Artificial neural networks for mechanical strength prediction of lightweight mortar

Razavi, S.V. and Jumaat, Mohd Zamin and Ei-Shafie, A.H. and Mohammadi, P. (2011) Artificial neural networks for mechanical strength prediction of lightweight mortar. Scientific Research and Essays, 6 (16). pp. 3406-3417. ISSN 19922248, DOI https://doi.org/10.5897/SRE11.311.

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Abstract

In this paper, the practical results of mechanical strength of different lightweight mortars made with 0, 5,10, 15, 20, 25, 30, 35, 40, 45, 50,55, 60, 65, 70, 75, 80, 85, 90, 95 and 100 of scoria instead of sand and 0.55 water-cement ratio and 350 kg/m3 cement content have been used to generate artificial neural networks (ANNs). Totally, 52 feed-forward back-propagation neural networks (FFBNN) with different parameters have been investigated in the case of 80 data for training, 15 data for verifying, and 10 data for testing. The performance for producing networks was evaluated by root mean squared error (RMSE) and the correlation coefficient between data. The two selected networks, N1 (Net Architecture 2-10-2) and N2 (Net Architecture 2-10-5-2) had (0.020, 0.027) and (0.017, 0.018) as (Training, Testing) RMSE set and 0.997 and 0.982 as testing correlation coefficient.

Item Type:	Article
Funders:	UNSPECIFIED
Additional Information:	Cited By (since 1996): 1 Export Date: 6 January 2013 Source: Scopus Language of Original Document: English Correspondence Address: Razavi, S. V.; Civil Engineering Department, University MalaysiaMalaysia; email: Vahidrazavy@yahoo.com References: Abedi, J., Mollahi, A., An Investigation in Mechanical Property of Lightweight Concrete with Rice Stalk (2005) 2nd International Conferences on Concrete & Development, p. 112. , Tehran. Iran; Ahmet, O., Murat, P., Erdog, O., Erdog, K., Naci, �., Asghar, M.B., Predicting the compressive strength and slump of high strength concrete using neural network (2006) Constr. 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Civil-Comp Press
Uncontrolled Keywords:	Artificial neural networks, Feed-forward back-propagation neural networks, Scoria.
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Engineering
Depositing User:	Mr Jenal S
Date Deposited:	29 Apr 2013 02:13
Last Modified:	05 Feb 2020 04:37
URI:	http://eprints.um.edu.my/id/eprint/5942

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