Problem
Research questions and friction points this paper is trying to address.
first-passage
dynamic reliability
non-Gaussian wind excitations
linear systems
First-passage reliability sensitivity analysis of linear systems subjected to non-Gaussian wind excitations by surface decomposition method
🤖 AI Summary
本文提出了一种表面分解方法，用于分析非高斯风激励下线性系统的首次穿越动态可靠性敏感性，通过分解复杂积分并使用嵌套采样算法有效估计。
📝 Abstract
This contribution develops a surface decomposition method for first-passage dynamic reliability sensitivity analysis of linear systems exposed to non-Gaussian wind excitations. The first-passage failure probability sensitivity is formulated as a system surface integral over a highly non-smooth and high-dimensional hypersurface. This complex integral is first decomposed into a collection of component surface integrals over the truncated smooth quadratic hypersurfaces. The dominant components are then identified based on the relative magnitudes of the first-order approximations of the component failure probabilities. A nested sampling algorithm is constructed to efficiently estimate the sum of these component surface integrals, in which the number of system limit-state function evaluations equals the number of outer-level samples while remaining independent of the inner-level sample size. A key advantage of the present approach is that the function evaluation results can be reused across different design parameters. Two numerical examples are explored to demonstrate the effectiveness of the proposed method. The results indicate that the number of function evaluations required is typically below 100 to achieve a target coefficient of variation of 0.1.
Innovation
Methods, ideas, or system contributions that make the work stand out.
surface decomposition method
first-passage dynamic reliability
non-Gaussian wind excitations
nested sampling algorithm
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