Table of Contents
Advances in Artificial Neural Systems
Volume 2014, Article ID 629137, 9 pages
http://dx.doi.org/10.1155/2014/629137
Research Article

Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks

Department of Civil Engineering, Malaviya National Institute of Technology Jaipur, JLN Marg, Jaipur, Rajasthan 302017, India

Received 29 August 2014; Accepted 20 October 2014; Published 11 November 2014

Academic Editor: Ping Feng Pai

Copyright © 2014 Vinay Chandwani et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Artificial neural networks (ANNs) have been the preferred choice for modeling the complex and nonlinear material behavior where conventional mathematical approaches do not yield the desired accuracy and predictability. Despite their popularity as a universal function approximator and wide range of applications, no specific rules for deciding the architecture of neural networks catering to a specific modeling task have been formulated. The research paper presents a methodology for automated design of neural network architecture, replacing the conventional trial and error technique of finding the optimal neural network. The genetic algorithms (GA) stochastic search has been harnessed for evolving the optimum number of hidden layer neurons, transfer function, learning rate, and momentum coefficient for backpropagation ANN. The methodology has been applied for modeling slump of ready mix concrete based on its design mix constituents, namely, cement, fly ash, sand, coarse aggregates, admixture, and water-binder ratio. Six different statistical performance measures have been used for evaluating the performance of the trained neural networks. The study showed that, in comparison to conventional trial and error technique of deciding the neural network architecture and training parameters, the neural network architecture evolved through GA was of reduced complexity and provided better prediction performance.