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http://dx.doi.org/10.12989/sem.2013.45.3.323

Application of the ANFIS model in deflection prediction of concrete deep beam  

Mohammadhassani, Mohammad (Department of Civil Engineering, University of Malaya)
Nezamabadi-Pour, Hossein (Department of Electrical Engineering, Shahid Bahonar University of Kerman)
Jumaat, MohdZamin (Department of Civil Engineering, University of Malaya)
Jameel, Mohammed (Department of Civil Engineering, University of Malaya)
Hakim, S.J.S. (Department of Civil Engineering, University of Malaya)
Zargar, Majid (Department of Civil Engineering, University of Malaya)
Publication Information
Structural Engineering and Mechanics / v.45, no.3, 2013 , pp. 323-336 More about this Journal
Abstract
With the ongoing development in the computer science areas of artificial intelligence and computational intelligence, researchers are able to apply them successfully in the construction industry. Given the complexities indeep beam behaviour and the difficulties in accurate evaluation of its deflection, the current study has employed the Adaptive Network-based Fuzzy Inference System (ANFIS) as one of the modelling tools to predict deflection for high strength self compacting concrete (HSSCC) deep beams. In this study, about 3668measured data on eight HSSCC deep beams are considered. Effective input data and the corresponding deflection as output data were recorded at all loading stages up to failure load for all tested deep beams. The results of ANFIS modelling and the classical linear regression were compared and concluded that the ANFIS results are highly accurate, precise and satisfactory.
Keywords
ANFIS; deflection; deep beams; fuzzy inference system; linear regression;
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Times Cited By KSCI : 4  (Citation Analysis)
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1 Abraham, A. (2005), Rule-based expert systems, Sydenham PH, Thorn R Handbook of measuring system design, Wiley, New York.
2 Alavi, N., Nozari, V., Mazloumzadeh, S.M. and Nezamabadi-pour, H. (2010), "Irrigation water quality evaluation using adaptive network-based fuzzy inference system", Paddy Water Environ, 8, 259-266. DOI 10.1007/s10333-010-0206-6.   DOI
3 Bilgehan, M. (2011), "Comparison of ANFIS and NN models-with a study in critical buckling load estimation", Applied Soft Computing, 11, 3779-3791.   DOI   ScienceOn
4 Hakim, S.J.S., Noorzaei, J., Jaafar, M.S., Jameel, M. and Mohammadhassani, M. (2011), "Application of artificial neural networks to predict compressive strength of high strength concrete", International Journal of the Physical Sciences (IJPS), 6(5), 975-981.
5 Herrera, F. and Lozano, M. (2003), "Fuzzy adaptive genetic algorithm: design, taxonomy, and future directions", Soft Comput., 7, 545-562.   DOI   ScienceOn
6 Hwang, S.J. and Lee, H.J. (2002), "Strength prediction for discontinuity regions failing in diagonal compressions by softened strut-and-tie model", J. Struct. Eng., ASCE, 128(12), 1519-1526.   DOI   ScienceOn
7 Jang, J.S.R. (1993), "ANFIS: Adaptive network based fuzzy inference system", IEEE Transactions Systems, Man and Cybernetics, 23(3), 665-685.   DOI   ScienceOn
8 Lee, M.H. (2011), "Estimation of structure system input force using the inverse Fuzzy estimator", Structural Engineering and Mechanics, 37(4), 351-365.   DOI   ScienceOn
9 Lee, M.H. and Chen, T.C. (2010), "Intelligent fuzzy weighted input estimation method for the input force on the plate structure", Structural Engineering and Mechanics, 34(1), 1-14.   DOI   ScienceOn
10 Lu, W.Y., Hwang, S.J. and Lin, I.J. (2010), "Deflection prediction for reinforced concrete deep beams", Computers and Concrete., 7(1), 1-16.   DOI   ScienceOn
11 Mazloumzadeh, S.M., Shamsi, M. and Nezamabadi-pour, H. (2010), "Fuzzy logic to classify date palm trees based on some physical properties related to precision agriculture", Precision Agric, 11, 258-273. DOI 10.1007/s11119-009-9132-2 .   DOI
12 Mamdani, E. and Assilian, S. (1975), "An experiment in linguistic synthesis with a fuzzy logic controller", Int. J. Man Mach. Stud., 7(1),1-13.   DOI   ScienceOn
13 Mohammadhassani, M., Jumaat, M.Z., Ashour, A. and Jameel, M. (2011), "Failure modes and serviceability of high strength self compacting concrete deep beams", Engineering Failure Analysis, 18, 2272-2281.   DOI   ScienceOn
14 Mohammadhassani, M., Jumaat, M.Z. and Jameel, M. (2012), "Experimental investigation to compare the modulus of rupture in high strength self compacting concrete deep beams and high strength concrete normal beams", Construction and Building Materials, 30, 265-273.   DOI   ScienceOn
15 Mohammadhassani, M., Jumaat, M.Z., Jameel, M. and Arumugam, A.M.S. (2012a), "Ductility and performance assessment of High Strength Self Compacting Concrete (HSSCC) deep beams: an experimental investigation", Nuclear Engineering and Design, 250, 116-124.   DOI
16 Yang, K.H., Eun, H.C. and Chung, H.S. (2006), "The influence of web openings on the structural behaviour of reinforced high-strength concrete deep beams", Engineering Structures, 28,1825-1834.   DOI   ScienceOn
17 Mohammadhassani, M., NezamAbadiPour, H., Jumaat, M.Z., Jameel, M. and Arumugam, A.M.S. (2013), "Application of artificial neural network (ANN) and linear regressions (LR) in predicting the deflection of concrete deep beams", Computer and concrete, 11(3). (In print)
18 Takagi, T. and Sugeno, M. (1985), "Fuzzy identification of systems and its applications to modeling and control", Systems, Man and Cybernetics, IEEE Transactions, 35(1), 116-132.
19 Wilson, C.M.D. (2012), "Effects of multiple MR dampers controlled by Fuzzy-based strategies on structural vibration reduction", Structural Engineering and Mechanics, 41(3), 349-363.   DOI   ScienceOn
20 Yilmaz, I. and Kaynar, O. (2011), "Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils", Expert Systems with Applications, 38, 5958-5966.
21 Zadeh, L.A. (1965), "Fuzzy sets", Inform. Control, 8(3), 338-353.   DOI