Deep Learning-Based Minute-Scale Digital Prediction Model of Temperature-Induced Deflection of a Cable-Stayed Bridge: Case Study

Abstract

The evolution rule of temperature-induced deflection in main girders is an important index to evaluate the service performance of long-span cable-stayed bridges, which directly reflects the coupling effect between the vertical stiffness of the main girder and the tension of multiple cables. However, temperature-induced deflection is caused by the complex temperature field of the main girder, cable tower and cable, while monitoring data have documented a time-lag effect between the temperature and temperature-induced deflection. Hence, it is difficult to accurately describe and model the behavior of the temperature-induced deflection in a long-span cable-stayed bridge in service. To this end, by utilizing the advantage of long short-term memory (LSTM) network for time series prediction, a digital model in minute scale based on monitoring data and deep learning can be developed to predict temperature-induced deflection, and resolve the low precision caused by the single-point input and time-lag effect. Compared with traditional machine learning algorithm and linear regression, a deep learning LSTM network has the best performance. For the cable-stayed bridge in this paper, the mean absolute error of the LSTM model was even less than 0.5 mm, and with the combined hypothesis test, the early warning accuracy for the abnormal change of temperature-induced deflection could achieve a minimum of 0.3%.