Development and implementation of a networklevel pavement optimization model
|
|
- Geoffrey Fisher
- 5 years ago
- Views:
Transcription
1 The University of Toledo The University of Toledo Digital Repository Theses and Dissertations 2011 Development and implementation of a networklevel pavement optimization model Shuo Wang The University of Toledo Follow this and additional works at: Recommended Citation Wang, Shuo, "Development and implementation of a network-level pavement optimization model" (2011). Theses and Dissertations This Thesis is brought to you for free and open access by The University of Toledo Digital Repository. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of The University of Toledo Digital Repository. For more information, please see the repository's About page.
2 A Thesis entitled Development and Implementation of a Network-Level Pavement Optimization Model by Shuo Wang Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Science Degree in Civil Engineering Dr. Eddie Y. Chou, Committee Chair Dr. George J. Murnen, Committee Member Dr. Liangbo Hu, Committee Member Dr. Patricia Komuniecki, Dean College of Graduate Studies The University of Toledo December 2011
3 Copyright 2011, Shuo Wang This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author.
4 An Abstract of Development and Implementation of a Network-Level Pavement Optimization Model By Shuo Wang Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Science Degree in Civil Engineering The University of Toledo December 2011 Optimal use of pavement maintenance and rehabilitation dollars is essential in a constrained budget environment such as now. A network-level optimization tool, which could generate the best maintenance and rehabilitation strategies for the entire pavement network, has become necessary for many highway agencies. This thesis presents the development and implementation of a network-level optimization tool within a pavement management information system for the Ohio Department of Transportation (ODOT). Future pavement condition is predicted based on historical pavement data using a Markov transition probability model. Such transition probabilities are updated automatically when new condition data become available each year. The network-level optimization tool integrates a linear programming model and the Markov transition probability model. This optimization tool is capable of (1) calculating the minimum budget required to achieve a desired level of pavement network condition, (2) maximizing the improvements of pavement network condition with a given amount of budget, and (3) determining the corresponding optimal treatment policy and budget iii
5 allocations. It can be used by highway agencies as a decision support tool for networklevel pavement management. iv
6 Acknowledgements Many people have assisted me in conducting this study. I would like to thank my advisor Dr. Eddie Y. Chou for providing the opportunity and financial support for me to continue my graduate education. I appreciate his valuable guidance on my thesis. I am also grateful to the members of my thesis committee, Dr. Liangbo Hu and Dr. George J. Murnen, for their helpful suggestions and allowing me to defend this thesis. I would also like to acknowledge the Ohio Department of Transportation for funding this study. Special thanks must be given to Debargha Datta, Jun Zhang and Haobo Zhen, for their previous research efforts for the pavement management information system. Without their assistance, this study could not have been completed. v
7 Table of Contents Abstract... iii Acknowledgements...v Table of Contents... vi List of Tables... ix List of Figures... xi 1 Introduction Background Statement of Problem Objective of Study Literature Review Optimization Algorithms Pavement Performance Prediction Models Previously Developed Optimization Models Methodology Introduction Development of Markov Transition Probability Model Formulation of Network-Level Optimization Model Implementation...24 vi
8 4 Example Problems Overview Problem 1: Minimum Budget to Achieve a Desired Condition Level Problem Statement Optimized Results without Budget Constraints Optimized Results with Budget Constraints Comparison between Two Solutions Problem 2: Allowable Treatments Effects on Annual Budget Requirements Problem Statement Results and Discussions Problem 3: Budget Allocation among Different Treatments Problem Statement Results and Discussions Problem 4: Budget Allocation among Different Districts Problem Statement Applying Statewide Policy to Each District Optimizing Pavement Expenditure for Each District Comparison between Two Results Conclusions Summary of the Study Conclusions...52 References...54 Appendices...56 vii
9 A Optimization Model Formulation Flowchart...56 B Network-Level Pavement Optimization Model...57 viii
10 List of Tables 3.1 Example of Allowable Treatments Unit Cost of Maintenance and Rehabilitation Treatments Pavement Condition Classification Current Pavement Condition Distribution Allowable Treatments for Problem Recommended Treatment Budget for Problem 1 without Budget Constraints Projected Pavement Condition Distribution for Problem 1 (without Budget Constraints) Recommended Treatment Budget for Problem 1 with Budget Constraints Projected Pavement Condition Distribution for Problem 1 (with Budget Constraints) Allowable Treatments for Problem 2 (A) Allowable Treatments for Problem 2 (B) Flexible Pavements Treatment Policy for Year 2011 Obtained from Model A in Problem Required Budget Obtained by Applying Statewide Policy to Each District Predicted Deficiency Level Obtained by Applying Statewide Policy to Each District ix
11 4.14 Required Budget Obtained by Optimizing Pavement Expenditure for Each District Predicted Deficiency Level Obtained by Optimizing Pavement Expenditure for Each District x
12 List of Figures 4-1 Recommended Treatment Budget for Problem 1 (without Budget Constraints) Projected Pavement Condition Distribution for Problem 1 (without Budget Constraints) Recommended Treatment Budget for Problem 1 (with Budget Constraints) Pavement Condition Distribution for Problem 1 (with Budget Constraints) Recommended Budget versus Available Budget for Problem Comparison of Deficiency Level for Problem Impact of PM on Required Average Annual Budget Recommended Treatment Budget for Problem Pavement Condition Distribution for Problem Comparison of Deficiency Level Trends between Problem 1 (Model A) and Problem Comparison of Average Annual Budget for Each District xi
13 Chapter 1 Introduction 1.1 Background Transportation is essential for a nation s development and growth (Garber and Hoel 2001). Pavement networks are a key component of the transportation infrastructure system, especially in developed countries such as the United States, where a large number of pavement networks have been constructed (Li et al. 1998). As a result of the aging pavement network compounded by budget cuts at most agencies, maximizing the benefits of available maintenance and rehabilitation dollars has become necessary for many highway agencies (Akyildiz 2008; Li et al. 1998). Generally, pavement conditions can be classified into several categories, such as good, fair or poor and maintenance and rehabilitation treatments can also be grouped into a few types, such as preventive maintenance, minor rehabilitation, and reconstruction. The unit costs of those treatments vary significantly, and their effect on pavement condition improvement also varies (Abaza 2007). It is often a complicated problem for highway agencies to determine the optimal treatment policy and budget allocation to maintain the overall pavement condition above an acceptable level with the least agency cost. Some 1
14 highway agencies determine the annual budget level and allocate the available funds among different repair treatments based upon experience or engineering judgment, which is not always an efficient way of managing pavement networks, especially in a constrained budget environment such as now. Therefore, an effective pavement management system (PMS) that can find the optimal policy has become a necessity for highway agencies to determine the best maintenance and rehabilitation strategies. The optimization tool should be able to address two critical issues facing the decision-maker: (1) determining the minimum budget required to achieve a desired level of pavement network condition, and (2) maximizing the improvements of pavement network condition with a given amount of maintenance and rehabilitation dollars. A pavement management system is defined by AASHTO as a set of tools or methods that assist decision-makers in finding optimum strategies for providing, evaluating, and maintaining pavements in a serviceable condition over a period of time (Huang 2004). The pavement optimization tool, which is capable of generating the best maintenance and rehabilitation strategies for the entire pavement network, is a critical component of a PMS. The financial benefits of optimizing pavement expenditures can be very significant. For instance, the optimization system developed for the state of Arizona saved almost 1/3 of Arizona s pavement preservation budget during the first year of implementation (Golabi et al. 1982). Optimization of pavement expenditures can be conducted at either the network- or the project-level. The network-level optimization takes a global view of the entire pavement network, and focuses on the overall condition distribution and budget allocation problems (Huang 2004). The network-level 2
15 optimization is capable of estimating the total mileage of pavements to be repaired by the applicable treatments, and determining the amount of budget required to maintain the whole pavement network above a certain acceptable condition level (Bako et al. 1995). On the other hand, the project-level optimization concentrates on a specified subset of the whole network and generates maintenance and rehabilitation plans for each pavement section (Huang 2004). The network-level optimization is usually implemented by a macroscopic approach, in which the repair variables are introduced for each pavement condition category and they represent the proportions of pavement that should be treated by the applicable treatments (Abaza 2007). The project-level optimization is generally conducted by a microscopic approach, in which each pavement section is assigned a repair variable for each repair treatment and the value of this variable is 1 if the repair treatment is recommended for this pavement section, otherwise it is 0 (Abaza 2007; Bako et al. 1995). Generally, the linear programming model is used at the network-level, and the integer programming model is applied at the project-level. 1.2 Statement of Problem A pavement management information system (PMIS) has been developed to assist ODOT to manage the Ohio pavement network, which includes more than 40,000 lane miles of highways. The current PMIS is capable of generating various reports regarding the pavement condition, performance, project history, and so on. In order to generate pavement maintenance and rehabilitation plans, a set of decision trees has been 3
16 developed by ODOT and implemented using the PMIS. However, the work plan generated by the decision tree would recommend a rather large number of pavements be repaired and the funding required is far beyond the maximum available budget. Therefore, a more targeted decision support tool is necessary to help ODOT determine the most cost-effective budget allocation and select the best pavement treatment policy under the stringent budget environment. The main purpose of this research is to develop and implement an optimization tool within the current PMIS to support the decision making process in pavement management at the network-level. 1.3 Objective of Study The main objectives of this study are: 1. To review prior research in pavement network-level optimization. 2. To develop a Markov prediction model for pavement condition deterioration and effects of repair treatments based on historical pavement condition data. 3. To develop a linear optimization model for the network-level pavement optimization. 4. To develop a decision support tool by implementing the network-level optimization model using Microsoft Visual Basic.NET (2008) and IBM ILOG CPLEX
17 Chapter 2 Literature Review 2.1 Optimization Algorithms Linear and integer programming are two optimization algorithms utilized by most developed pavement optimization models. Selecting an appropriate algorithm is important in establishing an efficient optimization tool. Linear programming is a powerful mathematical technique for dealing with the problem of allocating limited resources among competing activities in a best possible way (Hillier and Lieberman 2010). All functions and constraints of a linear programming model are required to be linear functions. A linear programming model is generally utilized in a macroscopic approach for pavement optimization at the network-level (Abaza 2007). Thanks to the efficient solution algorithms and the rapid progress in computation power, linear programming models can be solved within an acceptable time period even if the problem size is quite large (Hillier and Lieberman 2010). Therefore, many researchers, such as Golabi et al. (1982), Bako et al. (1995), Chen et al. (1998), and Abaza (2007), have developed network-level optimization models using linear programming. 5
18 The more complete name for integer programming is integer linear programming, which indicates that the integer programming model is derived from the linear programming model by adding a restriction that all variables must be integers (Hillier and Lieberman 2010). Solving an integer programming model is much more difficult than solving a linear programming model, especially when the problem size is large or the constraints are complicated (Hillier and Lieberman 2010). Li et al. (1998) and Ferreira et al. (2002) use integer programming models, in which each pavement section is assigned a set of repair variables and a specific maintenance and rehabilitation plan can be generated for each pavement section. However, this approach results in a very large number of variables and makes the optimization model extremely difficult to solve, especially when it is used for a large pavement network (Abaza 2007). Therefore, it is often used in the project-level optimization, where the number of pavement sections is much less than that of the entire network (Ferreira et al. 2002; Li et al. 1998). 2.2 Pavement Performance Prediction Models An accurate and reliable pavement condition prediction model is essential for a pavement optimization model (Akyildiz 2008). There are two main types of prediction models, namely deterministic models and probabilistic models. De la Garza et al. (2010) developed a regression prediction model by deterministically computing pavement deterioration rates based on historical data. However, the pavement deterioration rates are often uncertain (Butt et al. 1994). Therefore, the probabilistic 6
19 model based on the Markov process is the most frequently used approach (Bako et al. 1995; Chen et al. 1996; Golabi et al. 1982; Abaza 2007). A critical component of this model is the Markov transition probability matrix. Most developed models use two transition matrices for each repair treatment: one for condition improvements in the first year the treatment is conducted, and the other for the deterioration trend after the treatment (Chen et al. 1996). Generally, the elements of the transition probability matrices are calculated based on historical pavement condition data, or are assumed when historical data are insufficient or not available (Bako et al. 1995). 2.3 Previously Developed Optimization Models Two optimization models utilizing the linear programming algorithm and the Markov prediction model are Arizona s model developed by Golabi et al. (1982), and Oklahoma s model developed by Chen et al. (1996). The first modern network-level pavement management system was developed by Golabi et al. (1982) for Arizona Department of Transportation (ADOT) (Ferreira et al. 2002). In Golabi et al. s optimization model, a total of 120 pavement conditions states are defined by the variables including present amount of cracking, change in amount of cracking during the previous year, the present roughness, and index to the first crack. The statewide pavement network is divided into nine road categories (sub-networks) based on traffic volume and a regional environmental factor. The maintenance actions are grouped into 17 types ranging from routine maintenance to substantial corrective measures. 7
20 Golabi et al. (1982) developed a Markov transition probability prediction model using historical pavement condition data to address the probabilistic aspect of pavement deterioration. A single Markov transition probability matrix is used to estimate the deterioration trend of pavements receiving routine maintenance, which is equivalent to Do Nothing in other researcher s models, no matter what repair action they receive before the routine maintenance (Chen et al. 1996). As a result, pavements with different repair treatments, such as reconstruction and thin overlay, are assumed to deteriorate at the same rate after the treatments are conducted, which is considered by Chen et al. (1996) as one of the major limitations of Golabi et al. s model. The network-level optimization model for Arizona is composed of a long-term model and a short-term model. The objective functions of the two models are to minimize the total expected cost. The long-term model calculates a maintenance policy that minimizes the expected long-term average cost to keep the pavement network condition at a desired level. The short-term model then seeks a maintenance policy over an analysis period T that minimizes the total expected cost to achieve the long-term standard within the first T years. (Golabi et al. 1982) The outcome of this optimization model includes the optimized maintenance policy, the expected minimum budget required, and the predicted pavement condition (Golabi et al. 1982). 8
21 Another network-level optimization model is established by Chen et al. (1996) for the Oklahoma Department of Transportation with the application of linear programming and the Markov decision process. Pavement conditions are divided into five states, namely excellent, good, fair, poor, and bad, in terms of the overall pavement condition index. Nine treatments are defined: thin, medium, thick overlay on both asphalt and concrete pavements, medium and thick asphalt reconstruction, and concrete reconstruction. Chen et al. (1996) uses a global optimization model which seeks the optimal solution for the entire network, although the network is divided into six pavement groups by traffic volume and pavement types. The main improvement of this optimization model is that it uses two Markov transition matrices for each repair treatment. One is for the immediate impact of the treatment on the pavement condition improvement when it is conducted in the first year. The other is for the deterioration trend after the treatment, which is also known as a Do Nothing matrix. In other words, the deterioration trends for different repair treatments are estimated separately. Therefore, this prediction model is more realistic and accurate than previous ones in that pavements with different last treatments tend to deteriorate at different rates (Chen et al. 1996). Both cost minimization and benefit maximization approaches are implemented in Chen et al. s optimization model. Two methods for estimating the benefits of pavement maintenance and rehabilitation are developed for the benefit maximization model. One method is to convert pavement conditions into benefit indexes. The benefit index is 9
22 determined subjectively by engineering judgment considering traffic volume and pavement condition (Chen et al. 1996). The other method is to estimate benefits on the basis of the area under the performance curve after a treatment is applied. The benefit index of conducting treatment a on a pavement in state i with last treatment b can be calculated by: N 1 N 1 B mibat WmjADTmjlaj WmjADTmjlbj / C j 2 j 1 miat (2.1) where the Wmj is the weight for pavement group m in state j; ADTmj is the average daily traffic of pavement group m in state j; l aj is the number of years that a pavement section with last treatment a staying in state j; and at time t on pavement group m in state i (Chen et al. 1996). Cmiatis the unit cost of conducting treatment a Other methodologies except linear programming and Markov prediction model, such as integer programming and deterministic prediction models, have also been utilized previously by other researchers. Li et al. (1998) presented an integer programming optimization model for pavement network maintenance and rehabilitation. A time-related Markov probabilistic model is established for pavement condition prediction considering both the immediate treatment effects and the potential impact on the rate of future condition deterioration, which is similar to the prediction model developed by Chen et al. (1996). The major difference 10
23 between the two Markov models is that Li et al. s model predicts the exact pavement condition state (PCS) score, such as pavement condition index (PCI) or pavement serviceability index (PSI), rather than the pavement condition category, such as excellent or poor. This approach facilitates the establishment of the cost-effectiveness-based integer programming optimization model, as the predicted PCS score can be used directly to estimate the benefit of a treatment in the following objective function: Maximize s s M PCS stm Ast Lst ESALs Dst X stm t 1 m 1 Lst Wst Cstm 1 R (2.2) where X stm is the decision variable which is equal to 1 if treatment m is recommended for Section s at Year t and is equal to 0 otherwise; PCS st is the generalized Pavement Condition State (such as PCI or PSI) for Pavement Section s at Year t; minimum acceptable level of PCS required for Pavement Section s at Year t; Ast is the Lst is the length of pavement Section s at Year t; ESALs is ESAL applications on Section s at Year t; D st is the number of service days for traffic flow by Section s at Year t if treatment m is selected; Wst is the width of Section s at Year t; C stm is the unit cost of treatment m; R is the discount rate for calculating the present value of future cost (Li et al. 1998). The network optimization model developed by Li et al. (1998) uses a multiyear integer programming model on a year-by-year basis. The objective of the optimization model is 11
24 to maximize the total value of cost-effectiveness in each analysis year, given the available budget constraints and other applicable constraints. The main output of this program consists of the optimal maintenance and rehabilitation treatment strategy and the predicted condition state for each pavement section in each analysis year (Li et al. 1998). However, integer programming models are much more difficult to solve than linear programming models especially when the problem size is large or the constraints are complicated (Hillier and Lieberman 2010). Therefore, integer programming is not appropriate for pavement maintenance and rehabilitation optimization at the networklevel. De la Garza et al. (2010) developed a network-level linear programming optimization model, in which a deterministic prediction model is utilized for pavement condition deterioration. Five pavement condition states are defined based on the Combined Condition Index (CCI) values. Nine maintenance and rehabilitation treatments, ranging from ordinary maintenance to reconstruction, are identified. Each treatment is allowed to be conducted on only one pavement condition category. De la Garza et al. s model assumes that the deterioration rates are fixed for each pavement condition state and that pavements only deteriorate from an upstream condition to the next downstream condition. The pavement deterioration rates are calculated deterministically from historical data. The objective function of the optimization model is stated as follows: Minimize w N1 i w2 N2i w3 N3 i w4 N4i w5 N5i i P 1 (2.3) 12
25 where P is a set of funding periods (1, 2, 3,, i,, 15); w 1, w 2, w 3, w 4, and w 5 are weighting coefficients for each condition state; Nki is the number of lane-miles in condition k at the end of period i (de la Garza et al. 2010). The model is subject to several sets of constraints such as performance targets and budget limitation. One important constraint representing pavement condition state transition is stated as follows: N5( i 1) X i1 X i2 X i3 X i4 N 5i N5( i 1) 0 (2.4) D U U U U 54 i1 i2 i3 i4 where D 54 is the deterioration rate from condition state 5 (excellent) to condition state 4 (good); X ij is the amount of money spent on treatment j within period i; U ij is the unit cost of treatment j within period i (de la Garza et al. 2010). This constraint indicates that the number of lane-miles in excellent condition at period i is equal to the sum of the following components: the number of lane-miles in excellent condition at period (i-1); minus the number of lane-miles deteriorating from excellent condition to good condition; plus the number of lane-miles in excellent condition restored from other conditions via corresponding treatments (de la Garza et al. 2010). Similar constraints are developed for the other four condition states. 13
26 The optimization model developed by de la Garza et al. (2010) can be used as a powerful decision support tool in pavement management at the network-level. The objective function can be modified to solve different problems. However, there are two limitations in the deterioration model: (1) the same deterioration rates are used for all pavements no matter whether the last treatment is reconstruction or thin overlay (Chen et al. 1996); (2) the deterministic prediction model cannot consider the uncertain aspect of pavement deterioration (Butt et al. 1994). 14
27 Chapter 3 Methodology 3.1 Introduction As discussed in the previous chapter, the Markov transition probability prediction model and linear programming algorithm are the most popular techniques utilized in networklevel optimization. The development of this optimization model is mainly based on the methodologies adapted from Arizona s model (Golabi et al. 1982) and Oklahoma s model (Chen et al. 1996). A Markov prediction model is developed using historical pavement condition data. Then a network-level optimization model is formulated by integrating a linear programming model with the Markov prediction model. Appendix A shows a flowchart of the formulation process of this optimization model. The main feature of this decision support tool is that it provides more flexibility in setting optimization objectives and defining constraints, resulting in more capability in analyzing pavement management problems at the network-level. 15
28 3.2 Development of Markov Transition Probability Model The Markov transition probability model assumes that the probabilities that a pavement deteriorates from a given condition state to other condition states are stationary transition probabilities (Hillier and Lieberman 2010; Chou et al. 2008). These transition probabilities can form a Markov transition matrix, expressed by Equation 3.1: p p P p n1 p p p n2 p 1n p p 2n nn (3.1) where pij is the probability that the a pavement section moves from state i in the current year to state j in the next year, and n is the total number of condition states (Chou et al. 2008). In this research, pavement conditions are categorized into five states, namely Excellent, Good, Fair, Poor and Very Poor, based on the pavement condition rating (PCR) score; pavement repair treatments are grouped into four types, respectively Preventive Maintenance, Thin Overlay, Minor Rehabilitation and Major Rehabilitation. Pavement deterioration rates can be influenced by many factors, such as pavement type, last repair treatment, traffic loading, construction quality, climate, underlying soil characteristics, and system priorities (Yu 2005). Markov transition probabilities should be estimated for each pavement group with similar characteristics. However, a pavement group must have a significant number of pavement sections at various condition states to develop a reliable 16
29 prediction model (Chou et al. 2008). Therefore, three critical factors, namely pavement type, system priorities and last repair treatment, are used as parameters to form pavement groups. Two transition probability matrices, namely treatment matrix and Do Nothing matrix, are developed for each repair treatment in each pavement group. The treatment matrix is for the condition improvement the first year the treatment is applied and the Do Nothing matrix is for the deterioration trend after the treatment. The elements of the transition probability matrices are derived from historical pavement condition data, and are updated automatically when new condition data become available each year. There are three challenges in estimating the Markov transition matrices from actual historical data. First, noises or outliers in the data need to be excluded to improve the accuracy of the estimation. An example of the noises is that a pavement section in poor condition may become in good condition the next year without any record of repair treatment. Such pavement sections are removed from the calculation process in this research. Therefore, the Do Nothing matrices are upper triangular matrices and treatment matrices are lower triangular matrices. Second, pavement condition data are often subject to attrition, also referred to as dropouts (Laird and Ware 2004). Overtime, only good performing pavements remain, while poor performing pavements are more likely to receive treatments and drop out ; therefore, prediction models that do not consider dropouts tend to overestimate future pavement conditions, particularly at the later stage of pavement life span (Chou et al. 2008). In this research, imputation is conducted for those drop out pavement sections to avoid overestimation (Laird and Ware 2004). Third, some pavement groups do not have a sufficient number of pavement 17
30 sections, which makes the transition matrices tend to be inaccurate and unrealistic. For this research, the total mileage of a pavement group should be at least 300 miles; otherwise, the transition probabilities are derived from other similar groups. 3.3 Formulation of Network-Level Optimization Model This section presents the development of a linear programming model for network-level pavement optimization based on the Markov transition probability model. In this research, the pavement network is divided into three sub-networks according to the pavement types (1, Concrete; 2, Flexible; 3, Composite). Each sub-network is divided into four groups according to the last repair treatments (1, Preventive Maintenance (PM); 2, Thin Overlay; 3, Minor Rehabilitation; 4, Major Rehabilitation). Each group is further divided into five pavement condition states (1, Excellent; 2, Good; 3, Fair; 4, Poor; 5, Very Poor) based on the PCR score. Each pavement condition class may be recommended for one of the five repair treatments (0, Do Nothing; 1, Preventive Maintenance; 2, Thin Overlay; 3, Minor Rehabilitation; 4, Major Rehabilitation). In the optimization model described in this chapter: N is the number of pavement types, K is the number of repair treatment types, I is the number of pavement condition states and T is the number of analysis years. Y ntk ' ik is the decision variable representing the proportion of pavement type n in condition state i with last treatment k receiving recommended repair treatment k in year t. Two assumptions are: (1) the total mileage of the pavement network remains constant and (2) the pavement types do not change for any pavement section during the analysis period. 18
31 Two objective functions are developed. The first one is to minimize the total repair cost of the pavement network to achieve a certain condition level goal (Equation 3.2): Minimize N T K n 1 t 1 k' 1 i 1 k 0 I K Y ntk ik C (3.2) ' ntk' ik where C ntk ' ik is the unit cost of applying treatment k in year t to pavement type n in state i with last treatment k. The second one is to maximize the proportion of pavements in Excellent, Good, and Fair condition over the analysis period with given budget constraints (Equation 3.3): Maximize N T K 3 K Y ntk ik n 1 t 1 k' 1 i 1 k 0 ' (3.3) There are four sets of required constraints namely non-negativity constraints, sum-to-one constraints, initial condition constraints, and state transition constraints. The nonnegativity constraints (Equation 3.4) ensure that all variables in the optimization model are non-negative. Y ntk' ik 0 for all n = 1,, N; t = 1,, T; k = 1,, K; i = 1,, I; k = 0,, K (3.4) 19
32 The sum-to-one constraints (Equation 3.5) ensure that the entire pavement network is divided into many proportions and each proportion is represented by a decision variable. N K n 1 k' 1 i 1 k 0 I K Y 1 for all t = 1,, T (3.5) ntk' ik The initial condition constraints (Equation 3.6) pass the values representing current pavement condition state distribution for each pavement group to the optimization model. K k 0 Y n1k ' ik Q nk' i for all n = 1,, N; k = 1,, K; i = 1,, I (3.6) where year. Q nk ' i is the proportion of pavement type n in state i with last treatment k in initial The state transition constraints (Equation 3.7) integrate the Markov transition probability model with the linear programming model. From the second analysis year on, the proportion of pavement type n in condition state j with last treatment k in year t is derived from two parts of pavement in various condition states in year t-1: one part with last treatment k receiving no new treatment (Do Nothing) and the other part receiving new treatment k. 20
33 K Y ntk' jk k 0 i 0 k 1 I K Y n( t 1) kik' P nk' ij I i 0 Y n( t 1) k' i0 DN for all n = 1,, N; t = 2,, T; k = 1,, K; j = 1,, I; (3.7) nk' ij where P nk ' ij is the probability that pavement type n receiving new treatment k transit from state i to state j and DN nk ' ij is the probability that pavement type n with last treatment k receiving no new treatment (Do Nothing) moves from state i to state j. In order to make the optimization model more practical, several sets of optional constraints are also introduced. The condition constraints (Equation 3.8 and Equation 3.9) ensure that the proportion of pavement in certain condition states is in a prescribed range. N K K n 1 k' 1 k 0 Y ntk ik for all t = 2,, T; selected i (3.8) ' it N K K n 1 k' 1 k 0 Y ntk ik for all t = 2,, T; selected i (3.9) ' it where it is the upper limit of proportion of pavement in condition i in year t and it is the lower limit of proportion of pavement in condition i in year t. For instance, pavements in Poor and Very Poor condition are considered as deficient. It may be desirable to limit the total amount of deficient pavements (or deficiency level) to a given percentage, say, 5%, of the entire network. If the desirable deficiency level is 21
34 much lower than the existing deficiency level, a significant amount of rehabilitation would be required to achieve the desired condition target immediately. Therefore, it is more reasonable to allow the condition target (in term of desired deficiency level) to be achieved gradually by linearly reducing the proportion of deficient pavements using Equation 3.10: i1 i i1 t 1 2 t t' it t' 1 (3.10) i t' t T where i is the desired proportion of condition state i; it is the upper limit of proportion of pavement in condition i in year t; t is the year to achieve condition target specified by the user and T is the number of analysis years. The allowable treatment constraints (Equation 3.11) ensure that certain treatments can only be applied to pavements in certain condition states or with certain last treatments. Yntk' ik 0 for all t = 1,, T; selected n, k, i, k (3.11) Experience reveals that some treatments are cost effective only when pavements are in certain condition states and with appropriate last treatments. For example, Thin Overlay is only cost effective on pavements in Fair and Poor condition as shown in Table 3.1, so the corresponding decision variables are set to zero to disallow Thin Overlay on pavements in other condition states. 22
35 Table 3.1: Example of Allowable Treatments Condition Do Nothing PM Thin Overlay Minor Rehab Major Rehab Excellent Yes Good Yes Yes Fair Yes Yes Yes Poor Yes Yes Yes Yes Very Poor Yes Yes The effectiveness of some treatments is also associated with the last treatment. For instance, if PM is conducted on pavements with last treatments of PM, the underlying distress of the pavement can only be masked for a short period of time and the distress may resurface quickly within a few years after treatment. However, PM is a lower cost treatment, which may cause the optimized solution to recommend PM treatments to be applied repeatedly. Therefore, it is necessary to add a set of constraints to disallow PM treatment on pavements with last treatment of PM. The budget constraints (Equation 3.12) ensure that the required budgets recommended by the optimized solution do not exceed the maximum available budget for each year. N T K n 1 t 1 k' 1 i 1 k 0 I K Y ntk ik Cntk ik L B for all t = 1,, T (3.12) ' ' t where L is the total length of entire pavement network and Bt is the maximum available budget in year t. 23
36 It is possible that the optimized repair policy obtained from the mathematical model would recommend a large number of pavements to be repaired in the first couple of years in order to minimize the total cost over the analysis period. However, the recommended budget may be far beyond the maximum available budget of the highway agency, which makes the optimized repair strategy unsuitable for practical use. For that reason, the budget constraints are included in the model. On the basis of the above objective functions and constraints, a linear programming model for pavement maintenance and rehabilitation optimization at the network-level is formulated as described in Appendix B. 3.4 Implementation The network-level optimization model is implemented using Microsoft Visual Basic.NET (2008) and IBM ILOG CPLEX The optimization tool is composed of four parts: pavement database, data preparation, optimization analysis and results output. The pavement database stores current and historical pavement conditions, project history, and road inventory data for analysis. The data preparation part enables the user to define pavement condition states (Excellent, Good, Fair, Poor, and Very Poor) by selecting the corresponding PCR thresholds; to generate the current pavement condition distribution table for further analysis; and to determine the year from which historical condition data are used to generate the Markov transition probability matrices. The optimization analysis part allows the user to select the pavement network for optimization; to input unit cost for each type of repair treatment; to choose appropriate objective functions; to 24
37 set pavement condition constraints; to select allowable treatments for pavements in different condition states; and to enter the maximum available budget for each year in the analysis period. The results output part enables the user to view the projected pavement condition distribution, the optimized recommended treatment policy, and the corresponding budget allocation for the analysis period of up to 30 years. 25
38 Chapter 4 Example Problems 4.1 Overview This chapter presents four examples problems solved by the optimization tool developed in this study. For the example runs, ODOT s priority system pavement network which consists of 11,941 lane miles of interstate highways, U.S. routes, and state routes is analyzed over the next 20 years. The unit costs of four types of maintenance and rehabilitation treatments are shown in Table 4.1. Table 4.1: Unit Cost of Maintenance and Rehabilitation Treatments Preventive Thin Minor Major Treatment Maintenance Overlay Rehab Rehab Cost ($1,000 per lane-mile) ,000 Pavement conditions are classified into five categories based on PCR scores as shown in Table
39 Table 4.2: Pavement Condition Classification Pavement Condition PCR score range Excellent PCR >= 85 Good 75 =< PCR < 85 Fair 65 =< PCR < 75 Poor 55 =< PCR < 65 Very Poor PCR < 55 Table 4.3 presents the current overall pavement condition distribution. Since pavements in poor and very poor conditions are considered to be deficient, the current network deficiency level is 2.7%. Table 4.3: Current Pavement Condition Distribution Pavement Condition Category Excellent Good Fair Poor Very Poor Proportion (%) Problem 1: Minimum Budget to Achieve a Desired Condition Level Problem Statement This problem is to calculate the minimum budget required to improve the overall pavement network condition by reducing the deficiency level from 2.7% to 1% within three years and to determine the corresponding fund allocation among different maintenance and rehabilitation treatments. Both the optimized results with and without budget constraints are analyzed and compared. Table 4.4 shows the allowable treatments for Problem 1. 27
40 Table 4.4: Allowable Treatments for Problem 1 Condition Do Nothing PM Thin Overlay Minor Rehab Major Rehab Excellent Yes Good Yes Yes Fair Yes Yes Yes Poor Yes Yes Yes Very Poor Yes Yes The minimization of total cost is used as the objective function for this problem Optimized Results without Budget Constraints The optimization model without budget constraints (Model A) yields a theoretical optimized solution for the problem. Since no maximum available annual maintenance and rehabilitation budget is defined, the mathematical optimization model could recommend any amount of pavement mileage to be repaired in each year in order to minimize the total cost over the analysis period, which is 20 years in this case. Table 4.5 and Figure 4-1 show the recommended budget allocation for each type of treatment. Table 4.6 and Figure 4-2 show the corresponding projected pavement condition distribution. 28
41 Table 4.5: Recommended Treatment Budget for Problem 1 without Budget Constraints Year Recommended Budget ($ Million) PM Thin Overlay Minor Rehab Major Rehab Total Budget
42 Budget ($ Million) 250 Recommended Treatment Budget for Problem 1 (without Budget Constraints) Year PM Thin Overlay Minor Rehab Major Rehab Figure 4-1: Recommended Treatment Budget for Problem 1 (without Budget Constraints) 30
43 Table 4.6: Projected Pavement Condition Distribution for Problem 1 (without Budget Constraints) Year Condition Distribution (%) Excellent Good Fair Poor Very Poor
44 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Pavement Condition Distribution for Problem 1 (without Budget Constraints) Year Very Poor Poor Fair Good Excellent Figure 4-2: Projected Pavement Condition Distribution for Problem 1 (without Budget Constraints) From Table 4.5 and Figure 4-1, it can be seen that the required budget for the year 2013 is $206.7 million, much higher than the other years. Table 4.6 and Figure 4-2 indicate that the deficiency level is reduced gradually from 2.7% to 1%. However, this result is not suitable for practical use, since the recommended budget for the third year may be far beyond the available maximum annual budget. Besides, the recommended annual budget varies significantly in the first several years, which makes the treatment strategy difficult to be implemented by highway agencies. It should be noted that the funds for years after 2014 are used to maintain the deficiency level at 1%, since pavements tend to deteriorate over years. 32
45 4.2.3 Optimized Results with Budget Constraints The optimization model with budget constraints (Model B) provides an optimal solution under the constraint that recommended budgets do not exceed the maximum available budget for each year. In this example run, it is assumed that the annual budget limitation is $150 million. All other constraints and objective functions are the same with the Model A described in Table 4.7 and Figure 4-3 show the recommended budget allocation for each type of treatment. Table 4.8 and Figure 4-4 show the corresponding projected pavement condition distribution over the next 20 years. Table 4.7: Recommended Treatment Budget for Problem 1 with Budget Constraints Year Recommended Budget ($ Million) PM Thin Overlay Minor Rehab Major Rehab Total Budget
46 Budget ($ Million) 160 Recommended Treatment Budget for Problem 1 (with Budget Constraints) Year PM Thin Overlay Minor Rehab Major Rehab Figure 4-3: Recommended Treatment Budget for Problem 1 (with Budget Constraints) 34
47 Table 4.8: Projected Pavement Condition Distribution for Problem 1 (with Budget Constraints) Year Condition Distribution (%) Excellent Good Fair Poor Very Poor
48 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Pavement Condition Distribution for Problem 1 (with Budget Constraints) Year Very Poor Poor Fair Good Excellent Figure 4-4: Pavement Condition Distribution for Problem 1 (with Budget Constraints) It can be seen from Table 4.7 and Figure 4-3 that the recommended annual budgets are all within the limit of $150 million during the analysis period. Table 4.8 and Figure 4-4 indicate that the deficiency level is reduced gradually from 2.7% to 1% in three years. Although the average annual pavement expenditure is $141 million, which is slightly higher than the theoretical optimized result ($140.6 million) obtained from Model A, this model yields a more practical and stable solution especially for the first several years Comparison between Two Solutions As shown in Figure 4-5, Model A (without budget constraints) recommends a large amount of pavement mileage be repaired in the third year, which may exceed the 36
49 $ Million available budget limitation; whereas the annual treatment budget recommended by Model B (with budget constraints) is more stable and practical. 250 Recommended Treatment Budget versus Available Budget for Problem Year Without Budget Constraint With Budget Constraints Available Budget Figure 4-5: Recommended Budget versus Available Budget for Problem 1 As shown in Figure 4-6, both Model A and Model B reach the goal to reduce the deficiency level from 2.7% to 1% within three years as described in the problem statement. 37
50 Deficiency Level 3.0% Comparison of Deficiency Level Trends for Problem 1 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% Year Without Budget Constraints With Budget Constraints Figure 4-6: Comparison of Deficiency Level for Problem 1 Model B yields a better condition level than Model A in the first several years, mainly because the total recommended budget in the first three years of Model B is $13.6 million higher than that of Model A. In conclusion, Model A provides a maintenance and rehabilitation strategy to minimize the total cost in the 20 years without considering the budget limitation; whereas Model B has one more set of constraints to ensure the recommended annual budgets do not exceed the maximum available budget limitation. The average annual pavement expenditure obtained from Model A is slightly lower than that of Model B, which means Model A yields a better solution than Model B if the total cost in the analysis period is the only 38
DEVELOPMENT AND IMPLEMENTATION OF A NETWORK-LEVEL PAVEMENT OPTIMIZATION MODEL FOR OHIO DEPARTMENT OF TRANSPORTATION
DEVELOPMENT AND IMPLEMENTATION OF A NETWOR-LEVEL PAVEMENT OPTIMIZATION MODEL FOR OHIO DEPARTMENT OF TRANSPORTATION Shuo Wang, Eddie. Chou, Andrew Williams () Department of Civil Engineering, University
More informationOptimization models for network-level transportation asset preservation strategies
The University of Toledo The University of Toledo Digital Repository Theses and Dissertations 2014 Optimization models for network-level transportation asset preservation strategies Shuo Wang University
More informationCity of Glendale, Arizona Pavement Management Program
City of Glendale, Arizona Pavement Management Program Current Year Plan (FY 2014) and Five-Year Plan (FY 2015-2019) EXECUTIVE SUMMARY REPORT December 2013 TABLE OF CONTENTS TABLE OF CONTENTS I BACKGROUND
More informationEvaluating Different Bridge Management Strategies Using The Bridge Management Research System (bmrs)
Purdue University Purdue e-pubs Open Access Theses Theses and Dissertations 2013 Evaluating Different Bridge Management Strategies Using The Bridge Management Research System (bmrs) Timothy Paul Stroshine
More informationHighway Engineering-II
Highway Engineering-II Chapter 7 Pavement Management System (PMS) Contents What is Pavement Management System (PMS)? Use of PMS Components of a PMS Economic Analysis of Pavement Project Alternative 2 Learning
More informationThe Cost of Pavement Ownership (Not Your Father s LCCA!)
The Cost of Pavement Ownership (Not Your Father s LCCA!) Mark B. Snyder, Ph.D., P.E. President and Manager Pavement Engineering and Research Consultants, LLC 57 th Annual Concrete Paving Workshop Arrowwood
More informationLong-Term Monitoring of Low-Volume Road Performance in Ontario
Long-Term Monitoring of Low-Volume Road Performance in Ontario Li Ningyuan, P. Eng. Tom Kazmierowski, P.Eng. Becca Lane, P. Eng. Ministry of Transportation of Ontario 121 Wilson Avenue Downsview, Ontario
More informationNCHRP Consequences of Delayed Maintenance
NCHRP 14-20 Consequences of Delayed Maintenance Recommended Process for Bridges and Pavements prepared for NCHRP prepared by Cambridge Systematics, Inc. with Applied Research Associates, Inc. Spy Pond
More informationCity of Sonoma 2015 Pavement Management Program Update (P-TAP 16) Final Report February 25, 2016 TABLE OF CONTENTS
City of Sonoma I. Introduction TABLE OF CONTENTS II. Methodology III. Pavement Condition Index (PCI) / Remaining Service Life (RSL) Report IV. Budget Analysis Reports A. Budget Needs Report Five Year B.
More informationMaintenance Management of Infrastructure Networks: Issues and Modeling Approach
Maintenance Management of Infrastructure Networks: Issues and Modeling Approach Network Optimization for Pavements Pontis System for Bridge Networks Integrated Infrastructure System for Beijing Common
More informationC ITY OF S OUTH E UCLID
C ITY OF S OUTH E UCLID T A B L E O F C O N T E N T S 1. Executive Summary... 2 2. Background... 3 3. PART I: 2016 Pavement Condition... 8 4. PART II: 2018 Current Backlog... 12 5. PART III: Maintenance
More informationPavement Asset Management Decision Support Tools: Ohio Department of Transportation Case Study
Pavement Asset Management Decision Suort Tools: Ohio Deartment of Transortation Case Study Eddie Chou Professor of Civil Engineering The University of Toledo Andrew Williams Administrator, Office of Technical
More informationScienceDirect. Project Coordination Model
Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 52 (2015 ) 83 89 The 6th International Conference on Ambient Systems, Networks and Technologies (ANT 2015) Project Coordination
More informationMulti-Year, Multi-Constraint Strategy to
Multi-Year, Multi-Constraint Strategy to Optimize Linear Assets Based on Life Cycle Costs Keivan Neshvadian, PhD Transportation Consultant July 2016 2016 AgileAssets Inc All Rights Reserved Pavement Asset
More informationRISK BASED LIFE CYCLE COST ANALYSIS FOR PROJECT LEVEL PAVEMENT MANAGEMENT. Eric Perrone, Dick Clark, Quinn Ness, Xin Chen, Ph.D, Stuart Hudson, P.E.
RISK BASED LIFE CYCLE COST ANALYSIS FOR PROJECT LEVEL PAVEMENT MANAGEMENT Eric Perrone, Dick Clark, Quinn Ness, Xin Chen, Ph.D, Stuart Hudson, P.E. Texas Research and Development Inc. 2602 Dellana Lane,
More informationDecision Supporting Model for Highway Maintenance
Decision Supporting Model for Highway Maintenance András I. Baó * Zoltán Horváth ** * Professor of Budapest Politechni ** Adviser, Hungarian Development Ban H-1034, Budapest, 6, Doberdo str. Abstract A
More informationPlanning Pavement Maintenance and Rehabilitation Projects in the New Pavement Management System in Texas 3. Feng Hong, PhD, PE
Planning Pavement Maintenance and Rehabilitation Projects in the New Pavement Management System in Texas 0 Feng Hong, PhD, PE Texas Department of Transportation, Austin, TX Email: Feng.Hong@TxDOT.gov Eric
More informationLinear Programming Model for Pavement Management
TRANSPORTATION RESEARCH RECORD 12 71 Linear Programming Model for Pavement Management CHRISTIAN F. DAVIS AND c. PETER VAN DINE A computer model, CONNP A VE, has been developed for the Connecticut Department
More informationHazim M Abdulwahid, MSC, MBA Hazim Consulting
Road Map for Establishing Pavement Maintenance Management System on the Strategic Level 13 th International O&M Conference in the Arab Countries,17-19 Nov 2015 Hazim M Abdulwahid, MSC, MBA Hazim Consulting
More informationA PROCEDURAL DOCUMENT DESCRIBING THE PROCESS OF DEVELOPING THE 4-YEAR PLAN
5-9035-01-P8 A PROCEDURAL DOCUMENT DESCRIBING THE PROCESS OF DEVELOPING THE 4-YEAR PLAN Authors: Zhanmin Zhang Michael R. Murphy TxDOT Project 5-9035-01: Pilot Implementation of a Web-based GIS System
More information1.0 CITY OF HOLLYWOOD, FL
1.0 CITY OF HOLLYWOOD, FL PAVEMENT MANAGEMENT SYSTEM REPORT 1.1 PROJECT INTRODUCTION The nation's highways represent an investment of billions of dollars by local, state and federal governments. For the
More informationEffective Use of Pavement Management Programs. Roger E. Smith, P.E., Ph.D. Zachry Department of Civil Engineering Texas A&M University
Effective Use of Pavement Management Programs Roger E. Smith, P.E., Ph.D. Zachry Department of Civil Engineering Texas A&M University 1 Pavement Management Is A Decision Making Process Effective Pavement
More informationSMEC PAVEMENT MANAGEMENT AND ROAD INVENTORY SYSTEM. Frequently Asked Questions
SMEC PAVEMENT MANAGEMENT AND ROAD INVENTORY SYSTEM Frequently Asked Questions SMEC COMPANY DETAILS SMEC Australia Pty Ltd Sun Microsystems Building Suite 2, Level 1, 243 Northbourne Avenue, Lyneham ACT
More informationAsset Management Ruminations. T. H. Maze Professor of Civil Engineering Iowa State University
Asset Management Ruminations T. H. Maze Professor of Civil Engineering Iowa State University Why Transportation Asset Management Has Nothing to Do With Systems to Manage Individual Transportation Assets
More informationOPTIMIZATION OF ROAD MAINTENANCE AND REHABILITATION ON SERBIAN TOLL ROADS
Paper Nº ICMP123 8th International Conference on Managing Pavement Assets OPTIMIZATION OF ROAD MAINTENANCE AND REHABILITATION ON SERBIAN TOLL ROADS Goran Mladenovic 1*, Jelena Cirilovic 2 and Cesar Queiroz
More informationDeveloping Optimized Maintenance Work Programs for an Urban Roadway Network using Pavement Management System
Developing Optimized Maintenance Work Programs for an Urban Roadway Network using Pavement Management System M. Arif Beg, PhD Principal Consultant, AgileAssets Inc. Ambarish Banerjee, PhD Consultant, AgileAssets
More informationA Stochastic Approach for Pavement Condition Projections and Budget Needs for the MTC Pavement Management System
A Stochastic Approach for Pavement Condition Projections and Budget Needs for the MTC Pavement Management System Rafael Arturo Ramirez-Flores Ph. D. Candidate Carlos Chang-Albitres Ph.D., P.E. April 16,
More informationHosten, Chowdhury, Shekharan, Ayotte, Coggins 1
Hosten, Chowdhury, Shekharan, Ayotte, Coggins 1 USE OF VDOT S PAVEMENT MANAGEMENT SYSTEM TO PROACTIVELY PLAN AND MONITOR PAVEMENT MAINTENANCE AND REHABILITATION ACTIVITIES TO MEET THE AGENCY S PERFORMANCE
More informationTransition from Manual to Automated Pavement Distress Data Collection and Performance Modelling in the Pavement Management System
Transition from Manual to Automated Pavement Distress Data Collection and Performance Modelling in the Pavement Management System Susanne Chan Pavement Design Engineer, M.A.Sc, P.Eng. Ministry of Transportation
More informationMONETARY PERFORMANCE APPLIED TO PAVEMENT OPTIMIZATION DECISION MANAGEMENT
MONETARY PERFORMANCE APPLIED TO PAVEMENT OPTIMIZATION DECISION MANAGEMENT Gordon Molnar, M.A.Sc., P.Eng. UMA Engineering Ltd., 17007 107 Avenue, Edmonton, AB, T5S 1G3 gordon.molnar@uma.aecom.com Paper
More information2016 PAVEMENT CONDITION ANNUAL REPORT
2016 PAVEMENT CONDITION ANNUAL REPORT January 2017 Office of Materials and Road Research Pavement Management Unit Table of Contents INTRODUCTION... 1 BACKGROUND... 1 DATA COLLECTION... 1 INDICES AND MEASURES...
More informationChapter 8: Lifecycle Planning
Chapter 8: Lifecycle Planning Objectives of lifecycle planning Identify long-term investment for highway infrastructure assets and develop an appropriate maintenance strategy Predict future performance
More informationAdvanced Operations Research Prof. G. Srinivasan Dept of Management Studies Indian Institute of Technology, Madras
Advanced Operations Research Prof. G. Srinivasan Dept of Management Studies Indian Institute of Technology, Madras Lecture 23 Minimum Cost Flow Problem In this lecture, we will discuss the minimum cost
More informationThe Optimization Process: An example of portfolio optimization
ISyE 6669: Deterministic Optimization The Optimization Process: An example of portfolio optimization Shabbir Ahmed Fall 2002 1 Introduction Optimization can be roughly defined as a quantitative approach
More informationDemonstrating the Use of Pavement Management Tools to Address GASB Statement 34 Requirements
Demonstrating the Use of Pavement Management Tools to Address GASB Statement 34 Requirements Angela S. Wolters and Kathryn A. Zimmerman Applied Pavement Technology, Inc. 3001 Research Road, Suite C Champaign,
More informationResidential Street Improvement Plan
Residential Street Improvement Plan Introduction Aging infrastructure, including streets, is a nationwide problem and it is one of the biggest challenges facing many cities and counties throughout the
More informationAppendices to NCHRP Research Report 903: Geotechnical Asset Management for Transportation Agencies, Volume 2: Implementation Manual
Appendices to NCHRP Research Report 903: Geotechnical Asset Management for Transportation Agencies, Volume 2: Implementation Manual This document contains the following appendices to NCHRP Research Report
More informationA Multi-Objective Decision-Making Framework for Transportation Investments
Clemson University TigerPrints Publications Glenn Department of Civil Engineering 2004 A Multi-Objective Decision-Making Framework for Transportation Investments Mashrur Chowdhury Clemson University, mac@clemson.edu
More informationIntegrated GIS-based Optimization of Municipal Infrastructure Maintenance Planning
Integrated GIS-based Optimization of Municipal Infrastructure Maintenance Planning Altayeb Qasem 1 & Dr. Amin Hammad 2 1 Department of Building, Civil& Environmental Engineering 1 2 Concordia Institute
More informationMaintenance Funding & Investment Decisions STACEY GLASS, P.E. STATE MAINTENANCE ENGINEER ALABAMA DEPARTMENT OF TRANSPORTATION
Maintenance Funding & Investment Decisions STACEY GLASS, P.E. STATE MAINTENANCE ENGINEER ALABAMA DEPARTMENT OF TRANSPORTATION Funding Allocations Routine State $ 166 Million Resurfacing Federal $ 260 Million
More informationProject 06-06, Phase 2 June 2011
ASSESSING AND INTERPRETING THE BENEFITS DERIVED FROM IMPLEMENTING AND USING ASSET MANAGEMENT SYSTEMS Project 06-06, Phase 2 June 2011 Midwest Regional University Transportation Center College of Engineering
More informationContinuing Education Course #287 Engineering Methods in Microsoft Excel Part 2: Applied Optimization
1 of 6 Continuing Education Course #287 Engineering Methods in Microsoft Excel Part 2: Applied Optimization 1. Which of the following is NOT an element of an optimization formulation? a. Objective function
More informationOPTIMAL CONDITION SAMPLING FOR A NETWORK OF INFRASTRUCTURE FACILITIES
MN WI MI IL IN OH USDOT Region V Regional University Transportation Center Final Report NEXTRANS Project No. 034OY02 OPTIMAL CONDITION SAMPLING FOR A NETWORK OF INFRASTRUCTURE FACILITIES By Rabi G. Mishalani,
More informationTransportation asset valuation
The University of Toledo The University of Toledo Digital Repository Theses and Dissertations 2014 Transportation asset valuation Dixhant Acharya University of Toledo Follow this and additional works at:
More informationUNIFIED TRANSPORTATION PROGRAM
2002 UNIFIED TRANSPORTATION PROGRAM Blank Page SUMMARY OF CATEGORIES CATEGORIES NUMBER, NAME AND YEAR ESTABLISHED PROGRAMMING AUTHORITY FUNDING BANK BALANCE (Yes/) RESPONSIBLE ENTITY RANKING INDEX OR ALLOCATION
More informationOptimum Allocation of Resources in University Management through Goal Programming
Global Journal of Pure and Applied Mathematics. ISSN 0973-1768 Volume 12, Number 4 (2016), pp. 2777 2784 Research India Publications http://www.ripublication.com/gjpam.htm Optimum Allocation of Resources
More informationMulti-Objective Optimization Model using Constraint-Based Genetic Algorithms for Thailand Pavement Management
Multi-Objective Optimization Model using Constraint-Based Genetic Algorithms for Thailand Pavement Management Pannapa HERABAT Assistant Professor School of Civil Engineering Asian Institute of Technology
More informationExamples of Decision Support Using Pavement Management Data
Examples of Decision Support Using Pavement Management Data John Coplantz, PE Pavement Management Engineer Oregon Department of Transportation October 27, 2016 Strategic Network (Tactical) Project (Operational)
More informationPAVEMENT PROGRAM PLANNING
CIVIL ENGINEERING STUDIES Illinois Center for Transportation Series No. 10-067 UILU-ENG-2010-2008 ISSN: 0197-9191 PAVEMENT PROGRAM PLANNING BASED ON MULTI-YEAR COST- EFFECTIVENESS ANALYSIS Prepared By
More informationDepartment of Public Works
Department of Public Works Bureau of Street Services Pothole Politics: The Road To Pavement Preservation Rev. Oct. 2008 William A. Robertson Director Potholes Are Like Diamonds They re Forever! HOW BIG
More informationDUALITY AND SENSITIVITY ANALYSIS
DUALITY AND SENSITIVITY ANALYSIS Understanding Duality No learning of Linear Programming is complete unless we learn the concept of Duality in linear programming. It is impossible to separate the linear
More informationAnalysis of Past NBI Ratings for Predicting Future Bridge System Preservation Needs
Analysis of Past NBI Ratings for Predicting Future Bridge System Preservation Needs Xiaoduan Sun, Ph.D., P.E. Civil Engineering Department University of Louisiana at Lafayette P.O. Box 4229, Lafayette,
More informationExamining Long-Term Trends in Company Fundamentals Data
Examining Long-Term Trends in Company Fundamentals Data Michael Dickens 2015-11-12 Introduction The equities market is generally considered to be efficient, but there are a few indicators that are known
More informationDetermining the Value of Information in Asset Management Decisions
Determining the Value of Information in Asset Management Decisions David Luhr Jianhua Li Pavement Management Unit Washington State DOT Simple Decision Tree Solve by calculating Expected Monetary Value
More informationProject Management and Resource Constrained Scheduling Using An Integer Programming Approach
Project Management and Resource Constrained Scheduling Using An Integer Programming Approach Héctor R. Sandino and Viviana I. Cesaní Department of Industrial Engineering University of Puerto Rico Mayagüez,
More informationTime and Cost Optimization Techniques in Construction Project Management
Time and Cost Optimization Techniques in Construction Project Management Mr.Bhushan V 1. Tatar and Prof.Rahul S.Patil 2 1. INTRODUCTION In the field of Construction the term project refers as a temporary
More informationDeck Preservation Strategies with a Bridge Management System. Paul Jensen Montana Department of Transportation
Deck Preservation Strategies with a Bridge Management System Paul Jensen Montana Department of Transportation Email : pjensen@mt.gov Development Of A Roadmap Definitions Outcomes Culture Models Performance
More informationUsing Asset Management Planning to Make Roadway Improvements
Using Asset Management Planning to Make Roadway Improvements 1 Presentation Overview Status of Municipal Infrastructure Asset Management 101 15+ Year Pavement Life cycle Data, M,R&R, Prediction Models,
More informationAsset Management. Linking Levels of Service and Lifecycle Management Strategies Andrew Grunda Peter Simcisko
Asset Management Linking Levels of Service and Lifecycle Management Strategies Andrew Grunda Peter Simcisko 1 Introduction Topics that we will address today Review of Ontario Regulation 588/17 Defining
More informationStrategy for the Development of Investment Decision-Making Framework for Road Asset Management for Queensland Department of Main Roads
Strategy for the Development of Investment Decision-Making Framework for Road Asset Management for Queensland Department of Main Roads By: Noppadol Piyatrapoomi, Arun Kumar, Neil Robertson and Justin Weligamage
More informationMemorandum. CITY OF DALLAS (Report No. A15-008) June 19, 2015
Memorandum CITY OF DALLAS (Report No. A15-008) DATE: June 19, 2015 TO: SUBJECT: Honorable Mayor and Members of the City Council Audit of the Paving and Maintenance Program / Capital Program 1 The Department
More informationHIGHWAY PROGRAMING, INFORMATION MANAGEMENT EVALUATION METHODS
HIGHWAY PROGRAMING, INFORMATION MANAGEMENT EVALUATION METHODS Kumares C. Sinha, Purdue University Cf. Enhancing Highway Safety Through Engineering Management, Transportation Research Board, Final Report
More informationFINAL REPORT FHWA/IN/JTRP-2004/34 AN EVALUATION OF THE COST-EFFECTIVENESS OF WARRANTY CONTRACTS IN INDIANA. Priyanka Singh Graduate Research Assistant
FINAL REPORT FHWA/IN/JTRP-2004/34 AN EVALUATION OF THE COST-EFFECTIVENESS OF WARRANTY CONTRACTS IN INDIANA By Priyanka Singh Graduate Research Assistant Samuel Labi Visiting Assistant Professor Bob G.
More informationTransportation Economics and Decision Making. Lecture-11
Transportation Economics and Decision Making Lecture- Multicriteria Decision Making Decision criteria can have multiple dimensions Dollars Number of crashes Acres of land, etc. All criteria are not of
More informationAnalysis of TAMC Investment Reporting Data for Network Level Modeling on the Locally Owned Road System in Michigan
Analysis of TAMC Investment Reporting Data for Network Level Modeling on the Locally Owned Road System in Michigan Photo Credit: Alcona County Road Commission Andy Manty, PE, Research Engineer Center for
More informationMarkov Decision Processes for Road Maintenance Optimisation
Markov Decision Processes for Road Maintenance Optimisation This paper primarily focuses on finding a policy for maintaining a road segment. The paper presents two methods for finding such a policy. The
More informationCity of Grand Forks Staff Report
City of Grand Forks Staff Report Committee of the Whole November 28, 2016 City Council December 5, 2016 Agenda Item: Federal Transportation Funding Request Urban Roads Program Submitted by: Engineering
More informationA Linear Programming Approach for Optimum Project Scheduling Taking Into Account Overhead Expenses and Tardiness Penalty Function
A Linear Programming Approach for Optimum Project Scheduling Taking Into Account Overhead Expenses and Tardiness Penalty Function Mohammed Woyeso Geda, Industrial Engineering Department Ethiopian Institute
More informationRevenue Sharing Program Guidelines
Revenue Sharing Program Guidelines For further information, contact Local VDOT Manager or Local Assistance Division Virginia Department of Transportation 1401 East Broad Street Richmond, Virginia 23219
More informationLecture 3: Factor models in modern portfolio choice
Lecture 3: Factor models in modern portfolio choice Prof. Massimo Guidolin Portfolio Management Spring 2016 Overview The inputs of portfolio problems Using the single index model Multi-index models Portfolio
More informationRevenue Sharing Program Guidelines
Revenue Sharing Program Guidelines For further information, contact Local VDOT Manager or Local Assistance Division Virginia Department of Transportation 1401 East Broad Street Richmond, Virginia 23219
More informationPredicting the Success of a Retirement Plan Based on Early Performance of Investments
Predicting the Success of a Retirement Plan Based on Early Performance of Investments CS229 Autumn 2010 Final Project Darrell Cain, AJ Minich Abstract Using historical data on the stock market, it is possible
More informationEssays on Some Combinatorial Optimization Problems with Interval Data
Essays on Some Combinatorial Optimization Problems with Interval Data a thesis submitted to the department of industrial engineering and the institute of engineering and sciences of bilkent university
More informationUniversity of Macau. Faculty of Social Sciences and Humanities. Department of Government and Public. Administration
University of Macau Faculty of Social Sciences and Humanities Department of Government and Public Administration World Financial Crisis and RMB Internationalization: A False or Real Historical Opportunity?
More informationThe Role of Cash Flow in Financial Early Warning of Agricultural Enterprises Based on Logistic Model
IOP Conference Series: Earth and Environmental Science PAPER OPEN ACCESS The Role of Cash Flow in Financial Early Warning of Agricultural Enterprises Based on Logistic Model To cite this article: Fengru
More informationThe City of Owen Sound Asset Management Plan
The City of Owen Sound Asset Management Plan December 013 Adopted by Council March 4, 014 TABLE OF CONTENTS 1 EXECUTIVE SUMMARY... 1 INTRODUCTION....1 Vision.... What is Asset Management?....3 Link to
More informationHDM-4 Applications. Project Appraisal. Project Formulation. Maintenance Policy Optimization. Road Works Programming. Network Strategic Analysis
HDM-4 Applications HDM-4 Applications Project Appraisal Project Formulation Maintenance Policy Optimization Road Works Programming Network Strategic Analysis Standards & Policies 2 Project Appraisal Concerned
More informationResearch Paper. Statistics An Application of Stochastic Modelling to Ncd System of General Insurance Company. Jugal Gogoi Navajyoti Tamuli
Research Paper Statistics An Application of Stochastic Modelling to Ncd System of General Insurance Company Jugal Gogoi Navajyoti Tamuli Department of Mathematics, Dibrugarh University, Dibrugarh-786004,
More informationAn Analysis of Sensitivity in Economic Forecasting for Pavement Management Systems
Utah State University DigitalCommons@USU All Graduate Theses and Dissertations Graduate Studies 5-2015 An Analysis of Sensitivity in Economic Forecasting for Pavement Management Systems Antonio Fuentes
More informationNew-Generation, Life-Cycle Asset Management Tools
New-Generation, Life-Cycle Asset Management Tools Dr.-Ing. Robert Stein Executive Partner: Prof. Dr.-Ing. Stein & Partner GmbH and S & P Consult GmbH Company Background 1 Prof. Dr.-Ing. Stein & Partner
More informationIncorporating Variability into Life Cycle Cost Analysis and Pay Factors for Performance-Based Specifications
Incorporating Variability into Life Cycle Cost Analysis and Pay Factors for Performance-Based Specifications Leanne Whiteley, BASc. MASc Candidate Susan Tighe, Ph.D., P.Eng. Canada Research Chair in Pavement
More informationRisk Based Inspection Planning for Ship Structures Using a Decision Tree Method
TECHNICAL PAPER Risk Based Inspection Planning for Ship Structures Using a Decision Tree Method Dianqing Li, Shengkun Zhang, Wenyong Tang ABSTRACT A theoretical framework of risk-based inspection and repair
More informationCHAPTER 13: A PROFIT MAXIMIZING HARVEST SCHEDULING MODEL
CHAPTER 1: A PROFIT MAXIMIZING HARVEST SCHEDULING MODEL The previous chapter introduced harvest scheduling with a model that minimized the cost of meeting certain harvest targets. These harvest targets
More informationTHE ECONOMICS OF PREVENTIVE MAINTENANCE
THE ECONOMICS OF PREVENTIVE MAINTENANCE C lyde B urke Vice President Roy Jorgensen Associates, Inc. Gaithersburg, Maryland H O W M U C H P R E V E N T IV E M A IN T E N A N C E? How do we know when we
More informationFramework and Methods for Infrastructure Management. Samer Madanat UC Berkeley NAS Infrastructure Management Conference, September 2005
Framework and Methods for Infrastructure Management Samer Madanat UC Berkeley NAS Infrastructure Management Conference, September 2005 Outline 1. Background: Infrastructure Management 2. Flowchart for
More informationCity of La Habra Heights. Benefit Assessment Districts. June 6, Presented by Pablo Perez, Director
City of La Habra Heights Benefit Assessment Districts June 6, 2016 Presented by Pablo Perez, Director 1 BENEFIT ASSESSMENT DISTRICTS Proposition 218 Defines Special Benefit as Particular and distinct benefit
More informationCopyright is owned by the Author of the thesis. Permission is given for a copy to be downloaded by an individual for the purpose of research and
Copyright is owned by the Author of the thesis. Permission is given for a copy to be downloaded by an individual for the purpose of research and private study only. The thesis may not be reproduced elsewhere
More informationProjected Funding & Highway Conditions
Projected Funding & Highway Conditions Area Commission on Transportation Gary Farnsworth ODOT Interim Region 4 Manager March, 2011 Overview ODOT is facing funding reductions that will require new strategies
More informationThe Consistency between Analysts Earnings Forecast Errors and Recommendations
The Consistency between Analysts Earnings Forecast Errors and Recommendations by Lei Wang Applied Economics Bachelor, United International College (2013) and Yao Liu Bachelor of Business Administration,
More informationGLOSSARY. At-Grade Crossing: Intersection of two roadways or a highway and a railroad at the same grade.
Glossary GLOSSARY Advanced Construction (AC): Authorization of Advanced Construction (AC) is a procedure that allows the State to designate a project as eligible for future federal funds while proceeding
More informationLOCAL MAJOR BRIDGE PROGRAM
LOCAL MAJOR BRIDGE PROGRAM The Local Major Bridge Program provides federal funds to counties and municipal corporations for bridge replacement or bridge major rehabilitation projects. A Local Major Bridge
More informationU.P.B. Sci. Bull., Series D, Vol. 77, Iss. 2, 2015 ISSN
U.P.B. Sci. Bull., Series D, Vol. 77, Iss. 2, 2015 ISSN 1454-2358 A DETERMINISTIC INVENTORY MODEL WITH WEIBULL DETERIORATION RATE UNDER TRADE CREDIT PERIOD IN DEMAND DECLINING MARKET AND ALLOWABLE SHORTAGE
More informationPresents Interstate (IM) and Non-Interstate (FM) Pavement Maintenance Programs
Presents Interstate (IM) and Non-Interstate (FM) Pavement Maintenance Programs Mark Waits Assistant Maintenance Bureau Chief Roadway Section Pavement Preservation Training Regional Pavement Preservation
More informationPolicy modeling: Definition, classification and evaluation
Available online at www.sciencedirect.com Journal of Policy Modeling 33 (2011) 523 536 Policy modeling: Definition, classification and evaluation Mario Arturo Ruiz Estrada Faculty of Economics and Administration
More informationAnalyzing Decision Making in Alternative Contracting for Highway Pavement Rehabilitation Projects
Florida International University FIU Digital Commons FIU Electronic Theses and Dissertations University Graduate School 6-10-2016 Analyzing Decision Making in Alternative Contracting for Highway Pavement
More informationMPO Staff Report Technical Advisory Committee: April 8, 2015 MPO Executive Board: April 15, 2015
MPO Staff Report Technical Advisory Committee: April 8, 2015 MPO Executive Board: April 15, 2015 RECOMMENDED ACTION: Approve the Final. RECOMMENDED ACTION from TAC: Accept the Final and include the NDDOT
More informationProject Selection Risk
Project Selection Risk As explained above, the types of risk addressed by project planning and project execution are primarily cost risks, schedule risks, and risks related to achieving the deliverables
More informationAn Application of Mathematical Model to Time-cost Trade off Problem (Case Study)
Australian Journal of Basic and Applied Sciences, 5(7): 208-214, 2011 ISSN 1991-8178 An Application of Mathematical Model to Time-cost Trade off Problem (ase Study) 1 Amin Zeinalzadeh 1 Tabriz Branch,
More informationPavement Preservation
Road Foreman Meeting West Windsor, Vermont March 24, 2015 Dan Patenaude, P.E. Hometown: Chester, VT Pavement Preservation Your Key to Pavement Management Success Since 1957 Corporate Headquarters Braintree,
More informationDevelopment Practices for Municipal Pavement Management Systems Application
Development Practices for Municipal Pavement Management Systems Application Mehran Kafi Farashah, MASc., EIT, University of Waterloo Dr. Susan L. Tighe, PhD, PEng, University of Waterloo Paper prepared
More information