For the structural reliability analysis, although many methods have been proposed, they still suffer from substantial computational cost or slow convergence rate for complex structures, the limit state function of which are highly non-linear, high dimensional, or implicit. A novel adaptive surrogate model method is proposed by combining support vector machine (SVM) and Monte Carlo simulation (MCS) to improve the computational efficiency of estimating structural failure probability in this paper. In the proposed method, a new adaptive learning method is established based on the kernel function of the SVM, and a new stop criterion is constructed by measuring the relative position between sample points and the margin of SVM. Then, MCS is employed to estimate failure probability based on the convergent SVM model instead of the actual limit state function. Due to the introduction of adaptive learning function, the effectiveness of the proposed method is significantly higher than those that employed random training set to construct the SVM model only once. Compared with the existing adaptive SVM combined with MCS, the proposed method avoids information loss caused by inconsistent distance scales and the normalization of the learning function, and the proposed convergence criterion is also more concise than that employed in the existing method. The examples in the paper show that the proposed method is more efficient and has broader applicability than other similar surrogate methods.

对于结构可靠性分析，尽管已经提出了许多方法，但是对于复杂结构，其极限状态函数是高度非线性，高维或隐式的，仍然存在大量计算成本或收敛速度慢的问题。本文提出了一种新的自适应替代模型方法，该方法将支持向量机（SVM）和蒙特卡洛模拟（MCS）相结合，以提高估计结构破坏概率的计算效率。该方法基于支持向量机的核函数，建立了一种新的自适应学习方法，并通过测量样本点与支持向量机边缘之间的相对位置，构造了新的停止准则。然后，MCS用于基于收敛SVM模型而不是实际极限状态函数来估计故障概率。由于引入了自适应学习功能，因此该方法的有效性明显高于那些采用随机训练集仅构建一次SVM模型的方法。与现有的结合MCS的自适应SVM相比，该方法避免了距离尺度不一致和学习函数归一化导致的信息丢失，并且所提出的收敛准则也比现有方法更简洁。本文的算例表明，与其他类似的替代方法相比，该方法更有效，适用性更广。与仅使用一次随机训练集构建SVM模型的方法相比，该方法的有效性明显更高。与现有的结合MCS的自适应SVM相比，该方法避免了距离尺度不一致和学习函数归一化导致的信息丢失，并且所提出的收敛准则也比现有方法更简洁。本文中的例子表明，与其他类似的替代方法相比，该方法更有效，适用性更广。与仅使用一次随机训练集构建SVM模型的方法相比，该方法的有效性明显更高。与现有的结合MCS的自适应SVM相比，该方法避免了距离尺度不一致和学习函数归一化导致的信息丢失，并且所提出的收敛准则也比现有方法更简洁。本文的算例表明，与其他类似的替代方法相比，该方法更有效，适用性更广。所提出的方法避免了因距离尺度不一致和学习函数的规范化而导致的信息丢失，并且所提出的收敛准则也比现有方法所采用的准则更为简洁。本文的算例表明，与其他类似的替代方法相比，该方法更有效，适用性更广。所提出的方法避免了因距离尺度不一致和学习函数的规范化而导致的信息丢失，并且所提出的收敛准则也比现有方法所采用的准则更为简洁。本文的算例表明，与其他类似的替代方法相比，该方法更有效，适用性更广。