93 -8.36 Years Since Major Work Actual 0.02455 0.001 0.02488 0.000 0.02666 0.000 Thickness Average Precipitation 0.000371 0.231 Average TEMP_MEAN_AVG (deg C)
Average FREEZE_INDEX 0.000840 0.037 0.000657 0.078 0.000244 0.213 Average FREEZE_THAW 0.01029 0.022 0.00737 0.053 0.00352 0.153 Average TEMP_MAX 0.1905 0.000 0.1964 0.000 0.1791 0.000 Average TEMP_MIN 0.0382 0.098 0.0285 0.190 Average DAYS_ABOVE_32_C -0.00897 0.082 -0.01200 0.008 -0.01150 0.012 Feature -0.394 0.131 -0.399 0.129 -0.381 0.168 Subgrade Strength 0.938 0.001 0.931 0.001 0.925 0.001 Surface Type - Current -0.464 0.004 -0.459 0.005 -0.497 0.002 Thickness*Subgrade Strength Deviance R-Sq 9.94% 9.80% 9.63% Deviance R-Sq(adj) 8.64% 8.59% 8.53% AIC 924.46 923.89 923.54 AICc 924.87 924.24 923.84 BIC 994.04 988.50 983.18 -----Step 7----- -----Step 8----- Coef P Coef P Constant -7.37 -7.45 Years Since Major Work Actual 0.02640 0.000 0.03018 0.000 Thickness Average Precipitation Average TEMP_MEAN_AVG (deg C)
Average FREEZE_INDEX Average FREEZE_THAW 0.00456 0.048 0.00531 0.020 Average TEMP_MAX 0.1498 0.000 0.1459 0.000 Average TEMP_MIN Average DAYS_ABOVE_32_C -0.00933 0.026 -0.00947 0.024 Feature -0.384 0.176 Subgrade Strength 0.920 0.001 0.893 0.002 Surface Type - Current -0.518 0.001 -0.511 0.002 Thickness*Subgrade Strength Deviance R-Sq 9.49% 9.13% Deviance R-Sq(adj) 8.48% 8.33% AIC 923.00 922.57 AICc 923.25 922.74 BIC 977.67 967.30
α to remove = 0.1 If a term has more than one coefficient, the largest in magnitude is shown.
172
Response Information
Variable Value Count Unhealthy vs Healthy
1 189 (Event)
0 875 Total 1064
Regression Equation
P(1) = exp(Y')/(1 + exp(Y'))
Subgrade Strength
Surface Type - Current
A AAC Y' = -7.448 + 0.03018 Years Since Major Work Actual + 0.005305 Average FREEZE_THAW + 0.1459 Average TEMP_MAX - 0.009471 Average DAYS_ABOVE_32_C
A AC Y' = -7.078 + 0.03018 Years Since Major Work Actual
+ 0.005305 Average FREEZE_THAW + 0.1459 Average TEMP_MAX - 0.009471 Average DAYS_ABOVE_32_C
A APC Y' = -7.960 + 0.03018 Years Since Major Work Actual
+ 0.005305 Average FREEZE_THAW + 0.1459 Average TEMP_MAX - 0.009471 Average DAYS_ABOVE_32_C
B AAC Y' = -7.250 + 0.03018 Years Since Major Work Actual
+ 0.005305 Average FREEZE_THAW + 0.1459 Average TEMP_MAX - 0.009471 Average DAYS_ABOVE_32_C
B AC Y' = -6.880 + 0.03018 Years Since Major Work Actual
+ 0.005305 Average FREEZE_THAW + 0.1459 Average TEMP_MAX - 0.009471 Average DAYS_ABOVE_32_C
B APC Y' = -7.761 + 0.03018 Years Since Major Work Actual
+ 0.005305 Average FREEZE_THAW + 0.1459 Average TEMP_MAX - 0.009471 Average DAYS_ABOVE_32_C
C AAC Y' = -6.556 + 0.03018 Years Since Major Work Actual
+ 0.005305 Average FREEZE_THAW + 0.1459 Average TEMP_MAX - 0.009471 Average DAYS_ABOVE_32_C
173
C AC Y' = -6.186 + 0.03018 Years Since Major Work Actual + 0.005305 Average FREEZE_THAW + 0.1459 Average TEMP_MAX - 0.009471 Average DAYS_ABOVE_32_C
C APC Y' = -7.067 + 0.03018 Years Since Major Work Actual
+ 0.005305 Average FREEZE_THAW + 0.1459 Average TEMP_MAX - 0.009471 Average DAYS_ABOVE_32_C
Coefficients
Term Coef SE Coef VIF Constant -7.45 1.06 Years Since Major Work Actual
0.03018 0.00657 1.06
Average FREEZE_THAW 0.00531 0.00227 1.26 Average TEMP_MAX 0.1459 0.0334 3.83 Average DAYS_ABOVE_32_C
-0.00947
0.00420 4.11
Subgrade Strength B 0.198 0.200 1.32 C 0.893 0.255 1.33 Surface Type - Current AC 0.370 0.257 1.90 APC -0.511 0.329 1.99
Odds Ratios for Continuous Predictors
Odds Ratio 95% CI Years Since Major Work Actual
1.0306 (1.0174, 1.0440)
Average FREEZE_THAW 1.0053 (1.0009, 1.0098)
Average TEMP_MAX 1.1571 (1.0838, 1.2353)
Average DAYS_ABOVE_32_C
0.9906 (0.9824, 0.9988)
Odds Ratios for Categorical Predictors
Level A Level B Odds Ratio 95% CI Subgrade Strength B A 1.2191 (0.8242,
1.8034) C A 2.4412 (1.4814,
4.0231) C B 2.0024 (1.2292,
3.2622) Surface Type - Current
174
AC AAC 1.4473 (0.8743, 2.3958)
APC AAC 0.5996 (0.3144, 1.1435)
APC AC 0.4143 (0.2505, 0.6851)
Odds ratio for level A relative to level B
Model Summary
Deviance R-Sq
Deviance R-Sq(adj) AIC AICc BIC
9.13% 8.33% 922.57 922.74 967.30 Goodness-of-Fit Tests
Test DF Chi-Square P-Value Deviance 1055 904.57 1.000 Pearson 1055 1027.40 0.723 Hosmer-Lemeshow
8 10.80 0.214
Analysis of Variance
Wald Test Source DF Chi-Square P-Value Regression 8 71.08 0.000 Years Since Major Work Actual
1 21.09 0.000
Average FREEZE_THAW 1 5.45 0.020 Average TEMP_MAX 1 19.11 0.000 Average DAYS_ABOVE_32_C
1 5.08 0.024
Subgrade Strength 2 12.54 0.002 Surface Type - Current 2 12.50 0.002
175
176
177
Distress 76 Alkali Silica Reaction
During the first iteration, 'Average DAYS_BELOW_0_C' had a VIF of 2157.50 and was removed from the predictor list. The second iteration Average TEMP_MIN had a VIF of 24.97 and was removed. The third iteration Average DAYS_ABOVE_32_C had a VIF of 12.08 and was removed. The fourth iteration had all predictors with a VIF less than 10 and are below.
Method
Link function Logit Categorical predictor coding
(1, 0)
Rows used 2337 Backward Elimination of Terms
Candidate terms: Years Since Major Work Actual, Thickness, Average Precipitation, Average TEMP_MEAN_AVG (deg C), Average FREEZE_INDEX, Average FREEZE_THAW, Average TEMP_MAX, Feature, Subgrade Strength, Average Precipitation*Average TEMP_MEAN_AVG (deg C), Thickness*Subgrade Strength
------Step 1----- -----Step 2----- Coef P Coef P Constant -12.05 -13.33 Years Since Major Work Actual 0.01770 0.000 0.01764 0.000 Thickness -0.150 0.294 -0.0509 0.024 Average Precipitation -0.00226 0.076 -0.00225 0.077 Average TEMP_MEAN_AVG (deg C) 0.1177 0.216 0.1153 0.225 Average FREEZE_INDEX -0.00265 0.038 -0.00265 0.038 Average FREEZE_THAW 0.03867 0.000 0.03846 0.000 Average TEMP_MAX 0.1820 0.000 0.1818 0.000 Feature -0.233 0.385 -0.233 0.375 Subgrade Strength -0.93 0.813 0.806 0.052 Average Precipitation*Average TEMP_MEAN_AVG (deg C)
0.000101 0.183 0.000102 0.182
Thickness*Subgrade Strength 0.106 0.765 Deviance R-Sq 19.65% 19.60% Deviance R-Sq(adj) 18.38% 18.51% AIC 916.41 912.98 AICc 916.62 913.13 BIC 1002.76 987.81 -----Step 3----- ------Step 4----- Coef P Coef P Constant -13.32 -14.30 Years Since Major Work Actual 0.01732 0.000 0.01788 0.000 Thickness -0.0532 0.017 -0.0504 0.024
178
Average Precipitation -0.00232 0.068 -0.000620 0.050 Average TEMP_MEAN_AVG (deg C) 0.1114 0.240 0.1956 0.007 Average FREEZE_INDEX -0.00257 0.044 -0.00271 0.033 Average FREEZE_THAW 0.03837 0.000 0.03983 0.000 Average TEMP_MAX 0.1831 0.000 0.1717 0.000 Feature Subgrade Strength 0.795 0.046 0.721 0.063 Average Precipitation*Average TEMP_MEAN_AVG (deg C)
0.000105 0.166
Thickness*Subgrade Strength Deviance R-Sq 19.42% 19.25% Deviance R-Sq(adj) 18.51% 18.43% AIC 910.97 910.89 AICc 911.08 910.98 BIC 974.29 968.45
α to remove = 0.1 If a term has more than one coefficient, the largest in magnitude is shown.
Response Information
Variable Value Count Unhealthy vs Healthy
1 148 (Event)
0 2189 Total 2337
Regression Equation
P(1) = exp(Y')/(1 + exp(Y'))
Subgrade Strength A Y' = -14.30 + 0.01788 Years Since Major Work Actual
- 0.05044 Thickness - 0.000620 Average Precipitation + 0.1956 Average TEMP_MEAN_AVG (deg C) - 0.002707 Average FREEZE_INDEX + 0.03983 Average FREEZE_THAW + 0.1717 Average TEMP_MAX
B Y' = -13.99 + 0.01788 Years Since Major Work Actual
- 0.05044 Thickness - 0.000620 Average Precipitation + 0.1956 Average TEMP_MEAN_AVG (deg C) - 0.002707 Average FREEZE_INDEX
179
+ 0.03983 Average FREEZE_THAW + 0.1717 Average TEMP_MAX
C Y' = -13.57 + 0.01788 Years Since Major Work Actual
- 0.05044 Thickness - 0.000620 Average Precipitation + 0.1956 Average TEMP_MEAN_AVG (deg C) - 0.002707 Average FREEZE_INDEX + 0.03983 Average FREEZE_THAW + 0.1717 Average TEMP_MAX
Coefficients
Term Coef SE Coef VIF Constant -14.30 2.05 Years Since Major Work Actual 0.01788 0.00468 1.13 Thickness -0.0504 0.0223 1.10 Average Precipitation -
0.000620 0.000316 1.64
Average TEMP_MEAN_AVG (deg C)
0.1956 0.0725 8.88
Average FREEZE_INDEX -0.00271 0.00127 3.23 Average FREEZE_THAW 0.03983 0.00563 5.39 Average TEMP_MAX 0.1717 0.0451 2.39 Subgrade Strength B 0.304 0.542 9.02 C 0.721 0.549 9.24
Odds Ratios for Continuous Predictors
Odds Ratio 95% CI Years Since Major Work Actual 1.0180 (1.0087,
1.0274) Thickness 0.9508 (0.9101,
0.9933) Average Precipitation 0.9994 (0.9988,
1.0000) Average TEMP_MEAN_AVG (deg C)
1.2160 (1.0550, 1.4017)
Average FREEZE_INDEX 0.9973 (0.9948, 0.9998)
Average FREEZE_THAW 1.0406 (1.0292, 1.0522)
Average TEMP_MAX 1.1873 (1.0868, 1.2970)
Odds Ratios for Categorical Predictors
Level A Level B Odds Ratio 95% CI Subgrade Strength
180
B A 1.3550 (0.4687, 3.9174)
C A 2.0568 (0.7014, 6.0311)
C B 1.5179 (1.0433, 2.2082)
Odds ratio for level A relative to level B
Model Summary
Deviance R-Sq
Deviance R-Sq(adj) AIC AICc BIC
19.25% 18.43% 910.89 910.98 968.45 Goodness-of-Fit Tests
Test DF Chi-Square P-Value Deviance 2327 890.89 1.000 Pearson 2327 3559482.47 0.000 Hosmer-Lemeshow
8 8.12 0.422
Analysis of Variance
Wald Test Source DF Chi-Square P-Value Regression 9 140.40 0.000 Years Since Major Work Actual
1 14.62 0.000
Thickness 1 5.10 0.024 Average Precipitation 1 3.85 0.050 Average TEMP_MEAN_AVG (deg C)
1 7.28 0.007
Average FREEZE_INDEX 1 4.54 0.033 Average FREEZE_THAW 1 50.00 0.000 Average TEMP_MAX 1 14.49 0.000 Subgrade Strength 2 5.52 0.063
181
182
APPENDIX B
USAF LOCALIZED MAINTENANCE ACTIONS
183
.
184
APPENDIX C
ACCROYNM LIST
Air Force Base (AFB)
Air Force Civil Engineer Center (AFCEC)
Air Force Instruction (AFI)
Airfield Pavement Evaluation (APE)
Allowable Gross Load (AGL)
American Association of State Highway and Transportation Officials (AASHTO)
Asphalt Concrete (AC)
Asphalt-Over-Asphalt-Concrete (AAC)
Asphalt-Over-Portland-Cement-Concrete (APC)
California Bearing Ratio (CBR)
Correct Deduct Value (CDV)
Dynamic Cone Penetrometer (DCP)
Highest Deduct Value (HDV)
Long Term Pavement Performance (LTPP)
Maintenance and Repair (M&R)
Modern Era Retrospective Analysis for Research and Applications (MERRA)
National Aeronautics and Space Administration (NASA)
National Oceanic and Atmospheric Administration (NOAA)
Pavement Condition Index (PCI)
Pavement Classification Number (PCN)
Performance Grade (PG)
Portland Cement Concrete (PCC)
Unified Facility Criteria (UFC)
United States Air Force (USAF)
