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Machine-learning-enhanced tail end prediction of structural response statistics in earthquake engineering

机译：地震工程结构应答统计的机器学习增强尾端预测

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摘要

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.
机译：评估非线性结构的响应统计数据构成了工程设计中的关键问题。因此，蒙特卡罗方法已被证明是有用的，尽管计算成本变得相当高。特别是，围绕系统的设计点近近结构故障，可靠地估计统计数据对于复杂的高维系统不可行。因此，在本文中，我们开发了一种用于非线性行为工程结构的机器学习增强的蒙特卡罗仿真策略。神经网络学习基于所选择的地面加速度记录的光谱特性生成的初始非间断地激励子集的结构的响应行为。然后使用神经网络的卓越的计算效率，可以预测完整采样集的响应统计，这远远大于初始训练样本集。为了确保在结构故障附近罕见事件的情况下，确保可靠的神经网络响应预测，我们建议扩展初始训练样本集，从而提高强度方差。我们表明，即使在分布的尾端，也可以使用此扩展初始示例集可实现响应统计数据的可靠预测。

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