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Support vector regression based metamodel by sequential adaptive sampling for reliability analysis of structures

机译：通过顺序自适应采样支持矢量回归的元模型，实现结构可靠性分析

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摘要

Support vector regression (SVR) based metamodel is a powerful mean to alleviate computational challenge of Monte Carlo simulation (MCS) based reliability analysis of structure involving implicit limit state function. But, the sample size requirement is an important issue to achieve accuracy of estimated reliability. A two-stage iterative algorithm is explored to address this issue. The algorithm is hinged on the prediction accuracy of a metamodel near the failure surface region. In the first stage, an initial design of experiment is built by a space-filling design over the entire physical domain of the random variables. In the next stage, based on the prediction at MCS points using the previous SVR model, a subset of MCS samples are selected. These are now used to enrich existing design by adding more data points sequentially such that the new points are closer to the limit state and also as far as possible from the existing points. A comparative performance of reliability estimate by SVR with the proposed sequential adaptive approach and that of obtained by the relevance vector machines, Kriging and moving least square method based metamodels are performed to numerically demonstrate the improved reliability estimation capability of the proposed approach.
机译：支持向量回归（SVR）的Metamodel是一种强大的意义，可以减轻蒙特卡罗仿真（MCS）基于结构的计算挑战的结构挑战，该结构涉及隐式限制状态功能的结构。但是，样本大小要求是实现估计可靠性准确性的重要问题。探索了两阶段迭代算法来解决这个问题。算法铰接在故障表面区域附近的元模型的预测精度上。在第一阶段，通过随机变量的整个物理领域的空间填充设计构建了实验的初始设计。在下一个阶段，基于使用先前SVR模型的MCS点的预测，选择了MCS样本的子集。这些现在用于通过顺序添加更多数据点来丰富现有设计，使得新点更接近极限状态，也可以尽可能地从现有点。通过SVR利用所提出的顺序自适应方法的可靠性估计的比较性能以及由相关的矢量机器，Kriging和移动最小二乘法获得的基于元典的元素进行了数值上展示了所提出的方法的提高可靠性估计能力。

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