Improvement study on response surface method for reliability analysis in geotechnical engineering

In order to enhance computational efficiency and precision of response surface methods, the global response surface method is improved. Using RBF neural network substitutes BP network as the approximation of implicit performance function, and the response surface is formed in the sphere of hyper-pyramid at iteration step. Compared with other response surface methods, numerical case studies show that the proposed method not only markedly reduces the times of iterations and finite element analysis to shorten the processing time, but also benefits to better accuracy. Moreover, the engineering examples of shallow foundation and retaining wall are analyzed on their structural reliability. The calculation results show that the proposed method may gain reliability index satisfyingly by quicker convergence rate and less machine time, and to be suitable for reliability analysis in geotechnical engineering.