LIFE-CYCLE COST ANALYSIS SYSTEM FOR PAVEMENT …€¦ · Life-cycle cost analysis system for...

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,

[email protected]

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.

mailto:[email protected]

mailto:[email protected]



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



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



4

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.



5

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.



6

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



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


Membrane anti-reflection of cracks €1.88/m2


Surface levelling (2 cm) €2.45/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.



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%.



9

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


Fat clays N

MH Elastic clay

Inorganic silts


Micaceous silts

N

S2 5 ≤ CBR < 10


Fat clays N

MH Elastic clay

Inorganic silts


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.



10

Table 6 – Pavement foundation classes defined in the Portuguese manual (adapted from JAE 1995)

Foundation soil class

CBR (%)


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.



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)


Poisson ´s ratio

Material

Thickness (cm)


Poisson ´s ratio

Material

Illustration:

Figure 2 – Characteristics of pavement structures



12

Figure 3 – Agency discounted costs throughout the project analysis period for T1 and T5 (€/m2)



13

Table 9 – Pavement structures recommended by the Portuguese manual and by using the OPTIPAV system

Traffic 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.



14

REFERENCES

AASHTO (1993). Guide for design of pavement structures. American Association of State

Highway and Transportation Officials, Washington, DC, USA, 4th ed., 1-640.

AI (2001). Thickness design: asphalt pavements for highways and street. Asphalt Institute,

Lexington, KY, USA, 1-98.

APA (2010). Pavement type selection. A position paper by Asphalt Pavement Alliance,

Lanham, MD, USA, 1-20.

ASTM (2006). Standard D2487 - Standard practice for classification of soils for engineering

purposes (Unified Soil Classification System). ASTM International, West

Conshohocken, PA, USA, 1-12.

Brunton, J., Brown, S. and Pell, P. (1987). Developments to the Nottingham analytical design

method for asphalt pavements. Proceedings of the 6th International Conference on

Structural Design of Asphalt Pavements, University of Michigan, Ann Arbor,

Michigan, USA, 1, 366-377.

Christopher, B., Schwartz, C. and Boudreau, R. (2006). Geotechnical aspects of pavements.

FHWA report nº NHI-05-037, Ryan R. Berg & Associates, Inc., Woodbury, MN,

USA, 1-598.

David, J., Loton, T., Gunvaldson, E., Bowen, C., Coad, N. and Jefford, D. (2006).

Professional Visual Studio 2005 Team System. Wiley Publishing, Inc., Indianapolis,

Indiana, USA, 1-660.

EP (2009). Pavimentação - métodos construtivos. Caderno de Encargos Tipo Obra, Vol. V:03,

capítulo 15.03, Estradas de Portugal, S. A., Almada, Portugal, 1-210 (in Portuguese).

Ferreira, A. (2001). Pavement maintenance optimization of road networks. PhD Thesis,

Coimbra University, Coimbra, Portugal, 1-383 (in Portuguese).

Ferreira, A., Picado-Santos, L. and Antunes, A. (2002). A segment-linked optimization model

for deterministic pavement management systems. The International Journal of

Pavement Engineering, 3 (2), 95-105.

Ferreira, A., Meneses, S. and Vicente, F. (2009). Pavement management system for Oliveira

do Hospital, Portugal. Proceedings of the Institution of Civil Engineers - Transport,

162 (3), 157-169.

Ferreira, A., Meneses, S. and Vicente, F. (2009b). Alternative decision-aid tool for pavement

management. Proceedings of the Institution of Civil Engineers - Transport, 162 (1), 3-

17.

Ferreira, A., Picado-Santos, L., Wu, Z. and Flintsch, G. (2011). Selection of pavement

performance models for use in the Portuguese PMS. International Journal of Pavement

Engineering, 12 (1), 87-97.

JAE (1995). Manual of pavement structures for the Portuguese road network. Junta Autónoma

de Estradas, Lisboa, Portugal, 1-54 (in Portuguese).

Jorge, D. and Ferreira, A. (2012), Road network pavement maintenance optimisation using

the HDM4 pavement performance prediction models, International Journal of

Pavement Engineering (ISI Journal), 13 (1), 39-51.

Khogeli, W. and Mohamed, E. (2004). Novel approach for characterization of unbound

materials. Journal of the Transportation Research Board, Washington, D.C., USA,

1874, 38-46.



15

Picado-Santos, L. and Ferreira, A. (2008). Contributions to the development of the Portuguese

road administration‟s pavement management system. Proceedings of the 3rd European pavement and asset management conference, Coimbra, Portugal, CD Ed., paper

1138.pdf, 1–10.

Randolph, N. and Gardner, D. (2008). Professional Visual Studio 2008. Wiley Publishing,

Inc., Indianapolis, Indiana, USA, 1-946.

Reddy, C. and Moorthy, N. (2005). Significance of bearing capacity of clayey subgrade in

flexible pavement design. International Journal of Pavement Engineering, 6 (3), 183-

189.

Santos, J. and Ferreira, A. (2011). Life-cycle cost analysis for pavement management at

project level. International Journal of Pavement Engineering, 1-14, Published online:

06 Oct 2011, DOI:10.1080/10298436.2011.618535,

http://dx.doi.org/10.1080/10298436.2011.618535.

Shell (1978). Shell pavement design manual - asphalt pavements and overlays for road traffic.

Shell International Petroleum Company Ltd., London, UK.

Sidess, A. and Uzan, J. (2009). A design method of perpetual flexible pavement in Israel.

International Journal of Pavement Engineering, 10 (4), 241- 249.

Tarefder, R., Saha, N., Hall, J. and Ng, P. (2008). Evaluating weak subgrade for pavement

design and performance predictions: a case study of US 550. Journal of

Geoengineering, 3 (1), 13-24.

Date post:	09-May-2020
Category:	Documents
Upload:	others
View:	6 times
Download:	0 times

LIFE-CYCLE COST ANALYSIS SYSTEM FOR PAVEMENT …€¦ · Life-cycle cost analysis system for...

Documents