Generalized Stratified Sampling for Efficient Reliability Assessment of Structures Against Natural Hazards
Generalized Stratified Sampling for Efficient Reliability Assessment of Structures Against Natural Hazards
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DOI：
10.1061/jenmdt.emeng-7021
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发表时间：
2023-05
期刊：
影响因子：
--
通讯作者：
S. Arunachalam;S. Spence
中科院分区：
文献类型：
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作者：
S. Arunachalam;S. Spence
基于性能的自然灾害工程有利于结构的设计和评估，严格评估其不确定的结构行为下，潜在的极端随机载荷表示的失效概率对规定的标准。因此，有效的随机模拟方案是中央的计算框架，旨在估计故障概率与多个极限状态使用有限的样本集。在这项工作中，广义分层抽样方案提出了两个阶段的抽样：第一个是专门的分层样本的生成和估计的分层概率，而第二个目的是在分层故障概率的估计。阶段I采样使得能够选择广义分层变量（即，不一定属于随机变量的输入集合），其概率分布先验地是未知的。为了提高效率，马尔可夫链蒙特卡罗第一阶段的抽样被认为是不可行的蒙特卡罗模拟和最佳的第二阶段的抽样实施基于用户指定的目标变异系数的极限状态的兴趣。这些系数的表达式推导出适当考虑到样本的相关性引起的马尔可夫链和估计的地层概率的不确定性。建议的随机模拟计划收获的好处，近最佳分层抽样的分层变量在高维可靠性问题的更广泛的选择与机制，以近似控制的故障概率估计的准确性。该方案的实用性证明了使用两个例子，涉及与高度非线性响应引起的风和地震激励的故障概率的估计。
Performance-based engineering for natural hazards facilitates the design and appraisal of structures with rigorous evaluation of their uncertain structural behavior under potentially extreme stochastic loads expressed in terms of failure probabilities against stated criteria. As a result, efficient stochastic simulation schemes are central to computational frameworks that aim to estimate failure probabilities associated with multiple limit states using limited sample sets. In this work, a generalized stratified sampling scheme is proposed in which two phases of sampling are involved: the first is devoted to the generation of strata-wise samples and the estimation of strata probabilities whereas the second aims at the estimation of strata-wise failure probabilities. Phase-I sampling enables the selection of a generalized stratification variable (i.e., not necessarily belonging to the input set of random variables) for which the probability distribution is not known a priori. To improve the efficiency, Markov Chain Monte Carlo Phase-I sampling is proposed when Monte Carlo simulation is deemed infeasible and optimal Phase-II sampling is implemented based on user-specified target coefficients of variation for the limit states of interest. The expressions for these coefficients are derived with due regard to the sample correlations induced by the Markov chains and the uncertainty in the estimated strata probabilities. The proposed stochastic simulation scheme reaps the benefits of near-optimal stratified sampling for a broader choice of stratification variables in high-dimensional reliability problems with a mechanism to approximately control the accuracy of the failure probability estimators. The practicality of the scheme is demonstrated using two examples involving the estimation of failure probabilities associated with highly nonlinear responses induced by wind and seismic excitations.