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

Author

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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)

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.

Suggested Citation

  • Qi-Neng Zhou & Ye Yuan & Dong Yang & Jing Zhang, 2022. "An Advanced Multi-Agent Reinforcement Learning Framework of Bridge Maintenance Policy Formulation," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10050-:d:887697
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    References listed on IDEAS

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    2. Robelin, Charles-Antoine & Madanat, S M, 2006. "Dynamic Programing based Maintenance and Replacement Optimization for Bridge Decks using History-Dependent Deterioration Models," University of California Transportation Center, Working Papers qt5k01s7x9, University of California Transportation Center.
    3. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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