Hybrid Monte Carlo method in the reliability analysis of structures
Journal Title: Computer Assisted Methods in Engineering and Science - Year 2011, Vol 18, Issue 3
Abstract
The paper develops the idea of [8], i.e., the application of Artificial Neural Networks (ANNs) in probabilistic reliability analysis of structures achieved by means of Monte Carlo (MC) simulation. In this method, a feed-forward neural network is used for generating samples in the MC simulation. The patterns for network training and testing are computed by a Finite Element Method (FEM) program. A high numerical efficiency of this Hybrid Monte Carlo Method (HMC) is illustrated by two examples of the reliability analysis that refer to a steel girder [4] and a cylindrical steel shell [2].
Authors and Affiliations
Joanna Kaliszuk
Probabilistic multiscale analysis of inelastic localized failure in solid mechanics
In this work, we discuss the role of probability in providing the most appropriate multiscale based uncertainty quantification for the inelastic nonlinear response of heterogeneous materials undergoing localized failure....
Problems of the equilibrium of a rigid body and mechanical systems. (Received in the final form August 12, 2009)
In this article one of the greatest generalized methods for establishing the equilibrium equations of a rigid body and the set of rigid bodies is proposed. It is related to six equations of moments of force about six the...
Neural modelling of compactibility characteristics of cohesionless soil. (Received in the final form July 30, 2010)
Compaction is the method of in-situ soil modification to improve its engineering properties. Two key compactibility parameters are: the maximum dry density ρd max and the corresponding optimum water content wopt. They ar...
Numerical simulation of single phase flow in a flotation machine
In the paper, the numerical model of the flow phenomena in the flotation machine is presented. The process of flotation consists of a number of phenomena which provide serious numerical difficulties. One can enumerate ro...
On improved evolutionary algorithms application to the physically based approximation of experimental data
In this paper an evolutionary algorithms (EA) application to the physically based approximation (PBA) of experimental and/or numerical data is considered. Such an approximation may simultaneously use the whole experiment...