Document Type

Article

Publication Date

1-1-2011

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 water-cement 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, feed-forward 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 3-10-1 (3 inputs, 10 neurons in the hidden layer, and 1 output) with the minimum error below 1 and maximum correlation coefficient close to 1.

Keywords

Artificial neural networks (ANNs), Compressive strength (CS), Feed-forward back propagation (FFBP), Scoria.

Divisions

fac_eng

Publication Title

International Journal of Physical Sciences

Volume

6

Issue

6

Publisher

International Journal of Physical Sciences

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. 345-358; Davies, J.M., Recent research advances in cold-formed steel structures (2000) J. Construct Steel Res., p. 55; Fatih, A., �zgür, 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., Rodríguez, A., Campos, P.L., Garabito, J., Calderón, V., Lightweight mortar made with recycled polyurethane foam (2010) Cement Concrete Comp., 32, pp. 672-677; 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. 117-125; 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, , Prentice-Hall. 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. 374-379. , Civil Engineering Group; Merikallio, T., Mannonen, R., Drying of Lightweight Concrete Produced From Crushed Expended Clay Aggregates (1996) Com Concer Res., 26, pp. 1423-1433; Short, M., Kinniburgh, W., (1978) Lightweight Concrete, pp. 443-455. , 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. 117-128. , College of Engineering. Al-Balqa' 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. 584-590

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