A new sampling strategy for SVM-based response surface for structural reliability analysis

LIF: A new Kriging based learning function and its application to structural reliability analysis

“…These methods obtain a compromise between complexity and nonlinearity of meta-models. Compared with polynomial models, machine learning methods (neural network [21,22], support vector machine (SVM) [23][24][25], Multi-Layer Perceptrons [26] and so on) are more suitable to matching performance function with highly nonlinear input-output relationships. Kriging-based methods [27,28] are also widely used in reliability analysis [9,[29][30][31][32][33][34] and Global Optimization [35,36].…”

Application of Support Vector Machine in the analysis of the technical state of development in the LGOM mining area

“…The analysis of the subject literature demonstrates that the SVM method may also be used in the case of approximating the response surface of a structure in the reliability analysis [1]. Therefore, it is planned to use this methodology for the risk assessment of building structures in mining areas subject to the reliability theory.…”

Probability and interval hybrid reliability analysis based on adaptive local approximation of projection outlines using support vector machine

“…Considering nonlinear performance functions in SRA-RI, the kernel function for the nonlinear classification should be chosen. Due to its excellent applicability as well as high flexibility shown in many previous researches (Alibrandi, Alani, & Ricciardi, 2015;Bourinet et al, 2011;Pan & Dias, 2017;Song et al, 2013), the Gaussian kernel is employed in this work. The parameter in Gaussian kernel is defined as the mean of the pairwise distances between two types of training samples (Jaakkola, Diekhans, & Haussler, 1999).…”