Table of Contents
ISRN Civil Engineering
Volume 2013, Article ID 609379, 5 pages
Research Article

Modeling the Effect of Crude Oil Impacted Sand on the Properties of Concrete Using Artificial Neural Networks

1Department of Civil Engineering, University of Ibadan, Ibadan, Nigeria
2Department of Civil and Environmental Engineering, Kwara State University, Malete, PMB 1530, Ilorin, Kwara State, Nigeria
3Segun Labiran and Associates, P.O. Box 6289 Agodi, Ibadan, Nigeria

Received 11 March 2013; Accepted 13 April 2013

Academic Editors: P. J. S. Cruz and H.-L. Luo

Copyright © 2013 W. O. Ajagbe 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.


A network of the feedforward-type artificial neural networks (ANNs) was used to predict the compressive strength of concrete made from crude oil contaminated soil samples at 3, 7, 14, 28, 56, 84, and 168 days at different degrees of contamination of 2.5%, 5%, 10%, 15%, 20% and 25%. A total of 49 samples were used in the training, testing, and prediction phase of the modeling in the ratio 32 : 11 : 7. The TANH activation function was used and the maximum number of iterations was limited to 20,000 the model used a momentum of 0.6 and a learning rate of 0.031056. Twenty (20) different architectures were considered and the most suitable one was the 2-2-1. Statistical analysis of the output of the network was carried out and the correlation coefficient of the training and testing data is 0.9955712 and 0.980097. The result of the network has shown that the use of neural networks is effective in the prediction of the compressive strength of concrete made from crude oil impacted sand.