An Advanced Multi-Agent Reinforcement Learning Framework of Bridge Maintenance Policy Formulation

Qi-Neng Zhou, Ye Yuan, Dong Yang and Jing Zhang ()
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Qi-Neng Zhou: Department of Civil Engineering, Hefei University of Technology, Hefei 230009, China
Ye Yuan: Department of Civil Engineering, The University of Hong Kong, Pokfulam, Hong Kong, China
Dong Yang: Department of Civil Engineering, Hefei University of Technology, Hefei 230009, China
Jing Zhang: Department of Civil Engineering, Hefei University of Technology, Hefei 230009, China

Sustainability, 2022, vol. 14, issue 16, 1-18

Abstract: In its long service life, bridge structure will inevitably deteriorate due to coupling effects; thus, bridge maintenance has become a research hotspot. The existing algorithms are mostly based on linear programming and dynamic programming, which have low efficiency and high economic cost and cannot meet the actual needs of maintenance. In this paper, a multi-agent reinforcement learning framework was proposed to predict the deterioration process reasonably and achieve the optimal maintenance policy. Using the regression-based optimization method, the Markov transition matrix can better describe the uncertain transition process of bridge components in the maintenance year and the real-time updating of the matrix can be realized by monitoring and evaluating the performance deterioration of components. Aiming at bridges with a large number of components, the maintenance decision-making framework of multi-agent reinforcement learning can adjust the maintenance policy according to the updated Markov matrix in time, which can better adapt to the dynamic change of bridge performance in service life. Finally, the effectiveness of the framework was verified by taking the simulation data of a simply supported beam bridge and a cable-stayed bridge as examples.

Keywords: bridge maintenance; deep reinforcement learning; multi-agent; Q-network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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