Fuzzy weight neural network in the analysis of concrete specimens and R/C column buckling tests

Journal Title: Computer Assisted Methods in Engineering and Science - Year 2011, Vol 18, Issue 4

Abstract

The paper describes the applications of back propagation neural networks with the ability to process input and output variables expressed as fuzzy numbers. The presentation of an algorithm for finding fuzzy neural network weights is followed by three examples of applications of this technique to the problems of implicit modelling of material and structure behaviour. The following problems are considered: prediction of concrete fatigue failure, high performance concrete strength prediction, and prediction of critical axial load for eccentrically loaded reinforced concrete columns.

Authors and Affiliations

Magdalena Jakubek

Keywords

Related Articles

Minimizing the memory usage with parallel out-of-core multi-frontal direct solver

This paper presents the out-of-core solver for three-dimensional multiphysics problems. In particular, our study focuses on the three-dimensional simulations of the linear elasticity coupled with acoustics. The out-of-co...

Neural identification of compaction characteristics for granular soils

The paper is a continuation of [9], where new experimental data were analysed. The Multi-Layered Perceptron and Semi-Bayesian Neural Networks were used. The Bayesian methods were applied in Semi-Bayesian NNs to the desig...

Application of artificial neural network in soil parameter identification for deep excavation numerical model

In this paper, an artificial neural network (ANN) is used to approximate response of deep excavation numerical model on input parameters. The approximated model is then used in minimization procedure of the inverse probl...

A meshless method using global radial basis functions for creating 3-D wind fields from sparse meteorological data

An efficient, global meshless method has been developed for creating 3-D wind fields utilizing sparse meteorological tower data. Meshless methods do not require the need for a mesh in order to connect node points. In thi...

Simulation of contact forces and contact characteristics during meshing of elastic beveloid gears.

Beveloid gears, also known as conical involute gears with very complex tooth shapes, gain more and more importance in industrial practice due to their ability to realize gear stages with crossed axes. This is why they ar...

Download PDF file
  • EP ID EP73985
  • DOI -
  • Views 152
  • Downloads 0

How To Cite

Magdalena Jakubek (2011). Fuzzy weight neural network in the analysis of concrete specimens and R/C column buckling tests. Computer Assisted Methods in Engineering and Science, 18(4), 243-254. https://europub.co.uk/articles/-A-73985