Network-Level Pavement Condition Data Collection Quality

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Network-Level Pavement Condition Data Collection Quality Management Plan

Connecticut Department of Transportation

Newington, CT

Prepared By:

Connecticut Transportation Institute

School of Engineering

University of Connecticut

August 21, 2018

Ver. # 1.0

A. Document Change Control

The following is the document control for revisions made to this document.

Version Number Date of Issue Author(s) Brief Description of Change

1.0 8/21/2018 CTDOT & CTI FHWA Submission

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B. Definitions

The following acronyms, definitions of terms, abbreviations, and standards are used in this document.

Term Definition

AASHTO American Association of State Highway Transportation Officials

AASHTO M328-14 Standard Specification for Inertial Profiler, 2017

The objective of this specification is to define the required attributes of an inertial profiler which, when combined with an operator, becomes a complete inertial profiling system (IPS).

AASHTO PP67-16 Standard Practice for Quantifying Cracks in Asphalt Pavement Surfaces from Collected Pavement Images Utilizing Automated Methods, 2017

This practice outlines the procedures for quantifying cracking distress at the network level in asphalt pavement surfaces utilizing automated methods.

AASHTO PP68-14 Standard Practice for Collecting Images of Pavement Surfaces for Distress Detection, 2017

This practice outlines the procedures for collecting images of pavement surfaces utilizing automated methods for the purpose of distress detection for both network- and project-level analysis.

AASHTO PP69-14 Standard Practice for Determining Pavement Deformation Parameters and Cross Slope from Collected Transverse Profiles, 2017

This practice outlines a method for deriving pavement deformation parameters such as rut depth and cross-slope in pavement surfaces utilizing a transverse profile.

AASHTO PP70-14 Standard Practice for Collecting the Transverse Pavement Profile, 2017

This practice outlines a method for collecting pavement transverse profile, including its relationship to a level horizontal reference in pavement surfaces utilizing automated measurement devices. The profile can subsequently be used to quantify cross-slope and pavement distresses such as transverse deformation, rut characteristics, water entrapment, or edge drop-off.

AASHTO R36-13 Standard Practice for Evaluating Faulting of Concrete Pavements

This protocol describes a method for evaluating faulting in jointed concrete pavement surfaces. Faulting is defined as the difference in elevation across a transverse joint or transverse crack.

AASHTO R43-13 Standard Practice for Quantifying Roughness of Pavements, 2013

This standard practice describes a method for estimating roughness for a pavement section. An International Roughness Index (IRI) statistic is calculated from a single longitudinal profile measured with a road profiler in both the inside and outside wheelpaths of the pavement. The average of these two IRI statistics is reported as the roughness of the pavement section.

AASHTO R48-10 Standard Practice for Determining Rut Depth in Pavements, 2013

This practice describes a method for determining rut depth in pavement surfaces from transverse profile measurements. Five transverse profile points are the minimum number of points required to determine rut depth.

AASHTO R55-10 Standard Practice for Quantifying Cracks in Asphalt Pavement Surfaces, 2013

This practice covers the procedures for quantifying cracking in asphalt pavement surfaces, both in wheelpath and non-wheelpath areas.

AASHTO R56-14 Standard Practice for Certification of Inertial Profiling Systems, 2014

This practice describes a certification procedure for test equipment used to measure a longitudinal surface elevation profile of highways based on an inertial reference system that is mounted on a host vehicle.

AASHTO R57-14 Standard Practice for Operating Inertial Profiling Systems, 2014

This practice describes the procedure for operating and verifying the calibration of an inertial profiling system. This practice is meant to be performed as a quality control/quality assurance (QC/QA) test for use with the appropriate smoothness specification for paving operations and for network-level data collection.

Acceptance (A) (quality acceptance) Activities to verify that PMS data meet established quality standards.

Accuracy The degree to which a measurement, or the mean of a distribution of measurements, tends to coincide with the true population mean (AASHTO 2011).

ARAN Automatic Road AnalyzerTM

highway pavement data collection vehicle; CTDOT vehicles identified as vans 8 and 9 are ARAN 9000 models.

ASTM American Society of Testing and Materials

ASTM E1166-00 (2005) Standard Guide for Network level Pavement Management

This guide outlines the basic components of a network level pavement management system (PMS).

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ASTM E1656-94/1656M-11 Standard Guide for Classification of Automated Pavement Condition Survey Equipment, 2016

Guide to classify the measuring capabilities of pavement condition survey equipment that operates at traffic speeds and collect some of the data useful in characterizing pavement conditions.

ASTM E1926-98 (2003) Standard Practice for Computing International Roughness Index of Roads from Longitudinal Profile Measurements

This practice covers the mathematical processing of longitudinal profile measurements to produce a road roughness statistic called the International Roughness Index (IRI).

ASTM E2133 – 03 (2013) Standard Test Method for Using a Rolling Inclinometer to Measure Longitudinal and Transverse Profiles of a Travelled Surface.

This test method describes the measurement of transverse and longitudinal surface profiles on paved road, bridge, and airport surfaces using a rolling inclinometer traveling at walking speed.

ASTM E950/E950M-09 (2018) Standard Test Method for Measuring the Longitudinal Profile of Traveled Surfaces with an Accelerometer-Established Inertial Profiling Reference

This test method covers the measurement and recording of the profile of vehicular-traveled surfaces with an accelerometer-established inertial reference on a profile measuring vehicle.

Calibration A set of operations that establish, under specified conditions, the relationship between values of quantities indicated by a measuring instrument or measuring system, or between values represented by a material measure or a reference material, and the corresponding values realized by standards (AASHTO 2011).

Corrective Actions Improvements/adjustments to an organization's processes taken to eliminate causes of non-conformities or other undesirable situations. Specifically, they are actions to resolve discovered problems with calibration, defective equipment, data errors or missing data.

Crack, (Cracking) A fissure or discontinuity of the pavement surface not necessarily extending through the entire thickness of the pavement. (FHWA HPMS)

Cracking Percent (Asphalt pavement) The percentage of the total area exhibiting visible fatigue type cracking for all severity levels in the wheelpath in each section. (FHWA HPMS)

Cracking Percent (Portland Cement Concrete pavement)

The percentage of slabs within the section that exhibit transverse cracking. (FHWA HPMS)

Cross Slope (crossfall) The average transverse slope of the pavement surface, typically expressed in percent.

CTDOT Connecticut Department of Transportation

Data Quality Management Plan (DQMP)

A document that specifies the quality management procedures (including quality standards, QC, acceptance, corrective actions and resources) that will be used and how the process will be implemented and assessed for effectiveness (adapted from ISO 2000).

DMI Distance Measuring Instrument – Automated vehicle onboard instrumentation and software to accurately measure and output distance travelled.

dTIMS™ Deighton's Total Infrastructure Management System; infrastructure asset management software used by CTDOT PMU.

Faulting Difference in elevation (i.e., vertical misalignment) across a Portland Cement Concrete joint

Geometrics Roadway geometrics include horizontal curves, vertical curves, tangents, radius of curvature, elevations, grade, cross slope, lane dimensions (widths, lengths), and other parameters that can be used to define positioning of the physical elements of the roadway

GIS Geographic Information System - A system for the management, display, and analysis of spatial information (FHWA HPMS)

GPS Global Positioning System

HPMS Highway Performance Monitoring System - a national level highway information system that includes data on the extent, condition, performance, use and operating characteristics of the nation's highways. (FHWA)

Independent assurance (IA) or verification

Independent verification is an unbiased and independent evaluation of data quality that is performed by someone other than the entity that collected or is receiving the data.

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International Roughness Index (IRI) A statistic used to determine the amount of roughness in a measured longitudinal profile. (AASHTO 2014)

JRCP Jointed Reinforced Concrete Pavement – A reinforced Portland cement concrete pavement with transverse joints placed at planned intervals (ASTM E867)

LRS Linear Referencing System - A set of procedures for determining and retaining a record of specific points along a highway. Typical methods used are milepoint, milepost, reference point, and link-node. (FHWA HPMS)

Longitudinal Grade The slope (hilliness) of the pavement in the longitudinal direction (direction of travel) typically measured and expressed in percent.

Longitudinal Profile The vertical deviations of the pavement surface taken along a line in the direction of travel referenced to a horizontal datum. (AASHTO 2014)

Mean Roughness Index (MRI) Average of IRI values for the left and right wheelpaths of a roadway traffic lane

NCHRP National Cooperative Highway Research Program, A forum for coordinated and collaborative research administered by the Transportation Research Board of the National Academies in Washington D.C.

Pavement Image A representation of the pavement that describes a characteristic (gray scale, color, temperature, elevation, etc.) of a matrix of points (pixels) on the pavement surface. Images are used for the detection and extraction of pavement cracking data.

PCI Pavement Condition Index – A formula developed by CTDOT expressing the combined relationship of five pavement indices: cracking, distortion, ride quality, disintegration and drainage, on a scale of 1-9.

PLU CTDOT Photolog Unit

PM Preventative maintenance; contracted annual maintenance for CTDOT ARAN vehicles

PMS Pavement Management System

PMU CTDOT Pavement Management Unit

Precision The degree of agreement among a randomly selected series of measurements; or the degree to which tests or measurements on identical samples tend to produce the same results (AASHTO 2011).

PSR Present Serviceability Rating – A mean rating of the serviceability of a pavement established by a rating panel under controlled conditions. (ASTM E867) Scale 0.1-5.0 (FHWA)

PWL Percent Within Limits – The cumulative area under a normal distribution curve which represents the estimated percentage of a population that falls above the Lower Specification Limit (LSL), beneath the Upper Specification Limit (USL), or between the Upper and Lower Specification Limits. (NETTCP 2014)

Quality Control (QC) Activities needed to adjust production processes toward achieving the desired level of quality of pavement condition data.

Quality Standards Quality standards define, when applicable, the resolution, accuracy, and repeatability or other standards that are used to determine the quality of each deliverable.

Radius of Curvature For a curve, it equals the radius of the circular arc which best approximates the curve at that point.

Reference Value (Ground Truth) A value that serves as an agreed-upon reference for comparison, and which is derived as a theoretical or established value, based on scientific principles, an assigned or certified value, based on experimental work of some national or international organization, or a consensus or certified value, based on collaborative experimental work under the auspices of a scientific or engineering group (AASHTO 2011).

Repeatability Degree of variation among the results obtained by the same operator repeating a test on the same material. The term repeatability is therefore used to designate test precision under a single operator (AASHTO 2011).

Reproducibility Degree of variation among the test results obtained by different operators performing the same test on the same material (AASHTO 2011).

Resolution The smallest increment that a characteristic measuring process must distinguish and display (ASTM E867).

Road Profile The cross-sectional shape of the road surface in relation to the road corridor traversing the surrounding landscape. (PennStateU)

Roughness The deviation of a surface from a true planar surface with characteristic dimensions that affect vehicle dynamics, ride quality, dynamic loads, and drainage. (ASTM E867)

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ROW Image Digital image record of the roadway right of way and adjacent visible surrounding area

Rutting (Rut) Longitudinal surface depressions in the wheel path. A rut is more specifically defined as—a broad longitudinal depression in the wheel path of the pavement surface with a depth of at least 0.080 in., a width of at least 1.0 ft., and with a longitudinal length of at least 100 ft. (AASHTO 2014)

Segment A 0.1 mile section of roadway used for HPMS and PMS to summarize pavement attribute data

Smoothness, (ride quality) (IRI) The measure of a pavement’s roughness reported with the International Roughness Index. A statistic used to estimate the amount of roughness in a measured longitudinal profile. (AASHTO 2014)

Tolerance The defined limits of allowable (acceptable) departure from the true value of a measured quantity. (ASTM E867)

Transverse Profile Vertical deviations of the pavement surface from a level horizontal reference perpendicular to the lane direction of travel. (AASHTO 2014)

Validation Sites (Annual)

Specified roadway sections for use as reference or “ground truth,” whose condition data have been measured by agency personnel using a reference profiler. These sites are used to verify proper data collection procedures, determine accuracy and/or for calibration of the equipment.

Verification Runs Collection of production-season data on validation sites or verification sections by more than one device so that data collected during the production phase can be compared to verify proper collection procedures, reproducibility and continued calibration of the equipment.

Verification Sections (Monthly)

Roadway sections used on a routine basis to verify proper data collection procedures, accuracy and/or reproducibility of the equipment.

Vision Desktop data processing and analysis software developed by FUGRO Roadware allowing users to browse and interact with CTDOT collected ARAN data.

Walking Profiler CTDOT utilizes a Surface Systems & Instruments (SSI) CS8800 Walking Profiler for laying out and checking validation sites.

Wheelpath CTDOT Wheelpath – A longitudinal strip of pavement 39 inches (1 meter) wide. The inner edges of both wheelpaths are offset from the center of the lane by 14.75 inches (0.375 meters), and are therefore 29.5 inches (0.75 meters) apart.

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TABLE OF CONTENTS PAGE

A Title Page (Document Change Control) 1

B Definitions 2

C Foreword 7

1 Quality Management Approach 8

2 Quality Management “Project Team” Roles, Responsibilities & Current Business Process 9

3 Certification Process for Persons Performing Manual Data Collection 11

4 DATA Collection Equipment, Calibration, Certification or Validation & Verification 13

5 Data Collection Quality Control Measures 16

6 Deliverables, Protocols and Quality Standards 18

7 Data Acceptance Criteria and Error Resolution Procedures 21

8 Quality Reporting Plan 25

9 CTDOT Data Quality Management Plan Endorsement 27

10 References 28

LIST OF TABLES PAGE

Table 2.1 Project Team Roles and Responsibilities 9

Table 4.1 Equipment Installed on CTDOT ARAN 9000 Series Vans 13

Table 5.1 Data Collection Quality Control Measures 17

Table 5.2 Specific QC Procedures 18

Table 6.1 Deliverables, Protocols and Quality Standards for Automated Data Collection 19

Table 6.2 Data Review Criteria for Automated Condition Data Collection 21

Table 7.1 General Acceptance Expectations and Deliverables 23

Table 7.2 Specific Acceptance Procedures 24

APPENDICES PAGE

Appendix A Example Crack Detection Rater Exam Problems 29

Appendix B Example Walking Profiler Refresher Training Evaluation Checklist 33

Appendix C Example ARAN Field Operations Training Evaluation Report 35

Appendix D Example FUGRO Preventative Maintenance Report 37

Appendix E Examples of CTDOT’s Calibration Reports 53

Appendix F Example of the Verification Survey Test Report 56

Appendix G Example of the Photolog Daily Collection Check List 59

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C. Foreword

This Pavement Data Collection Quality Management Plan (DQMP) is a first cut for CTDOT. As more

ground truth reference data are collected at validation sites, as staff assignments regarding quality

control and acceptance are implemented, and as activities are fine-tuned, this DQMP will be revised

over the coming years.

Should any portion of this Data Quality Management Plan become obsolete or otherwise be in need of

revision, every reasonable attempt will be made to update the appropriate section(s) in a timely

manner. Per 23 cRF490.319(c)(2) all proposed significant changes to the DQMP will be submitted to

FHWA for approval prior to implementing the change.

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1.0 QUALITY MANAGEMENT APPROACH

Quality Management (QM) assures the quality of the data collection deliverables and describes the

processes and procedures to be used for ensuring quality. The Connecticut Department of

Transportation (CTDOT) has worked together with the Connecticut Transportation Institute (CTI) at the

University of Connecticut to develop a quality management approach for data collection that addresses

quality control and quality acceptance for pavement management. Quality Control (QC) is conducted by

the CTDOT Photolog Unit (PLU) of the Roadway Information Systems Office in the Bureau of Policy and

Planning. Quality Acceptance (QA) is conducted primarily by CTDOT’s Pavement Management Unit

(PMU) in the Bureau of Engineering and Construction, with assistance from the CTI, as needed.

Currently, 7,438 directional miles (for the 3,719 centerline-mile-state-maintained roadway network) are

surveyed each year. This represents 100 percent of the Interstate, Primary and Secondary system of

Connecticut’s state highway network. In addition, approximately 328 miles of the local road network is

surveyed as needed for the HPMS program. Pavement condition data are collected during these surveys.

This DQMP identifies key activities, processes, and procedures for ensuring quality.

Below is a brief explanation of each of the sections of the DQMP that follow.

Section 2. Quality Team Roles,

Responsibilities & Current Business Processes

Quality-related roles and responsibilities and current business processes for data collection, data reduction, review, acceptance, and reporting for use in FHWA HPMS, CTDOT performance measures, and paving and preservation programs.

Section 3. Certification for Persons Performing Manual Data

Collection

Processes used to certify and validate manual pavement condition raters and CTDOT’s training procedures.

Section 4. Equipment, Calibration,

Certification or Validation & Verification

Detail and description of CTDOT’s pavement data collection equipment processes and protocols used to calibrate, certify or validate and verify data collection equipment.

Section 5. Quality Control (QC)

The QC activities that monitor, provide feedback, and verify that the data collection deliverables meet the defined quality standards.

Section 6. Deliverables, Protocols &

Quality Standards

The data collection deliverables subject to quality review, protocols used for collection, quality standards that are the measures used to determine a successful outcome for a deliverable, and criteria to describe when each deliverable is considered complete and correct. Deliverables are evaluated against these criteria before they are formally approved.

Section 7. Data Acceptance Criteria and Error Resolution Procedures

The acceptance testing used to determine if quality criteria are met, and corrective actions that must be taken for any deliverables not meeting the quality criteria.

Section 9. Quality Reporting Plan

The documentation of all QM activities―including quality standards, QC, acceptance, and corrective actions―and the format of the final QM report.

Section 10. DQMP Endorsement

Signature page for endorsement of the CTDOT Data Quality Management Plan.

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2.0 QUALITY MANAGEMENT “PROJECT TEAM” ROLES, RESPONSIBILITIES & CURRENT BUSINESS

PROCESS

The following identifies the quality-related responsibilities of each member of the Quality Management

“Project Team” and lists specific quality responsibilities.

Table 2.1 Project Team Roles and Responsibilities

Team Role Assigned Resource Quality Management Responsibilities Agency Managers Michael Connors

Leo Fontaine • Set/Approve quality standards, acceptance criteria, and corrective actions. • Approve each deliverable per quality standards. • Approve resolution of quality issues. • Assess effectiveness of the QM procedures. • Recommend improvements to quality processes.

Quality Assurance Supervisor

John Henault Recommend quality standards, acceptance criteria and corrective actions to Agency Managers

Assure deliverables meet broad set of data quality requirements.

Communicate as needed with Agency Managers on any issues that may arise.

• Communicate weekly with QC Supervisor. • Assure data acceptance checks. • Assure PMU data processing, analysis and reporting.

Monitor schedule and reporting deadline adherence

Monitor resolution of quality exceptions reported to QC Supervisor.

Assure quality issue resolution and report results to QC Supervisor and Agency Managers.

• Prepare QM report.

PMU Data Lead Jeanine Moriarty • Maintain acceptance log and submit quality exceptions to QA Supervisor and QC Supervisor.

Document quality audits of processed data • Report any problems using QC log. • Perform data & FIS video acceptance checks and document results. • Perform GIS checks and document results.

Maintain all Vision software LCMS, rating, classification and rutting templates settings/distress schemes are up to date and correct

Track reporting requirements/deadlines for completion of pavement condition data

Quality Control Supervisor

James Spencer • Recommend quality standards, acceptance criteria, and corrective actions to Agency Managers

• Assure deliverables meet broad set of data quality requirements. • Communicate as needed with Agency Managers on issues that may arise.

Communicate daily/weekly with QC Lead, Data Lead and Field Crew Lead. • Communicate daily/weekly with QA Supervisor and PMS Data Lead • Submit acceptance exceptions log to QC Lead, PLU Data Lead and Field

Crew Lead. • Supervise manual measurement of Verification and Validation sites. • Establish reference values with data collection team. • Monitor schedule adherence. • Assure quality issue resolution with QC Lead and report results to QA

Supervisor and Agency Managers.

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QC Lead Lester King • Assure QC practices are followed. • Assure proper protocols are used. • Assure any training addresses all personnel skill levels. • Assure reviews by Photolog Data Lead. • Assure performance of all quality activities and reporting of all data quality

exceptions using a QC log. • Assure correction of all quality issues and changes in procedures as

needed. • Perform and document a final deliverables quality review as needed. • Compile documentation of all QC activities.

PLU Data Lead Jin Sadlowski • Perform and document checks of total mileage, segment lengths, and comparison with master route file.

• Assure and document GIS checks of segment location and completeness. • Document quality audits of uploaded and processed data. • Maintain and report any problems using QC log. • Observe and maintain records of verification runs on validation sites;

Analyze and document results. • Perform initial data & video acceptance checks and document results. • Perform GIS checks and document results.

Field Crew Lead Mike Longo • Assure and document initial equipment configuration, calibration, and verification.

Assure performance of daily and/or periodic equipment start-up checks, tests, inspections, and calibrations.

• Assure daily review of data logs and video samples. • Assure real-time monitoring of data and video quality. • Assure performance of monthly verification runs on validation, sites. • Assure documentation of all field QM activities and reporting of any

problems using QC log.

Field Crew Robert Kasica, Anthony Alasso

• Perform daily and/or periodic equipment start-up checks, tests, inspections, and calibrations.

• Perform daily review of data logs and video samples. • Perform real-time monitoring of data and video quality. • Perform documentation daily reports including:

End-of-Day Report, QC Log, and ARAN Daily Mileage Summary

2.1 CURRENT BUSINESS PROCESSES

All pavement data collection and data reduction operations at CTDOT are performed in-house (i.e., no

vendors or consultants are used for data collection or processing using CTDOT’s two FUGRO 9000 Series

ARAN Vans and Vision Desktop Processing Software Environment.

The Photolog Unit (PLU) within the Roadway Information Systems Office of the Bureau of Policy and

Planning is responsible for collecting, processing, analyzing, and providing data and information annually

on pavement condition, geometric and imagery data, including quality control. This data and

information is utilized for multiple functions within and outside of CTDOT.

The Pavement Management Unit (PMU) within the Engineering Services Section, Bureau of Engineering

and Construction performs further processing; maintains databases to sort and store collected data, as

well as to calculate pavement condition indices; uses a strategic analysis program (dTIMS™) to evaluate

preservation strategies and suggest cost-effective paving projects to maintain highway condition; and

produces annual reports that provide the state of the condition of pavements within Connecticut. This

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unit is also responsible for quality assurance (denoted henceforth as Acceptance). The project-level

pavement design functions are also tasked to the PMU. Data that are prepared by the PMU that are

submitted annually to FHWA for HPMS are finalized within The Bureau of Policy and Planning, as are

published quarterly CTDOT pavement performance measures.

Currently, the PLU is responsible for the segmentation or matching of data to LRS, and for the post

processing of field data in Vision to produce IRI, curve and grade.

The PMU then utilizes Vision to extract and summarize information on cracking types, zones, severities

and extent, percent cracking, rut depths, faulting, and IRI, and for exporting data into a SQL Server

Express database for storage, further sorting, processing, generating reports for CTDOT and FHWA, and

finally for importing into a strategic analysis program (dTIMS™).

3.0 CERTIFICATION PROCESS FOR PERSONS PERFORMING MANUAL DATA COLLECTION

A certification process for persons performing manual rating of data must be included in this Data

Quality Management Plan (DQMP) according to 23CFR490.319(c)(1)(ii) . All of CTDOT pavement

condition data are collected by automated or semi-automated methods, with the exception of the

following two manual procedures: (1) For Validation Sites and Reference Checks where crack detection

is done manually using the Vision/Wise Cracks application where cracks are manually identified off

imagery collected from the field and (2) where data is collected manually using the SSI CS8800 Walking

Profiler for development of reference values on the validation sites. Each of these procedures requires

the experience and knowledge of qualified staff. CTDOT has highly knowledgeable and experienced

senior level, field and office staff within its Photolog and Pavement Management Units who have

performed these duties for many years. For this purpose, CTDOT considers qualified to be analogous to

certified.

Certification/Qualification of Staff Performing Crack Detection

Since the CTDOT will be using manual pavement condition data collection as part of its certification

process, manual raters must be capable of collecting data meeting the requirements of 23 CFR 490.309.

Accordingly, specialized training is included in this plan for manual raters, which includes refresher

training in order to keep staff current.

The lead manual rater for the PMU that is responsible for certifying individuals conducting the manual

ratings is Ms. Jeannine Moriarty. She has been involved with pavement condition assessment

throughout her 17 years’ experience working in the PMU, and is CTDOT’s subject matter expert on

pavement cracking. Ms. Moriarty has experience in using the Distress Identification Manual for the

Long-Term Pavement Performance Program and is familiar with the definitions of pavement condition

metrics identified in the HPMS Field Manual. In addition, Ms. Moriarty serves on the committee for

NCHRP 01-57A, Standard Definitions for Comparable Pavement Cracking Data, where the objective is to

develop standard definitions for common cracking types in flexible, rigid, and composite pavements.

CTDOT will adopt the Distress Identification Manual for the Long-Term Pavement Performance Program

as its reference manual for describing its pavement condition rating methodology. Ms. Moriarty will

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administer training and testing for any new Manual Data Collectors. This training will incorporate the

abovementioned distress manual, as well as the HPMS Field Manual. Manual Data Collectors will be

expected to be familiar with both reference manuals. After significant classroom time, Ms. Moriarty will

oversee the students as they conduct pavement condition surveys. The students will then be required

to pass a written exam administered by Ms. Moriarty. Students that pass the exam will be issued a

certification indicating that they meet manual data collection requirements. Manual Data Collectors will

be required to recertify every two years.

Example Crack Detection Rater Exam Problems are provided in Appendix A.

Certification/Qualification of Staff using SSI CS8800 Walking Profiler

As part of the procurement of the SSI CS-8800 Walking Profiler, the PLU staff was initially provided with

two days of certified vendor training on the calibration and use of the equipment to insure that accurate

ground truth reference measurements would be obtained at CTDOT field validation sites. Annually,

prior to the beginning of each collection season, a training refresher course will be conducted by the

PLU Senior Level QC Lead per manufacturer’s specifications and requirements as detailed within the

CS8800 Walking Profiler Operation Manual. During the Refresher Training each employee will be

evaluated by the QC Lead for their understanding and effective use of the walking profiler.

A copy of the Walking Profiler Refresher Training Evaluation Checklist has been included as Appendix B.

3.1 ARAN VAN DRIVER AND OPERATOR TRAINING

Field staff will attend an annual refresher course on the use and operation of the ARAN Van and its

subsystems provided by the Senior Level QC Lead. The training will cover the Manufacturers

specifications and procedures as outlined in the ARAN User Manual and will cover the policies and

procedures as outlined in Reference 1 - “Connecticut Department of Transportation, Photolog Field Data

Collection Standard Operating Procedures – Working Draft”.

New or replacement staff added to the PLU will be provided with vendor-based training on CTDOT’s

FUGRO ARAN vans, Vision Desktop Suite of software, and on the SSI CS-8800 Walking Profiler, as

required. At the State’s option this training may be provided by the product vendors (FUGRO Roadware

and SSI) via a procurement process or may be provided in-house by senior level staff members, as noted

above. After receiving the training, new staff will each be subject to a working test period in a

production environment/setting where they will be further supervised by senior level Field Crew Lead to

gauge the new employee’s competency, knowledge and skill. Once sufficient competency has been

shown, the new employee will be deemed qualified to perform the job duties.

A copy of the ARAN Field Operations Training Evaluation has been included as Appendix C.

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4.0 DATA COLLECTION EQUIPMENT, CALIBRATION, CERTIFICATION OR VALIDATION & VERIFICATION

4.1 COLLECTION EQUIPMENT

FUGRO 9000 Series ARAN Vans

CTDOT has two FUGRO ARAN 9000 series Sprinter Style Vans each equipped with 3-D laser scanning

technology for collection of transverse profile (rutting) and pavement surface images (cracking). A laser

crack measurement system (LCMS) uses high speed cameras, custom optics and laser line projectors to

acquire both 2D images (black and white intensity images) and high-resolution 3D profiles (surface

elevations) of the road. These ARAN Vans and their onboard systems and sensors meet all federally

mandated AASHTO and ASTM standards. A general list of installed equipment is included in Table 4.1

below.

Further detailed information about the data collection equipment can be found in Reference 1

“Connecticut Department of Transportation, Photolog Field Data Collection Standard Operating

Procedures – Working Draft”, pages 7-15.

Table 4.1 Equipment Installed on CTDOT ARAN 9000 Series Vans

CTDOT Systems/ Equipment

CTDOT ARAN Vehicle

VAN 8 VAN 9

Chassis ARAN 9000, 2010 Dodge Sprinter ARAN 9000, 2015 Mercedes Benz Sprinter

Geographic Coordinates

Real-Time Differential GPS +POS LV Inertial Positioning System (1 meter accuracy) using

OmniStar

Real-Time Differential GPS +POS LV Inertial Positioning System (1 meter accuracy) using

OmniStar

Distance Wheel-mounted

Distance Measurement Instrument (DMI) Measures linear distance within ± 0.005%

Wheel-mounted Distance Measurement Instrument (DMI) Measures linear distance within ± 0.005%

Roughness (IRI)/Longitudinal

Profile

South Dakota Profiler RoLine – 4” Footprint Line Laser (Laser SDP/2) in Front Bumper Enclosure.

Class 1 Profiler under ASTM E950, AASHTO R56-10 Certification & ASTM E1926

South Dakota Profiler RoLine – 4” Footprint Line Laser (Laser SDP/2) in Front Bumper Enclosure. Class 1 Profiler under ASTM E950, AASHTO R56-

10 Certification & ASTM E1926

Crack Detection, Classification & Rating, Texture,

Rutting & Transverse Profile

Pave3D Pavemetrics Laser Crack Measurement System (LCMS)

Pave3D Pavemetrics Laser Crack Measurement System (LCMS)

Right of Way (Front View) Imagery

SONY HD Camera w/90 Degree Field of View Lens SONY HD Camera w/90 Degree Field of View Lens

Data that are collected with, or derived (calculated) from, the ARAN vehicles includes: right-of-way

imagery, chainage (distance), GPS coordinates, longitudinal grade, transverse slope (cross slope),

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roughness (IRI), rutting, faulting, cracking, geometric curvatures (vertical and horizontal), and other

pavement surface distress conditions.

Surface Systems & Instruments, Inc. (SSI) CS8800 Walking Profiler

CTDOT purchased a SSI CS8800 Walking Profiler to establish ground truth reference measurements for

IRI. It is certified as meeting the following standards and criteria:

Classification: ASTM E2133 compliant. Rating: ASTM Class 1 and World Bank Standard Class 1. Ability: measure longitudinal distances to within +/- 0.1 percent of the actual distance. Profile Accuracy: +/- 2mm/50m plus level for non-closed loop surveys. Computed Roughness Index: IRI with ability to demonstrate repeatability of pavement

roughness data on multiple same surface test runs of at least:

0.98% for IRI waveband. 0.98% in the long waveband. 0.98% in the medium waveband. 0.94% in the short waveband.

4.2 EQUIPMENT CALIBRATION

FUGRO 9000 Series Vans

CTDOT has contracted with the equipment manufacturer, FUGRO Roadware, to: (1) perform annual

preventative maintenance and calibration of CTDOT’s two ARAN vans prior to the start of each

collection season in accordance their own calibration specifications and procedures, with AASHTO

standards or protocols as detailed in Table 6.1, ASTM standards, FHWA’s HPMS Field manual (2016), and

CTDOT documents and procedures to ensure that the ARAN vans are operating at peak efficiency and

are collecting the highest quality level data possible; and (2) provide documentation of the calibration

processes used and proof of the successful equipment calibration prior to certification testing. This

documentation is then signed by the Photolog Unit Supervisor and FUGRO Technician as approved.

An example of FUGRO’s Preventative Maintenance Report is provided as Appendix D.

In addition, Photolog field staff are responsible for calibration of the ARAN Vans in accordance with

FUGRO’s recommendations, on a monthly basis, after any significant repair work done on the vans, or as

requested when the results from the certification, validation and verification testing indicate the need

for recalibration. All equipment calibration records and testing results done on the ARAN Vans during

the data collection schedule will be maintained and will be provided to the Project Team (as detailed in

Table 2.1 Project Team Roles and Responsibilities) for review.

Examples of CDOT’s Calibration Reports are provided in Appendix E.

Further detail on ARAN Equipment calibration is included in Ref. 4: Fugro Roadware, “ ARAN 9000

Manual, Version 2.0”, Fugro Roadware, Mississauga, Ontario, November 29, 2016

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SSI CS8800 Walking Profiler

The CS8800 Walking Profiler is recalibrated prior to each use by trained Photolog staff in accordance

with manufacturer’s specifications and recommendations as outlined in the CS8800 User Manual.

Further detail on the CS8800 Walking Profiler is included in Ref. 5: Surface Systems & Instruments, Inc., “

CS8800 Profiler VS Operation Manual,” Version 3.2.7.11, Surface Systems & Instruments, Inc., Auburn ,

CA., March 27, 2011

4.3 EQUIPMENT CERTIFICATION/VALIDATION FUGRO’s South Dakota Profiler RoLine – 4” Footprint Line Lasers (Laser SDP/2) installed on each of the

CTDOT’s ARAN Vans are nationally recognized as Class 1 Profiling devices under ASTM E950, AASHTO

R56-10 Certification & ASTM E1926; however there are no nationally recognized certification procedures

currently available for the LCMS or for the other equipment components. In order to validate these sub-

equipment components lacking certification procedures, CTDOT has planned and will be implementing

new Validation Sites where annual and periodic verification testing will be conducted (per Table 5.1

Specific QC Procedures) to evaluate the accuracy and precision of the data (per Table 6.1 Deliverables,

Protocols and Quality Standards for Automated Data Collection) reported in the field under conditions

representative to the ones anticipated during actual data collection.

Validation Sites

Validation sites are selected and established by CTDOT to calibrate the distress rating process and to

establish the precision and bias for the roughness, faulting and rutting information on asphalt concrete

pavements (ACP) and jointed reinforced concrete pavements (JRCP), as appropriate. The validation

sites are up to of 1 mile in length, and they are purposely selected with various levels of roughness and

distress. CTDOT’s CS8800 Walking Profiler is used on the selected sites to establish ground truth

(reference values) for:

Transverse Profile

Rutting

Roughness (IRI) (longitudinal profile)

Faulting

Distance calibration (DMI)

Implementation Plan for Validation Sites

The validation sites outlined in Ref. 2: Connecticut Transportation Institute, “Manual for Quality Control

of Pavement Condition Data Collection – DRAFT,” will be developed and implemented during the

summer of 2018, and will be completed by September 30, 2018. The precise development schedule for

the validation sites is dependent on the identification of a funding source, as well as scheduling the

resources needed to conduct the field work. As each of the new Validation Sites is implemented, ARAN

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Verification Surveys will be performed to collect data for analysis of repeatability and reproducibility,

per Table 6.1 Deliverables, Protocols and Quality Standards for Automated Data Collection.

Specific details on Validation Sites Selection, Site Parameters and Site Implementation, are included

under Sections 2.2, 2.3 & 2.4 of Ref. 2: Connecticut Transportation Institute, “Manual for Quality Control

of Pavement Condition Data Collection – DRAFT,” pages 2-4 thru 2-12.

Interim Plan

Until the new validation sites are available, the current practice of performing monthly verification

surveys for reproducibility against historical survey data and repeatability between ARAN Vans on

CTDOT’s existing Control Sections, located on Brook Street and Elm Street in Rocky Hill, Thornbush Road

in Wethersfield, and Willard Avenue in Newington, will continue. Five consecutive repeat runs are done

with each ARAN Van on each site and then the data are analyzed to insure that results fall within

acceptability ranges for FHWA HPMS Reporting and that they meet the quality standards as detailed in

Table 6.1 below. Results of the verification surveys are then documented and sent to the Project Team.

An example of the Verification Survey Testing Report is provided as Appendix F.

4.4 EQUIPMENT VERIFICATION TESTING

CTDOT’s PLU will conduct verification survey testing on both ARAN Vans on a monthly basis, or

additionally when necessary, to verify proper data collection procedures, accuracy, reproducibility and

continued calibration of the equipment. Verification survey testing is comprised of performing 5

sequential runs on each of CTDOT’s Validation sites with each ARAN Van. The data collected are then

compared against the established ground truth for reproducibility and for comparability between vans

and historical data per Table 6.1. A Verification Survey Testing Report (Appendix F) is then provided to

the Project Team.

5.0 DATA COLLECTION QUALITY CONTROL MEASURES

The focus of Quality Control (QC) is on data collection deliverables and processes. The PLU is

responsible for proper operation and calibration of the ARAN vans and their data collection systems.

Quality Control procedures and measures will be performed by the PLU field crew both before data

collection begins and periodically during the data collection program in accordance with Table 5.1. The

Photolog Daily Collection Check List is used to confirm that the QC Procedures are followed.

An example of the Photolog Daily Collection Check List in provided as Appendix G.

As described above, all equipment is calibrated according to manufacturer’s recommendations during

annually scheduled Preventative Maintenance done by FUGRO Roadware before initiation of the annual

data collection activities. Calibration checks on the data collection equipment are then performed

periodically throughout the year, at manufacturer’s specified intervals to assure that the equipment

remains properly calibrated and functional. The PLU then monitors the collection process and

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deliverables to ensure that they are of acceptable quality and are complete and correct throughout the

collection season.

Further details on ARAN equipment calibration are included in Ref. 4: Fugro Roadware, “ ARAN 9000

Manual, Version 2.0”, Fugro Roadware, Mississauga, Ontario, November 29, 2016

Identified in Table 5.1 below are:

• The major deliverables tested for satisfactory quality level.

• The quality expectations for the deliverables.

• The QC activities executed to control and monitor the quality of the deliverables.

• How often or when the QC activities are performed.

Table 5.1 Data Collection Quality Control Measures

Deliverable Quality Expectations* QC Activity Frequency/Interval

Vehicle Configuration

Inspect and clean laser apertures, windshield, and camera lenses

Inspect hardware and mountings

Check tire pressure

Collect small sample route

Check Prior to daily collection

GPS accuracy ≤ 3 meters

Image quality and lane placement

Monitor ARAN collection system errors

Data completeness

Check During active collection

Bounce and block tests, crack measurement system height check

Validation Monthly

Profiler Bounce test ≤ 8 inch/mile

Block check ± 0.01 inch of appropriate height

Calibration Pre-Season PM and on monthly basis

DMI Pulse Counts

≤0.1 difference (five runs) Validation Pre-Season PM and on monthly basis

Location of Segment

Mileage – 100% compliance with standards

Validation Daily

IRI

Std. dev. ≤ 5% (five 0.1 mile runs)

Symmetrical appearance of mult. runs

Validation Pre-Season

≥ 30 inch/mile IRI ≤ 400 inch/mile

Left & right IRI values differ ≤ 50 in/mi

Check Daily/Weekly

Rutting

Std. dev. ≤ 0.40 inch (five 0.1 mile runs)


Values ≤ 0.35 inch

Left & right rutting values dif. ≤ 0.25 in

Check Weekly

Percent Cracking

Std. dev. ≤ 20% total length (five 0.1 mile runs)


AC pavement values ≤ 50%

JPCP pavement values ≤ 100%

CRCP pavement values ≤ 100%

Check Weekly

Faulting Values ≤ 1.0 inch • Faulting values > 0 Check Weekly

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when joints are present

Imagery 98 % compliance with standards

Focus, color, luminance quality

Check Daily

Check uploaded imagery Weekly


Horizontal and Vertical Curves

Std. dev. ≤ 10% (five 0.1 mile runs)

Check Daily


*NOTE: The Connecticut DOT Photolog Operating Procedures Manual (ref. 1) will be followed. However, if after appropriate

corrective action has been taken, an unacceptable quantity of data fall outside of acceptable thresholds, the equipment

manufacturer shall be brought in to find a viable solution.

Table 5.2 Specific QC Procedures

QC Procedure Action Performed Frequency Quantity

Preventive maintenance and calibration of ARAN equipment

Perform height sensor bounce tests, laser calibration block tests, accelerometer calibration checks, distance calibration, sample IRI calculation and other checks,

Annually, or as specified by manufacturer

As prescribed by manufacturer

Testing of reference validation sites Perform at least five runs each on designated sections for IRI, cracking, transverse profile, rutting and faulting

Start of season and following equipment upgrades or calibrations

~50 (5 runs on ~10 sections (or the number of sections designated))

Verification testing of reference validation sites during production

Collect same data with both ARAN vans

Monthly Run all verification sites

Real-time viewing of data and data collection systems operation

Monitor collection systems and data collected within the vehicles in real time

Continuous Per manufacturer allowance

Check for missing road segments or data elements, and if data are within expected ranges

Run collected data through software checks while downloading

Weekly during data download

All data screened

Periodic testing of designated verification sections

Collect data on designated verification sections(s) with both vans

Min. weekly At least 1 site

Record events for out of lane (construction zones), other lane deviations, and railroad crossings

Record events during data collection with ARANs

Daily, as events encountered

All noted events

Automated Global database checks.

Statistical routines that check for inconsistencies in the data

Completeness of data,

improper file structure,

start and end points for all segments match inventory,

null or negative values

reasonable range of values

Run software at or immediately after download

Weekly 100% of routes

6.0. DELIVERABLES, PROTOCOLS, AND QUALITY STANDARDS

The key deliverables, protocols used for data collection, processing and reporting, and associated quality

standards are described below in Tables 6.1 & 6.2. Quality standards define, when applicable, the

resolution, accuracy, and repeatability or other standards used to determine the quality of each

deliverable. See Section 7 for the Acceptance Testing Plan. Please note that these quality standards will

be evaluated and adjusted, if necessary, as data are collected and refinements can be made.

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Table 6.1 Deliverables, Protocols and Quality Standards for Automated Data Collection

Deliverable Reference Protocols/Standards

Required Measurement Resolution

Required Accuracy Limits (compared to reference values) **

Required Reproducibility Limits (between CTDOT vehicles)***

Required Repeatability Limits (for five consecutive runs)***

IRI (left, right, and MRI average over 0.1-mi sections)

AASHTO R 43-13 AASHTO R 56-14 AASHTO R 57-14 AASHTO M328-14 ASTM E1926-98 HPMS Field Manual (2016)

1 in/mi ± 8 percent Absolute Difference in IRI <10 in/mi (95% PWL****)

Each run within ± 5 percent of the mean of five runs (95% PWL****)

Rut depth (average of right and left wheelpath over 0.1-mi sections)

AASHTO R 48-10 AASHTO PP 70-14 AASHTO PP 69-14 HPMS Field Manual (2016)

≤0.04 in. ± 0.08 in. Absolute Difference in rut depth <0.06 in (95% PWL)

Within ± 0.06 in. Standard Deviation from mean of five runs (95%PWL)

Faulting (average of right wheel path including only faults over 0.2 in)

AASHTO R 36-13 AASHTO R-57 AASHTO M328-14 HPMS Field Manual (2016)

0.04 in ± 0.08 in. Absolute Difference in fault <0.06 in (95% PWL)

Within ± 0.06 in. Standard Deviation from mean of five runs (95%PWL)

Asphalt Pavement Cracking* a) HPMS Percent Cracking – wheelpath fatigue percent area per 0.1 lane-mile b) CTDOT Network – length per 10 lane-meters

AASHTO PP 68-14 AASHTO R 55-10 AASHTO PP 67-16 HPMS Field Manual (2016)

a) 1 percent (HPMS -fatigue area) b) 0.1 ft. (CTDOT - length)

± 30 percent <10% C.o.V. in Total Cracking (95%PWL) <20% C.o.V. in Longitudinal, Transverse, or Area Cracking (95% PWL); <40% C.o.V. in Total wheelpath Cracking (95% PWL); <60% C.o.V. in Total Non-wheelpath cracking (95% PWL)

<10% C.o.V. in Total Cracking (95% PWL) <15% C.o.V. in Longitudinal, Transverse, or Area Cracking (95% PWL); <30% C.o.V. in Total wheelpath Cracking (95% PWL); <50% C.o.V. in Total Non-wheelpath cracking (95% PWL)

Concrete Pavement Cracking a) HPMS - % cracked slabs b) CTDOT Network

HPMS Field Manual (2016) Cracked slabs and total

slabs counted using ROW imagery

a) 1percent (HPMS) b) 0.1 ft.(CTDOT)

± 20 percent ± 20 percent ± 20 percent

GPS (degrees of latitude or longitude)

N/A 0.00001 degrees ± 0.00005 degree ± 0.00005 degree ± 0.00005 degree

Cross slope (100*rise/run) AASHTO PP 69-14 AASHTO PP 70-14

0.01 percent ± 0.5 percent Absolute Difference < 0.5 percent (99% PWL)

St.Dev. <0.05 percent (99% PWL)

Longitudinal grade (100*rise/run)

N/A 0.01 percent ± 0.1 percent Absolute Difference < 0.1 percent (99% PWL)

St.Dev. <0.1 percent (99% PWL)

Radius of curvature N/A 1 ft. N/A N/A ± 10 percent

Linear Reference (DMI) FUGRO Manual AASHTO R57-14 AASHTO R56-14

0.0001 ft. Abs Diff < 0.15 percent (e.g.,998.5 – 1001.5 ft./1000ft)

N/A N/A

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Table 6.1 Deliverables, Protocols and Quality Standards for Automated Data Collection (continued)

Deliverable Reference Protocols/Standards

Required Measurement Resolution

Required Accuracy Limits (compared to reference values) **

Required Reproducibility Limits (between CTDOT vehicles)***

Required Repeatability Limits (for five consecutive runs)***

1) Road segments Start boundary End boundary 2) Segment Length

PMS Database Field Check**

1) 0.00006 mi 2) Max. Length 0.11 mi.(per HPMS)

All assigned segments surveyed and assigned correct location: Start point ± 0.05 mi

N/A N/A

ROW images FUGRO Manual Field Check**

10 in. letter height visible at 15 ft.

Signs legible, proper exposure and color balance

N/A N/A

Pavement images AASHTO PP 68-14 Field Check**

N/A 1/8-in wide cracking visible on asphalt and concrete pavements

N/A N/A

Notes: * CTDOT collects images per every 10 lane-meters ** Accuracy standards will be updated as validation sites are added and reference data for these sections determined over the next 1to 3 years. *** Values to be reviewed/refined as more data are collected ****Minimum percentage of data to meet the specified range of values PWL = Percent Within Limit; N/A = Not Applicable C.o.V. = Coefficient of Variation (Ratio of Standard Deviation over Mean); St.Dev. = Standard Deviation from mean value of 5 runs

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Table 6.2 Data Review Criteria for Automated Condition Data Collection [1]

Deliverable Criteria* for Data Checks (Routine 0.10 mile CTDOT Network Sections)

Criteria** for Data Checks (HPMS 0.1 mile Sections)

IRI (left, right, and MRI average per section)

40-450 in./mile (99%****) Min. 30 in/mi.

Max. 400 in/mi.

Rut Depth (average of right and left wheelpath per section)

≤0.5 in. (99% )

Max. - 1.00 in.

Faulting (average of right wheel path per section for faults greater than 0.2 in)

≤0.5 in. (90% )

Max. - 1.00 in.

Asphalt Pavement Cracking*** a) CTDOT Network – length per 10 lane-meters (pavement-surface image) b) HPMS Percent Cracking – wheelpath fatigue percent area per 0.1 lane-mile

≤300 ft./image (10 lane-m) (99%)

Min. 0% area per 0.1 lane (12-ft wide) mile.

Max. 54.0 % area per 0.1 lane-(12-ft wide) mile

Concrete Pavement Cracking a) CTDOT Network- cracked slabs and total slabs counted using ROW imagery b) HPMS – Percent Cracking – percent cracked slabs

<100% cracked slabs Min. 0% cracked slabs

Max. 100% cracked slabs

Cross Slope (100*rise/run) ≤10 %(100% ) N/A

Longitudinal Grade (100*rise/run) ≤16 %(99% ) N/A

Radius of curvature N/A N/A

1) Road Segments Start boundary End boundary 2) Segment Length

< 1 mismatch (0.1 mi.) segment per 10 miles (99% )

N/A

Pavement Images < 1 missing image per 0.062 mi. (99% )

N/A

[1] In general, when values for 0.1-mile sections fall outside criteria stated in this table, as a check the data should be reviewed and verified for validity. For HPMS, section lengths will vary. * Criteria was developed by CTI using CTDOT network data for years 2008-2016 ** Criteria from FHWA, Nov 2017 (ref. 3) *** CTDOT collects images per every 10 lane-meters ****Minimum percentage of data expected to fall within the specified range of values. See also note [1]

7.0. DATA ACCEPTANCE CRITERIA AND ERROR RESOLUTION PROCEDURES

The focus of Acceptance is to validate that deliverables meet the established quality standards. The

Pavement Management Unit in the Bureau of Engineering and Construction has the final authority to

reject data and information that cannot be reconciled and/or fails to meet quality standards upon

review and re-sampling. The Photolog Unit works cooperatively with the Pavement Management Unit

to resolve any discrepancies in data quality that are found during the quality management process.

This section of the QMP documents data sampling, review, and checking processes that the CTDOT will

perform to verify proper data format, completeness (including checks for missing data), consistency, and

range.

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The Photolog Unit will deliver data to the Pavement Management Unit in batches approximately every

two weeks throughout the data collection phase of the project. The delivered data includes pavement

smoothness (IRI) data elements, but post-processing of 3-D data with Vision software is required to

produce cracking, rutting and faulting data elements.

These Vision software post-processing procedures will be performed in batches. Once post-processing

of a batch is complete, the entire dataset of that batch will be screened for proper data format and

completeness by PMU staff. The data elements to be reviewed for proper format and completeness

include cracking, roughness (IRI), and rutting. In addition, grade, cross slope, surface type, location

information and video images will be screened.

Special post-processing of 3-D images with Vision software will be performed separately to produce

faulting data for JRCPs because this dataset is small enough that it can be broken out separately. JRCPs

only make up approximately 0.5% of the entire network in Connecticut. Each fault will be located in

Vision or DigitalHiway to validate whether an actual fault was measured, or if the measured value

represents some other feature, such as a bridge joint.

Cracking percent values for asphalt-surfaced pavements will be reviewed for completeness following

further post-processing calculations in SQL Server Express. Additional screening for completeness will

also be performed on the entire data set following each SQL Server Express procedure where data are

aggregated, such as from a 5-meter granularity to 0.1-mile granularity. Criteria contained in Table 6.2

will be checked both before and after these post-processing calculations.

DigitalHiway and/or Vision software images will be manually reviewed to determine cracking percent

values for JRCPs. PMU staff will count the number of cracked slabs according to the HPMS Field Manual

and divide that quantity by the total number of slabs. The quotient, expressed as a percentage, will be

reported as the cracking percent (JRCP pavements only).

Missing data will be flagged and evaluated to determine why data are missing, and whether any

roadways need to be resurveyed. For example, the evaluation will include a review of ROW imagery to

determine if any construction zones were encountered, or any other unusual events were encountered

that would result in missing data.

The criteria checked in Tables 6.1 and 6.2, as well as the specific acceptance procedures contained in

Table 7.2, do not include checks of current data with legacy data. This is because CTDOT recently

upgraded its pavement data collection systems to the most advanced 3-D laser systems that introduced

new data in a different format. Attempts to develop transfer functions/correlation factors to ensure

comparability of historical and newly collected data were unsuccessful, as a multitude of problems with

both the new and old equipment were encountered, and the development of criteria to make these

comparisons was therefore not possible.

Problems with the new equipment were addressed before beginning the 2017 data collection season,

and it is believed that the 2017 pavement condition data are reliable enough to make meaningful and

statistically valid comparisons to data collected in subsequent years. Next year, once the 2018 annual

cycle is complete, comparisons between 2018 and 2017 data will be performed, which will provide some

indication of expected tolerances, but at least three annual cycles for comparison would be preferred.

23

In the interim, ad hoc checks between current and legacy data will be performed to identify significant

differences, but it will be a couple of years before tolerances are incorporated in the QMP.

Errors can occur during data collection because of equipment failures, poor image quality, or insufficient

calibration; during data collection because operators are using incorrect procedures or standards; or

during post-processing because of flawed procedures to calculate pavement condition metrics. Error

resolution procedures will be followed and corrective actions will be taken when data do not meet

established quality requirements and defined acceptance criteria.

Error logs will be maintained throughout the entire process: beginning with data collection, during

quality control, and finally during post-processing. Corrective actions may be taken during each of these

processes, including re-collection, re-calibration of equipment, re-analyzing raw data, or even re-training

staff responsible for data collection or data analysis. Error resolution procedures contained herein

present actions that will be taken to reduce conflict when a problem is discovered.

Following in Table 7.1 is a description of acceptance testing, the frequency for testing, and corrective

actions for items that fail to meet criteria. Table 7.2 contains specific acceptance procedures. These are

CTDOTs data acceptance criteria and error resolution procedures.

Table 7.1 General Acceptance Expectations and Deliverables

Deliverable Acceptance *(Percent Within Limits)

Acceptance Testing & Frequency Corrective Action

IRI, rut depth, faulting, cracking, cross slope, longitudinal grade

See Tables 6.1 & 6.2 1. Monthly (min.) verification using validation sites 2. Global database check for range, consistency, logic, and completeness 3. Inspection of all suspect data

1. Re-calibration of vehicle equipment 2. Reject deliverable; data must be re-collected 3. Use of GIS for further inspection 4. Determine reason for suspect data; or reject deliverable, data must be re-collected

Segment (section) boundaries and lengths

See Table 6.1 1. Plot on base map using GIS. 2. Create list of unmatched segment boundaries.

Return deliverable for correction, as needed.

Pavement images See Table 6.1 1. Monthly inspection of validation site video. 2. 5 to 10 percent sample inspection upon delivery.

Reject deliverable; images must be re-collected.

*NOTE: If following corrective action and re collection of data, an unacceptable quantity of data fall outside of acceptable

thresholds, the equipment manufacturer(s) and/or software developers are contacted to solicit advice for a viable solution.

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Table 7.2 Specific Acceptance Procedures

Acceptance Procedures

Action Performed Frequency Quantity

Checks of Periodic testing of known validation sites during production

Review QC findings As needed 50%

Checks of Cross Measurements for reproducibility

Review QC findings As needed 50%

Global database checks:

Missing Routes

• Data exists for all road segments. • Data file structure. • Start and end boundaries for all road segments. • Null and negative values. • Minimum and maximum tolerance parameters. • Duplicate records. • Wrong pavement type. • Abrupt change in roughness and rut depth. • Reasonable maximum extent of distress. • Non-numeric data in a numeric field.

Check for missing routes Check for missing data by segment Check format of file structure Find and List segments containing incorrect boundaries; investigate Find and list out of tolerance data, investigate, edit as necessary Find and list out of tolerance data; Investigate, edit as necessary Find, list and delete Find, list and correct Check for excessive variability within segments Check quantities for unreasonable high values Find, list and correct

Annually Annually As needed Annually As needed As needed Annually Annually Annually Annually Annually

100% 100% As needed 100% As needed As needed 100% 100% 100% 100% 100%

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8.0. QUALITY REPORTING PLAN

The Data Collection Quality Control Supervisor monitors quality through QC activities, and records and

reports data quality exceptions as part of internal weekly status reporting, or more frequently if

conditions warrant. Overall quality is monitored through acceptance testing, and quality issues are

reported to the data collection team as soon as issues are discovered.

A QC log is used by the data collection team to itemize, document, and track to closure concerns and

issues reported through the QC process.

An acceptance log is used by the PMS Data Lead to itemize, document, and track to closure items

reported through the acceptance process. Examples of QC and Acceptance Log formats can be found in

reference 2, Manual for Quality Control of Pavement Condition Data Collection.

8.1 Quality Management Reporting

An annual quality management report will be prepared to address and summarize the QC, Acceptance

and Procedural issues that occurred over the previous data collection year.

QC Report

Upon delivery of the final database and other deliverables, the data collection team provides a copy of

the QC logs, a summary of scope and schedule (including any deviations from the planned schedule), a

list of the collection vehicles and personnel used on the project, documentation of equipment

calibration and maintenance, results of all verification runs on validation sites, and documentation of

other problems encountered (not listed on the QC log) and corrective actions taken. Specifically, as a

minimum, the QC report will address the following:

• Equipment and key personnel used during data collection.

• Documentation of initial and continuing calibration/checks/maintenance for field equipment, any

equipment problems, and corrective actions taken.

• Schedule adherence and the reasons for any changes.

• Documentation of collection procedures and protocols used.

• Reporting of any variances in standard operating procedures or changes in collection methods

made in the field.

• Applicable guidance documents.

• Reporting of all validation site testing and results.

• Summary of all QC activities and resultant checks of expected values.

• Log of all quality issues identified through QC activities, and corrective actions taken.

Summary of Annual review of all QC processes performed.

26

Acceptance and Quality Management Report

Upon acceptance of the final database and all other deliverables, the Data Processing Quality Assurance

Supervisor will prepare the final annual Quality Management Report (QMR), which will incorporate the

QC report and include a section on Acceptance. A copy of the QMR must be provided to the Data

Collection Quality Control Supervisor for review and feedback. The QMR report will include a summary

of scope and schedule, description of validation site testing (including reference values and an analysis

of results), description of global database checks and other sampling tests performed and the results,

and recommendations for improvement.

The acceptance portion of the report will specifically address the following:

• A description of quality standards and acceptance criteria.

• A description of validation sites and reference values used.

• An analysis of validation site testing results.

• Documentation of all global database checks performed, and the results.

• Documentation of all sampling checks, and the results.

• Documentation of all other acceptance checks, and the results.

• A log of all quality issues identified through acceptance checks and corrective actions taken.

• Summary of Annual review of Acceptance processes that were performed.

Recommendations for improvements.

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10.0. REFERENCES

Note: The general structure/format of this QUALITY MANAGEMENT PLAN is patterned after “FHWA

Practical Guide for Quality Management of Pavement Condition Data Collection,” Pierce, McGovern and

Zimmerman, Feb, 2013.

Ref. 1:

https://www.ct.gov/dot/lib/dot/documents/dpavement/ref_1_photolog_standard_operating_procedur

es_-_022818.pdf, Office of Roadway Information Systems, “Connecticut Department of Transportation,

Photolog Field Data Collection Standard Operating Procedures – Working Draft,” Connecticut

Department of Transportation, Newington, CT, May 2018.

Ref. 2:

https://www.ct.gov/dot/lib/dot/documents/dpavement/ref_2_manual_for_qc_of_pavt_data_collection

_5_18_18__submitted.pdf, Connecticut Transportation Institute, “Manual for Quality Control of

Pavement Condition Data Collection - DRAFT,” University of Connecticut, Storrs, CT, May 2018.

Ref. 3: Max G. Grogg. Highway Information Seminar, 2016 HPMS Pavement Data Submission, FHWA,

USDOT, Nov 16, 2017. https://www.fhwa.dot.gov/policyinformation/hisconf/thu03_hpms_and_tpm-

part_3_2016_hpms_pavement_data_submission_max_grogg.pdf Accessed on April 18, 2018.

Ref. 4: https://www.ct.gov/dot/lib/dot/documents/dpavement/ref_4_aran_user_manual_2.pdf, Fugro

Roadware, “ ARAN 9000 Manual, Version 2.0”, Fugro Roadware, Mississauga, Ontario, November 29,

2016

Ref. 5:

https://www.ct.gov/dot/lib/dot/documents/dpavement/ref_5_ssi_profiler_v3_walkpro_manual_32711.

pdf, Surface Systems & Instruments, Inc., “ CS8800 Profiler VS Operation Manual,” Version 3.2.7.11,

Surface Systems & Instruments, Inc., Auburn , CA, March, 2011

https://www.ct.gov/dot/lib/dot/documents/dpavement/ref_1_photolog_standard_operating_procedures_-_022818.pdf

https://www.ct.gov/dot/lib/dot/documents/dpavement/ref_1_photolog_standard_operating_procedures_-_022818.pdf

https://www.ct.gov/dot/lib/dot/documents/dpavement/ref_2_manual_for_qc_of_pavt_data_collection_5_18_18__submitted.pdf

https://www.ct.gov/dot/lib/dot/documents/dpavement/ref_2_manual_for_qc_of_pavt_data_collection_5_18_18__submitted.pdf

https://www.fhwa.dot.gov/policyinformation/hisconf/thu03_hpms_and_tpm-part_3_2016_hpms_pavement_data_submission_max_grogg.pdf

https://www.fhwa.dot.gov/policyinformation/hisconf/thu03_hpms_and_tpm-part_3_2016_hpms_pavement_data_submission_max_grogg.pdf

https://www.ct.gov/dot/lib/dot/documents/dpavement/ref_4_aran_user_manual_2.pdf

https://www.ct.gov/dot/lib/dot/documents/dpavement/ref_5_ssi_profiler_v3_walkpro_manual_32711.pdf

https://www.ct.gov/dot/lib/dot/documents/dpavement/ref_5_ssi_profiler_v3_walkpro_manual_32711.pdf

29

Appendix A Example Crack Detection Rater Exam Problems

Example Crack Detection Rater Exam Problems

1. Match these cracking distresses for pavements with asphalt concrete surfaces with their best

descriptions by placing the letter of the distress or Not Applicable (NA) next to the correct

description:

Distress Description

A. Longitudinal Cracking

___ Develops into many-sided, sharp-angled pieces in later stages.

B. Block Cracking ___ Cracks that are predominantly perpendicular to pavement centerline.

C. Fatigue Cracking ___ Bowl-shaped holes of various sizes in the pavement surface.

D. Transverse Cracking ___ Cracks in asphalt concrete overlay surfaces that occur over joints in concrete pavements.

___ There are two types of this distress according to the LTPP Distress Identification Manual: Wheel Path and Non-Wheel Path.

___ A pattern of cracks that divides the pavement into approximately rectangular pieces.

2. Given the wheel path cracking below and what is shown in Figure 1, calculate the Cracking

Percent according to the December 2016 HPMS Field Manual:

∑ Left Wheel Path Zone Cracking = 12 S.F.

∑ Right Wheel Path Zone Cracking = 15 S.F.

∑ Center Zone Cracking = 2 S.F.

∑ Left Exterior Zone Cracking = 1 S.F.

∑ Right Exterior Zone Cracking = 1 S.F.

Cracking Percent =

Figure 1

3. Name and define the three Longitudinal Cracking Severity Levels according to the LTPP Distress

Identification Manual.

4. Match pavement metrics with their respective December 2016 HPMS Field Guide definitions:

Pavement Metric HPMS Field Guide Definition

IRI

A longitudinal surface depression in an asphalt pavement derived from measurements of a profile transverse to the path of travel on a highway lane

Rutting A vertical misalignment of pavement joints in Portland Cement Concrete Pavements

Faulting A fissure or discontinuity of the pavement surface not necessarily extending through the entire thickness of the pavement

Cracking Percent A statistic used to estimate the amount of roughness in a measured longitudinal profile

Crack Detection Rater Evaluation Report

Check

Pass Fail

Candidate demonstrates thorough knowledge and understanding of the pavement condition rating methodology contained in the Distress Identification Manual for the Long-Term Pavement Performance Program.

Distresses for AC Surfaces

Comments:

Distress for Jointed PCC Surfaces:

Comments:

Candidate demonstrates thorough knowledge and understanding of the pavement condition metrics identified in the December 2016 HPMS Field Manual.

IRI Comments:

Rutting Comments:

Cracking Percent (AC Pavements)

Comments:

Cracking Percent (Jointed PCC Pavements)

Comments:

Candidate demonstrates the ability to apply above knowledge and understanding during manual pavement ratings.

I hereby certify that ______________________________________ has successfully demonstrated

thorough knowledge, skill, and proficiency to perform crack detection ratings.

Signature: ___________________________________, Date: _______________________

Jeannine Moriarty, Lead Manual Rater

Signature: ___________________________________, Date: _______________________

John Henault, Quality Assurance Supervisor

33

Appendix B Example Walking Profiler Refresher Training

Evaluation Checklist

SSI CS8800 Walking Profiler Training Evaluation Report Last modified July 10, 2018

Operator: Evaluator: Date: Evaluation Score Pass / Fail

Check Description Comments

Pass Fail

Candidate demonstrates thorough knowledge and understanding of setup and calibration of SSI CS8800 Walking Profiler

Software Understanding Comments:

Profiler Components Setup, Activation and Charging Calibration procedures

Candidate demonstrates thorough knowledge and understanding of use and effective collection data of SSI CS8800 Walking Profiler

Startup procedures Comments:

Collection Procedures Performed successful collection

I hereby certify that ______________________________________ has successfully demonstrated thorough knowledge

skill and proficiency with operation and use of the SSI CS8800 Walking Profiler.

Signature: _________________________________, Date: ______________________

Lester King, Quality Control Lead

Signature: _________________________________, Date: ______________________

James Spencer, Quality Control Supervisor

https://fugroroadware.atlassian.net/wiki/pages/diffpagesbyversion.action?pageId=86737093&selectedPageVersions=4&selectedPageVersions=5

35

Appendix C Example ARAN Field Operations Training

Evaluation Report

ARAN Field Operations Training Evaluation Last modified July 9, 2018 by Jim Spencer

Operator: Evaluator: Date: Evaluation Score Pass / Fail

Check Description Comments

Pass Fail

Candidate demonstrates thorough knowledge and understanding of pavement condition and geometric data

Transverse Profile Comments:

Longitudinal Profile IRI Faulting Cracking Curve Grade

Candidate demonstrates thorough knowledge of ARAN Van equipment, systems and operation and can locate and describe each.

Rights of Way Video Comments:

GPS Positioning (GPS) Pave3D LCMS System Roughness System (IRI) Position & Orientation System (POS-LV) Distance Measuring Unit (DMI) Grade Sensors

Candidate demonstrates through knowledge and understanding with successful equipment calibration Bounce Test Comments:

DMI Calibration Block Test Bucket Test LCMS Laser Height Check

Candidate demonstrates thorough knowledge and understanding of operational standards and ability to correctly navigate vehicle during collection operations

Comments:

Candidate demonstrates thorough knowledge and understanding of ARAN Van Safety (Operational Practices, equipment

Comments:

Candidate demonstrates thorough knowledge and understanding of acceptable environmental conditions for collection

Comments:

Candidate demonstrates effective use of Photolog Daily Mechanical Inspection Check List

Comments:

Candidate demonstrates effective operation of ARAN Vehicle Systems and performed successful collection of data

Comments:

https://fugroroadware.atlassian.net/wiki/pages/diffpagesbyversion.action?pageId=86737093&selectedPageVersions=4&selectedPageVersions=5

https://fugroroadware.atlassian.net/wiki/display/~dcoleman%40fugro.com

37

Appendix D Example FUGRO Preventative Maintenance

Report

53

Appendix E Examples of CTDOT’s Calibration Reports

Description

CTDOT DMI Calibration Site

Date

Van

Driver

Operator

Temperature & Weather

Conditions

Calibration Interval

(Per ARAN User Manual)

FUGRO Acceptance of

Calibration Criteria

FUGRO Calibration Verification


example Run 1 Run 2 Run 3 Run 4 Run 5 Average

Results (Pulses/Meter) 0 0 0 0 0 0 0

Results (Meters) 400 0 0 0 0 0 0

Results (Feet) 1312.33596 0 0 0 0 0 0

Issues

Comments

DMI Calibration AcceptanceSignature of Photolog Quality Control Lead

Calibration Results

Time

The DMI requires regular (monthly) calibration, but specifically when any of the tire characteristics have been changed (replacement of

tire, inflation of tire).

DMI calibration performed successfully with passing results

Calibration can be verified by collecting multiple runs on a flat, straight piece of road with a known exact distance (e.g. the DMI

calibration site). ARAN should be drive at highway speeds for 8-10 miles prior to DMI Calibration to ensure tires are at optimal pressure.

(Resolution = 0.0001ft), Abs Diff < 0.15% (example: (998.5 - 1001.5 ft)/1000ft )

7/31/2018

DMI Calibration Report

Collection

On a section of road that is 450 meters, the average calibration number should be between 755 and 990 pulses / meter. The difference

between the runs should be less than 0.3 pulses / meter. The calibration number is then internally converted to a length within the DMI

system depending on variables specific to each ARAN Van.

Tire Pressure (80 PSI Optimal)

Standards and Requirements

Description

CTDOT DMI Calibration Site

Date

Van

Driver

Operator

Temperature & Weather

Conditions

Calibration Requirements

Calibration Interval


FUGRO Calibration Criteria


FUGRO Calibration Verification

Acceptance


example Run 1 Run 2 Run 3 Run 4 Run 5 Average

Direction 1 0.723668768 0 0 0 0 0 0

Direction 2 -0.723668768 0 0 0 0 0 0

Zeroing Result 0 0 0 0 0 0 0

Issues

Comments

Transverse Profile (Grade

System) Calibration

Acceptance

7/31/2018

Transverse Profile (Grade System) Calibration Report

Collection

A reverse run is the collection of data in the exact same wheel path on a straight piece of road in both directions. The length of the run

should be around 500 m or 0.3 mile (minimally 350 m or 0.22 mile). Resolution = 0.01%, Abs Diff < 0.5% (99% PWL).

Standards and Requirements

Signature of Photolog Quality Control Lead

Calibration Results

Time

The grade system calibration is verified by performing reverse runs which should be done on a monthly basis, if a grade sensor or model is

replaced, or if there are any other changes to the ARAN Van that would impact grade readings.

The average of the raw crossfall of 3 back and forth runs should be zero to be acceptable.

The DMI and Position and Orientation System needs to be calibrated first.

56

Appendix F Example of the Verification Survey Test Report

Route Speed (km/h) LeftIRI (m/km)Check (±5%)

RightIRI (m/km)Check (±5%)

LeftRut (mm)Check (± 2mm)

ghtRut (mmCheck (±2mm)

ne_Width (ding (Degr Pitch (%) Grade (%)Check (±0.2%)

Roll (%)Check (±0.2%)

ossSlope (%Check (±0.2%)

BrookEastJul2018Van8run2 61.82137596 1.370889711 ‐0.98% 1.643752794 ‐0.08% 3.230071223 0.17 2.416878 0.31 3.543933 79.38575 0.422533 0.022531 0.10 3.823747 ‐0.58 2.982949 ‐0.17BrookEastJul2018Van8run3 60.32524265 1.366805298 ‐0.68% 1.642256868 0.02% 3.278165956 0.12 2.383445 0.35 3.532074 79.3659 0.450093 0.061764 0.06 3.756481 ‐0.52 2.963154 ‐0.15BrookEastJul2018Van8run4 59.9286708 1.376960567 ‐1.43% 1.666950071 ‐1.49% 3.192699288 0.21 2.385144 0.34 3.53495 79.41016 0.445057 0.061413 0.07 3.837104 ‐0.60 3.005195 ‐0.19BrookEastJul2018Van8run5 60.7981943 1.37272852 ‐1.12% 1.662745248 ‐1.23% 3.185618964 0.22 2.396608 0.33 3.554989 79.44747 0.439935 0.062954 0.06 3.833892 ‐0.59 3.020365 ‐0.21BrookEastJul2018Van8AVG 60.71837093 1.371846024 1.653926245 3.221638858 2.395519 3.541486 79.40232 0.439404 0.052166 3.812806 2.992916BrookEastJul2018Van9run1 45.39008485 1.357575142 ‐0.60% 1.642519522 ‐0.11% 3.402738284 0.09 2.729856 0.11 3.538586 79.43719 0.226777 0.126634 ‐0.03 3.239769 ‐0.02 2.811039 0.00BrookEastJul2018Van9run2 53.17101517 1.345794282 0.28% 1.62984571 0.67% 3.494847707 0.00 2.833657 0.01 3.542665 79.36412 0.208136 0.113488 ‐0.01 3.221955 0.00 2.802547 0.01BrookEastJul2018Van9run3 54.86204963 1.34412129 0.40% 1.663450136 ‐1.38% 3.527041861 ‐0.04 2.83118 0.01 3.544737 79.30734 0.18283 0.095295 0.01 3.173772 0.05 2.789394 0.02BrookEastJul2018Van9run4 53.63341635 1.347456218 0.15% 1.609650856 1.90% 3.512651911 ‐0.02 2.857485 ‐0.01 3.533 79.38837 0.199916 0.103855 0.00 3.194655 0.02 2.834109 ‐0.02BrookEastJul2018Van9run5 54.94715271 1.352581826 ‐0.23% 1.658513773 ‐1.08% 3.52085465 ‐0.03 2.960663 ‐0.12 3.559406 79.3605 0.160939 0.064267 0.04 3.26619 ‐0.05 2.826322 ‐0.01BrookEastJul2018Van9AVG 52.40074374 1.349505751 1.640795999 3.491626883 2.842568 3.543679 79.3715 0.19572 0.100708 3.219268 2.812682BrookWestJul2018Van8run1 54.09381234 1.164593287 0.06% 1.362750144 0.42% 1.751524282 ‐0.04 2.478063 ‐0.03 3.531438 259.4161 0.110421 ‐0.25277 0.01 4.360928 0.07 3.413657 0.03BrookWestJul2018Van8run2 61.47178572 1.16586755 ‐0.05% 1.37326083 ‐0.35% 1.673161753 0.04 2.462916 ‐0.01 3.548265 259.3378 0.127088 ‐0.23334 ‐0.01 4.516872 ‐0.09 3.457175 ‐0.01BrookWestJul2018Van8run3 60.64541988 1.175686047 ‐0.89% 1.37912427 ‐0.78% 1.687128357 0.03 2.506556 ‐0.06 3.534822 259.3328 0.102197 ‐0.25487 0.01 4.485622 ‐0.06 3.468116 ‐0.02BrookWestJul2018Van8run4 58.45880317 1.154254966 0.95% 1.359361172 0.66% 1.706366442 0.01 2.404391 0.04 3.527411 259.3607 0.090181 ‐0.26133 0.01 4.379893 0.05 3.436974 0.01BrookWestJul2018Van8run5 61.76822528 1.166143534 ‐0.07% 1.367784565 0.05% 1.746373082 ‐0.03 2.394954 0.05 3.515022 259.4379 0.125276 ‐0.23395 ‐0.01 4.40313 0.03 3.464759 ‐0.02BrookWestJul2018Van8AVG 59.28760928 1.165309076 1.368456196 1.712910783 2.449376 3.531392 259.3771 0.111033 ‐0.24725 4.429289 3.448136BrookWestJul2018Van9run1 46.78209144 1.149566134 ‐0.10% 1.323912435 1.69% 1.766515195 0.02 2.621819 0.21 3.502562 259.3532 ‐0.11986 ‐0.21393 0.01 3.682889 0.08 3.154138 0.05BrookWestJul2018Van9run2 49.53484004 1.134536737 1.21% 1.361016071 ‐1.07% 1.778183146 0.01 2.846973 ‐0.01 3.503213 259.3346 ‐0.11883 ‐0.21875 0.01 3.726301 0.04 3.202828 0.00BrookWestJul2018Van9run3 48.57363975 1.157456987 ‐0.78% 1.339014011 0.57% 1.811655042 ‐0.02 2.95768 ‐0.12 3.501013 259.2723 ‐0.11453 ‐0.20217 0.00 3.809481 ‐0.05 3.22146 ‐0.02BrookWestJul2018Van9run4 52.05502585 1.155397218 ‐0.60% 1.364757053 ‐1.35% 1.797464358 ‐0.01 2.892074 ‐0.06 3.498252 259.428 ‐0.12545 ‐0.208 0.00 3.799377 ‐0.04 3.222969 ‐0.02BrookWestJul2018Van9run5 49.63725707 1.145364124 0.27% 1.344419024 0.16% 1.786046898 0.00 2.848369 ‐0.01 3.501514 259.3245 ‐0.10727 ‐0.18268 ‐0.02 3.802413 ‐0.04 3.222566 ‐0.02BrookWestJul2018Van9AVG 49.31657083 1.14846424 1.346623719 1.787972928 2.833383 3.501311 259.3425 ‐0.11719 ‐0.20511 3.764092 3.204792ElmStJul2018Van8run1 55.35202929 1.288448342 3.43% 1.723306894 2.47% 1.887477812 0.00 1.708318 ‐0.14 3.149586 65.38432 ‐1.99116 ‐2.28899 0.03 3.386696 ‐0.07 2.637997 ‐0.04ElmStJul2018Van8run2 46.33388161 1.352274832 ‐1.35% 1.773579817 ‐0.37% 1.916486583 ‐0.03 1.543467 0.02 3.140972 65.27936 ‐1.99035 ‐2.25916 0.00 3.32717 ‐0.01 2.591324 0.00ElmStJul2018Van8run3 48.4447325 1.376287968 ‐3.15% 1.801195718 ‐1.94% 1.861395581 0.02 1.425099 0.14 3.153844 65.40727 ‐1.97646 ‐2.24967 ‐0.01 3.237766 0.08 2.536312 0.06ElmStJul2018Van8run4 48.09208033 1.330029271 0.31% 1.820581905 ‐3.03% 1.888287743 0.00 1.624431 ‐0.06 3.159639 65.43736 ‐1.97674 ‐2.25988 0.00 3.290913 0.02 2.606999 ‐0.01ElmStJul2018Van8run5 46.81803976 1.32409375 0.76% 1.716332113 2.87% 1.876461242 0.01 1.535333 0.03 3.14168 65.36086 ‐1.97995 ‐2.25137 ‐0.01 3.335764 ‐0.02 2.59747 0.00ElmStJul2018Van8AVG 49.0081527 1.334226833 1.766999289 1.886021792 1.56733 3.149144 65.37383 ‐1.98293 ‐2.26181 3.315662 2.59402ElmStJul2018Van9run1 48.88309146 1.227211097 2.61% 1.668139661 2.06% 1.920365943 0.02 1.524753 ‐0.01 3.242889 65.27087 ‐2.19519 ‐2.26182 ‐0.01 2.731355 0.07 2.407204 0.05ElmStJul2018Van9run2 51.18930766 1.305331509 ‐3.59% 1.718325189 ‐0.89% 1.916533202 0.03 1.491857 0.02 3.251846 65.26021 ‐2.20623 ‐2.26999 0.00 2.806682 0.00 2.462373 ‐0.01ElmStJul2018Van9run3 49.6318802 1.254274763 0.47% 1.7008196 0.14% 1.965691505 ‐0.02 1.541179 ‐0.03 3.231451 65.24885 ‐2.20426 ‐2.26238 0.00 2.863443 ‐0.06 2.489358 ‐0.03ElmStJul2018Van9run4 54.1950312 1.263969342 ‐0.30% 1.743336404 ‐2.36% 1.947347965 0.00 1.517774 0.00 3.229685 65.24328 ‐2.2018 ‐2.26354 0.00 2.830008 ‐0.02 2.469694 ‐0.01ElmStJul2018Van9run5 48.78657931 1.249915425 0.81% 1.685211005 1.05% 1.969244265 ‐0.03 1.500845 0.01 3.221987 65.27188 ‐2.22212 ‐2.27764 0.01 2.794452 0.01 2.445507 0.01ElmStJul2018Van9AVG 50.53717797 1.260140427 1.703166372 1.943836576 1.515281 3.235572 65.25902 ‐2.20592 ‐2.26707 2.805188 2.454827ThornbushJul2018Van8run1 54.80811369 2.772004521 ‐0.05% 3.079607678 ‐3.71% 3.643872495 0.23 5.862588 ‐0.30 3.665482 171.6422 0.443356 0.028724 ‐0.01 5.503471 ‐0.13 4.286607 ‐0.06ThornbushJul2018Van8run2 51.16196199 2.653963223 4.21% 2.759942883 7.06% 4.21230053 ‐0.33 5.167177 0.39 3.519061 171.5536 0.445975 0.017642 0.00 5.230762 0.14 4.132623 0.09ThornbushJul2018Van8run3 47.92390853 2.986714761 ‐7.80% 3.102917707 ‐4.49% 3.635809816 0.24 5.583336 ‐0.02 3.689796 171.4211 0.430861 ‐0.00848 0.02 5.419163 ‐0.05 4.27271 ‐0.05ThornbushJul2018Van8run4 47.72956156 2.641105612 4.67% 2.795361603 5.86% 4.119454896 ‐0.24 5.265402 0.29 3.538873 171.5433 0.466822 0.040344 ‐0.03 5.24634 0.13 4.150308 0.08ThornbushJul2018Van8run5 50.1904845 2.799288353 ‐1.03% 3.109405357 ‐4.71% 3.780196978 0.10 5.917327 ‐0.36 3.672661 171.6357 0.428866 ‐0.00451 0.02 5.470014 ‐0.10 4.291206 ‐0.06ThornbushJul2018Van8AVG 50.36280606 2.770615294 2.969447046 3.878326943 5.559166 3.617175 171.5592 0.443176 0.014745 5.37395 4.226691ThornbushJul2018Van9run1 47.84986937 2.493166031 10.75% 2.656068819 4.39% 4.136280429 ‐0.27 6.259006 0.02 3.519489 171.3659 0.158136 0.013902 0.01 4.54887 0.08 3.913701 0.06ThornbushJul2018Van9run2 46.39138837 2.681599265 4.01% 2.605209863 6.22% 3.942777881 ‐0.08 5.8962 0.38 3.514117 171.5622 0.174931 0.041472 ‐0.02 4.489284 0.14 3.845781 0.12ThornbushJul2018Van9run3 47.28954362 3.171526006 ‐13.53% 2.892748911 ‐4.13% 3.777016938 0.09 6.370538 ‐0.10 3.614171 171.4662 0.168082 0.031949 ‐0.01 4.690414 ‐0.06 4.034221 ‐0.06ThornbushJul2018Van9run4 47.50007731 2.604093043 6.78% 2.761636539 0.59% 3.951884038 ‐0.08 6.144249 0.13 3.536694 171.4753 0.148062 0.007229 0.02 4.620397 0.01 3.986313 ‐0.02ThornbushJul2018Van9run5 49.49302158 3.017538866 ‐8.02% 2.974625917 ‐7.08% 3.52733462 0.34 6.702226 ‐0.43 3.657804 171.5982 0.152126 0.027486 0.00 4.815425 ‐0.18 4.066411 ‐0.10ThornbushJul2018Van9AVG 47.70478005 2.793584642 2.77805801 3.867058781 6.274444 3.568455 171.4935 0.160267 0.024408 4.632878 3.969285

Verification Survey Test ReportV8 & V9 Control Sites on July 9 and 10, 2018 (Monday & Tuesday)

V8 & V9 Control Sites July 2018 Comparison

Route Speed (km/h) LeftIRI (m/km)Check (±5%)

RightIRI (m/km)Check (±5%)

LeftRut (mm)Check (± 2mm)

ghtRut (mmCheck (±2mm)

ne_Width (ding (Degr Pitch (%) Grade (%)Check (±0.2%)

Roll (%)Check (±0.2%)

ossSlope (%Check (±0.2%)

BrookEastJul2018Van8AVG 60.71837093 1.371846024 ‐0.82% 1.653926245 ‐0.40% 3.221638858 0.13 2.395519 0.22 3.541486 79.40232 0.439404 0.052166 0.02 3.812806 ‐0.30 2.992916 ‐0.09BrookEastJul2018Van9AVG 52.40074374 1.349505751 0.82% 1.640795999 0.40% 3.491626883 ‐0.13 2.842568 ‐0.22 3.543679 79.3715 0.19572 0.100708 ‐0.02 3.219268 0.30 2.812682 0.09BrookEastV8&V9Jun2018Average 56.55955734 1.360675888 1.647361122 3.35663287 2.619044 3.542583 79.38691 0.317562 0.076437 3.516037 2.902799

BrookWestJul2018Van8AVG 59.28760928 1.165309076 ‐0.73% 1.368456196 ‐0.80% 1.712910783 0.04 2.449376 0.19 3.531392 259.3771 0.111033 ‐0.24725 0.02 4.429289 ‐0.33 3.448136 ‐0.12BrookWestJul2018Van9AVG 49.31657083 1.14846424 0.73% 1.346623719 0.80% 1.787972928 ‐0.04 2.833383 ‐0.19 3.501311 259.3425 ‐0.11719 ‐0.20511 ‐0.02 3.764092 0.33 3.204792 0.12BrookWestV8&V9Jun2018Average 54.30209005 1.156886658 1.357539957 1.750441856 2.64138 3.516351 259.3598 ‐0.00308 ‐0.22618 4.096691 3.326464

ElmStJul2018Van8AVG 49.0081527 1.334226833 ‐2.86% 1.766999289 ‐1.84% 1.886021792 0.03 1.56733 ‐0.03 3.149144 65.37383 ‐1.98293 ‐2.26181 0.00 3.315662 ‐0.26 2.59402 ‐0.07ElmStJul2018Van9AVG 50.53717797 1.260140427 2.86% 1.703166372 1.84% 1.943836576 ‐0.03 1.515281 0.03 3.235572 65.25902 ‐2.20592 ‐2.26707 0.00 2.805188 0.26 2.454827 0.07ElmStJul2018V8&V9Average 49.77266533 1.29718363 1.735082831 1.914929184 1.541306 3.192358 65.31643 ‐2.09443 ‐2.26444 3.060425 2.524424

ThornbushJul2018Van8AVG 50.36280606 2.770615294 0.41% 2.969447046 ‐3.33% 3.878326943 ‐0.01 5.559166 0.36 3.617175 171.5592 0.443176 0.014745 0.00 5.37395 ‐0.37 4.226691 ‐0.13ThornbushJul2018Van9AVG 47.70478005 2.793584642 ‐0.41% 2.77805801 3.33% 3.867058781 0.01 6.274444 ‐0.36 3.568455 171.4935 0.160267 0.024408 0.00 4.632878 0.37 3.969285 0.13ThornbushJul2018V8&V9Average 49.03379305 2.782099968 2.873752528 3.872692862 5.916805 3.592815 171.5264 0.301722 0.019576 5.003414 4.097988

= Run doesn't meet the Acceptance CriteriaData Acceptance CriteriaIRI ‐ IRI from each repeated run is within ±5% of the mean for the runs.RUT ‐ RUT depth from each run is within ±2 mm of the mean for the runs.Roll ‐ Roll from each run is within ±0.2% of the mean for the runs.Grade ‐ Grade from each run is within ±0.2 % of the mean for the runs.Cross Slope ‐ Cross Slop from each run is within ±0.2 % of the mean for the runs.

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Appendix G Example of the Photolog Daily Check List

Photolog Daily Mechanical Inspection Check List Last modified April 30, 2018 by Jim Spencer

ARAN Van: Date: Time Start: End: Weather Conditions: Project: State / Local HMPS / Special / Control / Ramp / PM Driver AM: Operator AM: Driver PM: Operator PM:

Driver Check List Item Check Description Comments

1 Inspect/walk around outside of Van for damage 2 Check fuel level 3 Check oil level 4 Check DEF Diesel Additive (Van9 only) 5 Clean Exterior – Clean of dirt and debris 6 Inspect & clean the HD capture window 7 Inspect & clean grade sensors 8 Inspect & clean IRI lasers 9 Inspect & clean LCMS lasers 10 Check tires & inflate to proper pressure (80psi) 11 Adjust side mirrors 12 Check turn signals, break lights and flashers 13 Clean Interior ‐ No food or personal belongings left. 14 Inspect & tighten camera mount 15 Tool box present and complete

16 Safety Equip.: Shoes Vests Safety Hats Laser Protective Eyewear Cones Fire Ext.

17 Accident Reporting Paperwork Present 18 Van keys present 19 GPS Unit present and charged and activated 20 Cell Phone present & charged and activated 21 Mobil WiFi unit present & charged and activated

Operator Check List Item Check Description Comments

22 Start vehicle and listen for any unusual noises 23 Check to see if hard drives have been reformatted 24 Insert hard drives if none are currently in computers 25 Start Inverters and ARAN 9000 sub‐systems 26 Turn on Roof top AC 27 Is correct routing file loaded 28 Map & create network shares for hard drives 29 Check “Status States Screen” for problems

30 Open POS‐LV & run ARAN 9000 system diagnostics. Check for errors (15 min. idle time for GPS accuracy)

31 Run dummy file & review for discrepancies

32 Select routes to collect and add to list. (Ensure that checkpoints for routing are in proper order)

33 Verify data at end of day 34 Create end of day log & email to Jin & Anthony 35 Perform export of collected data 36 Make backup to third removable hard drives 37 Turn off Systems, AC, Inverters, Phone, GPS, & WiFi

Documents

Network-Level Pavement Condition Data Collection Quality