A Tunnel Structural Health Monitoring and Damage Identification Algorithm Based on Deep Reinforcement Learning: Fid-Bau Portal

A Tunnel Structural Health Monitoring and Damage Identification Algorithm Based on Deep Reinforcement Learning

Wei, Yike / Yang, Bin

With the continuous promotion of infrastructure construction such as water conservancy, transportation, and municipal engineering in China, the number of tunnels put into operation is increasing, making the healthy service and longterm operation of tunnel structures an urgent and important task. During tunnel operation, structural diseases such as cracks, voids, voids, and water seepage occur frequently. These diseases not only weaken the strength of the tunnel structure and shorten its service life, but also pose a serious threat to the safety of people's lives and property. This paper proposes a tunnel structure health monitoring and damage identification algorithm based on deep reinforcement learning (DRL) to address this issue. This algorithm fully utilizes the advantages of deep learning (DL) in feature extraction and pattern recognition, as well as the ability of reinforcement learning (RL) in decision optimization and adaptive learning. By constructing a model for the interaction between intelligent agents and the environment, it achieves real-time monitoring of the health status of tunnel structures and damage recognition. The experimental results show that the algorithm can accurately identify the damage situation of tunnel structures, effectively monitor the health status of tunnels, and provide strong technical support for the safe operation of tunnels.