Abstract
The potential of using genetic programming to predict engineering data has caught the attention of researchers in recent years. This paper utilizes a derivative of genetic programming to model the torsional strength of reinforced concrete beams using polynomial-like equations. Furthermore, the calculation results of current building codes are introduced into the learning of input-output functional mapping as potential inputs to improve prediction accuracy and to suggest improvements to these building codes. The results show that introducing European building codes significantly improves the prediction accuracy to a level that is significantly above that achievable using the initial parameters alone. In addition, the results highlight that improvements of particular building codes are relevant to different parameter combinations. Moreover, suggestions for future modifications of European building codes were brought out.
Similar content being viewed by others
References
ACI (2005). Building code requirements for structural concrete (ACI 318-05) and commentary (318R-05), ACI 318-2005, American Concrete Institute, Farmington Hills, MI, USA.
AS3600 (2001). Concrete structures, AS 3600, Standards Association of Australia, Sydney, Australia.
Babanajad, S. K., Gandomi, A. H., Mohammadzadeh S. D., and Alavi, A. H. (2013). “Numerical modeling of concrete strength under multiaxial confinement pressures using linear genetic programming.” Automation in Construction, Vol. 36, pp. 136–144, DOI: https://doi.org/10.1016/j.autcon.2013.08.016.
Baykasoglu, A., Güllü, H., Çanakçi, H., and Ozbakir, L. (2008). “Prediction of compressive and tensile strength of limestone via genetic programming.” Expert Systems with Applications, Vol. 35, Nos. 1–2, pp. 111–123, DOI: https://doi.org/10.1016/j.eswa.2007.06.006.
Baykasoglu, A., Oztas, A., and Ozbay, E. (2009). “Prediction and multi-objective optimization of high-strength concrete parameters via soft computing approaches.” Expert Systems with Applications, Vol. 36, No. 3, pp. 6145–6155, DOI: https://doi.org/10.1016/j.eswa.2008.07.017.
Berardi, L., Kapelan, Z., Giustolisi, O., and Savic, D. (2008). “Development of pipe deterioration models for water distribution systems using EPR.” Journal of Hydroinformatics, Vol. 10, No. 2, pp. 113–126, DOI: https://doi.org/10.2166/hydro.2008.000.
Bhattacharya, M., Abraham, A., and Nath, B. (2001). “A linear genetic programming approach for modeling electricity demand prediction in Victoria.” Proc. The Hybrid Information Systems, First International Workshop on Hybrid Intelligent Systems, Adelaide, Australia, pp. 379–393, DOI: https://doi.org/10.1007/978-3-7908-1782-9_28.
BS8110 (1985). Structural use of concrete: Part 2, BS 8110, British Standards, London, UK.
Cevik, A., Arslan, M. H., and Köroglu, M. A. (2010). “Genetic-programming-based modeling of RC beam torsional strength.” KSCE Journal of Civil Engineering, KSCE, Vol. 14, No. 3, pp. 371–384, DOI: https://doi.org/10.1007/s12205-010-0371-6.
Chaipimonplin, T. (2016). “Investigation internal parameters of neural network model for flood forecasting at upper river ping, Chiang Mai.” KSCE Journal of Civil Engineering, Vol. 20, No. 1, pp. 478–484, DOI: https://doi.org/10.1007/s12205-015-1282-3.
Chen, X. Y., Chau, K. W., and Busari, A. O. (2015). “A comparative study of population-based optimization algorithms for downstream river flow forecasting by a hybrid neural network model.” Engineering Applications of Artificial Intelligence, Vol. 46, pp. 258–268, DOI: https://doi.org/10.1016/j.engappai.2015.09.010.
Collins, C. D., Walsh, P. F., Archer, F. E., and Hall, A. S. (1965). “Reinforced concrete beams subjected to combined torsion and shear.” UNICIV Report, No. R-14, University of New South Wales, Sydney, Australia.
CSA (1994). Design of concrete structures: Structure design. CSA A23-3–94, Canadian Standard Association, Sydney, Australia.
Düenci, M., Aydemir, A., Esen, I., and Aydin, M. E. (2015). “Creep modelling of polypropylenes using artificial neural networks trained with bee algorithms.” Engineering Applications of Artificial Intelligence, Vol. 45, pp. 71–79, DOI: https://doi.org/10.1016/j.engappai.2015.06.016.
Eurocode 2 (2002). Design of concrete structures, prEN 1992-1–1, European Committee for Standardization, Brussel, Belgium.
Ferreira, C. (2001). “Gene expression programming: A new adaptive algorithm for solving problems.” Complex Systems, Vol. 13, No. 2, pp. 87–129, DOI: https://doi.org/10.13140/RG.2.1.1834.1208.
Fiore, A., Berardi, L., and Marano, G. C. (2012). “Predicting torsional strength of RC beams by using evolutionary polynomial regression.” Advances in Engineering Software, Vol. 47, No. 1, pp. 178–187, DOI: https://doi.org/10.1016/j.advengsoft.2011.11.001.
Fiore, A., Quaranta, G., Marano, G. C., and Monti, G. (2016). “Evolutionary polynomial regression-based statistical determination of the shear capacity equation for reinforced concrete beams without stirrups.” Journal of Computing in Civil Engineering, Vol. 30, No. 1, p. 04014111, DOI: https://doi.org/10.1061/(ASCE)CP.1943-5487.0000450.
Gandomi, A. H. and Roke, D. A. (2015). “Assessment of artificial neural network and genetic programming as predictive tools.” Advances in Engineering Software, Vol. 88, pp. 63–72, DOI: https://doi.org/10.1016/j.advengsoft.2015.05.007.
Giustolisi, O. and Savic, D. A. (2006). “A symbolic data-driven technique based on evolutionary polynomial regression.” Journal of Hydroinformatics, Vol. 8, No. 3, pp. 207–222, DOI: https://doi.org/10.2166/hydro.2006.020b.
Hsu, T. T. C. (1968). “Torsion of structural concrete-behavior of reinforced concrete rectangular members.” ACI Torsion of structural concrete SP-18, Farmington Hills., MI, USA, pp. 261–306.
Kang, B., Kim, Y. D., Lee, J. M., and Kim, S. J. (2015). “Hydro-environmental runoff projection under GCM scenario downscaled by artificial neural network in the Namgang Dam watershed, Korea.” KSCE Journal of Civil Engineering, Vol. 19, No. 2, pp. 434–445, DOI: https://doi.org/10.1007/s12205-015-0580-0.
Koza, J. R. (1992). Genetic programming: On the programming of computers by means of natural selection, MIT Press, MA, USA.
Lessig, N. N. (1959). “Determination of carrying capacity of reinforced concrete elements with rectangular cross-section subjected to flexure with torsion.” Institute Betona i Zhelezobetona, Vol. 5, pp. 5–28.
Nacar, S., Hınıs, M. A., and Kankal, M. (2017). “Forecasting daily streamflow discharges using various neural network models and training algorithms.” KSCE Journal of Civil Engineering, pp. 1–10, DOI: https://doi.org/10.1007/s12205-017-1933-7.
Oltean, M. and Dumitrescu, D. (2002). “Multi expression programming.” Technical Report, UBB-01-2002, Babes-Bolyai University, Cluj-Napoca, Romania.
Rausch, E. (1929). Design of reinforced concrete in torsion, Technische Hochschule, Berlin, Germany.
Shafabakhsh, G. A., Talebsafa, M., Motamedi, M., and Badroodi, S.K. (2015). “Analytical evaluation of load movement on flexible pavement and selection of optimum neural network algorithm.” KSCE Journal of Civil Engineering, Vol. 19, No. 6, pp. 1738–1746, DOI: https://doi.org/10.1007/s12205-014-0585-0.
Tsai, H. C. (2009). “Hybrid high order neural networks.” Applied Soft Computing, Vol. 9, pp. 874–881, DOI: https://doi.org/10.1016/j.asoc.2008.11.007.
Tsai, H. C. (2010). “Predicting strengths of concrete-type specimens using hybrid multilayer perceptrons with center-unified particle swarm optimization.” Expert Systems with Applications, Vol. 37, pp. 1104–1112, DOI: https://doi.org/10.1016/j.eswa.2009.06.093.
Tsai, H. C. (2011). “Using weighted genetic programming to program squat wall strengths and tune associated formulas.” Engineering Applications of Artificial Intelligence, Vol. 24, pp. 526–533, DOI: https://doi.org/10.1016/j.engappai.2010.08.010.
Yeh, I. C. and Lien, L.C. (2009). “Knowledge discovery of concrete material using genetic operation trees.” Expert Systems with Applications, Vol. 36, No. 3, pp. 5807–5812, DOI: https://doi.org/10.1016/j.eswa.2008.07.004.
Zia, P. and Hsu, T. T. C. (2004). “Design for torsion and shear in prestressed concrete flexural member.” PCI Journal, Vol. 49, No. 3, pp. 34–42, DOI: https://doi.org/10.15554/pcij.05012004.34.42.
Acknowledgements
The research presented in this paper was supported by the Ministry of Science and Technology, Taiwan under grant MOST 106-2221-E-011-019 held by H.-C. Tsai.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Tsai, HC., Liao, MC. Modeling Torsional Strength of Reinforced Concrete Beams using Genetic Programming Polynomials with Building Codes. KSCE J Civ Eng 23, 3464–3475 (2019). https://doi.org/10.1007/s12205-019-1292-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12205-019-1292-7