A utilization of the inverse response surface method for the reliability-based design of structures

摘要

This paper discusses the pitfalls of using response surface methods when solving inverse problems and presents an adaptive artificial neural network-based inverse response surface method. The procedure is based on a coupling of the adaptive response surface method and artificial neural network-based inverse reliability analysis. The validity and accuracy of the method are tested on several examples. The first is a problem with a theoretical explicit nonlinear limit state function and one design parameter. Here, the accuracy of surrogate models for design parameter identification was tested for cases with the target values of the identified parameter both inside and outside of the initial range of values. The absolute percentage errors were 11.79 % and 0.19 % after the first and the last iteration of the identification process, respectively. The other two examples represent practical applications of the reliability design of structures with multiple design parameters and multiple reliability constraints. In the former, the limit state functions are defined explicitly, while in the latter, they are defined implicitly in the form of a structural analysis using the nonlinear finite element method. When assessing the reliability index values, very low absolute percentage error values were obtained in both examples. For the explicit form of the limit state function, the values were up to 0.50 % in all iterations. In the case of the implicitly defined limit state function, the absolute percentage error was equal to 6.45 % after the fist iteration and 0.79 % after the second iteration.