SVM在地下工程可靠性分析中的应用

将支持向量机应用到地下工程可靠性分析中，通过将支持向量机分别与一阶二次矩和蒙特卡洛结合，提出了基于支持向量机的可靠性分析方法，利用数值模拟构造学习样本，通过支持向量机学习，建立变形与随机变量之间映射关系的支持向量机表达，进而实现隧道极限状态函数及其偏导数的显式表达，从而计算隧道的可靠性指标。该方法避免了传统可靠性分析的缺点。算例分析结果表明，该方法计算效率高、结果可靠，对含有大量随机变量的复杂岩土工程可靠性分析具有很大的潜力，具有广泛的应用前景和工程价值。

Application of support vector machine to reliability analysis of underground engineering

The support vector machine (SVM)is applied to reliability analysis of underground engineering. A new method of tunnel reliability analysis is proposed by combining the SVM, numerical simulation, first order second moment (FORM) and Monte Carlo simulation. The learning samples are built by numerical simulation; then the relationship between displacement of tunnel and the random variables is built. The performance function and its derivatives in tunnel reliability analysis were approximated by SVM. The statistical moments calculated from the performance function values and the corresponding gradients using SVM were then used in the calculation of the reliability index in tunnel reliability analysis. An example is given for illustrating the applicability of the proposed approach. The new method is effective in tunnel reliability analysis. And it has potential application to other reliability problems of complicated engineering structure with a considerably large number of random variables.