Optimizing Expert-Based Decision-Making of Pavement Maintenance using Artificial Neural Networks with Pattern-Recognition Algorithms

Hafez, Marwan; Ksaibati, Khaled; Atadero, Rebecca A.

doi:10.1177/0361198119851085

Cited by 20 publications

(5 citation statements)

References 22 publications

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“…Hafez et al [42] selected the appropriate maintenance and rehabilitation treatments for the low-volume routes utilizing MLPNN, which covered a range of treatment levels from waterproofing to stacking.…”

Section: Flexible Pavement Maintenancementioning

confidence: 99%

Artificial Neural Networks for Flexible Pavement

et al. 2023

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Transportation agencies are primarily responsible for building new roads and maintaining current roads. The main focuses of these agencies are to prioritize maintenance and make significant rehabilitation decisions to handle serious problems facing road authorities. Considerable efforts and an abundance of studies have been performed to determine the nature, mechanisms, test methods, and measurement of pavements for preservation and improvements of roadways. The presented study reports a state-of-the-art review on recent advances in the application of artificial intelligence in various steps of flexible pavement, including pavement construction, performance, cost, and maintenance. Herein, the challenges of gathering large amounts of data, parameter optimization, portability, and low-cost data annotating are discussed. According to the findings, it is suggested that greater attention should be paid to integrating multidisciplinary roadway engineering techniques to address existing challenges and opportunities in the future.

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Section: Flexible Pavement Maintenancementioning

confidence: 99%

Artificial Neural Networks for Flexible Pavement

et al. 2023

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“…With the help of programming tools, exact methods can fast analyze MOO problems and obtain real Pareto solutions. In addition, heuristics may need parameter calibration or a significant amount of training data [46][47][48], which could be difficult for M&R decision-making especially when decision makers do not have enough knowledge about the addressed problems. Compared to heuristics, exact methods are easier for applications.…”

Section: Optimization Algorithmmentioning

confidence: 99%

Network-level pavement maintenance and rehabilitation decision-making with the optimized annual budget

Chen

2023

PLoS ONE

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Maintenance and rehabilitation (M&R) is necessary to keep pavement networks in good condition. Due to the capital intensity, M&R funding is always insufficient. The annual budget, determining the available funding, is a critical criterion when planning M&R treatments. However, its values are often given, and the determination of the values is seldom discussed. To fill the gap, this paper focuses on both the determination of annual budgets and the budget allocations, and therefore enhances the network-level decision-making on M&R by developing a Multi-Objective Optimization (MOO) method. This method does not only optimize and trade off the annual budgets and their consequences, but also allocates the funding across the entire network through generating the optimized M&R decisions. According to a case study with 50 segments, the developed method successfully and effectively identified non-linear discrete relationship between the minimized annual budgets and the maximized M&R benefits subject to all the constraints, and generated the optimized annual budget allocation for each M&R decision. The achievements of this paper can be used to enhance the efficiency of M&R decisions and contribute to informed pavement management.

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“…Han et al [40] presented a pavement maintenance strategy employing the fourth-order method of moments that considers the failure probability and reliability of pavement performance to minimize the pavement cost per unit time. Another different approach utilizing Artificial Neural Network (ANN) with a pattern-recognition algorithm was performed by Hafez et al [41] to develop a decision-making model in specifying the optimum pavement maintenance and rehabilitation alternatives at a road network level based on drivability life (DL) of pavement condition.…”

Section: Literature Reviewmentioning

confidence: 99%

An Integrated Optimization Model for Life Cycle Pavement Maintenance Budgeting Problems

2022

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Driven by the demand to preserve the existing road pavement condition, the issue of selecting maintenance action at the appropriate time under budget limitation has attracted great attention from highway agencies. This study focuses on the strategy of how to manage pavement maintenance budget effectively on road network level based on life cycle cost analysis. The framework of resource-constrained project scheduling problem (RCPSP) is implemented to establish a maintenance action decision-making mechanism for allocating pavement maintenance budgets on the planning and controlling phases. In the RCPSP environment, a two-stage optimization model based on constraint programming techniques is developed to meet two different management goals such as 1. annual budget evaluation from planning points of view, and 2. actual budget adjustment from controlling points f view, by considering the factor of road usability. Model-I, the life cycle lifespan evaluation model solves the problem of annual budget evaluation to satisfy the maintenance requirements of all road sections. The optimal maintenance plan then can be made to maintain road performance and evaluate annual budget requirement for future years to maximize total maintenance benefits, in terms of the overall maximum pavement lifespan. Based on the suggested results of budget evaluation from Model-I, Model-II, the actual budget adjustment model deals with the actual budget allocation problem of how to keep up the original maintenance budget plan when the actual budget is always awarded insufficiently each year. Finally, the proposed two-stage integrated models provide an optimal maintenance strategy to respond to actual maintenance status and pavement deterioration as feedbacks to the actual budget adjustment model, and recursively make pavement maintenance strategy closer to actual conditions by budget adjustment yearly.

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Optimizing Expert-Based Decision-Making of Pavement Maintenance using Artificial Neural Networks with Pattern-Recognition Algorithms

Cited by 20 publications

References 22 publications

Artificial Neural Networks for Flexible Pavement

Artificial Neural Networks for Flexible Pavement

Network-level pavement maintenance and rehabilitation decision-making with the optimized annual budget

An Integrated Optimization Model for Life Cycle Pavement Maintenance Budgeting Problems

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