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A new method for reliability analysis of structures with mixed random and convex variables

机译：混合随机和凸变量结构的可靠性分析方法

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This paper proposes a method combining projection-outline-based active learning strategy with Kriging metamodel for reliability analysis of structures with mixed random and convex variables. In this method, it is determined that the approximation accuracy of projection outlines on the limit-state surface is crucial for estimation of failure probability instead of the whole limit-state surface. To efficiently improve the approximation accuracy of projection outlines, a new projection-outline-based active learning strategy is developed to sequentially obtain update points located around the projection outlines. Taking into account the influence of metamodel uncertainty on the estimation of failure probability, a quantification function of metamodel uncertainty is developed and introduced in the stopping condition of Kriging metamodel update. Finally, Monte Carlo simulation is employed to calculate the failure probability based on the refined Kriging metamodel. Four examples including the Burro Creek Bridge and a piezoelectric energy harvester are tested to validate the performance of the proposed method. Results indicate that the proposed method is accurate and efficient for reliability analysis of structures with mixed random and convex variables. (C) 2019 Elsevier Inc. All rights reserved.
机译：本文提出了一种将基于投影大纲的主动学习策略与Kriging Metomodel结合的方法，以实现混合随机和凸变量的结构的可靠性分析。在该方法中，确定极限状态表面上的投影轮廓的近似精度对于估计失效概率而不是整个极限状态表面是至关重要的。为了有效地提高投影轮廓的近似精度，开发了一种新的基于投影的主动学习策略以顺序地获得位于投影轮廓周围的更新点。考虑到Metamodel不确定度对失效概率估计的影响，在Kriging Metomodel更新的停止条件下开发并引入了元模型不确定性的量化函数。最后，使用Monte Carlo模拟来计算基于精制的Kriging Metomodel的故障概率。测试包括枪溪桥梁和压电能量收割机的四个例子，以验证所提出的方法的性能。结果表明，该方法对具有混合随机和凸变量的结构的可靠性分析是准确和有效的。 （c）2019 Elsevier Inc.保留所有权利。

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