车-桥随机系统行车安全指标极值预测的自适应代理模型方法

张迅1,韩艳1,王力东1,刘汉云1,蔡春声2

振动与冲击 ›› 2023, Vol. 42 ›› Issue (19) : 70-78.

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (19) : 70-78.
论文

车-桥随机系统行车安全指标极值预测的自适应代理模型方法

  • 张迅1,韩艳1,王力东1,刘汉云1,蔡春声2
作者信息 +

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

  • ZHANG Xun1, HAN Yan1, WANG Lidong1, LIU Hanyun1, CAI Chunsheng2
Author information +
文章历史 +

摘要

为提高桥上列车行车安全可靠性评估效率,本文提出了一种车-桥耦合随机振动系统行车安全指标极值预测的自适应代理模型方法。首先,通过GF偏差最小化准则建立随机变量初始样本点集和候选样本点集。其次,采用车-桥耦合振动系统理论模型获取初始样本点集对应的行车安全指标极值,并依此建立径向基函数代理模型;最后,利用准则函数和已建立的代理模型依次在候选样本点集中确定新的样本点,优化当前代理模型,直至代理模型的预测精度满足要求。为验证所提方法的有效性,分别以移动车轮加簧上质量过简支梁桥和车-桥竖向耦合振动模型为例,对比分析了自适应代理模型和一次性采样代理模型的训练样本点分布情况,以及两种代理模型的预测精度。结果表明:自适应代理模型具有良好的全局探索和局部开发能力,能够发现当前训练集中样本点稀疏区域和目标函数非线性较大区域,并对这些区域进行样本点加密,从而在不增加训练样本点数目的情况下,显著提高代理模型在整个样本空间内的预测精度。以车-桥耦合系统竖向随机振动模型的轮重减载率极值预测为例,自适应代理模型的预测精度较一次性采样代理模型提高了2.5倍。

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.

关键词

车-桥耦合振动 / 自适应代理模型 / 准则函数 / GF偏差 / 轮重减载率

Key words

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

引用本文

导出引用
张迅1,韩艳1,王力东1,刘汉云1,蔡春声2. 车-桥随机系统行车安全指标极值预测的自适应代理模型方法[J]. 振动与冲击, 2023, 42(19): 70-78
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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