Improved metaheuristic algorithm enhanced adaptive neuro-fuzzy inference system for structural response prediction and reliability analysis

Purpose – Complex structures are subjected to multiple sources of uncertainty during service life, and their intrinsic failure mechanisms are complicated. It is important to achieve accurate response prediction and reliability analysis as a prerequisite for ensuring structural safety. Design/methodology/approach – An improved metaheuristic algorithm enhanced adaptive neuro-fuzzy inference system (ANFIS) (short for IMA-EANFIS) is developed based on ANFIS, C-means clustering and improved snow geese algorithm (ISGA). In this method, the ANFIS is used to describe the black-box relationship between input variables and output responses; the C-means clustering partitions the input space to control the number of fuzzy rules and avoid the “rule explosion”; the ISGA is employed to replace the gradient descent method to achieve global optimization of the ANFIS premise parameters and reduce the risk of trapping in local optima. Findings – The results demonstrate that the IMA-EANFIS approach exhibits excellent modeling and simulation performance. Originality/value – The proposed IMA-EANFIS method can provide instructive valuable insights for the reliability design and operational support of engineering structures.