A deep reinforcement learning framework for life-cycle maintenance planning of regional deteriorating bridges using inspection data

Determination of regional deteriorating bridges’ maintenance strategies for minimizing life-cycle risks and costs constructs a complex optimization problem. Improper maintenance strategies lead to budget waste and ineffective maintenances. To optimize the life-cycle maintenance strategies of regional bridges, this study develops a deep reinforcement learning (DRL)-based framework to enable the agent to learn better maintenance actions in an interactive environment by trial and error. It is able to generate several optimal maintenance strategies to match with different budget constraints under the premise of increasing the maintenance cost-effectiveness to the greatest extent possible. The framework contains the whole optimization process from data collection to regional functions establishment to reinforcement learning training. The regional structural deterioration features and the effect of maintenance actions are determined by years of regional inspection reports. The regional probabilistic models are incorporated to simulate the stochastic process. The regional life-cycle maintenance strategies are optimized with the deep Q-networks model. The proposed framework is substantiated by developing 100-year maintenance strategies for part of highway bridges. The results show that the trained optimal maintenance strategies match well with the given budget constraints and maximize the life-cycle cost-effectiveness of maintenance actions. By employing the trained maintenance strategies, the conditions of regional bridges are controlled at a good level. Besides, the trained optimal DRL-based strategies have better performance than traditional condition-fixed strategies.