A multi-agent ranking proximal policy optimization framework for bridge network life-cycle maintenance decision-making

The deterioration of bridge networks poses a major threat to the availability and function of transportation systems and ultimately affects social development. Deep reinforcement learning is expected to provide intelligent decision support for bridge network maintenance. However, existing studies ha...

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Bibliographic Details
Published in:Structural and multidisciplinary optimization 2024-11, Vol.67 (11), p.194, Article 194
Main Authors: Zhang, Jing, Li, Xuejian, Yuan, Ye, Yang, Dong, Xu, Pengkai, Au, Francis T. K.
Format: Article
Language:English
Subjects:
Bridge maintenance
Civil engineering
Cost analysis
Cost effectiveness
Decision making
Deep learning
Earthquakes
Linear programming
Maintenance costs
Management decisions
Multiagent systems
Optimization
Performance evaluation
Ranking
Transportation networks
Transportation systems
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Summary:The deterioration of bridge networks poses a major threat to the availability and function of transportation systems and ultimately affects social development. Deep reinforcement learning is expected to provide intelligent decision support for bridge network maintenance. However, existing studies have neglected to explicitly consider the impact of maintenance behavior on the cost-effectiveness of bridge networks. The complex traffic environment and the interconnection of bridge networks also pose unique challenges in balancing maintenance costs and benefits. It is necessary to explore how to use the specific traffic data of each bridge in the bridge network to effectively balance cost-effectiveness and rationalize maintenance decisions. Aiming at the maintenance requirements of the bridge network, a multi-agent ranking proximal policy optimization framework is proposed. The performance of the proposed framework is rigorously evaluated using a real bridge network example. The results show that the maintenance policy based on the proposed framework can maximize the cost-effectiveness of the bridge network in its life cycle, effectively reduce the excessive risk cost and achieve a harmonious balance between different costs. In addition, the proposed framework is superior to the traditional maintenance policy and provides higher performance and efficiency.
ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-024-03902-y