Mechanics-Guided optimization of an LSTM network for Real-Time modeling of Temperature-Induced deflection of a Cable-Stayed bridge. (1st February 2022) - Yue, Zixiang; Ding, Youliang; Zhao, Hanwei; Wang, Zhiwen
Highlights: Summary of time lag and complex space–time distribution in temperature field of cable stayed bridge. Combining mechanical mechanism with digital signal processing, the determining temperature features of cable stayed bridge and the temperature-induced deflection are accurately extracted. Massive data of temperature and deflection of a cable-stayed bridge are collected. The optimized LSTM network, which belongs to deep learning technology, is used for modeling the temperature-induced deflection. High nonlinearity of deep learning can solve the challenge by time lag and complex space–time distribution. Abstract: The main girder of cable-stayed bridges will experience significant vertical deflection under the effect of a temperature field. If the regression model based on the inputting temperature and outputting temperature-induced deflection can be established, then the reference value of the temperature-induced deflection on the cable-stayed bridge's main girder can be obtained. The reference value has positive significance for bridge management and maintenance. However, because of the time lag between the temperature and temperature-induced deflection and the complexity on temperature features, the correlation between the temperature features and temperature-induced deflection shows a strong high-order nonlinearity. The output result of the temperature-induced deflection model obtained by linear regression has great amplitude error and phase error. The long short-term memory (LSTM) network, which belongs to deep learning, has strong nonlinear fitting performance and can consider time dependence. To temporally map the characteristics between temperature and temperature-induced deflection, a digital regression model of temperature-induced deflection based on an LSTM network is established. To make the LSTM network work well, it is necessary to extract the temperature features based on mechanical mechanism and input these temperature features to the digital regression model. At the same time, the architecture of the network must be optimized. For the LSTM network, one of the optimized parameters is the number of the cells in input layer, which determines the time length of the input data; the other is the number of hidden layers, which determines if the LSTM network can accommodate a larger amount of input information, that is, to obtain high generalization performance. Finally, the average temperature of the main girder, the vertical temperature difference of the main girder and the average temperature of the tower are taken as input temperature features. Driven by mechanical mechanism and deep learning, the output accuracy and stability of digital model built by the network with two LSTM hidden layers are exceedingly better than that of traditional linear regression model. The average error of the LSTM digital regression model is only 1.4%, and the maximum error is only 6%.