Neural and finite element analysis of a plane steel frame reliability by the classical Monte Carlo method

Abstract

The paper is a continuation of [4], where a feed-forward neural network was used for generating samples in the Monte Carlo methods. The patterns for network training and testing were computed by an FEM program. A high numerical efficiency of neural generating MC samples does not correspond to the much more time consuming FEM computation of patterns. This question and an evaluation of the number of random inputs is discussed in the presented paper on an example of plane steel frame, called in [5] a calibrating frame.