Active learning methods have recently surged in the literature due to their ability to solve complex structural reliability problems within an affordable computational cost. These methods are designed by adaptively building an inexpensive surrogate of the original limit-state function. Examples of such surrogates include Gaussian process models which have been adopted in many contributions, the most popular ones being the efficient global reliability analysis (EGRA) and the active Kriging Monte Carlo simulation (AK-MCS), two milestone contributions in the field. In this paper, we first conduct a survey of the recent literature, showing that most of the proposed methods actually span from modifying one or more aspects of the two aforementioned methods. We then propose a generalized modular framework to build on-the-fly efficient active learning strategies by combining the following four ingredients or modules: surrogate model, reliability estimation algorithm, learning function and stopping criterion. Using this framework, we devise 39 strategies for the solution of $20$ reliability benchmark problems. The results of this extensive benchmark (more than $12,000$ reliability problems solved) are analyzed under various criteria leading to a synthesized set of recommendations for practitioners. These may be refined with a priori knowledge about the feature of the problem to solve, i.e. dimensionality and magnitude of the failure probability. This benchmark has eventually highlighted the importance of using surrogates in conjunction with sophisticated reliability estimation algorithms as a way to enhance the efficiency of the latter.
翻译:最近,由于能够以负担得起的计算成本解决复杂的结构可靠性问题,文献中的积极学习方法最近激增。这些方法的设计是适应性地建立一个廉价的替代原始限值功能的替代器。这些替代器的例子包括在许多贡献中采用的高斯进程模型,最受欢迎的模型是高效的全球可靠性分析(EGRA)和活跃的克里吉·蒙特卡洛模拟(AK-MCS),这是该领域的两个里程碑式贡献。在本文件中,我们首先对最近的文献进行调查,显示拟议方法大多实际上包括修改上述两种方法的一个或多个方面。然后,我们提出一个通用模块框架,通过合并以下四个要素或模块在现场建立高效的积极学习战略:代孕模型、可靠性估算算法、学习功能和停止标准。我们利用这一框架设计了39项战略,以解决20美元可靠性基准问题。根据各种标准分析了这一广泛基准(超过12 000美元的可靠性问题)的结果,从而得出一套综合的从业人员建议。我们提议采用这一前期的组合模式框架框架,将精密性的积极学习战略加以完善,同时结合关于可靠性的精密性模型,最终将精密性推测度推磨度推,从而推推推推推推,从而推推推推测了精确地推推了精确地推了精确地推测了精确度。