Development of Maintenance and Rehabilitation Program

Development of Maintenance and Rehabilitation Program Pavement Assets Under Performance-Based Contracts Zaid Alyami and Susan L. Tighe the contract period. A PBC is a type of contract in which payments are explicitly linked to the contractor’s successfully meeting or exceeding certain clearly defined minimum performance indicators (6). The main feature of a PBC is that contractors are paid on the basis of the end result achieved, not on following the specified method of performing the work. Therefore, the contractor is paid on the basis of how well it meets the specified performance LOS. Payments are made in installments, usually monthly. Incentives and penalties may be introduced and consist of an increase or decrease of a payment owing to exceeding or falling short on achieving a performance LOS (7 ). Because PBCs define the success of contractors in regard to how well they meet the performance goals alone, PBCs spark contractor innovation and improve quality, which in turn creates opportunities for value engineering and improved efficiencies (8). In traditional contracting, the agencies prescribe the specifications, materials, construction methods, and so on. With this contract method, the contractor is limited to the risk of defining all requirements for the project and eliminating the unknown conditions. Then the public agency assumes the risk of any failure in the specifications, plans, designs, unexpected or additional work, and so on (9). However, in PBCs, the contractor is free to make the decisions of what to do, when, and how, as long as the specified LOS of the performance measures is achieved (6). With that, the contractor bears the risk of any failure or shortcomings of its decisions. The risk of failure in achieving a specified LOS could be a result of contractor error in (a) predicting deterioration of contracted assets; (b) ­determining appropriate design, specifications, and materials; (c) planning needed maintenance interventions; and (d ) estimating quantities (6). In long-term warranty contracts, such as PBCs, the development of maintenance and rehabilitation programs is a complex task owing to the pavement deterioration process and probability of failure to achieve the specified LOS for various performance measures along the contract period. Performance modeling is crucial to establish appropriate and effective maintenance activity that maintains the specified LOS of the performance measures for the intended period. However, pavement deterioration follows a stochastic behavior (10), and the deterioration process and improvement resulting from maintenance and rehabilitation activities varies based on many factors, such as environment, loading, and data used for the modeling; thus, a risk is posed to the contractor in such contract models.

Over the past decade, there has been a movement in North America toward a performance-based contract (PBC) model for maintaining and managing road networks. In traditional method-based contracts, the owner agency specifies techniques, materials, methods, and quantities, along with the time period for the contract. By contrast, in a PBC, the client agency specifies certain clearly defined minimum performance measures to be met or exceeded during the contract period. PBC is a type of contract in which payments are explicitly linked to the contractor’s successfully meeting or exceeding certain clearly defined minimum performance indicators. Therefore, the selection of a PBC model for maintenance and rehabilitation differs significantly from that of a traditional asset management contract. Also, a PBC is more complex because of the pavement deterioration process and probability of failure to achieve the specified level of service for various performance measures along the contract period. A novel framework was developed for the selection of maintenance and rehabilitation activities with a model for pavement performance prediction and ­linear optimization. A case study based on data from the second generation pavement management system of the Ministry of ­Transportation Ontario is used to illustrate the framework.

Traditionally, agencies specify their maintenance and rehabilitation contracts specifying the means and methods to be performed and the sequence of the job (1). However, according to road agencies around the world, this traditional way of contracting had shortcomings in achieving the agencies’ main goal to maintain the road networks at an acceptable level of service (LOS) while reducing the cost (1). Therefore, the challenge of maintaining the road networks at the best possible condition by investing the minimum amount of money will always keep transportation agencies searching for innovative approaches (2). As a result, road agencies have increased private-sector involvement through warranty contracts (3). According to road agencies around the world, there has been a movement over the past two decades toward performance-based contracts (PBCs), or long-term warranty contracts (2–5). In traditional method-based contracts, the owner agency specifies techniques, materials, methods, and quantities, along with the time period for the contract. In contrast, in a PBC, the client agency does not specify any methods, material, or techniques; however, it specifies minimum performance measures to be met or exceeded throughout Department of Civil and Environmental Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada. Corresponding author: Z. Alyami, [email protected].

Scope and Objective of Paper

Transportation Research Record: Journal of the Transportation Research Board, No. 2361, Transportation Research Board of the National Academies, Washington, D.C., 2013, pp. 1–10. DOI: 10.3141/2361-01

The objective of this paper is to present a novel framework that facilitates the selection of maintenance and rehabilitation activities for pavement assets under PBCs. The framework uses historical 1

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data, performance modeling, and linear optimization to establish and select the maintenance and rehabilitation program. A case study using data from the Ministry of Transportation Ontario (MTO) secondgeneration pavement management system (PMS2) is introduced to illustrate the use of the framework. Proposed Framework The framework is intended to develop a maintenance and rehabilitation program for the bidding stage, which ultimately results in an estimate of the overall cost of the project. Figure 1 shows the overview of the framework. Inputs of the framework include the contract performance specifications, contract warranty and warranty period, maintenance and rehabilitation deterioration models, and the pavement data. Pavement data include the current performance condition; historical performance; pavement information such as traffic, thickness, material; and so on. These inputs are fed into an optimization model that results in a maintenance and rehabilitation program. The framework is beneficial to the contractor to establish the optimum maintenance and rehabilitation program with the lowest cost to maintain the specified LOS of the performance measures along the contract period. In addition, during the bidding stage, agencies prepare preliminary estimates for assets to be contracted out under a PBC (6). The objective is to obtain a benchmark price for the contract against

which bids will be compared during the bids evaluation process (6). Agencies can use the framework to achieve this objective.

Contract Specifications As mentioned earlier, PBCs differ significantly from traditional method-based contracts. In PBCs, the client agency specifies minimum performance measures to be met or exceeded along the contract period. Thus, development of maintenance and rehabilitation programs is subject to the specified performance measures and ­associated LOS as well as the contract period.

Pavement Data The pavement data component is quite important in developing an ­effective maintenance and rehabilitation program for many reasons. First, identifying appropriate maintenance and rehabilitation alternatives rely on pavement information, such as thickness, current condition, traffic, and more. Pavement data should include the following: • Current condition of pavement, • Historical performance of pavement, • Pavement material,

Historical Data (Asset Management Data, LTPP data, etc.) Maintenance and Rehabilitation Activities

M/R Activity 1

M/R Activity 2

M/R Activity n

Improvement to PM1, PM2, … , PMi

Improvement to PM1, PM2, … , PMi

Improvement to PM1, PM2, … , PMi

Deterioration Rate PM1, PM2,..,PMi

Deterioration Rate PM1, PM2,..,PMi

Deterioration Rate PM1, PM2,..,PMi

Cost M/R 1

Cost M/R 2

Cost M/R n

Contracted Asset Information

Maintenance and Rehabilitation Program Optimization

Current Condition

Contract Specifications

Contract Period Phase-I Framework Output

Performance History Materials, Thickness, Traffic, etc.

Final Maintenance and Rehabilitation Program

PM1 LOS PM2 LOS .. . PMi LOS

Total Cost

FIGURE 1   Framework for development of maintenance and rehabilitation (M/R) program (PM = performance measure).

Alyami and Tighe

• Pavement thickness, • Soil type, • Traffic information, • Pavement geometry, • Environmental data, and • Construction and maintenance history. Good-quality pavement data are important for identifying feasible maintenance and rehabilitation activities and in establishing ­deterioration rates and improvements of these activities.

Maintenance and Rehabilitation Deterioration Modeling To evaluate feasible maintenance and rehabilitation alternatives and their impact on pavement performance, deterioration modeling is needed. As noted earlier, performance modeling is crucial for establishing the appropriate maintenance activity, and the appropriate time of application to maintain the specified LOS for different performance measures. To construct deterioration models, pavement data are needed. The data source can be a challenge; however, most agencies now have a pavement management system in place with a wide range of data. For example, MTO uses a pavement management system that was developed in 1985 (11). In addition, some countries maintain pavement performance database programs, such as the Long-Term Pavement Performance (LTPP) program developed by FHWA; it is an excellent source of data for pavements in Canada and the United States (12). It is imperative to obtain the necessary data to construct deterioration models as accurately as possible. To do so, homogeneous sections with the same characteristics, such as material, thickness range, soil type, traffic, and weather condition are to be identified, and the performance of such sections is analyzed. Also, the performance of various maintenance and rehabilitation treatments applied to these homogeneous sections can be evaluated.

Development and Optimization of Maintenance and Rehabilitation Program In traditional asset management, budget constraints dictate establishing priority programming of various maintenance and rehabilitation activities. In other words, with the available budget, managers and engineers determine how much work can be carried out. Different methods were established to develop priority programs, such as ranking, optimization, near optimization, and others (13). However, to establish a maintenance and rehabilitation program for a pavement asset under a PBC, the question is different: How much will it cost to maintain the specified performance measures LOS along the contract period? To successfully develop the program and increase the probability of winning the bid of the potential contract, another objective is added to that question. The objective is to minimize the total cost required to maintain the specified LOS over the contract period. To set up the optimization problem, an objective function is constructed by summing the total present worth of the applied maintenance and rehabilitation activities (TMRC) applied throughout the contract period.

3 n

Y

minimize TMRC = ∑ ∑ X iy × Ciy

(1)

i =1 y = 0

such that

1 X iy =  0

if treatment i is applied at year y otherwise

where Xiy is maintenance or rehabilitation activity i (of n total activities) applied at year y (of the Y years of the contract period) and Ciy is present worth cost of maintenance or rehabilitation activity i applied at year y (of the Y years of the contract period). In addition to the objective function, the optimization model should account for the constraint of the contract-specified LOS of the performance measures. In other words, the performance of each specified performance measure should satisfy the specified LOS of the ­performance measures: Pjiy ≥ PM j

∀ PM j ∈{PM1, PM 2 , . . . , PM j }

(2)

where Pjiy is the performance condition of performance measure (PM) j at year y as a result of the latest maintenance or rehabilitation activity i applied and PMj is the specified LOS of PM j. Case Study To demonstrate the application of the proposed framework, a case study of Highway 7 is developed. The case study uses data available from the MTO PMS2. Performance measures selected for case study include roughness and rutting, for they are widely used in PBCs. The specification on the performance measures were developed based on typical values found in the literature. Although the case study was developed for two performance measures, the framework can be extended to any number of performance measures and LOS. Maintenance and rehabilitation activities and their associated deterioration rates were developed using PMS2 data. Estimates of cost of these maintenance and rehabilitation activities were obtained from MTO for the purpose of this study. A discount rate of 5% was chosen.

Description of Project Highway 7 is located between Kitchener and Guelph in the province of Ontario. The selected section of road, as shown in Figure 2, is approximately 10 km. Highway 7 is a typical two-lane rural highway with signalized and unsignalized intersections. Land use adjacent to the highway ranges from commercial and industrial within the urban border to mainly agricultural, with some commercial land uses along the rural section. The highway was chosen for this case study because it represents typical rural two-lane highways. In addition, the highway is heavily trafficked, resulting in higher probability of deterioration and thus a higher need for maintenance and rehabilitation. A PBC period of 10 years or more has proved to be effective for sustained preservation of a pavement network (14). Therefore, a contract period of 10 years was chosen for this case study. For this case study, roughness and rutting are selected as specified performance measures with LOS. Roughness is specified by an

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FIGURE 2   View of Highway 7 site. (Source: Google Maps, 2012.)

international roughness index (IRI) no less than 2 m/km; rutting is specified at no less than 12 mm throughout the contract period. Pavement Management System of the Ministry of Transportation Ontario MTO’s PMS2 obtained for this study contains data collected from 1990 to 2010. The database includes 870 sections with data classified as historical data and survey data. Historical data include climatic zone (northern and southern); equivalent thickness; subgrade soil type; and pavement type, as well as the maintenance and rehabilitation activities applied throughout the pavement life cycle. Survey data include annual average daily traffic; truck percentage; equivalent single-axle load (ESAL); roughness (IRI m/km); rutting (cm); pavement condition index; and distress manifestation index. Table 1 shows a sample of the PMS2 data used in this study.

Development of Maintenance and Rehabilitation Deterioration Models Performance modeling is crucial for establishing the appropriate maintenance activity and the appropriate time of application to maintain the specified LOS for different performance measures. Performance models are classified as deterministic or probabilistic (13, 15–19). Probabilistic models predict the performance of a pavement by giving the probability with which the pavement would fall into a particular condition state (20). Probabilistic models are developed to characterize the uncertain behavior of pavement deterioration processes (16, 21). The Markov model has proved an effective performance modeling tool among various researchers (10, 13, 22, 23). The Markov model is commonly used for its ability to capture the probabilistic behavior of pavement and the time-dependent uncertainty deterioration process as well as for different maintenance and rehabilitation activities (21). The model is based on the change

TABLE 1   Sample Data of Second Generation Pavement Management System Func_class FWY FWY FWY FWY . . FWY FWY FWY

Sec#

Year

IRI

Rut_depth

1 1 1 1

2003 2002 1999 2000 . . 2010 2009 2008

1.18 1.23 1.08 1.17 . . 0.72 0.72 0.72

5.31 4.27 0 0 . . 6.26 6.08 6.25

. . 41 41 41

ESAL 938,155 976,979 982,315 1,013,832 . . 2,204,256 2,204,256 2,273,735

Length

Year R/M

R/M Act

Subgrade

Pave Type

Environ

Surfthick

4.428 4.428 4.428 4.428 . . 7.329 7.329 7.329

1996 1996 1996 1996 . . 2004 2004 2004

114 114 114 114 . . 112 112 112

Sandy silt Sandy silt Sandy silt Sandy silt . . Sandy silt Sandy silt Sandy silt

AC AC AC AC . . AC AC AC

SO SO SO SO . . SO SO SO

123 123 123 123 . .  92  92  92

Note: Func_class = function class; sec# = section number; year = year of data collection; ESAL = equivalent single axle load; year R/M = year of application of rehabilitation or maintenance activity; R/M act = rehabilitation or maintenance activity; pave type = pavement type; environ = environmental zone; surfthick = surface thickness; FWY = freeway; AC = asphalt concrete; SO = Southern Ontario.

Alyami and Tighe

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TABLE 2   Influence Factors and Corresponding Levels for Pavement Deterioration Influence Factor

Corresponding Levels

Pavement type

Asphalt cement Portland cement Composite Surface treated Low (500,000) Sandy silt Granular Lacustrine clay Varied clay Southern zone Northern zone Low (150)

ESALs

Subgrade material

Climatic zone Surface thickness (mm)

of a pavement from a given state to another over a period of time. Thus, Markov models are developed using the transition probability matrix (TPM). For the development of the Markov models, the following steps are used: • Data screening and evaluation, • Identifying homogeneous pavement section groups, and • Developing the TPM.

Data Analysis The pavement deterioration process is affected by many factors, such as environment, loading, and material. To construct accurate deterioration model of maintenance and rehabilitation activities, homogeneous pavement sections should be identified. MTO’s PMS2 was evaluated to identify influence factors and develop homogeneous sections for developing deterioration models of various maintenance and rehabilitation activities. Influence factors and corresponding ­levels, as presented in Table 2, are identified. On the basis of influence factors and corresponding levels presented in Table 2, homogeneous sections are formed. The MTO PMS2 database was used to obtain Highway 7 characteristics, as presented in Table 3. According to Highway 7 pavement characteristics,

TABLE 4   Maintenance Activities and Costs Activity Code 101 103 102 104

Activity Description

Cost ($/m2)

Number of Sections

19.49

17

27.85

 9

19.16

30

28.94

72

Hot-mix overlay one lift (45 mm) Hot-mix overlay two lifts (45 mm) Mill + hot-mix overlay one lift (45 mm) Mill + hot-mix overlay two lifts (45 mm)

sections with similar influence factors were obtained from PMS2 data for analysis and modeling deterioration rates for maintenance and rehabilitation activities. Four maintenance activities are identified for this study. Table 4 presents a summary of the maintenance activities, the number of pavement sections in the analyzed pavement homogeneous group, and the associated cost as provided by MTO. Each pavement section performance history for roughness and rutting since the maintenance activity was applied is used to construct the Markov deterioration models.

Development of Transition Probability Matrix The TPM is used to present the probability of pavement condition transitioning from one state to the other. For this study and based on the data analysis, pavement roughness conditions are presented as five condition states, while rutting is divided into eight condition states. It is assumed that the pavement will transition by only one state condition each year. In other words, the pavement will either stay in its condition state in the following year, or it will move to the ­following state (22). The TPM is presented in the form of a matrix of order (n × n) where n is the number of condition states identified, as shown in Figure 3. Pi is the probability of staying in the same state, while 1 − Pi is the probability of transitioning to the following state in 1 year. The 1 value at the last row of the matrix indicates a holding state where the pavement does not transition any further (22). For the determination

Characteristic

Asphalt Pavement

ESALs Soil type Environmental zone Surface thickness Roughness (m/km) (2010) Rutting (cm) (2010)

426,419 Sandy silt Southern zone 125 mm 2.01 4.28

Condition state at year t

Condition state at year t+1 TABLE 3   Pavement Characteristics of Highway 7

P1

1-P1

0

0

0

0

P2

1-P2

0

0

. . 0

. . 0

. . 0

. . Pi

. . 1-Pi

0

0

0

0

1

FIGURE 3   Example of transition probability matrix.

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of the probabilities, the proportion method is used (24, 25). In this method, probability is found as follows: nij Pij = n

TABLE 6   TPM for Maintenance Activity Used in Case Study: Rutting Condition State 1

(3)

2

3

4

5

6

7

8

Hot-Mix Overlay One Lift (45 mm)

where Pij = probability of a pavement section to transition from state i to state j, nij = number of pavement sections transitioned from state j to state j in 1 year, and n = total number of sections in state i. The procedure as described is used to establish the TPM for roughness and rutting for the four maintenance activities used in this study. The TPM is then used to predict the future conditions of pavement with application of a given maintenance or rehabilitation activity. Tables 5 and 6 present the TPM developed for the various maintenance activities used in this study.

1

0.33

0.67

0.00

0.00

0.00

0.00

0.00

0.00

2

0.00

0.54

0.46

0.00

0.00

0.00

0.00

0.00

3

0.00

0.00

0.42

0.58

0.00

0.00

0.00

0.00

4

0.00

0.00

0.00

0.61

0.39

0.00

0.00

0.00

5

0.00

0.00

0.00

0.00

0.19

0.81

0.00

0.00

6

0.00

0.00

0.00

0.00

0.00

0.61

0.39

0.00

7 8

0.00 0.00

0.00 0.00

0.00 0.00

0.00 0.00

0.00 0.00

0.00 0.00

0.50 0.00

0.50 1.00

Hot-Mix Overlay Two Lifts (45 mm) 1

0.50

0.50

0.00

0.00

0.00

0.00

0.00

0.00

2

0.00

0.33

0.67

0.00

0.00

0.00

0.00

0.00

3

0.00

0.00

0.67

0.33

0.00

0.00

0.00

0.00

4

0.00

0.00

0.00

0.83

0.17

0.00

0.00

0.00

Maintenance and Rehabilitation Program

5

0.00

0.00

0.00

0.00

0.63

0.37

0.00

0.00

6

0.00

0.00

0.00

0.00

0.00

0.76

0.24

0.00

The framework is implemented to develop the maintenance program for the case study. The objective is to develop a maintenance

7 8

0.00 0.00

0.00 0.00

0.00 0.00

0.00 0.00

0.00 0.00

0.00 0.00

0.50 0.00

0.50 1.00

Mill + Hot-Mix Overlay One Lift (45 mm) TABLE 5   TPM for Maintenance Activity Used in Case Study: Roughness Condition State 1

2

3

4

5

Hot-Mix Overlay One Lift (45 mm) 1 2 3 4 5

0.26 0.00 0.00 0.00 0.00

0.74 0.04 0.00 0.00 0.00

0.00 0.96 0.02 0.00 0.00

0.00 0.00 0.98 0.67 0.00

0.00 0.00 0.00 0.33 1.00

Hot-Mix Overlay Two Lifts (45 mm) 1 2 3 4 5

0.33 0.00 0.00 0.00 0.00

0.67 0.59 0.00 0.00 0.00

0.00 0.41 0.64 0.00 0.00

0.00 0.00 0.36 0.50 0.00

0.00 0.00 0.00 0.50 1.00

1

0.55

0.45

0.00

0.00

0.00

0.00

0.00

0.00

2

0.00

0.30

0.70

0.00

0.00

0.00

0.00

0.00

3

0.00

0.00

0.60

0.40

0.00

0.00

0.00

0.00

4

0.00

0.00

0.00

0.71

0.29

0.00

0.00

0.00

5

0.00

0.00

0.00

0.00

0.68

0.32

0.00

0.00

6

0.00

0.00

0.00

0.00

0.00

0.90

0.10

0.00

7 8

0.00 0.00

0.00 0.00

0.00 0.00

0.00 0.00

0.00 0.00

0.00 0.00

0.82 0.00

0.18 1.00

Mill + Hot-Mix Overlay Two Lifts (45 mm) 1

0.52

0.48

0.00

0.00

0.00

0.00

0.00

0.00

2

0.00

0.63

0.37

0.00

0.00

0.00

0.00

0.00

3

0.00

0.00

0.65

0.35

0.00

0.00

0.00

0.00

4

0.00

0.00

0.00

0.73

0.27

0.00

0.00

0.00

5

0.00

0.00

0.00

0.00

0.74

0.26

0.00

0.00

6

0.00

0.00

0.00

0.00

0.00

0.48

0.52

0.00

7 8

0.00 0.00

0.00 0.00

0.00 0.00

0.00 0.00

0.00 0.00

0.00 0.00

0.67 0.00

0.33 1.00

Mill + Hot-Mix Overlay One Lift (45 mm) 1 2 3 4 5

0.45 0.00 0.00 0.00 0.00

0.55 0.62 0.00 0.00 0.00

0.00 0.38 0.80 0.00 0.00

0.00 0.00 0.20 0.90 0.00

0.00 0.00 0.00 0.10 1.00

Mill + Hot-Mix Overlay Two Lifts (45 mm) 1 2 3 4 5

0.50 0.00 0.00 0.00 0.00

0.50 0.84 0.00 0.00 0.00

0.00 0.16 0.90 0.00 0.00

0.00 0.00 0.10 0.82 0.00

0.00 0.00 0.00 0.18 1.00

and rehabilitation program at the lowest cost while maintaining contract specifications. To achieve this objective, the optimization model presented earlier is implemented. An Excel worksheet is developed to apply the process of this framework. All inputs are formulated in the Excel cells. The formulated Excel functions for maintenance and rehabilitation selection in the spreadsheet follow the logic presented in Figure 4. As shown in Figure 4, the worksheet is set up to select a maintenance and to evaluate and monitor the deterioration process for each performance measure for the selected maintenance or rehabilitation activity every year. Once the specified LOS is reached, a maintenance or rehabilitation activity is selected. When it is

Alyami and Tighe

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Start year t

Apply M/R Activity j

M/R and year log

Performance Modeling PM1 PM2 .. . PMj

End

Yes

No

Contract period reached?

Year t+1

No

Satisfy PM1 LOS PM2 LOS . . PMj LOS

Yes

FIGURE 4   Flowchart of selecting and evaluating maintenance or rehabilitation activities.

selected, it is recorded in the database with the corresponding year of application. This process is continued until the contract period is reached. The process of selection of the combination of maintenance and rehabilitation activities that will maintain the specified LOS is repeated to arrive at the optimum maintenance and rehabilitation program. For assistance with the optimization process, Evolver software is used. Evolver is a genetic algorithm optimization add-in for Microsoft Excel (see palisade.com/Evolver). Figure 5 shows a screenshot of the developed Excel worksheet and use of Evolver. As seen in Figure 5, the model definition box on the left corner allows for identifying the variables and constraints to reach the objective function. The objective function shown in the figure is to minimize the total cost by changing the maintenance and rehabilitation activities applied in a given year while maintaining the performance measures constraints. The optimization model was run several times to ensure that the optimum initial program was developed. Table 7 presents the program developed from implementing this framework. As shown in Table 7, the output provides a variation of maintenance activities applied at various years throughout the contract period. The present worth of each activity for the case study section is also provided. Figure 6, a and b, illustrates performance models for roughness and rutting, respectively, over the contract period as a result of the proposed maintenance program. As shown, performance specifications for performance measures are maintained throughout the c­ ontract period.

Framework Sensitivity Analysis The proposed framework develops a maintenance and rehabilitation program based on various inputs. Inputs include alternative maintenance and rehabilitation activities and the corresponding cost and deterioration rates. Deterioration rates are developed on the basis of historical data, using a given modeling method. Regardless of modeling method, pavement deterioration is of stochastic nature, and uncertainty is present (10). In addition, the development and selection of maintenance and rehabilitation programs are subject to the specified LOS of the performance measures. Therefore, the proposed framework is evaluated for sensitivity to variability in the deterioration rates and the specified LOS of the performance measures in a range of +20% to −20%.

Deterioration Rates: Sensitivity Analysis In the case study, deterioration rates were developed by using the ­Markov model represented in the TPMs. TPMs were used for the sensitivity analysis by changing the probabilities by percentage range from −20% to 20%. The case study was used to develop a maintenance and rehabilitation program for using the TPM sets developed for the sensitivity analysis. For each trial, output was recorded, and total cost was presented. Figure 7 graphically presents the total cost variation as a result of the deterioration sensitivity analysis.

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O b j e c t i v e Fu n c t i o n

Co n st r a i n t s V a r ia b le s

FIGURE 5   Excel worksheet and optimization snapshot.

As noted in Figure 7, the increase in the deterioration rate significantly increases the total cost of the maintenance program. In other words, as the deterioration rate of a maintenance activity accelerates (i.e., pavement deteriorates faster), a maintenance or rehabilitation is to be applied sooner. That results in an increase in the number of maintenance or rehabilitation activities to be applied throughout the contract period. However, a decrease in the deterioration rate results in a slight decrease in total maintenance cost. Consequently, the deterioration rate greatly increases the maintenance program cost; therefore, contractors should implement similar sensitivity analyses during cost estimation as a means to quantify the risk accepted. TABLE 7   Output of Maintenance and Rehabilitation Framework Treatment

Present Worth Cost

0 HM overlay 2 1 No treatment 2 Mill + HM overlay 1 3 No treatment 4 HM overlay 1 5 No treatment 6 Mill + HM overlay 1 7 No treatment 8 HM overlay 2 9 No treatment 10 No treatment Total cost

$ 2,228,000.00 $0 $ 1,390,294.78 $0 $ 1,282,757.70 $0 $ 1,143,798.96 $0 $ 1,507,998.10 $0 $0 $ 7,552,849.54

Year

Note: HM = hot-mix.

Performance Specifications: Sensitivity Analysis Although the performance LOS is specified by the highway agency tendering the contract, it is valuable to study the impact on the total cost of such constraint. In the case study, performance measures are roughness and rutting. The specified LOS for each is 2 m/km and 12 mm, respectively. For the sensitivity analysis, the performance specification is relaxed and restricted by a range of −20% to 20%. The framework output of total cost for the performance ­specifications sensitivity analysis is presented in Figure 8. As seen Figure 8, relaxing the specified LOS slightly allows for more deterioration and therefore reduces the total cost of maintenance. However, restricting the specification, even by a slight percentage, increases the total maintenance cost significantly. On the basis of sensitivity analysis of performance LOS, it is evident that the LOS specified has a high influence on total maintenance cost; thus, contracting agencies should take that into account and carefully select the appropriate LOS.

Conclusions The following are conclusions reached about the framework: • The framework is intended to develop an optimized maintenance and rehabilitation program for the bidding stage that will ultimately result in an estimate of the overall cost of the project. • The framework is beneficial to the contractor for establishing the optimum maintenance and rehabilitation program with the lowest cost to maintain the specified LOS of the performance measures along the contract period.

Alyami and Tighe

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Roughness (m/km)

3 2.5 2 1.5 1 0.5 0 0

1

2

3

4 5 6 7 Pavement Age (Year) (a)

8

9

10

11

10

11

12

12 Rutting (mm)

10 8 6 4 2 0

0

1

2

3

4 5 6 7 8 Pavement Age (Year)

9

(b)

Maintenance Total PW Cost

FIGURE 6   Performance over contract period: (a) roughness and (b) rutting. $12,000,000.00 $10,000,000.00 $8,000,000.00 $6,000,000.00 $4,000,000.00 $2,000,000.00 $0.00

+20%

+10%

0

-10%

-20%

Deterioration Rates Sensitivity FIGURE 7   Deterioration rates: sensitivity analysis (PW = present worth).

Maintenance Total PW Cost

$12,000,000.00 $10,000,000.00 $8,000,000.00 $6,000,000.00 $4,000,000.00 $2,000,000.00 $0.00

+20%

+10% 0 -10% Deterioration Rates Sensitivity

FIGURE 8   Performance specifications: sensitivity analysis.

-20%

10

• The framework can be used to prepare preliminary estimates for assets to be contracted out under a PBC as a benchmark price against which bids will be compared during the bid evaluation process. • Good-quality pavement data are important for identifying feasible maintenance and rehabilitation activities and in establishing deterioration rates and improvements of these activities. • Performance modeling is crucial for establishing the appropriate maintenance activity and the appropriate time of application to maintain the specified LOS for different performance measures. • Sensitivity analysis of performance LOS showed that the specified LOS has a strong influence on the total maintenance cost; thus, contracting agencies should take that into account and carefully select the appropriate LOS. • Deterioration rate sensitivity analysis showed that deterioration rate has a great impact on the maintenance program cost. As a result, it is recommended that contractors implement similar sensitivity analysis during cost estimation and maintenance program development as a means to quantify the risk accepted. References  1. Piñero, J. C., and M. Jesus. Issues Related to the Assessment of Performance-Based Road Maintenance Contracts. ASCE, Reston, Va., 2004.  2. Piñero, J. C. A Framework for Monitoring Performance-Based Road Maintenance. PhD dissertation. Virginia Polytechnic Institute and State University, Blacksburg, 2003.  3. Queiroz, C. Contractual Procedures to Involve the Private Sector in Road Maintenance and Rehabilitation. Transport Sector Familiarization Program, World Bank, Washington, D.C., 1999.   4. Manion, M., and S. L. Tighe. Performance-Specified Maintenance Contracts: Adding Value Through Improved Safety Performance. In Transportation Research Record: Journal of the Transportation Research Board, No. 1990, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 72–79.   5. Giglio, J. M., and W. D. Ankner. Public–Private Partnerships: Brave New World. TR News, No. 198, 1998, pp. 28–33.  6. Performance-Based Contracting for Preservation and Improvement of Road Assets. Transport Note No. TN-27, World Bank, Washington, D.C., 2005.   7. Hyman, W. A. NCHRP Synthesis of Highway Practice 389: PerformanceBased Contracting for Maintenance. Transportation Research Board of the National Academies, Washington, D.C., 2009.   8. Segal, G. F., A. T. Moore, and S. McCarthy. Contracting for Road and Highway Maintenance. Reason Foundation and Public Policy Institute, Los Angeles, Calif., 2003.   9. Moynihan, G., H. Zhou, and Q. Cui. Stochastic Modeling for Pavement Warranty Cost Estimation. Journal of Construction Engineering and Management, Vol. 135, No. 5, 2009, pp. 352–359.

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10. Madanat, S., S. Bulusu, and A. Mahmoud. Estimation of Infrastructure Distress Initiation and Progression Models. Journal of Infrastructure Systems, Vol. 1, No. 3, 1995, pp. 146–150. 11. Kazmierowski, T., Z. He, and B. Kerr. A Second Generation PMS for the Ministry of Transportation of Ontario. Proc., 4th International Conference on Managing Pavements, Seattle, Wash., Transportation Research Board of the National Academies, Washington, D.C., 2001. 12. Long-Term Pavement Performance Program. http://www.ltpp-products. com. Accessed July 2012. 13. Haas, R. C. G., W. R. Hudson, and J. P. Zaniewski. Modern Pavement Management. Krieger Publishing Company, Malabar, Fla., 1994. 14. Haas, R., S. Tighe, J. Yeaman, and L. C. Falls. Long Term Warranty Provisions for Sustained Preservation of Pavement Networks. Proc., Annual Conference and Exhibition, Transportation Association of Canada, Toronto, Ontario, Canada, Transportation Association of Canada, Ottawa, Ontario, Canada, 2008. 15. Analysis of PMS Data for Engineering Applications—Reference Manual. NHI Course No. 131105. FHWA, U.S. Department of Transportation, 2002. 16. Li, Z. A Probabilistic and Adaptive Approach to Modeling Performance of Pavement Infrastructure. PhD dissertation. University of Texas at Austin, 2005. 17. Mahoney, J. Performance Prediction. Introduction to Prediction Models and Performance Curves. Advanced Course on Pavement Management, FHWA, U.S. Department of Transportation, 1990. 18. Morcous, G. Modeling Bridge Deterioration Using Case-Based Reasoning. Journal of Infrastructure Systems, Vol. 8, No. 3, 2002, pp. 86–95. 19. McCullouch, B. G., K. C. Sinha, and P. C. Anastasopoulos. Performance-Based Contracting for Roadway Maintenance Operations in Indiana. Joint Transportation Research Program, Purdue University, West Lafayette, Ind., 2009. 20. Durango, P. L. Adaptive Optimization Models for Infrastructure Management. PhD dissertation. University of California, Berkeley, 2002. 21. Panthi, K. A Methodological Framework for Modeling Pavement Maintenance Costs for Projects with Performance-Based Contracts. Florida International University, Miami, 2009. 22. Butt, A. A., M. Y. Shahin, K. J. Feighan, and S. H. Carpenter. Pavement Performance Prediction Model Using the Markov Process. In Transportation Research Record 1123, TRB, National Research Council, Washington, D.C., 1987, pp. 12–19. 23. Li, N. Development of a Probabilistic Based, Integrated Pavement Management System. PhD dissertation. University of Waterloo, Waterloo, Ontario, Canada, 1997. 24. Jiang, Y., M. Saito, and K. C. Sinha. Bridge Performance Prediction Model Using the Markov Chain. In Transportation Research Record 1180, TRB, National Research Council, Washington, D.C., 1988, pp. 25–32. 25. Ortiz-García, J. J., S. B. Costello, and M. S. Snaith. Derivation of Transition Probability Matrices for Pavement Deterioration Modeling. Journal of Transportation Engineering, Vol. 132, No. 2, 2006, pp. 141–161. The Maintenance and Operations Management Committee peer-reviewed this paper.