Razavi, S.V. and Jumaat, Mohd Zamin and EiShafie, A.H. and Mohammadi, P. (2011) General regression neural network (GRNN) for the first crack analysis prediction of strengthened RC oneway slab by CFRP. International Journal of Physical Sciences, 6 (10). pp. 24392446. ISSN 19921950, DOI https://doi.org/10.5897/IJPS10.578.

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Abstract
In this study, six strengthened RC oneway slabs with different lengths and thicknesses of CFRP were tested and compared with a similar RC slab without CFRP. The dimensions of the slabs were1800 x 400 x 120 mm and the lengths of CFRP used were 700, 1100, and 1500 mm, with different thicknesses of 1.2 and 1.8 mm. The results of the experimental operation for the first crack were used to generate general regression neural networks (GRNNs). Concerning the limited data for training and testing, the different data were extracted seven times for use as training and testing data. In this case, the optimum run was evaluated and compared with the experimental results. The results indicate that the amount of MSE and RMSE was acceptable and the correlation coefficient was close to 1.
Item Type:  Article 

Funders:  UNSPECIFIED 
Additional Information:  Cited By (since 1996): 2 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: Ahmed, A.N.E., Karim, A., Jaffar, O., Evaluation the efficiency of Radial Basis Function Neural Network for Prediction of water quality parameters (2009) Eng. Intell. Syst, 17 (4), pp. 221231; Amen, A., Laurent, M., Manuel, L., Patric, H., Strengthening slab using externallybonded strip composite (2008) Compos, 39, pp. 11251135; Clarke, J.K., Waldron, P., The reinforcement of concrete structures with advanced composites (1996) Struct. Eng, 74 (3), pp. 283288; Altun, F., Ã�zgÃ¼r, K., Kamil, A., Predicting the compressive strength of steel fiber added lightweight concrete using neural network (2008) Comput. Mater. Sci, 42, p. 2; Jamal, A.A., Elsanosi, A., Abdelwahab, A., Modeling and simulation of shear resistance of R/C beams using artificial neural network (2007) J. Franklin Inst, 344, pp. 741756; Kasperkiewics, J., Racz, J., Dubrawski, A., HPC strength prediction using ANN (1995) ASCE J. Comput. Civ. Eng, 9, pp. 279284; Lai, S., Sera, M., Concrete strength prediction by means of neural network (1997) Constr. Build. Mater, 11, pp. 9398; Lange, N.T., New mathematical approaches in hydrological modeling an application of artificial neural networks (1999) Phys. Chem. Earth (B), 24 (12), pp. 3135; Lee, S.C., Prediction of concrete strength using artificial neural Networks (2003) Eng. Struct, 25, pp. 849857; Mehmet, I., Modeling ultimate deformation capacity of RC columns using artificial Eng (2007) Struct, 29, pp. 329335; Naci, C., Muzaffer, E., Zeynep, D.Y., Mehmet, S., (2007) Neural Networks In 3dimensional Dynamic Analysis of Reinforced Concrete Buildings; Ola, E., Joakim, L., TÃ¤ljsten, B., Piotr, R., Thomas, O., CFRP strengthened openings in twoway concrete slabs  An experimental and numerical study (2007) Constr. Build. Mater, 21, pp. 810826; Pannirselvam, N., Raghunath, P.N., Suguna, K., Neural Networks for performance of Glass Fiber Reinforced polymer plated RC beam (2008) Am. J. Eng. Appl. Sci, 1, pp. 8288; Smith, S.T., Kim, S.J., (2008) Strengthening of Oneway Spanning RC Slabs With Cutouts Using FRP Composites, , The University of Hong Kong. University of Technology, Sydney, Australia; Specht, D.F., Probabilistic neural networks (1990) Neur. Netw, 3, pp. 109118; Sudheer, K.P., Gosain, A.K., Mohana, R.D., Saheb, S.M., Modelling evaporation using an artificial neural network algorithm (2002) Hydrol. Process. Lfi, pp. 31893202; Taljsten, B., Elfgren, L., Strengthening of concrete beams for shear using CFRPmaterials: Evaluation of different application methods (2000) Compos., Part B, Eng, 31, pp. 8796; Yeh, I.C., Modeling of strength of HPC using ANN (1998) Cem. Concr. Res, 28, pp. 17971808 
Uncontrolled Keywords:  CFRP, GRNN (general regression neural network), MSE, RMSE. 
Subjects:  T Technology > TA Engineering (General). Civil engineering (General) 
Divisions:  Faculty of Engineering 
Depositing User:  Mr Jenal S 
Date Deposited:  25 Apr 2013 01:25 
Last Modified:  05 Feb 2020 04:34 
URI:  http://eprints.um.edu.my/id/eprint/5941 
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