Optimization of Pavement Maintenance Planning in Cambodia Using a Probabilistic Model and Genetic Algorithm

Purpose: Systematic pavement management plays a vital role in the better Road Asset Management system. The lifespan of pavement can be preserved and prolonged by adequate maintenance measures in proper time. By early detection and repair of defects at initial stages with predictive maintenance reduces the rapid deterioration of the pavement. Moreover, inaccuracy in pavement deterioration prediction leads to an improper maintenance with wastage of time, cost and labor strength. To overcome all of these obstacles, this paper attempts an approach on optimal pavement maintenance strategy by means of fuzzy rule-based system. Materials and Methods: In this research, four pavement indicators of PCI, IRI, PSR and PSI have been taken into account as antecedents and five different types of pavement maintenance as consequents. In this research, ASTM D-6433, Road Lab Pro software, AASHTO guidelines and Fuzzy Logic (Mamdani) model have been used to evaluate the current pavement condition and optimal pavement maintenance strategies. Findings: According to research area, 130 total pavement sample units were undertaken to study for the mean pavement condition of PCI value 54.93 (poor), IRI 7.01 (fair), PSR 3.33 (fair) and PSI 3.35 (fair). Due to 13 years-old life span of traffic volume, temperature and other external factors, this expressway is suffering from potholes, bleeding, longitudinal and transverse cracking, weathering, joint reflection cracking for Asphalt Concrete (AC) overlay and linear cracking, joint seal failure, scaling, faulting, depression, lane/shoulder drop off for existing concrete pavement. According to the fuzzy logic model results, 42 numbers of fuzzy If-Then maintenance rules were verified as the most relevant pavement maintenance and rehabilitation strategies for Yangon-Mandalay Expressway. Implications to Theory, Practice and Policy: This research provide implications to the study and contributions to theory, practice and policy. This research paper insights into effective infrastructure maintenance and investment, emphasizing the role of advanced computational techniques in transportation economics. The key advantage of this research paper is to be effective, supportive, easy and accessible decision-making tool for transportation and pavement engineers, expressway planners and highways department authorities especially from developing countries.

Angular fuzzy logic application for pavement maintenance and rehabilitation strategy in Ohio

The pavement maintenance and rehabilitation (M&R) strategy selection problem is an exceedingly hard problem to solve optimally. In this paper, a novel Adaptive Hybrid Genetic Algorithm (AHGA) is proposed which incorporates Local Search (LS) techniques into Genetic Algorithms (GA) to improve the overall efficiency and effectiveness of the search. Specifically, it contains two dynamic learning mechanisms to guide and combine the exploration and exploitation search processes adaptively. The first learning mechanism aims to assess the worthiness of conducting an LS reactively and to control the computational resources allocated to the application of this search technique efficiently. The second learning mechanism uses instantaneously learned probabilities to select from a set of pre-defined LS operators which compete against each other for selection which is the most appropriate at any particular stage of the search to take over from the evolutionary-based search process. The new AHGA is compared to a non-hybridized version of the GA by applying the algorithms to several case studies in order to determine the best pavement M&R strategy that minimizes the present value of the total M&R costs. The results show that the proposed AHGA statistically outperforms the traditional GA in terms of efficiency.

To provide a low-carbon economy maintenance strategy is the most challenging problem faced by pavement management authorities under the restricted budget and significant environmental repercussions. The development of a multi-objective optimization model for pavement maintenance decision making is essential to formulate pavements. Nevertheless, the existing automatic detection can only recognize and classify pavement distress. However, few studies are able to accurately determine the precise dimensions of specific distresses such as cracks and potholes, especially combined with the actual size of the image. This limitation hinders the ability to provide specific maintenance recommendations and make optimal maintenance decisions. Therefore, this paper develops a comprehensive and effective multi-objective decision-making framework for pavement maintenance. This framework consists of four distinct components: (1) recognizing the dimensions of pavement distresses based on the pavement image segmentation technique; (2) compiling a list of viable pavement maintenance strategies; (3) assessing the costs and carbon emissions of these strategies; and (4) optimizing decisions on pavement maintenance. We used the U-Net algorithm to accurately recognize the dimensions of pavement distresses, while an improved entropy-weighted TOPSIS model was proposed to determine the optimal pavement maintenance strategy with the lowest cost and carbon emissions. The results indicated that the pavement distress dimension recognition model achieved a high accuracy of 96.88%, and the TOPSIS model identified the optimal maintenance strategy with a score of 99.16. This maintenance strategy achieved a substantial reduction of 30.80% in carbon emissions and a cost reduction of 20.81% compared to the highest values among all maintenance strategies. This study not only provides a scientifically objective method for making pavement maintenance decisions but also offers specific, quantifiable maintenance programs, marking a stride towards more environmentally friendly and cost-effective road maintenance. It also contributes to the sustainability of pavement maintenance.

This paper presents a new pavement management system (PMS) to achieve the optimal pavement maintenance and rehabilitation (M&R) strategy for a highway network using genetic algorithms (GAs). Optimal M&R strategy is a set of pavement activities that both minimise the maintenance cost of a highway network and maximise the pavement condition of the road sections on the network during a certain planning period. NSGA-II, a multi-objective GA, is employed to perform pavement maintenance optimisation because of its robust search capabilities and constraint handling method that deal with the multi-objective and multi-constrained optimisation problems. In the proposed approach, both deterministic and probabilistic pavement age gain models are utilised for evaluating the evolution of pavement condition over time because of their simplicity of application. The proposed PMS is applied to a case study network that consists of different kinds of road sections. The results obtained indicate that the model is a valuable toolbox for pavement engineers.

Joint optimization of pavement design, resurfacing and maintenance strategies with history-dependent deterioration models

Knowledge acquisition and representation model for developing an expert system for pavement maintenance and REhabilitation strategy in the State of Ohio (ESPRESSO)

Road network serves as backbone of country as it provides support for development and growth of nation. If road condition is good and network is adequate it enables quick, safe and comfortable movement for goods and passengers between two places. Pavement maintenance plays quite important role in upkeeping service life of any road. After the introduction of PradhanMantri Gram SadakYojna significant improvement is there in village road development. There is need for maintenance system for these PMGSY roads as no systematical maintenance practices are followed by concerned agencies for these low volume roads. Pavement maintenance system comprises of inspecting pavement condition, rating pavement condition, prioritizing road network and optimizing various pavement maintenance strategies in systematic manner. First of all, data is collected from field by conducting various experiments regarding pavement condition and entered into database. This collected data is analyzed according to codal guidelines and based on that various maintenance strategies are optimized. Soft computingbased Pavement maintenance system would be more beneficial rather than conventional pavement maintenance system due to budgetary constraints of various government road agencies. Pavement maintenance system would be helpful in achieving maximum benefits under allocated budget. Road network prioritization and optimization of alternative maintenance strategies would be helpful in awarding contracts and arranging labors, materials, equipments for small scale pavement maintenance projects.

The first ultra-thin whitetopping (UTW) project in the United States on a general aviation (GA) airport runway was built at the Savannah-Hardin County Airport (SNH) in Tennessee. The asphalt concrete (AC) surface at SNH Runway 18-36 was aged and had significant amounts of cracking. Existing surface conditions were assessed using the pavement condition index procedure and pavement structural capacity was estimated using the falling weight deflectometer and dynamic cone penetrometer; insitu and laboratory CBR tests were also conducted. Current pavement conditions, structural capacity, and subsurface soil conditions were evaluated to determine pavement design parameters. UTW deflection and bending stresses (estimated using 2-D ILLI-SLAB code), because of FAA C-2 classification aircraft, were used in the Portland Cement Association fatigue law to determine the allowable number of load repetitions. A 100-mm thick UTW on top of the existing AC surface with a 1220-mm joint spacing was recommended. Strain gages were installed near the surface and bottom of the Portland cement concrete (PCC), and on the top of the milled AC surface for performance monitoring by determining if the UTW is completely bonded, completely unbonded or partially bonded to the milled AC surface. Strains were measured in the winter and summer seasons during both morning and afternoon hours using a loaded dump truck. A paper presented at the 2001 ASCE Airfield Pavement Specialty Conference in Chicago, Illinois, described the UTW design process and the results of the field-testing conducted during the summer months. This paper provides results of winter and summer testing and compares the summer and winter test results. Analysis of both summer and winter field test data indicated that the UTW is performing satisfactorily and is adequately bonded to the existing AC surface.

The goal of this paper is to summarize some practical issues of date research concerning the development and implementation of the risk management application Risk3M for Pavement Maintenance and Management System (PMMS). The paper also illustrates the need for potential development of such a risk system for analysis and risk factors and mitigation in pavement maintenance and rehabilitation strategies. We use a particular implementation of the k-nearest neighbors (k-NN) algorithm as a non parametric alternative method for reference class forecasting, based on mathematical kernel density estimate.

A pavement management system should help a decision maker to select the best preservation program, decide which preservation treatment to use, and where and when to apply it to maximize the use of the available resources. One of the essential roles of pavement management is to provide a rational, cost-effective optimal funding planning and allocation strategy for highway agencies. Researchers have previously developed deterministic optimization methods for programming pavement maintenance and rehabilitation strategies. However, pavement infrastructure deterioration is a dynamic, complicated, and stochastic process affected by a variety of factors such as traffic loading, environmental conditions, and structural capacities, as well as certain unobserved factors. Ignoring these fundamental characteristics may limit the usefulness of an optimal solution. To take the uncertainties into consideration, some researchers have introduced stochastic programming techniques into pavement maintenance management. However, difficulties in characterizing the distribution of data and the substantial computational challenge have compromised the practical application of those techniques. A project-level robust optimization method for maintenance budget planning to overcome these difficulties is presented. The solutions from this proposed method are computationally tractable and not overly sensitive to any specific realization of the uncertainties. An application of this method is demonstrated by using long-term pavement performance data collected during the past 20 years, yielding promising preliminary results.

Pavement maintenance and repair (M&R) is an important social need that should be accessible to all regardless of social or economic circumstances. In this project, pavement condition is analyzed through the use of machine learning algorithms and then compared to the socioeconomic factors of the surrounding communities. This analysis is crucial for assessing inequities that disadvantaged communities often face, especially pertinent toward pavement M&R policies. In addition to the goal of determining equity, a secondary goal of this project was to develop a methodology that can be repeated across any city. Utilizing an image-set provided by Google Street-View's API and census data of Kansas City, Missouri, a comparative analysis was conducted to determine whether equity between advantaged and disadvantaged groups was present. To aid in pavement distress identification, YOLOv5 (You only look once), a popular deep learning algorithm, was used to identify seven unique pavement distresses across Kansas City Road segments. The resulting methods were able to demonstrate a comparison between pavement conditions and socioeconomic metrics, demonstrating a trend indicating road segments in disadvantaged communities show slightly worse conditions. The strongest correlative factor borne out of analysis shows that median household income demonstrates the greatest gap between advantaged and disadvantaged census blocks.

The problem of determining optimal pavement maintenance and rehabilitation strategies is a special case of a more general problem termed the asset depreciation problem. Perhaps the most general formulation and solution of the asset depreciation problem is the semi-Markov formulation. This paper illustrates how the semi-Markov formulation and solution of the general asset depreciation problem can be applied to pavements. The semi-Markov formulation, like the Markov formulation, characterizes pavement deterioration probabilistically and represents human intervention (maintenance and rehabilitation) as slowing or modifying the basic probabilities of deterioration. The Markov formulation, first implemented for the state of Arizona, is shown to be a special case of the more general, less computationally intensive semi-Markov formulation. The application of the semi-Markov formulation is illustrated at the project level for a heavy-duty pavement in Manitoba. Key words: asset depreciation, infrastructure management, pavement management, probabilistic modelling, Markov, semi-Markov, maintenance optimization, project level.

Performance-based maintenance contracts differ significantly from material and method-based contracts that have been traditionally used to maintain roads. Road agencies around the world have moved towards a performance-based contract approach because it offers several advantages like cost saving, better budgeting certainty, better customer satisfaction with better road services and conditions. Payments for the maintenance of road are explicitly linked to the contractor successfully meeting certain clearly defined minimum performance indicators in these contracts. Quantitative evaluation of the cost of performance-based contracts has several difficulties due to the complexity of the pavement deterioration process. Based on a probabilistic analysis of failures of achieving multiple performance criteria over the length of the contract period, an effort has been made to develop a model that is capable of estimating the cost of these performance-based contracts. One of the essential functions of such model is to predict performance of the pavement as accurately as possible. Prediction of future degradation of pavement is done using Markov Chain Process, which requires estimating transition probabilities from previous deterioration rate for similar pavements. Transition probabilities were derived using historical pavement condition rating data, both for predicting pavement deterioration when there is no maintenance, and for predicting pavement improvement when maintenance activities are performed. A methodological framework has been developed to estimate the cost of maintaining road based on multiple performance criteria such as crack, rut and, roughness. The application of the developed model has been demonstrated via a real case study of Miami Dade Expressways (MDX) using pavement condition rating data from Florida Department of Transportation (FDOT) for a typical performance-based asphalt pavement maintenance contract. Results indicated that the pavement performance model developed could predict the pavement deterioration quite accurately. Sensitivity analysis performed shows that the model is very responsive to even slight changes in pavement deterioration rate and performance constraints. It is expected that the use of this model will assist the highway agencies and contractors in arriving at a fair contract value for executing long term performance-based pavement maintenance works.

In order to ensure that pavement investment decisions are made in an optimum manner, the State highway agencies (SHAs) are continually examining their pavement maintenance, rehabilitation, and reconstruction (MRR) strategies and their repair and rehabilitation (R&R) techniques to maintain and preserve the nation’s highways. As the economy has grown in recent years, highways in the United States are being subjected to more and more vehicles with heavy loads. This effect along with the variety of climatic conditions has caused the vast network of the nation’s highways to deteriorate in a number of different ways. Consequently, current decisions associated with the repair of this deterioration have long reaching implications for many future generations. With respect to portland cement concrete (PCC) pavements, the state of practice of MRR is good, but it can be better. In the last 10 to 15 years, significant improvements have been made in our understanding of PCC pavement failure mechanisms, pavement condition evaluation techniques, repair materials, equipment, and techniques for MRR. The challenge is how to use this information to preserve the highway investment that has been made over the years while maintaining pavement structures in a serviceable condition. These and other factors have caused SHAs greater concern regarding the effectiveness of their maintenance, rehabilitation, and reconstruction (MRR) strategies and their repair and rehabilitation (R&R) treatments in maintaining and preserving the nation’s highways. Consequently, techniques are sought after in repair treatment selection that will enhance the effectiveness of strategies for maintenance, rehabilitation, and reconstruction of concrete pavements and to develop better decision-making processes to select MRR strategies and R&R treatments. These types of devices will help highway agencies and engineers select treatments that will ensure expected performance.