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Accounting for Ground-Motion Spectral Shape Characteristics in Structural Collapse Assessment through an Adjustment for Epsilon

机译:通过调整Epsilon在结构塌陷评估中考虑地面运动频谱形状特征

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One of the challenges of assessing structural collapse performance is the appropriate selection of ground motions for use in the nonlinear dynamic collapse simulation. The ground motions should represent characteristics of extreme ground motions that exceed the ground-motion intensities considered in the original building design. For modern buildings in the western United States, ground motions that cause collapse are expected to be rare high-intensity motions associated with a large magnitude earthquake. Recent research has shown that rare high-intensity ground motions have a peaked spectral shape that should be considered in ground-motion selection and scaling. One method to account for this spectral shape effect is through the selection of a set of ground motions that is specific to the building's fundamental period and the site hazard characteristics. This selection presents a significant challenge when assessing the collapse capacity of a large number of buildings or for developing systematic procedures because it implies the need to assemble specific ground-motion sets for each building. This paper proposes an alternative method, whereby a general set of far-field ground motions is used for collapse simulation, and the resulting collapse capacity is adjusted to account for the spectral shape effects that are not reflected in the ground-motion selection. The simplified method is compared with the more direct record selection strategy, and results of the two approaches show good agreement.
机译:评估结构倒塌性能的挑战之一是在非线性动态倒塌模拟中适当选择地面运动。地面运动应代表极端地面运动的特征,这些特征超过了原始建筑设计中考虑的地面运动强度。对于美国西部的现代建筑,导致坍塌的地面运动预计将是与大地震相关的罕见的高强度运动。最近的研究表明,稀有的高强度地面运动具有峰值频谱形状,在地面运动的选择和缩放中应予以考虑。解决这种频谱形状影响的一种方法是通过选择一组特定于建筑物基本周期和场所危害特征的地面运动。在评估大量建筑物的倒塌能力或制定系统程序时,此选择提出了重大挑战,因为这意味着需要为每座建筑物组装特定的地面运动集。本文提出了一种替代方法,其中将一组通用的远场地面运动用于塌陷模拟,并对产生的塌陷能力进行调整,以解决未反映在地面运动选择中的频谱形状效应。将简化方法与更直接的记录选择策略进行了比较,两种方法的结果显示出很好的一致性。

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