Estimation of system reliability by using the PLS-regression based corrected response surface method

jszych kwadratow A new computational method, referred as PLS-regression (PLSR) based corrected response surface method, has been developed for predicting the reliability of structural and mechanical systems subjecting to random loads, material properties, and geometry. The method involves a Corrected-Response Surface Model (C-RSM) based on the Partial Least Squares Regression Method (PLSRM) combined with some correction factors, and Monte Carlo Simulation (MCS), which is named as the Corrected-Partial Least Squares Regression-Response Surface Method (C-PLSRRSM). In order to develop an accurate surrogate model for the region determining the reliability of the system, a proper coefficient is presented to determine the sampling region of the input random variables. Due to a small number of original function evaluations, the proposed method is effective, particularly when a response evaluation entails costly finite-element, mesh-free, or other numerical analysis. Three numerical examples involving reliability problems of two structural systems and a mechanical system illustrate the method developed. Results indicate that the proposed method provides accurate and computationally efficient estimates of reliability. The proposed correction method, the PLSR based corrected response surface (C-PLSR-RS), can be the accurate surrogate model for calculating system reliabilities, especially for the implicit performance functions.