非 线 性 随 机 响 应 单 侧 尾 部 分 布 的 代 理 模 型 算 法

In the realm of stochastic nonlinear response analysis for large and intricate structures，the Monte Carlo simulation method stands out as a pivotal approach. However，its widespread practicality is hampered by its exorbitant computational costs. To surmount this challenge，researchers have endeavored to develop the active learning-based Gaussian process surrogate model algorithm. Despite its promise in reducing computational expenses，the optimization strategy associated with active learning necessitates further refinement to meet the exacting demands of engineering applications. For this purpose，we introduce a search function endowed with‘intelligent’attention capabilities. This function is meticulously crafted to concentrate on exceedingly high-risk one-sided tail events in engineering scenarios. By incorporating this search function，we have engineered an algorithm that surpasses existing methodologies. Our algorithm finds successful application in the analysis of complex adhesive anchoring structures within subway tunnel rings and linings. Compared to conventional methodologies，our algorithm exhibits a remarkable 30% reduction in the estimation error of single-tailed probabilities. This advancement facilitates a more precise estimation of the one-tailed probability distribution governing the stochastic response of complex structures. Consequently，it enhances the precision of assessing the occurrence probability of extreme events. These findings yield invaluable insights for decision-making processes in pertinent engineering domains and insurance sectors.