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Improving analytical models of circular concrete columns with genetic programming polynomials

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Abstract

This study improves weighted genetic programming and uses proposed novel genetic programming polynomials (GPP) for accurate prediction and visible formulas/polynomials. Representing confined compressive strength and strain of circular concrete columns in meaningful representations makes parameter studies, sensitivity analysis, and application of pruning techniques easy. Furthermore, the proposed GPP is utilized to improve existing analytical models of circular concrete columns. Analytical results demonstrate that the GPP performs well in prediction accuracy and provides simple polynomials as well. Three identified parameters improve the analytical models—the lateral steel ratio improves both compressive strength and strain of the target models of circular concrete columns; compressive strength of unconfined concrete specimen improves the strength equation; and tie spacing improves the strain equation.

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References

  1. J.B. Mander, M.J.N. Priestley, R. Park, Observed stress-strain behavior of confined concrete. J. Struct. Eng. 114(8), 1827–1849 (1988)

    Article  Google Scholar 

  2. M. Saatcioglu, S.R. Razvi, Strength and ductility of confined concrete. J. Struct. Eng. 118(6), 1590–1607 (1992)

    Article  Google Scholar 

  3. K. Hoshikuma, K. Kawashima, K. Nagaya, A.W. Taylor, Stress-strain model for confined reinforced concrete in bridge piers. J. Struct. Eng. 123(5), 624–633 (1997)

    Article  Google Scholar 

  4. J. Sakai, Effect of Lateral Confinement of Concrete and Varying Axial Load on Seismic Response of Bridges. Doctor of Engineering Dissertation, Dept. of Civil Engineering, Tokyo Institute of Technology, Tokyo (2001)

  5. G.G. Penelis, A.J. Kappos, Earthquake-Resistant Concrete Structures, (E&FN Spon, London, 1997) Sec. 7.4, pp. 177–196

  6. M. Mehrjoo, N. Khaji, H. Moharrami, A. Bahreininejad, Damage detection of truss bridge joints using Artificial Neural Networks. Expert Syst. Appl. 35(3), 1122–1131 (2008)

    Article  Google Scholar 

  7. E. Mesbahi, Y. Pu, Application of ANN-based response surface method to prediction of ultimate strength of stiffened panels. J. Struct. Eng. 134(10), 1649–1656 (2008)

    Article  Google Scholar 

  8. Y.Q. Ni, H.F. Zhou, J.M. Ko, Generalization capability of neural network models for temperature- frequency correlation using monitoring data. J. Struct. Eng. 135(10), 1290–1300 (2009)

    Article  Google Scholar 

  9. H.-C. Tsai, Hybrid high order neural networks. Appl. Soft Comput. 9, 874–881 (2009)

    Article  Google Scholar 

  10. N.B. Dash, S.N. Panda, R. Remesan, N. Sahoo, Hybrid neural modeling for groundwater level prediction. Neural Comput. App. 19(8), 1251–1263 (2010)

    Article  Google Scholar 

  11. H.-C. Tsai, Predicting strengths of concrete-type specimens using hybrid multilayer perceptrons with center-unified particle swarm optimization. Expert Syst. Appl. 37, 1104–1112 (2010)

    Article  Google Scholar 

  12. A. Nazari, G. Khalaj, S. Riahi, ANFIS-based prediction of the compressive strength of geopolymers with seeded fly ash and rice husk-bark ash. Neural Comput. App. 1–13 (2011)

  13. S. Cawley, F. Morgan, B. McGinley, S. Pande, L. McDaid, S. Carrillo, J. Harkin, Hardware spiking neural network prototyping and application. Genet. Program. Evol. M. 12(3), 257–280 (2011)

    Article  Google Scholar 

  14. H. Yaprak, A. Karaci, I. Demir, (2011) Prediction of the effect of varying cure conditions and w/c ratio on the compressive strength of concrete using artificial neural networks. Neural Comput. App. 1–9 (2011)

  15. H.-C. Tsai, Y.-W. Wu, Y.-Y. Tyan, Y.-H. Lin, Programming Squat Wall Strengths and Tuning Associated Codes with Pruned Modular Neural Network. Neural Comput App, Accepted (2012)

  16. J.R. Koza, Genetic programming: On the programming of computers by means of natural selection (MIT Press, Massachusetts, 1992)

    MATH  Google Scholar 

  17. A. Baykasoglu, H. Güllü, H. Çanakçi, L. Ozbakir, Prediction of compressive and tensile strength of limestone via genetic programming. Expert Syst. Appl. 35(1–2), 111–123 (2008)

    Article  Google Scholar 

  18. M. Oltean, D. Dumitrescu, Multi expression programming, technical report, UBB-01-2002, Babes-Bolyai University, Cluj-Napoca, Romania (2002). www.mep.cs.ubbcluj.ro

  19. C. Ferreira, Gene expression programming: A new adaptive algorithm for solving problems. Compl. Syst. 13(2), 87–129 (2001)

    MATH  Google Scholar 

  20. M. Bhattacharya, A. Abraham, B. Nath, A linear genetic programming approach for modeling electricity demand prediction in Victoria. In Proceedings of the hybrid information systems, first international workshop on hybrid intelligent systems, Adelaide, Australia, pp 379–393 (2001).

  21. A. Baykasoglu, A. Oztas, E. Ozbay E, Prediction and multi-objective optimization of high-strength concrete parameters via soft computing approaches. Expert Syst. Appl. 36(3), 6145–6155 (2009)

    Article  Google Scholar 

  22. I.-C. Yeh, L.-C. Lien, Knowledge discovery of concrete material using genetic operation trees. Expert Syst. Appl. 36(3), 5807–5812 (2009)

    Article  Google Scholar 

  23. H.-C. Tsai, Using weighted genetic programming to program squat wall strengths and tune associated formulas. Eng. Appl. Artif. Intell. 24, 526–533 (2011)

    Article  Google Scholar 

  24. O. Giustolisi, D.A. Savic, A symbolic data-driven technique based on evolutionary polynomial regression. J. Hydroinf. 8(3), 207–222 (2006)

    Google Scholar 

  25. L. Berardi, Z. Kapelan, O. Giustolisi, D. Savic, Development of pipe deterioration models for water distribution systems using EPR. J. Hydroinf. 10(2), 113–126 (2008)

    Article  Google Scholar 

  26. A. Doglioni, D. Mancarella, V. Simeone, O. Giustolisi, Inferring groundwater system dynamics from hydrological time-series data. Hydrol. Sci. J. 55(4), 593–608 (2010)

    Article  Google Scholar 

  27. H.-C. Tsai, Y.-H. Lin, Predicting high-strength concrete parameters using weighted genetic programming. Eng. Comput. 27(4), 347–355 (2011)

    Article  MathSciNet  Google Scholar 

  28. J.H. Holland, Adaptation in neural and artificial systems (The University of Michigan Press, Ann Arbor, 1975)

    Google Scholar 

  29. A.W.C. Oreta, K. Kawashima, Neural network modeling of confined compressive strength and strain of circular concrete columns. J. Struct. Eng. 129(4), 554–561 (2003)

    Article  Google Scholar 

  30. J. Sakai, K. Kawashima, H. Une, K. Yoneda, Effect of tie spacing on stress-strain relation of confined concrete. J. Struct. Eng. 46A(3), 757–766 (2000)

    Google Scholar 

  31. M. Scardi, L.W. Harding, Developing an empirical model of phytoplankton primary production: a neural network case study. Ecol. Modell. 120(2), 213–223 (1999)

    Article  Google Scholar 

  32. C.-H. Peng, I.-C. Yeh, L.-C. Lien, Modeling strength of high-performance concrete using genetic operation trees with pruning techniques. Comput. Concrete 6(3), 203–223 (2009)

    Google Scholar 

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Correspondence to Hsing-Chih Tsai.

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Tsai, HC., Pan, CP. Improving analytical models of circular concrete columns with genetic programming polynomials. Genet Program Evolvable Mach 14, 221–243 (2013). https://doi.org/10.1007/s10710-012-9176-3

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  • DOI: https://doi.org/10.1007/s10710-012-9176-3

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