Deep learning-driven thermal response estimation for an in-service cable stayed bridge

In order to enhance the accuracy of thermal response estimation, this study proposes the utilization of a deep learning-driven method, specifically the Long Short-Term Memory (LSTM) network, for predicting temperature-induced strains and deflections in the Queensferry Crossing, UK. Initially, an examination of the temperature fields of the bridge is conducted based on monitored temperature data. Subsequently, the LSTM network is introduced as a tool to estimate thermal response utilizing temperature field information. The study proceeds to discuss the results of temperature-induced response estimation, including strains and deflections, obtained through the LSTM network, with a comparative analysis against a linear regression model. It is observed that the temperature discrepancies among steel components within the bridge are lesser in comparison to those of concrete components. The LSTM network exhibits higher estimation accuracy than the linear model, manifesting in an improvement of mean square error from 4.45 to 0.89 for strain estimation. In addition, the LSTM network can partially address time-lag effects. However, the estimation performance of the LSTM network is influenced by the structural response type and the resolution of employed sensors. Specifically, the correlation coefficient (R) associated with strain estimation is measured at 0.980 using the LSTM network, whereas R for deflection estimation stands at 0.825. Generally, local structural responses, such as strains, outperform global responses like deflections. Furthermore, sensors with higher resolutions yield enhanced estimation accuracy.