Direct integration method based on dual neural networks to solve the structural reliability of fuzzy failure criteria

Abstract

In practical engineering, fuzzy failure criteria can reflect the actual conditions of the normal use and durability of structures. Therefore, this topic has garnered considerable research attention. First, a fuzzy set and a membership function were proposed in this study. A fuzzy reliability mathematical model of structures was obtained by means of the fuzzy random event probability. Second, the distribution forms of common membership functions were introduced, and the optimal membership function was selected based on the Akaike information criterion. Third, considering the difficulty of calculating multiple integrals in the fuzzy reliability mathematical model, a direct integration method based on dual neural networks was introduced. This method provides a new approach for calculating structural reliability with the fuzzy failure criteria. Finally, the proposed method was verified by numerical examples. The results showed that this method could solve structural fuzzy reliability problems with multidimensional random variables with high computational efficiency and accuracy.

A method for time‐varying reliability calculation of RC bridges considering random deterioration process

“…Neural networks have been increasingly applied in reliability analysis, particularly for establishing nonlinear mappings in limited essential variables 25,26 . This makes them ideal for processing complex reliability problems 27 .…”

“…23,24 Neural networks have been increasingly applied in reliability analysis, particularly for establishing nonlinear mappings in limited essential variables. 25,26 This makes them ideal for processing complex reliability problems. 27 With the ability to learn and generalize from a set of training data, neural networks can efficiently analyze and predict complex structural behaviors and system performance over time.…”

Multi-Extremum Adaptive Fuzzy Network Method for Dynamic Reliability Estimation Method of Vectoring Exhaust Nozzle

“…Zhai et al [18] combined the improved response surface model with the static test and the Monte Carlo method to propose a stochastic model updating strategy for improving the accuracy and efficiency of the complex structure calculation model, and studied the reliability of a simply supported beam and aero-engine stator system. Du et al [19] established a mathematical model of structural fuzzy reliability using the fuzzy random probability method, selected the optimal membership function, and proposed a direct integration method based on a dual neural network for the problem of the difficult multiple integration calculation in the fuzzy reliability mathematical model, which solved the structural fuzzy reliability problem with multidimensional random variables well and had high computational efficiency and accuracy. Xiao et al [20] used existing relevant reliability data to perform error comparison analysis on test set data, conducted simulation training, and established a three-layer continuous optimization feedforward neural network model for the reliability prediction of a CNC machine tool spindle.…”