An adaptive surrogate model approach for random vibration analysis of the train–bridge system. (1st March 2023) - Zhang, Xun; Han, Yan; Wang, Lidong; Liu, Hanyun; Cai, C.S.
Highlights: A new approach is proposed for random vibration analysis of the train–bridge system based on the adaptive sampling surrogate model. The approach adds the new samples one by one base on learning function until the surrogate model reaches satisfactory accuracy. The prediction accuracy of the proposed approach is one order of magnitude higher than that of the traditional method in prediction the operational failure probability of trains. Abstract: To improve the efficiency of random vibration analysis of the train–bridge system, a new approach is proposed based on the adaptive sampling (AS) surrogate model in this paper. First, an initial sample set and a candidate sample set are generated by the method based on Generalized F (GF)-discrepancy. Second, a theoretical model is used to calculate the maximum dynamic response of the train–bridge system corresponding to the initial sample set, and a surrogate model based on Gaussian process regression (GPR) is constructed. Third, the learning function is used to identify new samples in the candidate sample set to optimize the current surrogate model until it meets satisfactory prediction accuracy. Finally, the maximum dynamic response of each sample in the representative sample set is predicted by the final surrogate model, and then the statistical properties of the dynamic response of the train–bridge system are analyzed. To verify the effectiveness of the proposed method, the distribution of training sample points and prediction accuracy of the AS surrogate model and the one-stage sampling (OS) surrogate model are compared and analyzed, taking the prediction of the maximum wheel load reduction rate (WLRR) of a train on the bridge as an example. On this basis, the prediction accuracy of the AS and OS surrogate models for the random characteristics of the train on the bridge is evaluated. The results show that the proposed method can significantly improve the prediction accuracy of the surrogate model without increasing the number of training samples.