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A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems

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Abstract

This paper presents a new approach for behavioral modeling of structural engineering systems using a promising variant of genetic programming (GP), namely multi-gene genetic programming (MGGP). MGGP effectively combines the model structure selection ability of the standard GP with the parameter estimation power of classical regression to capture the nonlinear interactions. The capabilities of MGGP are illustrated by applying it to the formulation of various complex structural engineering problems. The problems analyzed herein include estimation of: (1) compressive strength of high-performance concrete (2) ultimate pure bending of steel circular tubes, (3) surface roughness in end-milling, and (4) failure modes of beams subjected to patch loads. The derived straightforward equations are linear combinations of nonlinear transformations of the predictor variables. The validity of MGGP is confirmed by applying the derived models to the parts of the experimental results that are not included in the analyses. The MGGP-based equations can reliably be employed for pre-design purposes. The results of MSGP are found to be more accurate than those of solutions presented in the literature. MGGP does not require simplifying assumptions in developing the models.

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References

  1. Walter E, Pronzato L (1997) Identification of parametric models from experimental data. Springer, London

    MATH  Google Scholar 

  2. Metenidis MF, Witczak M, Korbicz J (2004) A novel genetic programming approach to nonlinear system modelling: application to the DAMADICS benchmark problem. Eng Appl Art Int 17:363–370

    Google Scholar 

  3. Guzelbey IH, Cevik A, Gogus MT (2006) Prediction of rotation capacity of wide flange beams using neural networks. J Constr Steel Res 62(10):950–961

    Google Scholar 

  4. Guzelbey IH, Cevik A, Erklig A (2006) Prediction of web crippling strength of cold-formed steel sheeting using neural networks. J Constr Steel Res 62(10):962–973

    Google Scholar 

  5. Gandomi AH, Alavi AH (2011) Applications of computational intelligence in behavior simulation of concrete materials. In: Yang XS, Koziel S (eds) Chapter 9 in Computational optimization and applications in engineering and industry. Springer, Berlin, pp 225–249

  6. Guven A (2011) A multi-output descriptive neural network for estimation of scour geometry downstream from hydraulic structures. Adv Eng Softw 42:85–93

    MATH  Google Scholar 

  7. Pala M (2006) A new formulation for distortional buckling stress in cold-formed steel members. J Constr Steel Res 62:716–772

    Google Scholar 

  8. Koza JR (1992) Genetic programming, on the programming of computers by means of natural selection. MIT Press, Cambridge

    MATH  Google Scholar 

  9. Gandomi AH, Alavi AH, Arjmandi P, Aghaeifar A, Seyednour R (2010) Genetic programming and orthogonal least squares: a hybrid approach to modeling of compressive strength of CFRP-confined concrete cylinders. J Mech Mater Struct 5(5):735–753

    Google Scholar 

  10. Gandomi AH, Alavi AH (2011) Multi-stage genetic programming: a new strategy to nonlinear system modeling. Inf Sci. doi:10.1016/j.ins.2011.07.026

  11. Gandomi AH, Alavi AH, Yun GJ (2011) Nonlinear modeling of shear strength of SFRC beams using linear genetic programming. Struct Eng Mech 38(1):1–25

    Google Scholar 

  12. Cevik A, Gög˘üs MT, Güzelbey IH, Filiz H (2010) Soft computing based formulation for strength enhancement of CFRP confined concrete cylinders. Adv Eng Softw 41:527–536

    MATH  Google Scholar 

  13. Gandomi AH, Alavi AH, Yun GJ (2011) Formulation of uplift capacity of suction caissons using multi expression programming. KSCE J Civil Eng 15(2):363–373

    Google Scholar 

  14. Gandomi AH, Alavi AH, Mirzahosseini MR, Moqaddas NF (2011) Nonlinear genetic-based models for prediction of flow number of asphalt mixtures. J Mater Civil Eng ASCE 23(3):248–263

    Google Scholar 

  15. Gandomi AH, Tabatabaei SM, Moradian MH, Radfar A, Alavi AH (2011) A new prediction model for the load capacity of castellated steel beams. J Construct Steel Res 67(7):1096–1105

    Google Scholar 

  16. Cevik A (2007) A new formulation for longitudinally stiffened webs subjected to patch loading. J Construct Steel Res 63:1328–1340

    Google Scholar 

  17. Cevik A, Sonebi M (2008) Modelling the performance of self-compacting SIFCON of cement slurries using genetic programming technique. Comput Concr 5:475–490

    Google Scholar 

  18. Cevik A, Guzelbey IH (2007) A soft computing based approach for the prediction of ultimate strength of metal plates in compression. Eng Struct 29:383–394

    Google Scholar 

  19. Cevik A (2007) A new formulation for web crippling strength of cold-formed steel sheeting using genetic programming. J Construct Steel Res 63:867–883

    Google Scholar 

  20. Cevik A (2007) Genetic programming based formulation of rotation capacity of wide flange beams. J Construct Steel Res 63:884–893

    Google Scholar 

  21. Searson DP, Willis MJ, Montague GA (2007) Co-evolution of nonlinear PLS model components. J Chemometr 2:592–603

    Google Scholar 

  22. Searson DP, Leahy DE, Willis MJ (2010) GPTIPS: an open source genetic programming toolbox for multigene symbolic regression. Proceedings of international multi conference on engineering computer science, Hong Kong

  23. Searson DP (2009) GPTIPS: genetic programming and symbolic regression for MATLAB

  24. Mousavi M, Gandomi AH, Alavi AH, Vesali M (2010) Modeling of compressive strength of HPC mixes using a combined algorithm of genetic programming and orthogonal least squares. Struct Eng Mech 36(2):225–241

    Google Scholar 

  25. Yeh IC (2006) Analysis of strength of concrete using design of experiments and neural networks. J Mater Civil Eng 18(4):597–604

    Google Scholar 

  26. Yeh IC, Lien L (2009) Knowledge discovery of concrete material using genetic operation trees. Expert Syst Appl 36:5807–5812

    Google Scholar 

  27. Chen L (2003) A study of applying macro evolutionary genetic programming to concrete strength estimation. J Comput Civ Eng 17:290–294

    Google Scholar 

  28. Chen L, Wang T (2010) Modeling strength of high-performance concrete using an improved grammatical evolution combined with macro genetic algorithm. J Comput Civil Eng 24:281–288

    Google Scholar 

  29. Calladine CR (1988) Theory of shell structures. Cambridge University Press, Cambridge

    Google Scholar 

  30. Shahin M, Elchalakani M (2008) Neural networks for modelling ultimate pure bending of steel circular tubes. J Constr Steel Res 64:624–633

    Google Scholar 

  31. Sherman DR (1986) Inelastic flexural buckling of cylinders, “steel structures”. Proceedings of steel structural conference on: record advances in application design, Budva, pp 339–357

  32. Sherman DR (1976) Tests of circular tubes in bending. J Struct Eng 102(ST11):2181–2195

    Google Scholar 

  33. Lou SM (1997) Development of four in-process surface recognition systems to predict surface roughness in end milling. Ph D thesis, Iowa State University, Iowa

  34. Lou SM, Chen CJ, Li MC (1998) Surface roughness prediction technique for CNC end-milling. J Ind Technol 15(1):1–6

    Google Scholar 

  35. Brezocnik M, Kovacic M, Ficko M (2004) Prediction of surface roughness with genetic programming. J Mater Proc Tech 157–158:28–36

    Google Scholar 

  36. Huang L, Chen JC (2001) A multiple regression model to predict inprocess surface roughness in turning operation via accelerometer. J Ind Technol 17(2):1–8

    Google Scholar 

  37. Fonseca ET, Vellasco MMBR, Vellasco PCGS, De Andrade SAL, Pacheco MAC (2008) A neuro-fuzzy evaluation of steel beams patch load behavior. Adv Eng Softw 39:558–572

    Google Scholar 

  38. Bergfelt A (1979) Patch loading on slender web. Influence of horizontal and vertical web stiffeners on the load carrying capacity, S79:1. Chalmers University of Technology Publication, Goteborg, pp 1–143

    Google Scholar 

  39. Gozzi J (2007) Patch loading resistance of plated girders—ultimate and serviceability limit state. Doctoral Thesis, Lulea University of Technology

  40. Smith GN (1986) Probability and statistics in civil engineering. Collins, London

    Google Scholar 

  41. Pan Y, Jiang J, Wang R, Cao H, Cui Y (2009) A novel QSPR model for prediction of lower flammability limits of organic compounds based on support vector machine. J Haz Mater 68:962–969

    Google Scholar 

  42. Frank IE, Todeschini R (2002) The data analysis handbook. Elsevier, Amsterdam, The Netherland, 1994

  43. Golbraikh A, Tropsha A (2001) Beware of q2. J Mol Graph Model 20:269–276

    Google Scholar 

  44. Alavi AH, Ameri M, Gandomi AH, Mirzahosseini MR (2011) Formulation of flow number of asphalt mixes using a hybrid computational method. Constr Build Mater 25:1338–1355

    Google Scholar 

  45. Kraslawski A, Pedrycz W, Nyström L (1999) Fuzzy neural network as instance generator for case-based reasoning system: an example of selection of heat exchange equipment in mixing. Neural Comput Appl 8(2):106–113

    Google Scholar 

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Authors and Affiliations

  1. Department of Civil Engineering, University of Akron, Akron, OH, 44325-3905, USA

    Amir Hossein Gandomi

  2. School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

    Amir Hossein Alavi

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  1. Amir Hossein Gandomi
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  2. Amir Hossein Alavi
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Correspondence to Amir Hossein Gandomi.

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Gandomi, A.H., Alavi, A.H. A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems. Neural Comput & Applic 21, 171–187 (2012). https://doi.org/10.1007/s00521-011-0734-z

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  • DOI: https://doi.org/10.1007/s00521-011-0734-z

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