Using generalized regression neural network (GRNN) for mechanical strength prediction of lightweight mortar

Razavi, S.V. and Jumaat, Mohd Zamin and Ahmed, E.S.H. and Mohammadi, P. (2012) Using generalized regression neural network (GRNN) for mechanical strength prediction of lightweight mortar. Computers and Concrete, 10 (4). pp. 379-390. ISSN 15988198

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In this paper, the 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 percentage of scoria instead of sand and 0.55 water-cement ratio and 350 kg/m3 cement content is investigated. The experimental result showed 7.9, 16.7 and 49 decrease in compressive strength, tensile strength and mortar density, respectively, by using 100 scoria instead of sand in the mortar. The normalized compressive and tensile strength data are applied for artificial neural network (ANN) generation using generalized regression neural network (GRNN). Totally, 90 experimental data were selected randomly and applied to find the best network with minimum mean square error (MSE) and maximum correlation of determination. The created GRNN with 2 input layers, 2 output layers and a network spread of 0.1 had minimum MSE close to 0 and maximum correlation of determination close to 1.

Item Type: Article
Additional Information: Export Date: 6 January 2013 Source: Scopus Language of Original Document: English Correspondence Address: Razavi, S.V.; Jundi-Shapur University of Technology, Dezful, Iran; email: References: Aydin, A.C., Karakoç, M.B., Düzgün, O.A., Bayraktutan, M.S., Effect of low quality aggregates on the mechanical properties of lightweight concrete (2010) Sci. Res. Essays, 5 (10), pp. 1133-1140; (1996) Determination of the Dry Density of Lightweight Aggregate Concrete with Open Structure, , BS EN 992, British Standards Institution; Specht, D.F., Probabilistic neural networks (1990) Neural Networks, 3 (1), pp. 109-118; Famili, H., (1997) A Project About Lightweight concrete, , Published by University of Elmo sanat, Iran, Tehran; Gadea, J., Rodríguez, A., Campos, P., Garabito, J., Calderón, V., Lightweight mortar made with recycled polyurethane foam (2010) Cement Concrete Comp., 32 (9), pp. 672-677; Kendrick, R., Acton, D., Duncan, A., Phase-diversity wave-front sensor for imaging systems (1994) Appl. Optics, 33 (27), pp. 6533-6547; Lanzon, M., Garcia-Ruiz, P.A., Lightweight cement mortars: Advantages and inconveniences of expanded perlite and its influence on fresh and hardened state and durability (2008) Construction and Building Materials, 22 (8), pp. 1798-1806. , DOI 10.1016/j.conbuildmat.2007.05.006, PII S095006180700133X; Lo, T.Y., Cui, H.Z., Effect of porous lightweight aggregate on strength of concrete (2004) Mater. Lett., 58 (6), pp. 916-919; Mahesh, P., Surinder, D., Modeling pile capacity using support vector machines and generalized regression neural network (2008) J. Geotech. Geoenviron., 134 (7), p. 1021; Mahmut, F., Gungor, M., Generalized regression neural networks and feed forward neural networks for prediction of scour depth around bridge piers (2007) Adv. Eng. Softw., 40 (8), pp. 731-737; Mannan, M.A., Alexander, J., Ganapathy, C., Teo, D., Quality improvement of oil palm shell (OPS) as coarse aggregate in lightweight concrete (2006) Build. Environ., 41 (9), pp. 1239-1242; Merikallio, T., Mannonen, R., Penttala, V., Drying of lightweight concrete produced from crushed expanded clay aggregates (1996) Cement and Concrete Research, 26 (9), pp. 1423-1433. , DOI 10.1016/0008-8846(96)00116-0, PII S0008884696001160; Mukesh, D., Introduction to neural networks: Application of neural computing for process chemists, part 1 (1996) J. Chem. Educ., 73 (5), pp. 431-434; Rajamane, N.P., Annie Peter, J., Ambily, P.S., Prediction of compressive strength of concrete with fly ash as sand replacement material (2007) Cement and Concrete Composites, 29 (3), pp. 218-223. , DOI 10.1016/j.cemconcomp.2006.10.001, PII S095894650600179X; Razavi, S.V., Jumaat, M.Z., El-Shafie, A.H., Mohammadi, P., Artificial neural network for mechanical strength prediction of lightweight mortar (2011) Sci. Res. Essays, 6 (16), pp. 3406-3417; Sanahi, G., (1998) Application of Perlit in Construction Process, , University of Tabriz, Iran; Sari, D., Pasamehmetoglu, A., (2004) The Effect of Grading and Admixture on the Pumice Lightweight Aggregate Concrete, Depart. Civil Eng., , Atilim University. Ankara. Turkey; Shideler, J., Lightweight aggregate concrete for structural use (1975) ACI J., 54 (4), pp. 299-328; Shorabi, M., Rigi, A., Application of lightweight concrete properties with lightweight grain of taftan in construction method (2005) 2nd International Conferences on Concrete & Development, p. 109. , Tehran, Iran; Short, M., Kinniburgh, W., (1978) Lightweight Concrete, p. 464. , Galliard; Topcu, I.B., Semi lightweight concretes produced by volcanic slags (1997) Cement and Concrete Research, 27 (1), pp. 15-21. , PII S0008884696001901; Unal, O., Uygunoglu, T., Yildiz, A., Investigation of properties of low-strength lightweight concrete for thermal insulation (2007) Building and Environment, 42 (2), pp. 584-590. , DOI 10.1016/j.buildenv.2005.09.024, PII S0360132305004294; Wehenkel, L., Contingency severity assessment for voltage security using non-parametric regression techniques (1996) IEEE Transactions on Power Systems, 11 (1), pp. 101-111; Wei, Y., Yang, J., Lin, Y., Chuang, S., Wang, H., Recycling of harbor sediment as lightweight aggregate (2008) Mar. Pollut. Bull., 57 (6-12), pp. 867-872; Williams, T., Gucunski, N., Neural networks for backcalculation of modula from SASW test (1995) J. Comput. Civil Eng., 9 (1), pp. 1-9
Uncontrolled Keywords: ANN, GRNN, Mechanical strength, MSE, Scoria, Cement content Generalized regression neural networks, Input layers, Lightweight mortars, Maximum correlations, Minimum mean square error (mse), Output layer, Water-cement ratio, Cements, Mortar, Network layers, Neural networks, Tensile strength, Strength of materials.
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering
Depositing User: Mr Jenal S
Date Deposited: 23 Apr 2013 00:49
Last Modified: 05 Feb 2020 04:38

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