Abstract
The inverse first-order reliability method (FORM) is considered to be one of the most widely used methods in inverse reliability analysis. It has been recognized that there are shortcomings of the inverse FORM in solving inverse reliability problems with implicit response functions, primarily inefficiency and difficulties involved in evaluating derivatives of the implicit response functions with respect to random variables. In order to apply the inverse FORM to structural inverse reliability analysis, response surface methods can be used to overcome the shortcomings. In the present paper, two different response surface methods, namely the polynomial-based response surface method and the artificial neural network-based response surface method, are developed to solve the inverse reliability problems with implicit response functions, and the accuracy and efficiency of the two response surface methods are demonstrated through two numerical examples of steel structures. It is found that the polynomial-based response surface method is more efficient and accurate than the artificial neural network-based response surface method. Recommendations are made regarding the suitability of the two response surface methods to solve the inverse reliability problems with implicit response functions.
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