RM5Tree: Radial basis M5 model tree for accurate structural reliability analysis

Abstract The surrogate models-based prediction of performance functions is an efficient and accurate methodology in structural reliability analyses. In this paper, the M5 model tree (M5Tree) is improved based on radial basis training data set and it is named as Radial basis M5Tree (RM5Tree). To predict the performance function, the random input variables are transferred from ordinal space to radial space using several effective points for nonlinear calibrated model of RM5Tree. The input datasets are controlled using the radial dataset for high-dimensional reliability problems to reduce computational efforts to evaluate the performance function. The abilities of RM5Tree using Monte Carlo Simulation (MCS) with respect to accuracy and efficiency are investigated through five nonlinear reliability problems. The results indicate that the proposed RM5Tree performs superior manner in accuracy and efficiency compared to the M5Tree, response surface method (RSM) and first order reliability method.

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