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J. Bai, S. Wild, J. Ware, B. Sabir, Using neural networks to predict workability of concrete incorporating metakaolin and fly ash, Adv. Eng. Softw., 34 (2003) 663-669.

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Article

Prediction of Compressive Strength of Plain Concrete Confined with Ferrocement using Artificial Neural Network (ANN) and Comparison with Existing Mathematical Models

1Urban & Infrastructure Engineering, NED University of Engineering & Technology, Karachi, Pakistan

2Civil Engineering Department, NED University of Engineering & Technology, Karachi, Pakistan


American Journal of Civil Engineering and Architecture. 2013, Vol. 1 No. 1, 7-14
DOI: 10.12691/ajcea-1-1-2
Copyright © 2013 Science and Education Publishing

Cite this paper:
S. U. Khan, T. Ayub, S. F. A. Rafeeqi. Prediction of Compressive Strength of Plain Concrete Confined with Ferrocement using Artificial Neural Network (ANN) and Comparison with Existing Mathematical Models. American Journal of Civil Engineering and Architecture. 2013; 1(1):7-14. doi: 10.12691/ajcea-1-1-2.

Correspondence to: S. U. Khan, Urban & Infrastructure Engineering, NED University of Engineering & Technology, Karachi, Pakistan. Email: sadaquat78@hotmail.com

Abstract

This paper is an extension of the work published in year 2010 in which compressive strength of plain concrete confined with Ferrocement was estimated using mathematical models and compared with 55 experimental results. In this paper, predictive model of compressive strength for plain concrete confined with Ferrocement has been developed by using MATLAB Artificial Neural Network (ANN) simulation. Out of 55, 19 experimental results are selected for training of multilayer feed forward neural network. Comparative analysis of the results showed that compressive strength estimated by ANN predictive model are very close to the experimental results than existing theoretical models.

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