The application of Latin Hypercube and Monte Carlo (MC) sampling techniques for ground motion selection purposes is investigated. Latin Hypercube Sampling (LHS) works by first stratifying a probability distribution domain into multiple equally spaced and non-overlapping stripes and then by permutationally drawing samples from those stripes. To examine the efficiency of these two distinct sampling methods, a set of conditional multivariate distributions was fit to an intensity measure vector based on a single, two, or average of more-than-two (average) conditioning intensity measure. LHS was then utilized for sampling purposes from the conditional multivariate distributions, which in turn demonstrated superiority over MC given the same number of realization samples. Accordingly, it was utilized as an underlying peace of a broader ground motion selection framework to facilitite the selection of a number of ground motion suites based on different methods of conditioning. Using the selected suites, response history and subsequent damage/loss analyses were conducted on a generic 4-story non-ductile reinforced concrete building. The outcomes of these latter studies demonstrated that the ground motion suite selected based on an average-intensity-measure conditioning criterion performed better than those selected through single- and two-intensity-measure conditioning criteria.

研究了拉丁超立方体和蒙特卡洛（MC）采样技术在地面运动选择中的应用。拉丁超立方体采样（LHS）的工作方式是，首先将概率分布域分层为多个等距且不重叠的条纹，然后从这些条纹中进行置换以抽取样本。为了检查这两种不同采样方法的效率，将一组条件多元分布拟合到基于单个，两个或多于两个（平均）调节强度度量的强度度量矢量。然后，LHS被用于从条件多元分布中进行抽样，这反过来又证明了优于MC给出相同数量的实现样本。因此，它被用作更广泛的地面运动选择框架的基础，以方便基于不同的调节方法选择多个地面运动套件。使用选定的套件，在通用的4层非延性钢筋混凝土建筑上进行了响应历史和随后的损坏/损失分析。后面这些研究的结果表明，基于平均强度测量条件准则选择的地面运动套件的性能优于通过单强度测量和双重强度度量条件准则选择的地面运动套件。