Ranking method of the severest input ground motion for underground structures based on composite ground motion intensity measures

强度(物理) 地震动 结构工程 排名(信息检索) 强地震动 岩土工程 地质学 工程类 计算机科学 人工智能 物理 量子力学
作者
Zhiyi Chen,Wei Yu,Hehua Zhu,Lili Xie
出处
期刊:Soil Dynamics and Earthquake Engineering [Elsevier BV]
卷期号:168: 107828-107828 被引量:19
标识
DOI:10.1016/j.soildyn.2023.107828
摘要

Underground structure seismic response analyses and structural safety seismic designs are based on the scientific and reasonable selection of ground motion records. Different input ground motions lead to varies seismic response due to the strong randomness of ground motion. The severest input ground motion for the seismic analysis of underground structures cannot be obtained via a single intensity measure (IM). For this purpose, a ranking method is proposed based on composite IM for the severest input ground motion for underground structures. Four composite ground motion IMs constructed using the partial least squares regression (PLSR) method are proposed as representative ground motion IMs of the seismic failure potential of underground structures. These composite ground motion IMs correspond to the evaluation of four engineering demand parameters (EDPs), including the moment, shear force, axial force, and relative displacement of the central column of a subway station. Elastic-plastic time-history analyses of a two-story three-span subway station are performed given 64 ground motion records to provide the necessary data samples. After the construction, a bilinear logarithmic regression analysis is carried out. The results show that the proposed composite ground motion IMs have a higher correlation with the seismic dynamic responses than single IMs with respect to effectiveness, practicability, and proficiency. Finally, a ranking method for the severest input ground motion based on these composite ground motion intensity parameters is proposed. This method can be used to obtain the severest design ground motion of underground structures for seismic design.
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