A probabilistic deep reinforcement learning approach for optimal monitoring of a building adjacent to deep excavation

During a deep excavation project, monitoring the structural health of the adjacent buildings is crucial to ensure safety. Therefore, this study proposes a novel probabilistic deep reinforcement learning (PDRL) framework to optimize the monitoring plan to minimize the cost and excavation-induced risk. First, a Bayesian-bi-directional general regression neural network is built as a probabilistic model to describe the relationship between the ground settlement of the foundation pit and the safety state of the adjacent building, along with the actions in a dynamic manner. Subsequently, a double deep Q-network method, which can capture the realistic features of the excavation management problem, is trained to form a closed decision loop for continuous learning of monitoring strategies. Finally, the proposed PDRL approach is applied to a real-world deep excavation case in No. 14 Shanghai Metro. This approach can estimate the time-variant probability of damage occurrence and maintenance actions and update the state of the adjacent building. According to the strategy proposed via PDRL, monitoring of the adjacent buildings begins in the middle stage rather than on the first day of the excavation project if there is full confidence in the quality of the monitoring data. When the uncertainty level of data rises, the starting day might shift to an earlier date. It is worth noting that the proposed PDRL method is adequately robust to address the uncertainties embedded in the environment and model, thus contributing to optimizing the monitoring plan for achieving cost-effectiveness and risk mitigation.