Nonlinear analysis of load-deflection testing of reinforced one-way slab strengthened by carbon fiber reinforced polymer (CFRP) and using artificial neural network (ANN) for prediction

Far, M.R.H. and Jumaat, Mohd Zamin and Vahid Razavi T., S. and Mohammadi, P. and Mohammadi, H. (2011) Nonlinear analysis of load-deflection testing of reinforced one-way slab strengthened by carbon fiber reinforced polymer (CFRP) and using artificial neural network (ANN) for prediction. International Journal of Physical Sciences, 6 (13). pp. 3054-3061. ISSN 19921950, DOI https://doi.org/10.5897/IJPS11.553.

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Abstract

Load-deflection curve is the most important part of the structural analysis of RC beam and slab. The load-deflection analysis of the RC one-way slab strengthened by CFRP using experimental work, finite element analysis (FEA), artificial neural network (ANN), and a comparison of them together are the important objective of this study. The dimension of the one-way slab was 1800�400�120 mm which was strengthened by different length and width of carbon fiber reinforced polymer (CFRP). The experimental results sufficiently adapted with FEA and ANNs output. The feed forward back-propagation (FFB) was the best ANN for prediction of load-deflection curve with minimum error below 1, and maximum correlation coefficient close to 1.

Item Type:	Article
Funders:	UNSPECIFIED
Additional Information:	Export Date: 6 January 2013 Source: Scopus Language of Original Document: English Correspondence Address: Far, M. R. H.; Civil Engineering Department, Islamic Azad University, Dezful BranchIran; email: halvaefar2006@gmail.com References: Akbulut, S., Hasilog¢lu, A.S., Pamukcu, S., (2004) Soil Dynamics and Earthquake Engineering, 24, pp. 805-814; Amen, A., Laurent, M., Manuel, L., Patric, H., Strengthening slab using externally-bonded strip composite (2008) Composite, 39, pp. 1125-1135; Bimal, B.A., Hiroshi, M., Prediction of shear strength of steel fiber RC beams using neural networks (2006) Construction Build. Mater., 20 (9), pp. 801-811; Caudill, M., Butler, C., (1990) Neural Intelligent System, , MIT Press. Cambridge. Ma; Chen, H.M., Tsai, K.H., Qi, G.Z., Yang, J.C.S., Amini, F., Neural networks for structural control (2005) J. Computational Civil Eng., 9 (2), pp. 168-176; Christopher, K.Y., Zhongfan, C., Stephen, K.L., Effect of size on the failure of FRP strengthened reinforced concrete beams (2002) Adv. Build. Technol., pp. 797-801; Clarke, J.K., Waldron, P., The reinforcement of concrete structures with advanced composites (1996) Struct. Eng., 74, p. 1996; Consolazio, G.R., Iterative equation solver for bridge analysis using neural networks (2000) Computer-Aided Civil Infrastructure Eng., 15 (2), pp. 107-119; Hadi, M.N.S., Neural network applications in concrete structures (2003) Comput. Struct. Elsevier Sci. Ltd, 81, pp. 373-381; Hawley, D.D., John, D.J., Dijjotam, R., Artificial Neural System: A New Tool Financial Decision Making (1990) Fin. Anal. J., pp. 63-72; Haykin, S., (1994) Neural Networks: A Comprehensive Foundation, , Macmillan College Publishing Company Inc. New York. United States; Hong-Guang, N., Ji-Zong, W., Prediction of compressive strength of confined concrete by neural networks (2000) Cement Concrete Res. Elsevier Sci., 30, pp. 1245-1250. , Ltd; Ilker, B.T., Mustafa, S., Prediction of rubberized mortar properties using artificial neural network and fuzzy logic (2008) J. Mater. Process. Technol., pp. 108-118; Jamal, A.A., Elsanosi, A., Abdelwahab, A., Modeling and simulation of shear resistance of R/C beams using artificial neural network (2007) J. Franklin Institute, 344 (5), pp. 741-756; Kasperkiewics, J., Racz, J., Dubrawski, A., HPC strength prediction using ANN (1995) ASCE J. Comput. Civil Eng., 9, pp. 279-284; Kerh, T., Yee, Y.C., Analysis of a deformed three-dimensional culvert structure using neural networks (2000) Adv. Eng. Software, 31 (5), pp. 367-375; Lee, J.J., Lee, J.W., Yi, J.H., Yun, C.B., Jung, H.Y., Neural network-based damage detection for bridges considering errors in baseline finite element models (2005) J. Sound Vib., 280, pp. 555-578; Li, L.J., Guo, Y.C., Liu, F., Bungey, J.H., An experimental and numerical study of the effect of thickness and length of CFRP on performance of repaired reinforced concrete beam (2005) Construction Building Mater., 20, pp. 901-909; Mansour, M.Y., Dicleli, M., Lee, J.Y., Zhang, J., Predicting the shear strength of reinforced concrete beams using artificial neural networks (2004) Engine. Struct., 26, pp. 781-799; Naci, C., Muzaffer, E., Zeynep, D.Y., Mehmet, S., (2007) Neural networks in 3-dimensional dynamic analysis of reinforced concrete buildings; Rajagopalan, P.R., Prakash, J., Naramimhan, V., Correlation between ultrasonic pulse velocity and strength of concrete (1973) Indian Concrete J., 47 (11), pp. 416-418; Ripley, B.D., (1996) Pattern recognition and neural networks, , Cambridge University Press. New York; Smith, S.T., Kim, S.J., (2008) Strengthening of one-way spanning RC slabs with cutouts using FRP composites, , The University of Hong Kong. University of Technology Sydney, Australia; Taljsten, B., Elfgren, L., Strengthening of concrete beams for shear using CFRP-materials: Evaluation of different application methods (2000) Composites. Part B. Eng., 31, pp. 87-96; Wasserman, R., Bentur, A., Interfacial interactions in lightweight aggregate concretes and their influence on the concrete strength (1996) Cement Concrete Composites, 18, pp. 67-76; Yeh, I.C., Modeling of strength of HPC using ANN (1998) Cem. Concr. Res., 28, pp. 1797-1808; Yeung, W.T., Smith, J.W., Damage detection in bridges using neural networks for pattern recognition of vibration signatures (2005) Eng. Struct., 27, pp. 685-698
Uncontrolled Keywords:	Artificial neural network (ANN), Carbon fiber reinforced polymer (CFRP), Feed forward back-propagation (FFB), Finite element analysis (FEA).
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Engineering
Depositing User:	Mr Jenal S
Date Deposited:	07 May 2013 02:46
Last Modified:	05 Feb 2020 04:33
URI:	http://eprints.um.edu.my/id/eprint/5987

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