An empirical model for shear capacity of RC deep beams using genetic-simulated annealing

• Autorzy: Gandomi A. H. , Alavi A. H. , Mohammadzadeh Shadmehri D. , Sahab M. G.
• Języki publikacji: EN

Abstrakty

EN

This paper presents an empirical model to predict the shear strength of RC deep beams. A hybrid search algorithm coupling genetic programming (GP) and simulated annealing (SA), called genetic simulated annealing (GSA), was utilized to develop mathematical relationship between the experimental data. Using this algorithm, a constitutive relationship was obtained to make pertinent the shear strength of deep beams to nine mechanical and geometrical parameters. The model was developed using an experimental database acquired from the literature. The results indicate that the proposed empirical model is properly capable of evaluating the shear strength of deep beams. The validity of the proposed model was examined by comparing its results with those obtained from American Concrete Institute (ACI) and Canadian Standard Association (CSA) codes. The derived equation is notably simple and includes several effective parameters.

Słowa kluczowe

EN

shear capacity; R/C deep beam; genetic-simulated annealing; empirical formula

PL

belka żelbetowa; nośność na ścinanie; wyżarzanie

• Wydawca: Springer ; Wrocław University of Science and Technology
• Czasopismo: Archives of Civil and Mechanical Engineering
• Rocznik: 2013
• Tom: Vol. 13, no. 3
• Strony: 354--369
• Opis fizyczny: Bibliogr. 48 poz., rys., tab., wykr.

Twórcy

autor

Gandomi A. H.

• Department of Civil Engineering, The University of Akron, Akron, OH44325-3905, USA

autor

Alavi A. H.

• Department of Civil and Environmental Engineering, Engineering Building, Michigan State University, East Lansing, MI 48824, USA

autor

Mohammadzadeh Shadmehri D.

• Civil Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran

autor

Sahab M. G.

• College of Civil Engineering, Tafresh University, Iran

Bibliografia

• [1] ACI, Committee 318. Building Code Requirements for Structural Concrete (ACI 318-05) and Commentary (ACI 318R-05), USA, 2005.
• [2] A.H. Alavi, M. Ameri, A.H. Gandomi, M.R. Mirzahosseini, Formulation of flow number of asphalt mixes using a hybrid computational method, Construction and Building Materials 25 (3) (2011) 1338–1355.
• [3] A.H. Alavi, A.H. Gandomi, A robust data mining approach for formulation of geotechnical engineering systems, Engineering Computations 28 (3) (2011) 242–274.
• [4] A.H. Alavi, A.H. Gandomi, A.A.R. Heshmati, Discussion on soft computing approach for real-time estimation of missing wave heights, Ocean Engineering 37 (13) (2010) 1239–1240.
• [5] A.H. Alavi, A.H. Gandomi, J.Boloury, A. Mollahasani, Linearand Tree-based genetic programming for solving geotechnical engineering problems, in: X.S. Yang et al. (Ed.), Chapter 12 in Metaheuristics in Water, Geotechnical and Transportation Engineering, Elsevier, Waltham, MA, 2013, pp. 289–310.
• [6] P. Aminian, H. Niroomand, A.H. Gandomi, A.H. Alavi, M. Arab Esmaeili, New design equations for assessment of load carrying capacity of castellated steel beams: a machine learning approach. Neural Computing & Applications, in press. doi:10.1007/s00521-012-1138-4.
• [7] A. Arabzadeh, A. R. Rahaie, R. Aghayari, Asimplestrut-and-tie model for prediction of ultimate shear strength of rc deep beams, International Journal of Civil Engineering 7 (3) (2009) 141–153.
• [8] A.F. Ashour, L.F. Alvarez, V.V. Toropov, Empirical modeling of shear strength of RC deep beams by genetic programming, Computers and Structures 81 (2003) 331–338.
• [9] M. Brameier, W. Banzhaf, A comparison of linear genetic programming and neural networks in medical data mining, IEEE Transactions on Evolutionary Computation 5 (1) (2001) 17–26.
• [10] M. Brameier, W. Banzhaf, Linear Genetic Programming, Springer Science Business Media, LLC, 2007.
• [11] K.K. Choi, A.G. Sherif, M.M. Reda Taha, L. Chung, Shear strength of slender reinforced concrete beams without web reinforcement: a model using fuzzy set theory, Engineering Structures 31 (2009) 768–777.
• [12] M. Conrads, O. Dolezal, F.D. Francone, P. Nordin, Discipulus Lite TM fast Genetic Programming Based on AIM Learning Technology, Register Machine Learning Technologies Inc., Littleton, 2004.
• [13] CSA, Committee A23.3. Design of Concrete Structures: Structures (Design) A National Standard of Canada, Canadian Standards Association, 1994.
• [14] L.M. Deschaine, F.A. Zafran, J.J. Patel, D. Amick, R. Pettit, F.D. Francone, P. Nordin, E. Dilkes, L.V. Fausett, Solving the unsolved using machine learning, Data mining and knowledge discovery to model a complex production process, in: Proceedings of Advanced Simulation Technologies Conference, Washington DC, USA, 2000.
• [15] Y.I. Elbahy, M. Nehdi, M.A. Youssef, Artificial neural network model for deflection analysis of superelastic shape memory alloy reinforced concrete beams, Canadian Journal of Civil Engineering 37 (6) (2010) 855–865.
• [16] G. Folino, C. Pizzuti, G. Spezzano, Genetic programming and simulated annealing: a hybrid method to evolve decision trees, in: R. Poli, W. Banzhaf, W.B. Langdon, J.F. Miller, P. Nordin, T.C. Fogarty (Eds.), Genetic Programming, Proceedings of EuroGP’2000, vol. 1802, Springer-Verlag, Edinburgh, 2000, pp. 294–303.
• [17] F.D. Francone, Discipulus Lite TM Owner’s Manual, Machine Learning Technologies Inc., Littleton, CO, USA, 2004.
• [18] I.E. Frank, R. Todeschini, The Data Analysis Handbook, Elsevier, The Netherland, Amsterdam, 1994.
• [19] A.H. Gandomi, A.H. Alavi, Expression programming techniques for formulation of structural engineering systems, in: A.H. Gandomi et al. (Ed.), Chapter 19 in Metaheuristic Applications in Structures and Infrastructures, Elsevier, Waltham, MA, USA, 2013.
• [20] A.H. Gandomi, A.H. Alavi, M. Mousavi, S.M. Tabatabaei, A hybrid computational approach to derive new ground-motion attenuation models, Engineering Applications of Artificial Intelligence 24 (4) (2011) 717–732.
• [21] A.H. Gandomi, A.H. Alavi, G.J. Yun, Nonlinear modeling of shear strength of sfrc beams using linear genetic programming, Structural Engineering and Mechanics 38 (1) (2011) 1–25.
• [22] A.H. Gandomi, S.K. Babanajad, A.H. Alavi, Y. Farnam, A novel approach to strength modeling of concrete under triaxial compression, Journal of Materials in Civil Engineering 24 (9) (2012) 1132–1143.
• [23] A.H. Gandomi, X.S. Yang, S. Talatahari, A.H. Alavi, Metaheuristics in modeling and optimization, in: A.H. Gandomi et al. (Ed.), Chapter 1 in Metaheuristic Applications in Structures and Infrastructures, Elsevier, Waltham, MA, USA, 2013.
• [24] A.H. Gandomi, G.J. Yun, A.H. Alavi, An evolutionary approach for modeling of shear strength of RC deep beams, Materials and Structures, in press, http://dx.doi.org/10.1617/s11527-013-0039-z.
• [25] A.H. Gandomi, A.H. Alavi, M.G. Sahab, M. Gandomi, M. Safari Gorji, Empirical models for the prediction of flexural resistance and initial stiffness of welded beam-column joints, in: Proceedings of the 11th East Asia-Pacific Conference on Structural Engineering & Construction, Taiwan, Paper no. 320, 2008.
• [26] A. Golbraikh, A. Tropsha, Beware of q2, Journal of Molecular Graphics and Modeling 20 (2002) 269–276.
• [27] S. Kirkpatrick, Gelatt C.D.J.R., M.P. Vecchi, Optimisation by simulated annealing, Science 220 (4598) (1983) 671–680.
• [28] F.K. Kong, G.R. Sharp, S.C. Appleton, C.J. Beaumont, L.A. Kubik, Structural idealization for deep beams with web openings: further evidence, Magazine of Concrete Research 30 (103) (1978) 89–95.
• [29] F.K. Kong, Reinforced Concrete Deep Beams, Taylor & Francis e-Library, Van Nostrand Reinhold New York, 2002.
• [30] J.R. Koza, Genetic Programming on the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge MA, USA, 1992.
• [31] A.B. Matamoros, K.H. Wong, Design of simply supported deep beams using strut-and-tie models, ACI Structural Journal 100 (6) (2003) 704–712.
• [32] S.T. Mau, T.T.C. Hsu, A formula for the shear strength of deep beams, ACI Structural Journal 86 (5) (1989) 516–523.
• [33] N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, E. Teller, Equation of state calculations by fast computing mechanics, Journal of Chemical Physics 21 (1953) 1087–1092.
• [34] E. Morsch, Concrete-Steel Construction, (Der Eisenbetonbau), English translation of the 3rd German Edition, McGraw-Hill Book Company, New York, 1909 (368 pp.).
• [35] A.C. Oreta, Simulating size effect on shear strength of RC beams without stirrups using neural networks, Engineering Structures 26 (5) (2004) 681–691.
• [36] Y. Pan, J. Jiang, R. Wang, H. Cao, Y. Cui, A novel QSPR model for prediction of lower flammability limits of organic compounds based on support vector machine, Journal of Hazardous Materials 168 (2009) 962–969.
• [37] J.W. Park, D. Kuchma, Strut-and-Tie model analysis for strength prediction of deep beams, ACI Structural Journal 104 (6) (2007) 657–666.
• [38] J.L. Perez, A. Cladera, J.R. Rabunal, F.M. Abella, Optimal adjustment of EC-2 shear formulation for concrete elements without web reinforcement using Genetic Programming, Engineering Structures 32 (2010) 3452–3466.
• [39] N.K. Raju, Advanced Reinforced Concrete Design, CBS Publishers & Distributors, New Delhi Bangalore, 2005.
• [40] W. Ritter, Die Bauweise Hennebique, Schweizerische Bauzeitung 33 (1899) 59–61.
• [41] P.P. Roy, K. Roy, On some aspects of variable selection for partial least squares regression models, QSAR & Combinatorial Science 27 (2008) 302–313.
• [42] A. Sanad, M.P. Saka, Prediction of ultimate shear strength of reinforced concrete deep beams using neural networks, Journal of Structural Engineering 127 (7) (2001) 818–827.
• [43] G.N. Smith, Probability and Statistics in Civil Engineering, Collins, London, 1986.
• [44] C.W. Tang, Using radial basis function neural networks to model torsional strength of reinforced concrete beams, Computers and Concrete 3 (5) (2006) 335–355.
• [45] C.Y. Tang, K.H. Tan, Interactive mechanical model for shear strength of deep beams, Journal of Structural Engineering 30 (10) (2004) 1534–1544.
• [46] R.S. Torres, A.X. Falcao, M.A. Goncalves, J.P. Papa, B. Zhang, W. Fan, E.A. Fox, A genetic programming framework for content-based image retrieval, Pattern Recognition 42 (2) (2009) 283–292.
• [47] L.A. Zadeh, Fuzzy sets, Information and Control 8 (1965) 338–353.
• [48] N. Zhang, K.H. Tan, Direct strut-and-tie model for single span and continuous deep beams, Engineering Structures 29 (11) (2007) 2987–3001.