Search

Although some parameters are uncertain in pavement Maintenance and Rehabilitation (M&R) planning, most of the literature has considered these parameters deterministic. Likewise, minimizing risk has been an immense concern as a significant amount of money should be spent on the M&R of pavement networks. To this end, this study aims to propose a new approach to consider risk and uncertainty in a pavement M&R optimization problem. A Multistage Stochastic Integer Programming including risk constraints is applied for the problem modeling. Conditional Value at Risk index is applied to minimize the mentioned variables and avoid unacceptable risks. Subsequently, a case study is used to analyze the proposed technique's effectiveness. The results indicate that the total pavement M&R agency cost equals 6.33 billion Tomans considering a confidence level of 95%. Furthermore, the proposed approach can detect the optimal value of the annual budget for pavement network M&R, which is 1.96 billion Tomans for the case study. Applying preventive maintenance can compensate for the negative effects of risk on projects. Therefore, the proposed approach can help decision-makers, policymakers, and transport agencies to analyze and control risk in the pavement M&R optimization problems and determine the optimal M&R budget. [ABSTRACT FROM AUTHOR]

The sustainability of pavement, especially in Maintenance and Rehabilitation (M&R) scheduling, has become an immense concern and has received limited attention in previous studies. Therefore, this study aimed to develop the M&R scheduling optimization based on sustainability. To this end, a novel sustainability index was introduced, in which all the sustainable development aspects were considered, including highway agency cost, environmental impacts, and social effects. A conventional model was used to assess the sustainable model's effectiveness. Two new constraints are introduced to reduce the budget fluctuation and not to apply the M&R treatments for two consecutive years to make the model practical. On the other hand, highway agencies face large-scale networks, in which the optimization of M&R scheduling has computational complexities. Thus, the novel and powerful metaheuristic algorithm, named Marine Predator Algorithm (MPA), was applied to solve the pavement M&R scheduling problem. A large-scale pavement network, including 110 sections, was analyzed over a 5-year plan as the case study. The results indicated that using the sustainable model rather than the conventional one leads to a 6.5% reduction in CO2 emission. Besides, utilizing the sustainable approach enhances the equity and safety indices by 40.7% and 2.5% compared to the conventional treatment. However, the highway agency cost is increased by 1.1% using the sustainable model. [ABSTRACT FROM AUTHOR]

Pavements often are divided into short segments for data collection, analysis, and management in practice. However, in pavement maintenance and rehabilitation (M&R) planning, it is impractical and uneconomical to create M&R projects based on such short segments. Road agencies generally group consecutive pavement segments to facilitate larger projects. However, the grouping is not easy, especially for large networks, and inappropriate grouping might result in resource wastage and ineffective M&R plans. Thus, an advanced and effective grouping method is required for decision makers when making M&R plans. This study focused on grouping consecutive pavement segments in the context of network-level multiyear M&R planning and developed a multiobjective optimization (MOO)-based grouping method. The MOO model was established with three conflicting objectives: minimizing the total agency costs, minimizing the total road user costs, and maximizing the network pavement performance, subject to the constraints of annual budgets, individual and network pavement performance, and the minimal and maximal length required for a M&R project. The proposed MOO-based grouping method was tested and compared with the clustering-based grouping method and no grouping method using a real road network–based case study in the context of a 5-year pavement M&R plan. The results showed that the proposed method can help decision-makers effectively conduct segment grouping and generate cost-effective solutions when conducting pavement M&R planning at the multiyear network level. [ABSTRACT FROM AUTHOR]

One of the key objectives of a pavement management system (PMS) is to find the optimal maintenance strategy, which minimises the sum of a road agency and user costs by maximising the net benefit to society. The PMS helps road agencies to better apply a selection of the right pavement treatments. The objective of the present paper is to show how the HDM-4 (Highway Development and Management 4) approach allows to develop a policy road leading to the optimal and robust selection of maintenance strategies under budgetary constraints, which minimises the sum of agency costs and road user costs in present value or maximises the net benefit to society over an analysis period. The findings could help road agencies to better apply the right pavement treatments. The present study is based on the exploitation of road network data, obtained from pavement structural condition survey and performed by the National Studies and Road Research Center. The study applies a long-term simulation (20 years) of maintenance budget needs under different budget scenarios. [ABSTRACT FROM AUTHOR]

Road agencies employ different criteria and methods to compare treatment alternatives and develop maintenance and rehabilitation (M&R) programmes for their networks. The maximisation of benefits has been integrated in several leading pavement management systems and used for network-level programming since the 1980s. The approach defines benefit as the area between the performance curves with and without M&R actions based on an aggregated index, representing the overall pavement condition. Further simplifications like the screening of solutions using an efficiency frontier and the incremental benefit–cost technique have made it possible to apply the method with the available computing power at the time. Based on a case study of 1000 road sections, this paper analyses the effect of benefit maximisation on annual budget, network condition, treatment selection, trigger values and remaining life. M&R programmes minimising costs and maximising benefits are compared at the project and network level for various budget scenarios. The results show that the maximisation of benefits based on an aggregated condition index leads to substantially higher agency costs and favours the selection of expensive treatments with earlier timing, irrespective of actual failure causes. The conclusions of this work may be useful to road agencies in developing more efficient budget-allocation polices. [ABSTRACT FROM AUTHOR]

Worldwide, more than 1.25 million people die annually in road traffic accidents and between 20 and 50 million more are injured. By 2030, highwayrelated crashes are projected to be the 5th leading cause of death in the world. Road accidents have several contributing factors, including roadway conditions, vehicle conditions, and factors related to the road users. While some of these factors have been studied extensively by researchers very few focused on quantifying the relationship between accidents frequency and pavement quality. Before 1990s, due to the lack of pavement data collection technology, it was very difficult to carry out state-wide scale studies relating pavement quality and road safety. However, in the past decades, there has been a huge growth and awareness in the importance of road safety, leading to a significant increase of research in the topic. Researchers start ed to study other contributing factors to accidents occurrence and due to the development of high-speed friction measurement tools, agencies can now include friction into network level Pavement Management Systems (PMSs). The objective of this article is to contribute to the incorporation of safety concerns into Pavement Management by performing an exploratory analysis to assess the impact in the agency, user and total costs of performing two different maint enance policies. The methodology consisted in evaluating the evolution of the condition of the road pavements using pavement performance prediction models, followed by the prediction of the expected number of accidents and, finally the calculation of the agency and user costs. The results obtained provided a strong starting point to understand the requirements to solve this type of problems that not only affect road agencies but also the society in general. [ABSTRACT FROM AUTHOR]

Highway maintenance and rehabilitation (M&R) planning is a multifaceted decision-making process that requires the consideration of multiple but often conflicting objectives. Traditionally, state highway agencies (SHAs) have focused on maximizing the condition of a pavement network or minimizing agency costs needed to keep the network at an acceptable physical and functional level of service. However, other objectives like minimizing the road user costs and greenhouse gas (GHG) emissions are seldom factored into the M&R decision-making process. In this paper, a mixed integer program that accounts for these three objectives is presented. The proposed model is then implemented on a subset network in Texas consisting of 100 pavement sections. The results demonstrate the Pareto-optimal relationships that exist between the road condition improvements, road user costs, and GHG emissions. Notably, there is a positive relationship between condition score improvements and the observed marginal GHG emissions from the case network implementation. Furthermore, this network's results indicate that GHG emissions from materials and construction contribute to the most significant proportion of the total estimated GHG emissions. The results of the sensitivity analysis confirm the observed trends in the network case examined and provide valuable insights concerning the impact of variability in the budget size. The principal contribution of this study is in providing an approach for highway decision-makers to perform trade-offs in road condition improvements, road user costs, and GHG emissions of alternate pavement M&R programs or policies. [ABSTRACT FROM AUTHOR]

Process of sustainable pavement maintenance and rehabilitation (M&R) management can be conducted using various technological and organizational systems. Such systems must meet the implementation conditions of a given project involving pavement M&R, and trade-offs between economic, environmental, and social impacts from various aspects involved. Hence, this study examines a systematic framework for large-scale pavement network decision-making considering sustainability dimensions, constructs a multi-stage decision model considering different modes and options using attribute mapping techniques, and develops a decision support system that (1) determines comprehensive sustainability drivers and quantification methods in the field of pavement management to evaluate the sustainability characteristics of predetermined M&R strategies; and (2) allows the execution of decision space dimensionality reduction process for any given network scales and M&R techniques dimensions considering spatial-temporal distribution, then generate a specific set of sustainable budget allocation and M&R strategy combination scenarios using a redesigned multi-objective optimization model, which can coordinate segment-level and network-level management. A case study was conducted to demonstrate the application of the proposed system. Results show that the approach can significantly reduce the computational cost compared to the scenario without dimensionality reduction, and also facilitates the improvement of agency costs, environmental impacts, as well as user costs from the life cycle perspective. Furthermore, the balance of M&R budget allocation depends on the mapped attributes selected in the dimensionality reduction process, the sustainability and reliability of the decision results can be improved by selecting as many attributes as possible while satisfying the pavement management system. The study provides an exciting opportunity to support decision-making for large-scale transportation infrastructure management. [ABSTRACT FROM AUTHOR]

This paper presents the development and implementation of a Multi-Objective Decision-Aid Tool (MODAT) tested with data from the Estradas de Portugal's Pavement Management System. The MODAT uses a multi-objective deterministic section-linked optimisation model with different possible goals: minimisation of agency costs, minimisation of user costs, maximisation of the residual value of pavements, etc. The MODAT also uses the deterministic pavement performance model used in the AASHTO flexible pavement design method. The application of the new Decision-Aid Tool is illustrated with a case study involving the main road network of Castelo Branco, a district of Portugal. In this application, the Knee point, that represents the most interesting solution of the Pareto frontier, corresponds to an agency costs weight value of 4% and an user costs weight value of 96%, demonstrating that user costs, which are generally much higher than agency costs, dominate the decision process. [ABSTRACT FROM PUBLISHER]

In pavement management systems, it is beneficial to consider the economies of scale stemming from the synchronization of repairs conducted on neighboring sections within a single work zone. However, finding the globally optimal solution of the repair and work zone policy for a large-scale pavement network along a long-term planning horizon can be computationally cumbersome. In this study, as a benchmark, we first propose an exact solution algorithm based on dynamic programming. Then we second propose a computationally feasible methodology, a time-invariant simplified rule, to determine desirable (near-optimal) policies. The proposed methodology is applied to two numerical studies: (i) Case 1 for a small-scale road pavement system to compare life cycle costs and computational times between the rule-based methodology and the exact solution algorithm, and (ii) Case 2 for a real-scale road pavement system to discuss the effectiveness of the rule-based methodology. In Case 1, the rule-based methodology derives a near-optimal solution with a significantly shorter computational time than the exact solution algorithm. Case 2 shows that the rule-based methodology can find a superior policy to the aggregation of the optimal solutions independently found for each of decomposed sub-systems in a feasible computational time. Through sensitivity analyses, we find that the repair and work zone policies should vary depending on the deterioration process, cost factors, and weight between agency and user costs for society's view or available budget for the agency's perspective. • Jointly optimize long-term pavement repair and work zone policies. • Consider the economies of scale stemming from the synchronization of repairs. • Develop optimal solution and rule-based approximation. • Apply the proposed methods to two numerical studies for validation. • Derive practical insights through sensitivity analyses. [ABSTRACT FROM AUTHOR]

Sustainable development requires decision-making systems based on sustainability criteria and overpassing just economic and financial criteria. Integration of life-cycle cost analysis (LCCA) and life-cycle assessment (LCA) to select pavement alternative is a solution to address economic and environmental issues simultaneously. While most of current methods related to sustainable design of pavements consider environmental parameters as economic factors i.e. cost, this paper takes the two criteria of carbon emissions and energy consumption into account. Moreover the presented method considers the embedded managerial flexibilities which are available due to existing uncertainties in pavement project valuation procedure. Agency costs, user costs, carbon emissions, and energy consumption are considered as the main criteria, and Monte Carlo simulation (MCS) technique is used with a recursive procedure for analyzing flexibility in each time step of pavement life-cycle. For making the best decision in each time step and the best overall alternative, technique for order of preference by similarity to ideal solution (TOPSIS) is used, and the best alternatives are reported. The model is applied in a highway project in Iran to demonstrate its capabilities to analyze the sustainability of different asphalt and concrete alternatives for pavement projects. Various alternatives of the two pavement types are compared and it is clarified that the best asphalt alternative has lower life-cycle costs but more carbon emissions and much more energy consumption. Selection of concrete pavement instead of asphalt pavement in last decade in Iran, will approximately 35% increase related costs but it will result in about 2,000,000 tons of carbon emissions reduction and 700,000 GJ reduction in energy consumption annually. • An integrating LCA and LCCA model is proposed for sustainable pavement selection. • Recursive dynamic programming method is used for consideration of managerial flexibilities. • TOPSIS is used for multi criteria decision-making with preferred weights. • Cement pavements have 12% less environmental effects than Asphalt pavements. • Cement pavement have 55% less energy consumption than Asphalt pavements. [ABSTRACT FROM AUTHOR]