Computational Technology Resources - CCP

Paper IV.3

Prediction of Strength for Concrete Specimens using Artificial Neural Networks

A. Kaveh and A. Khalegi

Iran University of Technology, Narmak, Tehran, Iran, Building and Housing Research Centre, Tehran, Iran

doi:10.4203/ccp.53.4.3

A. Kaveh, A. Khalegi, "Prediction of Strength for Concrete Specimens using Artificial Neural Networks", in B.H.V. Topping, (Editor), "Advances in Engineering Computational Technology", Civil-Comp Press, Edinburgh, UK, pp 165-171, 1998. doi:10.4203/ccp.53.4.3

Keywords: concrete strength, admixture, artificial neural networks, backpropagation algorithm.

Abstract

Artificial Neural Networks are trained for different types of concrete mixtures, in order to predict the 7-day and 28-day strength of concrete specimens. Both plain and admixture concretes are considered. Employing the Backpropagation algorithm, neural nets with one, two and three hidden layers are trained and compared. The most efficient networks are then selected and used for predicting the strength of concrete mixtures, with reasonably small errors.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 53 ADVANCES IN ENGINEERING COMPUTATIONAL TECHNOLOGY Edited by: B.H.V. Topping Paper IV.3 Prediction of Strength for Concrete Specimens using Artificial Neural Networks A. Kaveh and A. Khalegi Iran University of Technology, Narmak, Tehran, Iran, Building and Housing Research Centre, Tehran, Iran doi:10.4203/ccp.53.4.3 purchase the full-text of this paper Full Bibliographic Reference for this paper A. Kaveh, A. Khalegi, "Prediction of Strength for Concrete Specimens using Artificial Neural Networks", in B.H.V. Topping, (Editor), "Advances in Engineering Computational Technology", Civil-Comp Press, Edinburgh, UK, pp 165-171, 1998. doi:10.4203/ccp.53.4.3 Keywords: concrete strength, admixture, artificial neural networks, backpropagation algorithm. Abstract Artificial Neural Networks are trained for different types of concrete mixtures, in order to predict the 7-day and 28-day strength of concrete specimens. Both plain and admixture concretes are considered. Employing the Backpropagation algorithm, neural nets with one, two and three hidden layers are trained and compared. The most efficient networks are then selected and used for predicting the strength of concrete mixtures, with reasonably small errors. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £75 +P&P)
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