Optimization-Based Decision-Making Approach for Incorporating the Length Constraint of Preventive Maintenance into Pavement Maintenance and Rehabilitation Planning
Publication: Journal of Transportation Engineering, Part B: Pavements
Volume 148, Issue 4
Abstract
Most existing pavement maintenance and rehabilitation (M&R) decision-making approaches ignore the length constraint of preventive maintenance (PM). This study proposed a practical optimization-based decision-making approach that incorporates the length constraint of PM. For the current popular bottom-up decision-making approach, incorporating the length constraint of PM will cause its solution methods (i.e., evolutionary algorithms) to fail. Therefore, this study developed a sliding-window random repair (SWRR) method based on repair methods and sliding-window methods. The SWRR method was inserted into the evolutionary algorithm (i.e., genetic algorithm) of a two-stage bottom-up approach to solve this dilemma. That is, the proposed decision-making approach is composed of the SWRR method and the two-stage bottom-up approach. A parametric study showed that the M&R decision-making plan recommended by the proposed approach was less expensive than the actual engineering plan, but the achieved performance was 5.7% higher. The results prove that the proposed approach can indeed solve the dilemma caused by the length constraint of PM and produce a better decision-making plan.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request, including:
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basic data of Qilin Freeway (i.e., the length of pavement segments, the performance surface condition index detected in December 2019 and 2020, road age, and actual maintenance and rehabilitation projects in 2020);
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optimization decision-making model (facility-level optimization model, and network-level optimization model); and
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three copies of MATLAB code (code of exhaustive method, code of genetic algorithm, and code of sliding-window random repair algorithm).
Acknowledgments
Thanks to the Shanxi Department of Transportation for providing basic research data.
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Information
Published In
Journal of Transportation Engineering, Part B: Pavements
Volume 148 • Issue 4 • December 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Oct 22, 2021
Accepted: May 5, 2022
Published online: Jul 20, 2022
Published in print: Dec 1, 2022
Discussion open until: Dec 20, 2022
ASCE Technical Topics:
- Algorithms
- Architectural engineering
- Building management
- Business management
- Construction engineering
- Construction methods
- Decision making
- Engineering fundamentals
- Infrastructure
- Maintenance and operation
- Mathematics
- Models (by type)
- Optimization models
- Parameters (statistics)
- Pavements
- Practice and Profession
- Rehabilitation
- Statistics
- Transportation engineering
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Cited by
- Qingwei Zeng, Feng Xiao, Xiaorui Sun, Shunxin Yang, Qixuan Cui, An Improved Two-Stage Bottom-Up Optimization Approach for Pavement Maintenance and Rehabilitation Decision Making, Journal of Transportation Engineering, Part B: Pavements, 10.1061/JPEODX.PVENG-1537, 150, 3, (2024).
- Qingwei Zeng, Feng Xiao, Hui Zhang, Shunxin Yang, Qixuan Cui, Data Cleaning Framework for Pavement Maintenance and Rehabilitation Decision-Making in Pavement Management System Based on Artificial Neural Networks, Journal of Infrastructure Systems, 10.1061/JITSE4.ISENG-2479, 30, 3, (2024).
- Feng Xiao, Xinyu Chen, Shunxin Yang, Jianchuan Cheng, Bi-objective pavement maintenance and rehabilitation optimization decision-making model incorporating the construction length of preventive maintenance projects, Structure and Infrastructure Engineering, 10.1080/15732479.2023.2184394, (1-15), (2023).