Probabilistic seismic hazard analysis for spatially distributed infrastructure

被引:52
|
作者
Han, Yeliang [2 ]
Davidson, Rachel A. [1 ]
机构
[1] Univ Delaware, Dept Civil & Environm Engn, Newark, DE 19716 USA
[2] Tsinghua Univ, Dept Engn Phys, Beijing 100084, Peoples R China
来源
EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS | 2012年 / 41卷 / 15期
基金
美国国家科学基金会; 中国国家自然科学基金;
关键词
hazard; regional; scenarios; lifelines; simulation; optimization; AVERAGE HORIZONTAL COMPONENT; GROUND-MOTION; RESPONSE SPECTRA; RISK-ASSESSMENT; MODEL; CALIFORNIA; SCENARIOS; NETWORKS; PERIODS; WIND;
D O I
10.1002/eqe.2179
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Two key issues distinguish probabilistic seismic risk analysis of a lifeline or portfolio of structures from that of a single structure. Regional analysis must consider the correlation among lifeline components or structures in the portfolio, and the larger scope makes it much more computationally demanding. In this paper, we systematically identify and compare alternative methods for regional hazard analysis that can be used as the first part of a computationally efficient regional probabilistic seismic risk analysis that properly considers spatial correlation. Specifically, each method results in a set of probabilistic ground motion maps with associated hazard-consistent annual occurrence probabilities that together represent the regional hazard. The methods are compared according to how replicable and computationally tractable they are and the extent to which the resulting maps are physically realistic, consistent with the regional hazard and regional spatial correlation, and few in number. On the basis of a conceptual comparison and an empirical comparison for Los Angeles, we recommend a combination of simulation and optimization approaches: (i) Monte Carlo simulation with importance sampling of the earthquake magnitudes to generate a set of probabilistic earthquake scenarios (defined by source and magnitude); (ii) the optimization-based probabilistic scenario method, a mixed-integer linear program, to reduce the size of that set; (iii) Monte Carlo simulation to generate a set of probabilistic ground motion maps, varying the number of maps sampled from each earthquake scenario so as to minimize the sampling variance; and (iv) the optimization-based probabilistic scenario again to reduce the set of probabilistic ground motion maps. Copyright (C) 2012 John Wiley & Sons, Ltd.
引用
收藏
页码:2141 / 2158
页数:18
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