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LIFE-CYCLE COST ANALYSIS SYSTEM FOR PAVEMENT MANAGEMENT –SENSITIVITY NALYSIS TO THE PAVEMENT FOUNDATION
ADELINO FERREIRA, DEPARTMENT OF CIVIL ENGINEERING, UNIVERSITY OF COIMBRA, [email protected]ÃO SANTOS, DEPARTMENT OF CIVIL ENGINEERING, UNIVERSITY OF COIMBRA,
This is an abridged version of the paper presented at the conference. The full version is being submitted elsewhere.Details on the full paper can be obtained from the author.
Life-cycle cost analysis system for pavement management FERREIRA, Adelino; SANTOS, João
13th WCTR, July 15-18, 2012 – Rio de Janeiro, Brazil
1
LIFE-CYCLE COST ANALYSIS SYSTEM FOR PAVEMENT MANAGEMENT – SENSITIVITY ANALYSIS TO THE
PAVEMENT FOUNDATION
Adelino Ferreira
Department of Civil Engineering, University of Coimbra, [email protected]
João Santos
Department of Civil Engineering, University of Coimbra, [email protected]
ABSTRACT
This paper presents a LCCA system called OPTIPAV that can consider construction costs,
maintenance and rehabilitation costs, user costs, and the residual value of the pavement. The
OPTIPAV is constituted by a deterministic segment-linked optimization model that is solved
by an heuristic method based on genetic-algorithm principles. The OPTIPAV system has the
following components: the objectives of the analysis; the data and the models about the road
pavements; the constraints that the system must guarantee; and the results. One objective that
can be considered in the analysis is the minimisation of total costs, i.e., construction costs,
agency costs, user costs, and the residual value of the pavements.
The OPTIPAV uses the deterministic pavement performance model of the AASHTO flexible
pavement design method to predict the future quality of pavements in terms of the Present
Serviceability Index (PSI). The OPTIPAV was applied to the alternative flexible pavement
structures included in the Portuguese Road Administration pavement design catalogue. The
analysis was carried out using construction costs and information on maintenance strategies
adopted on flexible pavement structures in the main road network of Portugal.
The final part of the paper contains the main conclusions and presents the developments
planned for the near future.
Keywords: pavement design, life-cycle cost analysis, deterministic pavement performance
models, pavement maintenance and rehabilitation, optimisation models, genetic algorithms.
Life-cycle cost analysis system for pavement management FERREIRA, Adelino; SANTOS, João
13th WCTR, July 15-18, 2012 – Rio de Janeiro, Brazil
2
INTRODUCTION
The alignments for most of the highway projects do not always follow the site topography.
Due to a great variety of cuts and fills that will be required, the lithology nature of the soils
found at project site it is not the same in both depth and length. Consequently, the in-situ
geotechnical conditions available to build the pavement structure support are not always the
best, and as it is known the conditions and the preparation of the foundation are extremely
important to ensure a long-lasting pavement structure that does not require excessive
maintenance costs. To overcome these limitations many highway agency have as current
practise to build, upon the roadbed, a layer of compacted roadbed soil or selected borrow
material, called subgrade (Christopher et al. 2006). The main purpose of the subgrade is to
provide a platform for construction of the pavement and to support the pavement without
excessive deflection that would impact the pavement‟s performance. For pavements constructed on-grade or in cuts, if the in-situ natural soils present good qualities, the subgrade
is the natural in-situ soil at the site (Christopher et al. 2006). The stiffness of this layer must
be sufficient to allow compaction of the overlying pavement structure in order to obtain
adequate density in the granular and asphalt layers to ensure a good performance of the
pavement (APA 2010). Although there is a consensus about the importance of the foundation
strength and stiffness for the design, construction and performance of the pavement, until now
there are few research works in the literature that have assessed the impact of structural
capacity of pavement subgrade in pavement design and pavement performance prediction
(Khogeli and Mohamed 2004, Tarefder et al. 2008). Moreover, the research studies that have
been carried out are based on pavement design methods which consider only design criteria,
usually fatigue and rutting modes of pavement failure. Reddy and Moorthy (2005) assessed
the adequacy of flexible pavement design thickness based on California Bearing Ratio (CBR)
method against possible risk of shear failure in clayey subgrade. The pavement thickness
designs based on CBR method over clayey subgrades of different compressibility were
compared with a methodology proposed for flexible pavement design based on safe bearing
capacity (SBC) of subgrade soils. They concluded that it is preferable to adopt higher design
thickness values obtained from SBC approach to construct flexible pavements that are safe
against the aspects of shear failure and excessive settlement in subgrade. However, in case of
lime treated soils, the risk against shear failure of subgrade may not be there and hence design
based on CBR value of subgrade may be valid and used. Sidess and Uzan (2009) presented a
design method of perpetual flexible pavement in Israel. The total perpetual pavement
thickness is calculated using the Israeli design method. The HMA layers thickness is
determined as the minimum thickness at which the tensile strain at the bottom of the HMA
layer meets one of the following two criteria: (1) crack initiation at the end of the 30 years
design period or (2) an „endurance‟ limit of 70 µS. The effect of subgrade strength on HMA layers was studied. The authors verified that the value of the HMA thickness decreased by
only 30 mm when the CBR of the subgrade increased from 2 to 10%.
This paper is a step forward in the evaluation of the influence of pavement subgrade soils in
pavement design since the study presented here was carried out on the application of a new
LCCA system, called OPTIPAV (Santos and Ferreira 2011), which considers pavement
performance and the following costs: construction costs; maintenance costs throughout the
Life-cycle cost analysis system for pavement management FERREIRA, Adelino; SANTOS, João
13th WCTR, July 15-18, 2012 – Rio de Janeiro, Brazil
3
project analysis period; user costs throughout the project analysis period; and the pavement
residual value at the end of the project analysis period.
LIFE-CYCLE COST ANALYSIS SYSTEM
Introduction
The LCCA system called OPTIPAV, proposed by Santos and Ferreira (2011), consists of the
components shown in Figure 1: the objective of the analysis, the road pavement data and
models, the constraints that the system must guarantee and the results. The OPTIPAV system
was implemented using Microsoft Visual Studio programming language (David et al. 2006,
Randolph and Gardner 2008) adapting and introducing new functionalities to an existing
genetic algorithm program called GENETIPAV-D (Ferreira 2001, Ferreira et al. 2002,
Ferreira et al. 2009a) previously developed to solve deterministic optimisation models. The
results of the application of the OPTIPAV system consist of the optimal pavement structure,
the predicted annual pavement quality, the construction costs, the M&R plan and costs, the
user costs, and the pavement residual value at the end of the project analysis period. The
objective of the analysis, the road pavement data and models, and the constraints that the
system must guarantee are described in the following section.
Minimisation of total costs
(construction costs, M&R costs, user costs, residual value of pavements)
Verifying the minimum quality levels
Using only the M&R actions defined by the infrastructure manager
Not exceeding the maximum number of M&R actions during the project analysis period
Number of years of the project analysis period
Discount rate
Traffic
Pavement width and length
Admissible pavement layers and construction costs
M&R actions and unit agency costs
Pavement foundation class
Performance model
User costs model
Residual value model
Minimum quality levels to guarantee
Optimal pavement structure
Predicted annual pavement quality
Construction costs
M&R plan and costs
User costs
Residual value in the end of the project analysis period
Data and models
Objective
Constraints
Results
Figure 1 – OPTIPAV system components
Life-cycle cost analysis system for pavement management FERREIRA, Adelino; SANTOS, João
13th WCTR, July 15-18, 2012 – Rio de Janeiro, Brazil
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Optimisation model formulation
The optimisation model introduced above can be formulated as follows:
1,11 11
01
1
1
1
1
1 Min
TsT
T
t
T
t
sttrstrstt
R
r
s RVd
UCd
XMCd
CC
(1)
TtSsXXXXZΦZ RstRsstssst ,...,1;,...,1),,...,,...,,...,,( 11110 (2)
TtSsZZst ,...,1;,...,1,
(3)
TtSsRrZX strst ,...,1;,...,1;,...,1, (4)
TtSsX rst
R
r
,...,1 ;,...,1,1
1
(5) SsThMcCC slsls ,...,1,,0 (6) TtSsRrXZaMC rststrst ,...,1;,...,1;,...,1,, (7) TtSsZuUC stst ,...,1;,...,1, (8) SsZCCΘRV TssTs ,...,1,, 1,01, (9)
SsNX s
R
r
T
t
rst ,...,1,max
2 1
(10)
Where: R is the number of alternative M&R operations; S is the number of pavement
structures generated for analysis; T is the number of years of the project analysis period; CCs0 is the construction cost of a pavement structure s in year 0 in function of the material and
thickness of each layer; MCrst is the maintenance cost for applying operation r to pavement
structure s in year t; UCst is the user cost for pavement structure s in year t; RVs,T+1 is the
residual value for a pavement structure in year T+1; Xrst is equal to one if operation r is
applied to pavement structure s in year t, otherwise it is equal to zero; d is the discount rate;
Zst are the condition variables for pavement structure s in year t; Z are the warning levels for
the condition variables of pavement structures; Msl is the material of layer l of pavement
structure s; Thsl is the thickness of layer l of pavement structure s; Nmaxs is the maximum
number of M&R operations that may occur in pavement structure s over the project analysis
period; Φ are the pavement condition functions; Θ are the residual value functions; c are
the construction cost functions;a are the agency cost functions for M&R; u are the user
cost functions; are the feasible operations sets.
Equation (1), the objective-function of this quite complex, highly non-linear discrete
optimization model, expresses the minimisation of total discounted costs over the project
analysis period, while keeping a pavement structure above specified quality standards. Total
costs include construction costs, M&R costs, user costs and the residual value of a pavement
structure, i.e. its value at the end of the project analysis period.
Constraints (2) correspond to the pavement condition functions, expressing pavement
condition in each year as a set of functions of the initial pavement state and the M&R
operations previously applied to the pavement. These functions can describe the pavement
condition with regard to variables such as cracking, rutting, longitudinal roughness, surface
disintegration (potholing and ravelling) and overall quality of pavements, etc.
Life-cycle cost analysis system for pavement management FERREIRA, Adelino; SANTOS, João
13th WCTR, July 15-18, 2012 – Rio de Janeiro, Brazil
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In Portugal, the Pavement Management System (PMS) of the Portuguese Road
Administration (Picado-Santos and Ferreira 2008, Ferreira et al. 2011), and other municipal
PMS (Ferreira et al. 2009a, Ferreira et al. 2009b), uses the pavement performance model of
the flexible pavement design method developed by the American Association of State
Highways and Transportation Officials (AASHTO 1993) to predict the future quality of
pavements. Thus, the first application of the LCCA system (Santos and Ferreira 2011) has
also considered the AASHTO flexible pavement design method. The basic design equation
used for flexible pavements is Equation (11). This pavement design method considers the
structural coefficients (SN) presented in Table 1, the initial and terminal present serviceability
index (PSI) values presented in Table 2 and the statistic design values (ZR and S0) presented in
Table 3. Equation (11) can be transformed into Equation (12) to be directly used in the
prediction of the PSI value in each year of the design period. The PSI value ranges between
0.0 and approximately 4.5 (the value for a pavement immediately after construction).
Equation (13) is used to calculate the SN value for each pavement structure. Equation (14) is
used to compute the number of 80 kN equivalent single axle load (ESAL) applications until
any year of the project analysis period.
M
+SN
PSI
+SNSZ=W R0R 8.07-log2.32+
1
1094+0.40
1.5-4.2log
+0.2-1log9.36+log 10
5.19
10
108010
(11)
5.19101080101
10944.007.8log2.32-0.21log9.36log
0 101.5-4.2-+SN
M+SNSZW
tt
Rt0Rt
PSIPSI (12)
L
l
dl
ell CCHSN
1
(13)
h
tYh
hg
gAADTW
t
1)1(365
80
(14)
Where: W80 is the number of 80 kN equivalent single axle load applications estimated for a
selected design period and design lane; ZR is the standard normal deviate; S0 is the combined
standard error of the traffic prediction and performance prediction; PSI is the difference
between the initial or present serviceability index (PSI0) and the terminal serviceability index
(PSIt); SN is the structural number indicative of the total required pavement thickness; MR is
the sub-grade resilient modulus (pounds per square inch); elC is the layer (structural)
coefficient of layer l; dlC is the drainage coefficient of layer l; and lH is the thickness of layer
l; PSIt is the Present Serviceability Index in year t; PSI0 is the Present Serviceability Index of
a pavement immediately after construction (year 0); t
W80
is the number of 80 kN equivalent
single axle load (ESAL) applications in year t (million ESAL/lane); SNt is the structural
number of a pavement structure in year t; AADTh is the annual average daily heavy traffic in
the year of construction or the last rehabilitation, in one direction and per lane; gh is the
annual average growth rate of heavy traffic; tY is the time since the construction of the
pavement or its last rehabilitation (years); is the average heavy-traffic damage factor or
simply truck factor.
Life-cycle cost analysis system for pavement management FERREIRA, Adelino; SANTOS, João
13th WCTR, July 15-18, 2012 – Rio de Janeiro, Brazil
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Table 1 – Structural coefficients
Material Description e
nC /cm
AC-S Asphalt concrete - surface layer 0.17323 DAC-Bi Dense asphalt concrete - binder layer 0.17323 AC-Bi Asphalt concrete - binder layer 0.13386 AC-B Asphalt concrete - base layer 0.13386 G-B Granular material - base layer 0.05512
GC-B Granular material treated with hydraulic cement - base layer 0.09055 G-SB Granular material - sub-base layer 0.04331
Table 2 – Initial and terminal PSI values
Road class PSI0 PSIt
Highways 4.2 – 4.5 2.5 – 3.0 National roads 4.2 – 4.5 2.0
Municipal roads 4.2 – 4.5 1.5
Table 3 – Statistic design values
Confidence level (%) ZR S0
50 -0.000
0.40 – 0.50
60 -0.253
70 -0.524
75 -0.674
80 -0.841
85 -1.037
90 -1.282
91 -1.340
92 -1.405
93 -1.476
94 -1.555
95 -1.645
96 -1.751
97 -1.881
98 -2.054
99 -2.327
99.9 -3.090
99.99 -3.750
Constraints (3) are the warning level constraints which define the maximum (or in relation to
the PSI, the minimum) level for the pavement condition variables. The warning level adopted
in this study considering the AASHTO pavement design method was a PSI value of 2.0 which
corresponds to the PSI terminal value for national roads (Table 2). A corrective M&R
operation appropriate for the rehabilitation of a pavement structure must be performed when
the PSI value is lower than 2.0.
Constraints (4) represent the feasible operation sets, i.e. the M&R operations that can be
applied to maintain or rehabilitate the pavement structure in relation to its quality condition.
In this application of the OPTIPAV system two M&R operations were considered (Table 4).
The M&R operation 1, that corresponds to “do nothing”, is applied to a pavement structure if the PSI value is above the warning level; that is, if the PSI value is greater than 2.0. The M&R
operation number 2 is the operation that must be applied to a pavement structure when the
warning level is reached; that is, this operation is applied to rehabilitate the pavement
Life-cycle cost analysis system for pavement management FERREIRA, Adelino; SANTOS, João
13th WCTR, July 15-18, 2012 – Rio de Janeiro, Brazil
7
structure. The M&R operation costs, in the same way as the construction costs, were obtained
from the PMS of the Portuguese road administration and correspond to the 85th percentile.
Table 4 – Maintenance and rehabilitation operations
M&R operation Description Cost M&R actions involved Cost
1 Do nothing €0.00/m2 No actions €0.00/m2
2 Structural rehabilitation €21.29/m2
Wearing layer (5 cm) €6.69/m2
Tack coat €0.41/m2
Base layer (10 cm) €8,63/m2
Tack coat €0.41/m2
Membrane anti-reflection of cracks €1.88/m2
Tack coat €0.41/m2
Surface levelling (2 cm) €2.45/m2
Tack coat €0.41/m2
Constraints (5) indicate that only one M&R operation should be performed per pavement
structure in each year. Constraints (6) represent the construction costs, which are computed in
relation to the material and thickness of each pavement layer. Constraints (7) represent the
M&R costs, which are computed in relation to the pavement condition and the M&R
operation applied to the pavement in a given year. Constraints (8) represent the user cost
functions. They express the costs for road users as a function of the pavement condition in a
given year. Equation (15) was adopted for calculating the user costs because it is already used
in some Portuguese PMS for calculating this type of costs (Ferreira et al. 2009b).
32 00042.000709.003871.039904.0 tttt PSIPSIPSIUC (15)
Where: UCt are the user costs in year t (€/km/vehicle); PSIt is the Present Serviceability Index
in year t.
Constraints (9) represent the residual value functions. They express the value of the pavement
structure at the end of the project analysis period as a function of the construction cost and the
pavement condition at that time. Equation (16) is used for calculating the residual value of
pavements structures, which is also used in Portuguese PMS for the same purpose (Jorge and
Ferreira 2012). Constraints (10) were included in the model to avoid frequent M&R
operations on the same pavement structure.
5.15.4
5.1101
TT
PSICCRV
(16)
Where: RVT+1 is the residual value for a pavement structure in year T+1; CC0
is the
construction cost of a pavement structure in year 0 depending on the material and thickness of
each layer; PSIT+1 is the Present Serviceability Index in year T+1.
Life-cycle cost analysis system for pavement management FERREIRA, Adelino; SANTOS, João
13th WCTR, July 15-18, 2012 – Rio de Janeiro, Brazil
8
SENSITIVITY ANALYSIS TO THE PAVEMENT FOUNDATION
Introduction
The Portuguese manual (JAE 1995) recommends 16 different flexible pavement structures for
different combinations between traffic and pavement foundation. These pavement structures
were defined using the Shell pavement design method (Shell 1978), with verification by using
the University of Nottingham (Brunton et al. 1987) and Asphalt Institute (AI 2001) pavement
design methods. The traffic class, which varies between T1 and T6, is defined by the number
of 80 kN equivalent single axle load (ESAL) applications for a design life or design period
calculated depending on the annual average daily heavy-traffic (AADTh), the annual average
growth rate of heavy-traffic (gh) and the average heavy-traffic damage factor or, simply, truck
factor (α). On the other hand, the pavement foundation class, which varies between F1 and
F4, is defined depending on the geotechnical characteristics of both subgrade and underlying
soils until 1 meter deep. The Portuguese soils that traditionally can be found until that depth
are categorized by the Portuguese manual in 6 classes (S0 to S5) taking into account their
geotechnical characteristics defined by the Unified Soil Classification System (ASTM 2006)
and their CBR values. Their characteristics and applicability domain as subgrade layer are
presented in the Table 5. By analysing this Table we can see that all types of soils belonging
to soil classes S3, S4, and S5 and only one belonging to soil class S2 (SC) can be used as
subgrade layer. However, beyond the specifications presented in the Table 5, the soils
characteristics also must verify other specifications defined by the Portuguese road
administration (EP 2009).
Table 6 indicates the thickness of an available soil classified in a specific class that should be
used in the subgrade layer in order to obtain one specific pavement foundation class above an
existent soil also classified in a specific soil class. For example, it is possible to obtain a F2
pavement foundation class constructing a subgrade layer with 30 centimetres thick of a S3
soil above a S2 soil. In this case, the same foundation class can also be obtained constructing
a subgrade layer with 15 centimetres thick of a S4 soil above a S2 soil. Additionally, Table 6
also shows the CBR values, the stiffness modulus (Ef) values, including the design stiffness
modulus (Efd) value that characterize each pavement foundation class.
In order to compare the best solutions in terms of global costs for the final choice of the
pavement structure for a national road or highway, the OPTIPAV system was applied to 384
combinations of traffic (6 different values), foundation (4 different values of the foundation
design stiffness modulus) and pavement structure (16 different flexible pavement structures)
using a total costs optimisation strategy. The objective to achieve through this analysis is to
select the pavement structure that minimises Net Present Value (NPV), calculated by adding
the construction costs, the annual maintenance costs, the annual user costs and deducting the
residual value of pavements at the end of the project analysis period, while always keeping
the pavements PSI value above the warning level of 2.0. This economic analysis was done
using a discount rate equal to 3%.
Life-cycle cost analysis system for pavement management FERREIRA, Adelino; SANTOS, João
13th WCTR, July 15-18, 2012 – Rio de Janeiro, Brazil
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Table 5 – Foundation soil classes defined in the Portuguese manual (adapted from JAE 1995)
Class CBR (%) Soil classification
Portuguese manual description Applicability
as subgrade layer Group symbol Group name
S0 CBR < 3
OL Organic clay Organic silts
Organic clayey silts of low plasticity N
OH Organic clay Organic clays of medium to high plasticity
Organic silts N
CH Fat clay Inorganic clays of high plasticity
Fat clays N
MH Elastic clay
Inorganic silts
Micaceous fine sands
Micaceous silts
N
S1 3 ≤ CBR < 5
OL Organic clay Organic silt
Organic clayey silts of low plasticity N
OH Organic clay Organic clays of medium to high plasticity
Organic silts N
CH Fat clay Inorganic clays of high plasticity
Fat clays N
MH Elastic clay
Inorganic silts
Micaceous fine sands
Micaceous silts
N
S2 5 ≤ CBR < 10
CH Fat clay Inorganic clays of high plasticity
Fat clays N
MH Elastic clay
Inorganic silts
Micaceous fine sands
Micaceous silts
N
CL Lean clay
Inorganic clays of low to medium plasticity
Gravelly clays, sandy clays,
silty clays and lean clays
N
ML Silt
Inorganic silt and very fine sands
Fine, silty or clayey sands
Clayey silts of low plasticity
N
SC Clayey sand Clayey sand
Clayey sand with gravel P
S3 10 ≤ CBR < 20
SC Clayey sand Clayey sand
Clayey sand with gravel A
SM Silty sand Silty sand
Silty sand with gravel A
SP Poorly graded sand Poorly graded sands
Poorly graded sands with gravel A
S4 CBR ≥ 20
SW Well-graded sand Well-graded sands
Well-graded sands with gravel A
GC Clayey gravel Clayey gravel
Clayey gravel with sand A
GM Silty gravel Silty gravel
Silty gravel with sand A
GP Poorly graded gravel Poorly graded gravel
Poorly graded gravel with sand A
S5 CBR ≥40
GM Silty gravel Silty gravel
Silty gravel with sand A
GP Poorly graded gravel Poorly graded gravel
Poorly graded gravel with sand A
GW well-graded gravel Well-graded gravel
Well-graded gravel with sand A
Notes: N - not admissible; P - possible; A - admissible.
Life-cycle cost analysis system for pavement management FERREIRA, Adelino; SANTOS, João
13th WCTR, July 15-18, 2012 – Rio de Janeiro, Brazil
10
Table 6 – Pavement foundation classes defined in the Portuguese manual (adapted from JAE 1995)
Foundation soil class
CBR (%)
Pavement foundation class
F1 F2 F3 F4
Ef (MPa)
30 < Ef ≤ 50 50 < Ef ≤ 80 80 < Ef ≤ 150 > 150
Efd (MPa)
30 60 100 150
S0 CBR < 3 Special study In bedrock or in embankments with rock-soil
materials, with a subgrade layer in rock material with thickness ≥ 15 cm
S1 3 ≤ CBR < 5 30 S2 or 20 S3 60 S3 or 40 S4
S2 5≤ CBR < 10 (1) 30 S3 or 15 S4 60 S3 or 30 S4
S3 10 ≤ CBR < 20 - (1) 20 S4
S4/S5 CBR ≥ 20 - - (1)
Notes:
Thickness in cm
Ef – foundation stiffness modulus (including the subgrade layer with the thickness indicated in the table)
Efd - design foundation stiffness modulus (including the subgrade layer with the thickness indicated in the table)
CBR – California Bearing Ratio
(1) - in excavation, the soil should be scarified and compacted in the necessary depth to guarantee a final well-
compacted thickness of 30 cm; in embankment, the foundation conditions are guaranteed.
Results of the sensitivity analysis to the pavement foundation
The results presented in this paper were obtained for the following data and conditions: six
traffic classes (T1 to T6) characterized in Table 7; four classes of pavement foundation (F1 to
F4) with the characteristics presented in Table 8; sixteen different pavement structures with
the characteristics presented in Figure 2; a project analysis period of 40 years; and a discount
rate of 3%. Table 7 also shows the pavement structures recommended in the Portuguese
manual for traffic classes T1 to T6 and pavement foundation classes F1 to F4.
Figure 3 presents the agency discounted costs throughout the project analysis period for T1
and T5. Considering these costs directly related to a highway operator or highway agency, i.e.
constructions costs, M&R costs and the residual pavement of pavement structures, we can
conclude that pavement structure P3 is the optimum pavement structure for traffic class T5,
constructed above a pavement foundation F4. On the other hand, pavement structure P16 is
the optimum pavement structure for traffic class T1, constructed above a pavement
foundation F4. For example, pavement structure P3 has the following values: construction
costs (€24.53/m2); maintenance costs (€0.00/m
2); residual value (€4.53/m2). Pavement
structure P4 has the following values: construction costs (€26.25/m2); maintenance costs
(€0.00/m2); residual value (€5.44/m2). We can see that P3 has lower construction costs (less
€1.72/m2), no maintenance costs as P4, and a lower residual value (less €0.91/m
2).
Considering these costs, P3 allows savings of €0.81 per m2. For a road with 100 kilometres
long and 10 meters wide it corresponds to a saving of €810,000.00.
Life-cycle cost analysis system for pavement management FERREIRA, Adelino; SANTOS, João
13th WCTR, July 15-18, 2012 – Rio de Janeiro, Brazil
11
Table 7 – Traffic classes and corresponding values
Traffic class
AADT AADTh gh (%) α ESAL (20 years)
Pavement structures for foundation class
F1 F2 F3 F4
T6 1500 150 3 2 0.29x107 NAF P3 P2 P1
T5 3000 300 3 3 0.88x107 NAF P7 P4 P3
T4 5000 500 4 4 2.17x107 NAF P11 P6 P5
T3 8000 800 4 4.5 3.91x107 NAF P13 P9 P8
T2 12000 1200 5 5 7.24x107 NAF P15 P12 P10
T1 20000 2000 5 5.5 13.28x107 NAF P16 P14 P12
Note: NAF - not an adequate foundation for a flexible pavement with an asphalt base layer according to the Portuguese manual.
Table 8 – Pavement foundation class characteristics
Pavement foundation class Design stiffness modulus (MPa) CBR (%)
F1 30 5
F2 60 10
F3 100 20
F4 150 30
32
20
200
0.35
G
26
4000
0.35
AC
P16
30
20
200
0.35
G
24
4000
0.35
AC
P15
28
20
200
0.35
G
22
4000
0.35
AC
6
4000
0.35
AC
P14
28
20
200
0.35
G
23
4000
0.35
AC
5
4000
0.35
AC
P13
26
20
200
0.35
G
20
4000
0.35
AC
6
4000
0.35
AC
P12
25
20
200
0.35
G
20
4000
0.35
AC
5
4000
0.35
AC
P11
24
20
200
0.35
G
18
4000
0.35
AC
6
4000
0.35
AC
P10
24
20
200
0.35
G
19
4000
0.35
AC
5
4000
0.35
AC
P9
22
20
200
0.35
G
17
4000
0.35
AC
5
4000
0.35
AC
P8
22
20
200
0.35
G
18
4000
0.35
AC
4
4000
0.35
AC
P7
21
20
200
0.35
G
16
4000
0.35
AC
5
4000
0.35
AC
P6
19
20
200
0.35
G
14
4000
0.35
AC
5
4000
0.35
AC
P5
18
20
200
0.35
G
14
4000
0.35
AC
4
4000
0.35
AC
P4
16
20
200
0.35
G
12
4000
0.35
AC
4
4000
0.35
AC
P3
12
20
200
0.35
G
8
4000
0.35
AC
4
4000
0.35
AC
P2
HMA
Surface
Layer
HMA
Base
Layer
Sub-
base
Layer
Total HMA Layer Thickness (cm)
Thickness (cm)
Stiffness Modulus (MPa)
Poisson ´s ratio
Material
10
20
200
0.35
G
6
4000
0.35
AC
4
4000
0.35
AC
P1
Flexible Pavement Design Alternatives
5.385945.118224.850504.811134.582784.409554.315064.275694.007973.968603.874113.606393.433163.165442.63000Structural Number 2.36228
Key:
AC - Asphalt Concrete
G - Granular Material
HMA - Hot Mix Asphalt
6
4000
0.35
AC
6
4000
0.35
AC
Thickness (cm)
Stiffness Modulus (MPa)
Poisson ´s ratio
Material
Thickness (cm)
Stiffness Modulus (MPa)
Poisson ´s ratio
Material
Illustration:
Figure 2 – Characteristics of pavement structures
Life-cycle cost analysis system for pavement management FERREIRA, Adelino; SANTOS, João
13th WCTR, July 15-18, 2012 – Rio de Janeiro, Brazil
12
Figure 3 – Agency discounted costs throughout the project analysis period for T1 and T5 (€/m2)
Life-cycle cost analysis system for pavement management FERREIRA, Adelino; SANTOS, João
13th WCTR, July 15-18, 2012 – Rio de Janeiro, Brazil
13
Table 9 – Pavement structures recommended by the Portuguese manual and by using the OPTIPAV system
Traffic class
Pavement foundation class
F1 F2 F3 F4
Pa
ve
me
nt str
uctu
res
OP
TIP
AV
T6 P 16 P 7 P 3 P 1
T5 P 16 P 15 P 5 P 3
T4 P 16 P 16 P 11 P 5
T3 P 16 P 16 P 15 P 10
T2 P 16 P 15 P 16 P 13
T1 P 16 P 16 P 16 P 16
Po
rtu
gu
ese
man
ua
l T6 NAF P 3 P 2 P 1
T5 NAF P 7 P 4 P 3
T4 NAF P 11 P 6 P 5
T3 NAF P 13 P 9 P 8
T2 NAF P 15 P 12 P 10
T1 NAF P 16 P 14 P 12
Note: NAF - not an adequate foundation for a flexible pavement with an asphalt base layer according to the Portuguese manual.
CONCLUSIONS
The results of a sensitivity analysis to the pavement foundation presented in this paper
demonstrate the importance of a right choice of the pavement foundation, in order to
minimize the costs for the highway agency during a long project analysis period, particularly
now that Portugal and other European countries are facing an economic crisis. A good
decision in the selection of the pavement foundation, specifically in the application of a
LCCA to pavement management at project-level, is advantageous not only for the highway
agencies, which can apply the available budget better on construction and M&R operations,
but also for the users, who will benefit from roads with better levels of quality, comfort and
safety. The outcomes obtained with the sensitivity analysis to the pavement foundation, when
applying the OPTIPAV system to a case study, permit us to draw the following conclusions:
(1) The pavement foundation to consider depends on the available soils in the zone of the road
construction;
(2) The agency costs (the sum of the construction costs and the M&R costs, deducting the
residual value of pavements) always decreases with the increase of the structural capacity of
the pavement foundation;
(3) Sometimes, it is better to spend some money on improving the structural capacity of the
pavement foundation since it will save more money in terms of agency costs during a long
project analysis period.
In the near future, in terms of sensitivity analysis, our research will follow with the
consideration of other input parameters, such as, for example, the project analysis period.
Life-cycle cost analysis system for pavement management FERREIRA, Adelino; SANTOS, João
13th WCTR, July 15-18, 2012 – Rio de Janeiro, Brazil
14
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