发表机构
Johns Hopkins University; LIMOS, CNRS (UMR 6158), Université Clermont-Auvergne; Indian Institute of Science（约翰斯·霍普金斯大学; 利莫斯实验室，法国国家科学研究中心（联合研究单位6158），克莱蒙奥弗涅大学; 印度科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文综述了结构可靠性建模领域十年来的进展，涵盖时不变与时变可靠性问题，比较了解析法、重要性抽样、粒子分裂及机器学习等方法，并指出了未来研究方向。
AI 中文摘要
本文回顾了过去十年计算结构可靠性建模领域的进展。讨论聚焦于构件层面的时变/时不变可靠性问题。考虑了三大类问题：（a）涉及静态问题的时不变可靠性问题；（b）由随机激励驱动的确定性参数动力系统的时变可靠性分析问题；（c）受随机激励作用的随机参数动力系统的时变可靠性分析。讨论了四类方法：（a）基于一阶/二阶可靠性分析（用于时不变可靠性分析）的解析方法，以及用于时变可靠性问题的基于水平穿越的方法；（b）基于重要性抽样策略的方法（包括用于动力系统的基于Girsanov变换的方法）；（c）基于粒子和轨迹分裂的方法；（d）利用基于机器学习的工具（主要涉及代理模型开发和主动学习策略）来解决可靠性问题的方法。讨论的重点是方法论的进展，与具体应用相关的问题不在讨论范围内。本综述对替代方法的相对优缺点（在计算效率、精度、可扩展性、稀有事件处理、处理与失效面相关的几何复杂性的能力以及适用于高维问题的能力方面）进行了批判性讨论，并指出了未来研究的几个方向。
英文摘要
This paper reviews advances over the last decade in the field of computational structural reliability modelling. The discussion is focused on problems of time-variant/time-invariant reliability at the component level. Three broad classes of problems are considered: (a) time-invariant reliability problems involving static problems, (b) problems of time-variant reliability analysis for deterministically parametered dynamical systems driven by random excitations, and (c) time-variant reliability analysis of randomly parametered dynamical systems subjected to random excitations. Four classes of approaches are discussed: (a) analytical methods based on first/second order reliability analyses (for time-invariant reliability analysis) and level crossing based approaches for time-variant reliability problems, (b) methods based on importance sampling strategies (including the Girsanov transformation based method for dynamical systems), (c) particle and trajectory splitting based methods, and (d) methods that employ machine learning based tools (primarily involving development of surrogate models and active learning strategies) in tackling reliability problems. The focus of the discussions is on methodological advances, and questions related to specific applications are not addressed. The review presents critical discussions on the relative merits of alternative approaches (in terms of computational efficiency, accuracy, scalability, treatment of rare events, ability to handle geometric complexities linked to failure surface, and suitability for high-dimensional problems) and identifies several directions for future research.
