Machine‐learning‐enhanced tail end prediction of structural response statistics in earthquake engineering
Machine‐learning‐enhanced tail end prediction of structural response statistics in earthquake engineering
复制标题
地震工程中结构响应统计的机器学习增强尾部预测
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
F. Bamer
中科院分区：
文献类型：
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作者：
Denny Thaler;M. Stoffel;B. Markert;F. Bamer
评估非线性结构的响应统计是工程设计中的一个关键问题。由此，蒙特卡罗方法被证明是有用的，尽管计算成本相当高。特别是，在接近结构失效的系统设计点周围，对于复杂的高维系统，可靠的统计估计是不可行的。因此，在本文中，我们开发了一种针对非线性行为工程结构的机器学习增强蒙特卡罗模拟策略。神经网络学习结构在初始非平稳地面激励子集下的响应行为，该子集是根据所选地面加速度记录的频谱特性生成的。然后利用神经网络卓越的计算效率，可以预测整个样本集的响应统计量，该样本集比初始训练样本集大得多。为了确保在结构失效附近发生罕见事件时可靠的神经网络响应预测，我们建议扩展初始训练样本集，增加强度的方差。我们表明，使用这个扩展的初始样本集可以对响应统计数据进行可靠的预测，即使在分布的尾部也是如此。
Evaluating the response statistics of nonlinear structures constitutes a key issue in engineering design. Hereby, the Monte Carlo method has proven useful, although the computational cost turns out to be considerably high. In particular, around the design point of the system near structural failure, a reliable estimation of the statistics is unfeasible for complex high‐dimensional systems. Thus, in this paper, we develop a machine‐learning‐enhanced Monte Carlo simulation strategy for nonlinear behaving engineering structures. A neural network learns the response behavior of the structure subjected to an initial nonstationary ground excitation subset, which is generated based on the spectral properties of a chosen ground acceleration record. Then using the superior computational efficiency of the neural network, it is possible to predict the response statistics of the full sample set, which is considerably larger than the initial training sample set. To ensure a reliable neural network response prediction in case of rare events near structural failure, we propose to extend the initial training sample set increasing the variance of the intensity. We show that using this extended initial sample set enables a reliable prediction of the response statistics, even in the tail end of the distribution.
DOI：
10.1016/j.cma.2020.112875
发表时间：
2020-05-01
影响因子：
7.2
作者：
Heider, Yousef;Wang, Kun;Sun, WaiChing
通讯作者：
Sun, WaiChing