Development of a Decision-Making Tool to Select Optimum Preventive Maintenance Treatments in a Hot and Humid Climate

Mousa, Momen R.; Elseifi, Mostafa A.; Zhang, Zhongjie; Gaspard, Kevin

doi:10.1177/0361198119898397

Cited by 4 publications

(2 citation statements)

References 14 publications

(25 reference statements)

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“…Advance M&R decision should consider various decision criteria, including M&R benefits, pavement condition, funding efficiency, etc., when planning M&R treatments [19][20][21]. As an important criterion, the annual budget determines the available funding for M&R treatments of the entire pavement network in every year.…”

Section: Background and Literature Reviewmentioning

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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Section: Background and Literature Reviewmentioning

confidence: 99%

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

Chen

2023

PLoS ONE

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“…(Marcelino et al, 2019) Machine Learning (ML) ML was employed in the prediction of pavement performance. (Mousa et al, 2020)…”

Section: Authormentioning

confidence: 99%

A Case-Based Reasoning and Random Forest Framework for Selecting Preventive Maintenance of Flexible Pavement Sections

Dabous

Hamad

Al‐Ruzouq

et al. 2022

BJRBE

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Pavement maintenance decision-making is receiving significant attention in recent research, since pavement infrastructure is aging and deteriorating. The decision-making process is mainly related to selecting the most appropriate maintenance intervention for pavement sections to ensure performance and enhance safety. Several preventive maintenance methods have been proposed in the previous studies, yet the potential of implementing Case-Based Reasoning (CBR) in pavement maintenance decision-making has been investigated rarely. The CBR is an artificial intelligence technique, it is knowledge-based on several known cases, which are used to adapt a solution for a new case through retrieving similar cases. This research introduces the CBR to the area of pavement management to select the most appropriate preventive maintenance strategy for flexible pavement sections. The needed database was extracted from maintenance cases at Long-Term Pavement Performance Program. The criteria used to characterize condition of each section were identified based on the common practices in pavement maintenance published in the literature and implemented in the field. To assign weights to the selected criteria, different machine learning techniques were tested, and subsequently, Random Forest (RF) algorithm was selected to be integrated with the proposed CBR method producing the CBR-RF framework. A case study was analyzed to validate the proposed framework and a sensitivity analysis was conducted to assess the influence of each criterion on case retrieval accuracy and overall framework performance. Results indicated that the CBR-RF approach could assist effectively in the preventive maintenance decision-making with regard to new cases by learning from the previous similar cases. Accordingly, several agencies can depend on the proposed framework, while facing similar decision-making problems. Future research can compare the CBR-RF framework with other machine learning algorithms using the same dataset included in this research.

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Optimizing the Cost-Effectiveness and Time of Application of Microsurfacing

Mousa

Elseifi

Bashar

et al. 2021

J. Transp. Eng., Part B: Pavements

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Development of a Decision-Making Tool to Select Optimum Preventive Maintenance Treatments in a Hot and Humid Climate

Cited by 4 publications

References 14 publications

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

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

A Case-Based Reasoning and Random Forest Framework for Selecting Preventive Maintenance of Flexible Pavement Sections

Optimizing the Cost-Effectiveness and Time of Application of Microsurfacing

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