4.6 Article

Developing efficient scalar and vector intensity measures for IDA capacity estimation by incorporating elastic spectral shape information

期刊

EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS
卷 34, 期 13, 页码 1573-1600

出版社

WILEY
DOI: 10.1002/eqe.496

关键词

performance-based earthquake engineering; incremental dynamic analysis; capacity; intensity measure; limit-state; non-linear

向作者/读者索取更多资源

Scalar and vector intensity measures are developed for the efficient estimation of limit-state capacities through incremental dynamic analysis (IDA) by exploiting the elastic spectral shape of individual records. IDA is a powerful analysis method that involves subjecting a structural model to several ground motion records, each scaled to multiple levels of intensity (measured by the intensity measure or IM), thus producing curves of structural response parameterized by the IM on top of which limit-states can be defined and corresponding capacities can be calculated. When traditional IMs are used, such as the peak ground acceleration or the first-mode spectral acceleration, the IM-values of the capacities can display large record-to-record variability, forcing the use of many records to achieve reliable results. By using single optimal spectral values as well as vectors and scalar combinations of them on three multistorey buildings significant dispersion reductions are realized. Furthermore, IDA is extended to vector IMs, resulting in intricate fractile IDA surfaces. The results reveal the most influential spectral regions/periods for each limit-state and building, illustrating the evolution of such periods as the seismic intensity and the structural response increase towards global collapse. The ordinates of the elastic spectrum and the spectral shape of each individual record are found to significantly influence the seismic performance and they are shown to provide promising candidates for highly efficient IMs. Copyright (c) 2005 John Wiley & Sons, Ltd.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据