Resumen
The intensity non-stationarity is one of the basic characteristics of ground motions, the influences of which on the dynamic responses of structures is a pressing issue in the field of earthquake engineering. The BP neural network modified by the genetic algorithm was adopted in this research to investigate the influence of intensity nonstationary inputs on the structural dynamic responses from a new perspective. Firstly, many training data were generated from the prediction formula of dynamic response. The BP neural network was then pre-trained by sparsely selected data to optimize the initial weights and biases. Finally, the BP neural network was trained by all data, and the mean square error of predicted responses compared with the target response were less than 10-5. The calculation formula of sensitivity was also derived here to quantify the influence of the input change on the output. The presented method combines the advantages of neural networks in nonlinear multi-variable fitting and provides a new perspective for the study of earthquake nonstationary characteristics and their influence on the structural dynamic responses.