Adaptive surrogate model method for extreme value prediction of driving safety indexes of train-bridge coupled random system

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (19) : 70-78.

ZHANG Xun1, HAN Yan1, WANG Lidong1, LIU Hanyun1, CAI Chunsheng2

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Abstract

In order to improve the efficiency of the reliability evaluation of train running safety on bridge, a method of adaptive surrogate model for predicting the extreme value of the running safety indexes of the train-bridge stochastic system is proposed in this paper. Firstly, the initial sample set and candidate sample set are generated by minimizing the GF-discrepancy; Secondly, the theoretical model is used to calculate the extreme value of safety indexes of the train-bridge system corresponding to the initial sample set, and the radial basis function surrogate model is established; Finally, the learning function and established surrogate model are used to determine the new sample in the set of the candidate sample set to optimize the current surrogate model until the surrogate model meets the satisfactory prediction accuracy. In order to verify the effectiveness of the proposed method, taking the moving wheel and sprung mass passing through simply supported beam bridge and the train-bridge vertical coupling vibration model as examples, the distribution of training sample points of the adaptive surrogate model and the one-stage sampling surrogate model, as well as the prediction accuracy of the two surrogate models, are compared and analyzed. The results show that it can find the sparse regions of sample points in the current training set and the strong nonlinearity regions of the objective function, and make the sample points in these regions more densely. Thus, the prediction accuracy of the surrogate model in the whole sample space is significantly improved without increasing the number of training samples. Taking the vertical model of the train-bridge system as an example, the prediction accuracy of the adaptive surrogate model is 2.5 times higher than that of the one-time sampling surrogate model.

Key words

train-bridge coupled system / adaptive surrogate model / learning function / GF-discrepancy / wheel load reduction rate

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ZHANG Xun1, HAN Yan1, WANG Lidong1, LIU Hanyun1, CAI Chunsheng2. Adaptive surrogate model method for extreme value prediction of driving safety indexes of train-bridge coupled random system[J]. Journal of Vibration and Shock, 2023, 42(19): 70-78

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