不确定度量化
不确定性传播
概率逻辑
不确定度分析
计算机科学
地震动
模块化设计
工程类
极限(数学)
地震风险
敏感性分析
结构工程
可靠性工程
数学优化
土木工程
数学
算法
模拟
操作系统
机器学习
数学分析
人工智能
作者
Angshuman Deb,Joel P. Conte,José I. Restrepo
摘要
Abstract This study complements and extends a recent work on the development of a rigorous framework for risk‐targeted performance‐based seismic design/assessment of ordinary standard bridges (OSBs) in California. Rooted in the formulation of this framework is an updated fully probabilistic performance‐based earthquake engineering (PBEE) assessment methodology wherein metrics of structural performance are formulated in terms of the mean return periods of exceedances for several strain‐based limit‐states (LSs). The originally proposed framework explicitly considering: (1) the uncertainty in the seismic input, and (2) the uncertainty in the capacity of the various LSs, is extended in this study to account for the following additional pertinent sources of uncertainty: (i) the aleatory uncertainty associated with finite element (FE) model parameters, and (ii) the epistemic parameter estimation uncertainty associated with using finite datasets to estimate the parameters of the probability distributions characterizing the FE model parameters and LS fragilities. These additional sources of uncertainty are commonly omitted or neglected in PBEE often by invoking that the earthquake ground motion uncertainty is the predominant source of uncertainty. However, their inclusion and consistent propagation in seismic performance‐based assessment of OSBs is imperative to obtain a more complete picture of seismic performance, thereby leading to a more comprehensive, transparent, and reliable design of these simple, yet essential bridges which represent an integral part of lifeline infrastructure systems especially in earthquake‐prone regions. The analytical and computational framework previously assembled is extended via modular incorporation of these additional sources of uncertainty. Four OSB testbeds and their risk‐targeted re‐designed versions are analyzed with and without these additional sources of uncertainty to evaluate their significance.
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