In contrast to approaches based on scaling of recorded seismograms, using extensive inventories of numerically simulated earthquakes avoids the need for any selection and scaling of motions which implicitly requires assumptions on intensity measures (IMs) that correlate with structural response. This study has the objectives to examine seismogram features that control the collapse response of tall buildings and to develop efficient and reliable collapse classification algorithms. To that end, machine learning techniques are applied to the results of nonlinear response history analyses of a 20-story tall building performed using about two million simulated ground motions. Feature selection of ground motion IMs generally confirms current understanding of collapse predictors based on previous studies using scaled recorded motions. In addition, interrogations of the large collection of hazard-consistent simulations demonstrate the utility of different IMs for collapse risk assessment in a way that is not possible with recorded motions. Finally, a small subset of IMs is identified and used in development of an efficient collapse classification algorithm which is tested on benchmark simulated data at several sites in the Los Angeles basin.

中文翻译：

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基于 SCEC Cyber​​Shake 地面运动模拟的高层建筑倒塌预测的有效强度测量和机器学习算法

与基于记录地震图缩放的方法相比，使用大量的数值模拟地震库存避免了对运动的任何选择和缩放的需要，这隐含地需要对与结构响应相关的强度测量 (IM) 进行假设。本研究的目标是检查控制高层建筑倒塌响应的地震图特征，并开发高效可靠的倒塌分类算法。为此，机器学习技术被应用于使用大约 200 万次模拟地面运动对 20 层高建筑进行非线性响应历史分析的结果。地面运动 IM 的特征选择通常证实了当前对基于使用缩放记录运动的先前研究的坍塌预测因子的理解。此外，对大量危险一致模拟的询问证明了不同 IM 以记录运动无法实现的方式用于倒塌风险评估的效用。最后，确定了一小部分 IM 并将其用于开发有效的坍塌分类算法，该算法在洛杉矶盆地的几个站点的基准模拟数据上进行了测试。