Razavi, S.V. and Jumaat, Mohd Zamin and EiShafie, A.H. (2011) Using feedforward back propagation (FFBP) neural networks for compressive strength prediction of lightweight concrete made with different percentage of scoria instead of sand. International Journal of Physical Sciences, 6 (6). pp. 13251331. ISSN 19921950, DOI https://doi.org/10.5897/IJPS11.204.

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Abstract
Artificial neural networks (ANNs) are the result of academic investigations that use mathematical formulations to model nervous system operations. Neural networks (NNs) represent a meaningfully different approach to using computers in the workplace, and have been used to recognize patterns and relationships in data. In this paper, the compressive strength (CS) of lightweight material with 0, 20, 30, and 50 of scoria instead of sand, and different watercement ratios and cement content for 288 cylindrical samples were studied. Out of these, 36 samples were randomly selected for use in this research. The CS of these samples was used to teach ANNs CS prediction to achieve the optimal value. The ANNs were formed by MATLAB software so that the minimum error in information training and maximum correlation coefficient in data were the ultimate goals. For this purpose, feedforward back propagation (FFBP) with TRAINBR training function, LEARNGD adaption learning function, and SSE performance function were the last networks tried. The end result of the FFBP was 3101 (3 inputs, 10 neurons in the hidden layer, and 1 output) with the minimum error below 1 and maximum correlation coefficient close to 1.
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 Malaya (UM)Malaysia; email: Vahidrazavy@yahoo.com References: Bishop, C.M., (1995) Neural networks for pattern recognition, , Oxford University Press. Oxford. England; Carpenter, W.C., Barthelemy, J.F., Common Misconceptions about Neural Networks as Approximators (1994) ASCE J. Comput. Civil Eng., 8, pp. 345358; Davies, J.M., Recent research advances in coldformed steel structures (2000) J. Construct Steel Res., p. 55; Fatih, A., Ã�zgÃ¼r, K., Kamil, A., Predicting the compressive strength of steel fiber added lightweight concrete using neural network (2008) Comp. Mater. Sci., 42, p. 2; Gadea, J., RodrÃguez, A., Campos, P.L., Garabito, J., CalderÃ³n, V., Lightweight mortar made with recycled polyurethane foam (2010) Cement Concrete Comp., 32, pp. 672677; Haykin, S., (1999) Neural networks: A comprehensive foundation, , Prentice Hall Inc. Englewood Cliffs. N. J; Ilker, B.T., Mustafa, S., Prediction of properties of waste AAC aggregate concrete using artificial neural network (2007) Comput. Mater. Sci., 41, pp. 117125; Kulkarni, A.D., (1994) Artificial neural networks for image understanding, , Van Nosrand Reinhold. NY. USA; Laurene, V.F., (1994) Fundamentals of neural networks: Architectures, algorithms, and applications, , PrenticeHall. Englewood Cliffs. NJ; Manish, A.K., Rajiv, G., Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks (2006) Birla Inst. Technol. Sci. Autom. Construct., 15, pp. 374379. , Civil Engineering Group; Merikallio, T., Mannonen, R., Drying of Lightweight Concrete Produced From Crushed Expended Clay Aggregates (1996) Com Concer Res., 26, pp. 14231433; Short, M., Kinniburgh, W., (1978) Lightweight Concrete, pp. 443455. , Applied Science Publishers. London; Raghu, B.K., Eskandari, H., Venkatarama, B.V., (2001) Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN, 23, pp. 117128. , College of Engineering. AlBalqa' Applied University. Construct. Build. Mater; Rumelhart, D.E., Hinton, G.E., Williams, R.J., Learning Internal Representations by Error Propagation (1986) Parallel Distributed Processing Foundations, 1. , In, Rumelhart DE, and McClelland J L. The MIT Press; Unal, O., Uygunog, T., Yildiz, A., Investigation of properties of low strength lightweight concrete for thermal insulation (2007) Build. Environ., 42, pp. 584590 
Uncontrolled Keywords:  Artificial neural networks (ANNs), Compressive strength (CS), Feedforward back propagation (FFBP), Scoria. 
Subjects:  T Technology > TA Engineering (General). Civil engineering (General) 
Divisions:  Faculty of Engineering 
Depositing User:  Mr Jenal S 
Date Deposited:  29 Apr 2013 02:15 
Last Modified:  05 Feb 2020 04:30 
URI:  http://eprints.um.edu.my/id/eprint/5944 
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