Structural dominant failure modes searching method based on deep reinforcement learning

Abstract

The dominant failure modes (DFMs) of a structural system are significant for structural analysis and failure probability estimation. However, existing failure modes (FMs) searching methods often face problems of combinatorial explosion. To address this issue, a deep reinforcement learning (DRL)-based method is proposed for DFMs searching, which transforms the probability-based failure component selection process into a sequential decision process. First, the failure stages and the selected failure components of a structural system are transformed to be the states and actions in the DRL. Second, a deep neural network (DNN) is established to observe the failure stages and select failure components. Finally, a new reward function is designed to guide the network to learn the failure component selection policy. The proposed method was tested through a roof truss structure and a truss bridge structure. It was demonstrated that the trained DNN could learn to observe the failure stages and select the most critical components in a completely unknown testing set. High accuracy of the identified DFMs can be achieved. In comparison with the calculation results of Monte Carlo Simulation (MCS) and β-unzipping method, this proposed method shows significant computational efficiency advantages with high accuracy in dealing with combinatorial explosion.