Abstract
A method of bridge structure seismic response identification combining signal processing technology and deep learning technology is proposed. The short-time energy method is used to intelligently extract the non-smooth segments in the sensor acquired signals, and the short-time Fourier transform, continuous wavelet transform, and Meier frequency cestrum coefficients are used to analyze the spectrum of the non-smooth segments of the response of the bridge structure, and the response feature matrix is extracted and used to classify sequences or images in the LSTM network and the Resnet50 network. The results show that the signal processing techniques can effectively extract the structural response features and reduce the overfitting phenomenon of neural networks, and the combination of signal processing techniques and deep learning techniques can recognize the seismic response of bridge structures with high accuracy and efficiency.