In order to make up for the shortcomings of previous scholars, this paper puts forward the modified theory and method of real-time prediction of structural response under earthquake, and improves the defects of the previously established neural network model. In this paper, the effectiveness of real-time prediction of structural response under earthquake by using neural network is demonstrated, and the omissions in previous theories are pointed out. This paper focuses on the problems faced in the practical application, including the preprocessing of the training set and the application method of the model after training. On the basis of correcting the deficiency of the theory, this paper deeply discusses the method of using the training model to predict the structural response, and provides a practical method for predicting the real-time response of the structure under earthquake. The preprocessing method of the data set is improved to ensure that the explosive accumulation of errors will not occur in the model prediction. In order to improve the accuracy and efficiency of structural response prediction, an EraquseqNet model is introduced, which is based on codec neural network model, bi-directional neural network module and attention mechanism. Compared with other neural network methods, the problem of decreasing accuracy caused by information redundancy is solved by using attention mechanism, and the problem of rapid decline of response prediction accuracy under long-term earthquake is solved by bi-directional neural network module.
Key words
Earthquake action /
Structural response /
Attention mechanism /
Bidirectional neural network
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