Displacement response reconstruction method for bridge subjected to moving load based on IPSO-BiLSTM network

Bridge dynamic displacement reconstruction methods based on neural networks usually use single-input neural networks, and most of the hyperparameters are determined by experience, which seriously affect the reconstruction accuracy. In this paper, a reconstruction method for bridge displacement response induced by moving load is proposed by using a small number of sensors and a triple-input IPSO-BiLSTM network. Firstly, the input strain and acceleration data are normalized in advance for data fusion. Secondly, IPSO-BILSTM network model with three-time sequence responses as input is constructed, and IPSO algorithm is used to optimize the network hyperparameters. Finally, three-time sequence responses are input into the trained iterative particle swarm optimization (IPSO)-Bidirectional LSTM (IPSO-BiLSTM) neural network to reconstruct the bridge displacement response. The proposed IPSO-BiLSTM network realizes the data fusion of three-time sequence responses and automatically establishes the relationship between input response and output displacement. Numerical examples indicate that the reconstruction accuracy is sensitive to road roughness and measurement noise. Experimental studies reveal that the reconstruction accuracy is insensitive to vehicle velocity and weigh.