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Cable’s fundamental frequency (CFF) is an important characteristic of the working state of long-span cable-stayed bridges. The change in the bridge’s temperature field will influence CFF by altering the cable’s tension and the cables’ sags. An accurate regression model between the temperature-induced variation of CFF and the real-time changing temperature field should be established. Then, the reference value of the temperature-induced variation of CFF can be obtained after inputting the real-time temperature data. In this study, an intelligent real-time prediction model for CFF is proposed based on the full-bridge temperature field, including the average temperature of the main beam, the vertical temperature difference of the main beam, and the temperature of the cable tower. Besides, a machine learning method named the long short-term memory (LSTM) network is exploited to ensure the nonlinear fitting performance of the model, and a paradigm for optimal hyperparameter selection and training strategy selection is provided. To verify the superiority of the LSTM-based model, the output accuracy of the linear regression, BP network, and LSTM network was tested and compared using the monitoring data collected from cable sensors in the main span and side span, which provides an important basis for the intelligent maintenance and sustainable operation of the bridge cables.
Li et al. (Thu,) studied this question.