Bridge Temperature Prediction Model Based on Long-Short Term Memory Neural Network

Abstract

Based on the pavement temperature data of Sutong Bridge, a prediction model based on Long-Short Term Memory neural network was proposed and optimized in this study, in an effort to improve the prediction and early warning reliability of bridge state. Next, sufficient time was guaranteed for the sake of fault processing by dividing the data model into three parts: observation window, warning window and prediction window. The experimental results show that in comparison with the traditional time series prediction model, the proposed prediction model based on LSTM network is more accurate in the peak prediction, and it can effectively reduce the occurrence of false early warning. Moreover, the comparison results of root mean square error manifest that the proposed model displays a better stability in the long-term prediction.