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Smart Structures and Systems Volume 30, Number 6, December 2022 , pages 571-582 DOI: https://doi.org/10.12989/sss.2022.30.6.571 |
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Deep reinforcement learning for optimal life-cycle management of deteriorating regional bridges using double-deep Q-networks |
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Xiaoming Lei and You Dong
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| Abstract | ||
| Optimal life-cycle management is a challenging issue for deteriorating regional bridges. Due to the complexity of regional bridge structural conditions and a large number of inspection and maintenance actions, decision-makers generally choose traditional passive management strategies. They are less efficiency and cost-effectiveness. This paper suggests a deep reinforcement learning framework employing double-deep Q-networks (DDQNs) to improve the life-cycle management of deteriorating regional bridges to tackle these problems. It could produce optimal maintenance plans considering restrictions to maximize maintenance cost-effectiveness to the greatest extent possible. DDQNs method could handle the problem of the overestimation of Q-values in the Nature DQNs. This study also identifies regional bridge deterioration characteristics and the consequence of scheduled maintenance from years of inspection data. To validate the proposed method, a case study containing hundreds of bridges is used to develop optimal life-cycle management strategies. The optimization solutions recommend fewer replacement actions and prefer preventative repair actions when bridges are damaged or are expected to be damaged. By employing the optimal life-cycle regional maintenance strategies, the conditions of bridges can be controlled to a good level. Compared to the nature DQNs, DDQNs offer an optimized scheme containing fewer low-condition bridges and a more costeffective life-cycle management plan. | ||
| Key Words | ||
| condition assessment; deteriorating structures; life-cycle management; regional bridges; reinforcement learning | ||
| Address | ||
| Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China. | ||