Table of Contents Author Guidelines Submit a Manuscript
Advances in Materials Science and Engineering
Volume 2016, Article ID 7648467, 10 pages
http://dx.doi.org/10.1155/2016/7648467
Research Article

Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming

1Department of Computer Science and Engineering, Thapar University, Patiala 147004, India
2Department of Civil Engineering, Thapar University, Patiala 147004, India

Received 23 June 2015; Accepted 18 August 2015

Academic Editor: Luigi Nicolais

Copyright © 2016 Palika Chopra 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

An effort has been made to develop concrete compressive strength prediction models with the help of two emerging data mining techniques, namely, Artificial Neural Networks (ANNs) and Genetic Programming (GP). The data for analysis and model development was collected at 28-, 56-, and 91-day curing periods through experiments conducted in the laboratory under standard controlled conditions. The developed models have also been tested on in situ concrete data taken from literature. A comparison of the prediction results obtained using both the models is presented and it can be inferred that the ANN model with the training function Levenberg-Marquardt (LM) for the prediction of concrete compressive strength is the best prediction tool.