Prediction model of deflection based on wavelet and LSTM+GAN

Bridge deflection is an important indicator to evaluate bridge safety, and accurate deflection prediction is an important way to ensure the healthy operation of bridges. At present, the methods applied to bridge deflection prediction are mainly long-term short-term memory networks (LSTM), but for bridge deflection data, the prediction effect of LSTM in the case of large mutation data often does not meet the ideal standard. In this paper, this paper proposes an LSTM+GAN hybrid network based on wavelet transform, which decomposes the deflection of the bridge into flat data and live load data by the wavelet transform, uses LSTM to predict the flat data, and the GAN network generates the predicted live load data, and finally sums the predicted values of LSTM and GAN to obtain the final predicted value. Experiments show that the proposed method has a better prediction accuracy improvement than LSTM.