Browse > Article
http://dx.doi.org/10.12989/sem.2010.36.2.225

Modeling of compressive strength of HPC mixes using a combined algorithm of genetic programming and orthogonal least squares  

Mousavi, S.M. (Department of Civil Engineering, Sharif University of Technology)
Gandomi, A.H. (Department of Civil Engineering, Tafresh University)
Alavi, A.H. (School of Civil Engineering, Iran University of Science and Technology)
Vesalimahmood, M. (School of Mathematics, Iran University of Science and Technology)
Publication Information
Structural Engineering and Mechanics / v.36, no.2, 2010 , pp. 225-241 More about this Journal
Abstract
In this study, a hybrid search algorithm combining genetic programming with orthogonal least squares (GP/OLS) is utilized to generate prediction models for compressive strength of high performance concrete (HPC) mixes. The GP/OLS models are developed based on a comprehensive database containing 1133 experimental test results obtained from previously published papers. A multiple least squares regression (LSR) analysis is performed to benchmark the GP/OLS models. A subsequent parametric study is carried out to verify the validity of the models. The results indicate that the proposed models are effectively capable of evaluating the compressive strength of HPC mixes. The derived formulas are very simple, straightforward and provide an analysis tool accessible to practicing engineers.
Keywords
high performance concrete; genetic programming; orthogonal least square; compressive strength; formulation;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
Times Cited By Web Of Science : 2  (Related Records In Web of Science)
Times Cited By SCOPUS : 6
연도 인용수 순위
1 Gandomi, A.H. and Alavi, A.H. (2010c), "Hybridizing genetic programming with orthogonal least squares for modeling of soil liquefaction", Computational Collective Intelligence and Hybrid Systems Concepts and Applications, IGI Global Publishing (in press).
2 Banzhaf, W., Nordin, P., Keller, R. and Francone, F. (1998), Genetic Programming - An Introduction. on the Automatic Evolution of Computer Programs and Its Application, San Francisco: (The Morgan Kaufmann Series in Artificial Intelligence), Morgan Kaufmann Publishers, Heidelberg.
3 Gandomi, A.H., Alavi, A.H. and Arjmandi, P. (2010a), "Genetic programming and orthogonal least squares: a hybrid approach to modeling of compressive strength of CFRP-confined concrete cylinders" J. Mech. Mater. Struct. (in press).
4 Gandomi, A.H., Alavi, A.H., Sahab, M.G. and Arjmandi, P. (2010b), "Formulation of elastic modulus of concrete using linear genetic programming", J. Mech. Sci. Tech., 24(6), 1011-1017.   과학기술학회마을   DOI
5 Goodspeed, C.H., Vanikar, S. and Cook, R. (1996), "High-performance concrete (HPC) defined for highway structures", Concrete Int., 18(2), 62-67.
6 Chen, L. (2003), "A study of applying macroevolutionary genetic programming to concrete strength estimation", Expert. Syst. Appl., 17(4), 290-294.
7 Basma, A.A., Barakat, S. and Oraimi, S.A. (1999), "Prediction of cement degree of hydration using artificial neural networks", Mater. J., 96(2), 166-172.
8 Billings, S., Korenberg, M. and Chen, S. (1988), "Identification of nonlinear outputaffine systems using an orthogonal least-squares algorithm", Int. J. Syst. Sci., 19(8), 1559-1568.   DOI   ScienceOn
9 Cao, H., Yu, J., Kang, L. and Chen, Y. (1999), "The kinetic evolutionary modelling of complex systems of chemical reactions", Comput. Chem. Eng., 23(1), 143-151.   DOI
10 Alavi, A.H., Gandomi, A.H., Sahab, M.G. and Gandomi, M. (2010), "Multi expression programming: a new approach to formulation of soil classification", Eng. Comput., 26(2), 111-118.   DOI   ScienceOn
11 Chen, L. and Wang, T. (2010), "Modeling strength of high-performance concrete using an improved grammatical evolution combined with macro genetic algorithm", J. Comput. Civil Eng., 24(3), 281-288.   DOI   ScienceOn
12 Jepsen, M.T. (2002), "Predicting concrete durability by using artificial neural network", Published in a Special NCR-publication, ID. 5268.
13 Ji, T., Lin, T. and Lin, X. (2006), "A concrete mix proportion design algorithm based on artificial neural networks", Cement Concrete Res., 36(7), 1399-1408.   DOI   ScienceOn
14 Johari, A., Habibagahi, G. and Ghahramani, A. (2006), "Prediction of soil-water characteristic curve using genetic programming", J. Geotech. Geoenviron. Eng., 132(5), 661-665.   DOI   ScienceOn
15 Chen, S., Billings, S. and Luo, W. (1989), "Orthogonal least squares methods and their application to non-linear system identification", Int. J. Control, 50(5), 1873-1896.   DOI   ScienceOn
16 Domone, P. and Soutsos, M. (1994), "An approach to the proportioning of high-strength concrete mixes", Concrete Int., 16(10), 26-31.
17 Gandomi, A.H., Alavi, A.H., Mirzahosseini, M.R. and Moghadas Nejad, F. (2010b), "Nonlinear genetic-based models for prediction of flow number of asphalt mixtures", J. Mater. Civil Eng. (ASCE), DOI: 10.1061/(ASCE)MT.1943-5533.0000154 (in press).
18 Yeh, I. and Lien, L. (2009), "Knowledge discovery of concrete material using genetic operation trees", Expert. Syst. Appl., 36, 5807-5812.   DOI   ScienceOn
19 Ryan, T.P. (1997), Modern Regression Methods, Wiley, New York.
20 Salajegheh, E. and Ali, H. (2005), "Optimum design of structures against earthquake by wavelet neural network and filter banks", Earthq. Eng. Struct. D., 34(1), 67-82.   DOI   ScienceOn
21 Yeh, I.C. (1998), "Modeling of strength of high-performance concrete using artificial neural networks", Cement Concrete Res., 28(12), 1797-808.   DOI   ScienceOn
22 Yeh, I.C. (1998), "Modeling of strength of high-performance concrete using artificial neural networks", Cement Concrete Res., 28(12), 1797-1808.   DOI   ScienceOn
23 Yeh, I.C. (2006a), "Exploring concrete slump model using artificial neural networks", J. Comput. Civil Eng., 20(3), 217-221.   DOI   ScienceOn
24 Yeh, I.C. (2006c), "Generalization of strength versus water-cementations ratio relationship to age", Cement Concrete Res., 36(10), 1865-1873.   DOI   ScienceOn
25 Yeh, I.C. (2007), "Modeling slump flow of concrete using second-order regressions and artificial neural networks", Cement Concrete Comp., 29, 474-480.   DOI   ScienceOn
26 Pearson, R.K. (2003), "Selecting nonlinear model structures for computer control", J. Process Contr., 13(1), 1-26.   DOI   ScienceOn
27 Gandomi, A.H., Alavi, A.H. and Sahab, M.G. (2010a), "New formulation for compressive strength of CFRP Confined concrete cylinders using linear genetic programming", Mater. Struct., 43(7), 963-983.   DOI   ScienceOn
28 Rajasekaran, S., Suresh, D. and Pai, G.A.V. (2002), "Application of sequential learning neural networks to civil engineering modeling problems", Eng. Comput., 18, 138-147.   DOI   ScienceOn
29 Yeh, I.C. (2006b), "Analysis of strength of concrete using design of experiments and neural networks", J. Mater. Civil Eng., 18(4), 597-604.   DOI   ScienceOn
30 Maravall, A. and Gomez, V. (2004), EViews Software, Ver. 5, Quantitative Micro Software, LLC, Irvine CA.
31 Raghu Prasad, B.K., Eskandari, H. and Venkatarama Reddy, B.V. (2009), "Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN", Constr. Build. Mater., 23(1), 117-128.   DOI   ScienceOn
32 Rajasekaran, S. and Amalraj, R. (2002), "Predictions of design parameters in civil engineering problems using SLNN with a single hidden RBF neuron", Comput. Struct., 80(31), 2495-2505.   DOI   ScienceOn
33 Rajasekaran, S. and Lavanya, S. (2007), "Hybridization of genetic algorithm with immune system for optimization problems in structural engineering", Struct. Multidiscip. O., 34(5), 415-429.   DOI   ScienceOn
34 Madar, J., Abonyi, J. and Szeifert, F. (2005a), "Genetic programming for the identification of nonlinear inputoutput models", Indian Eng. Chem. Res., 44(9), 3178-3186.   DOI   ScienceOn
35 Reeves, C.R. (1997), "Genetic algorithm for the operations research", Inf. J. Comput., 9, 231-250.   DOI   ScienceOn
36 Kasperkiewicz, J., Racz, J. and Dubrawski, A. (1995), "HPC strength prediction using artificial neural network", J. Comput. Civil Eng., 9(4), 279-284.   DOI   ScienceOn
37 Koza, J.R. (1992), Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge.
38 Madar, J., Abonyi, J. and Szeifert, F. (2005b), "Genetic programming for the identification of nonlinear inputoutput models", White Paper.
39 Madar, J., Abonyi, J. and Szeifert, F. (2004), "Genetic programming for system identification", Proceedings of the Intelligent Systems Design and Applications (ISDA 2004) Conference, Budapest, Hungary.