Adaptive reliability analysis based on a support vector machine and its application to rock engineering

The response surface method (RSM), a simple and effective approximation technique, is widely used for reliability analysis in civil engineering. However, the traditional RSM needs a considerable number of samples and is computationally intensive and time-consuming for practical engineering problems with many variables. To overcome these problems, this study proposes a new approach that samples experimental points based on the difference between the last two trial design points. This new method constructs the response surface using a support vector machine (SVM); the SVM can build complex, nonlinear relations between random variables and approximate the performance function using fewer experimental points. This approach can reduce the number of experimental points and improve the efficiency and accuracy of reliability analysis. The advantages of the proposed method were verified using four examples involving random variables with different distributions and correlation structures. The results show that this approach can obtain the design point and reliability index with fewer experimental points and better accuracy. The proposed method was also employed to assess the reliability of a numerically modeled tunnel. The results indicate that this new method is applicable to practical, complex engineering problems such as rock engineering problems.