Prediction model of deflection based on wavelet and LSTM+GAN

Zong Bao Liang; Ming Gao; Bo Chen; YunFei Yuan; Yang Song

doi:10.1117/12.2656835

20 October 2022 Prediction model of deflection based on wavelet and LSTM+GAN

Zong Bao Liang, Ming Gao, Bo Chen, YunFei Yuan, Yang Song

Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 124511H (2022) https://doi.org/10.1117/12.2656835
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

Abstract

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.

Citation Download Citation

Zong Bao Liang, Ming Gao, Bo Chen, YunFei Yuan, and Yang Song "Prediction model of deflection based on wavelet and LSTM+GAN", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 124511H (20 October 2022); https://doi.org/10.1117/12.2656835

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KEYWORDS

Data modeling

Gallium nitride

Wavelet transforms

Bridges

Wavelets

Data conversion

Data processing

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