Innovative Approach for Moment Capacity Estimation of Spirally Reinforced Concrete Columns Using Swarm Intelligence–Based Algorithms and Neural Network
Publication: Practice Periodical on Structural Design and Construction
Volume 26, Issue 4
Abstract
The purpose of this paper is to present an innovative equation to predict the moment capacity of spirally reinforced concrete columns with high accuracy using a combination of neural network and metaheuristic optimization algorithms. To this end, a large experimental database has been gathered to train a neural network with seven independent parameters that deal with the dimensional properties of the columns, reinforcements, materials, and also the forces. Furthermore, the authors improved the process of training with consideration of two optimization techniques: particle swarm optimization (PSO) and Harris hawks optimization (HHO). Then, the best model was selected to a statistical methodology to extract an empirical equation to predict the target, which makes the proposed system of this article more applicable, especially for the practical usages. The results indicated that the neural network with the PSO algorithm had better results than the other model. Also, it has been found that the proposed formulation could predict the moment capacity of the considered element with high performance. The presented equation of this article has many applications in civil engineering, such as retrofitting and rehabilitation.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
References
Ahmad, S. H., and S. P. Shah. 1982. “Complete tri-axial stress–strain curves for concrete.” ACI J. 79 (46): 484–490.
Berry, M., M. Parrish, and M. Eberhard. 2004. PEER structural performance database. Berkeley, CA: Univ. of California, Berkeley.
Carpenter, G. A. 1989. “Neural network models for pattern recognition and associative memory.” Neural Netw. 2 (4): 243–257. https://doi.org/10.1016/0893-6080(89)90035-X.
Cascardi, A., F. Micelli, and M. A. Aiello. 2017. “An artificial neural networks model for the prediction of the compressive strength of FRP-confined concrete circular columns.” Eng. Struct. 140 (Jun): 199–208. https://doi.org/10.1016/j.engstruct.2017.02.047.
Chan, W. W. 1955. “The ultimate strength and deformation of plastic hinges in reinforced concrete frameworks.” Mag. Concr. Res. 77 (21): 21. https://doi.org/10.1680/MACR.1955.7.21.121.
Chen, M. Y. 2013. “A hybrid ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clustering.” Inf. Sci. 220 (Jan): 180–195. https://doi.org/10.1016/j.ins.2011.09.013.
Desayi, P., K. T. Sundara Raja Iyengar, and T. Sanjeeva Reddy. 1978. “Equation for stress-strain curve of concrete confined in circular steel spiral.” Matériaux et Constr. 11 (5): 339–345. https://doi.org/10.1007/BF02473875.
Eberhart, R., and J. Kennedy. 1995. “A new optimizer using particle swarm theory.” In Proc., 6th Int. Symp. on Micro Machine and Human Science. Piscataway, NJ: IEEE.
Heidari, A. A., S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen. 2019. “Harris hawks optimization: Algorithm and applications.” Future Gener. Comput. Syst. 97 (Aug): 849–872. https://doi.org/10.1016/j.future.2019.02.028.
Inel, M. 2007. “Modeling ultimate deformation capacity of RC columns using artificial neural networks.” Eng. Struct. 29 (3): 329–335. https://doi.org/10.1016/j.engstruct.2006.05.001.
Kalman Sipos, T., and P. Parsa. 2020. “Empirical formulation of ferrocement members moment capacity using artificial neural networks.” J. Soft Comput. Civ. Eng. 4 (1): 111–126. https://doi.org/10.22115/SCCE.2020.221268.1181.
Kent, D. C., and R. Park. 1971. “Flexural members with confined concrete.” Struct. Div. 97 (7): 1969–1990. https://doi.org/10.1061/JSDEAG.0002957.
Köroğlu, M. A., M. Ceylan, M. H. Arslan, and A. İlki. 2012. “Estimation of flexural capacity of quadrilateral FRP-confined RC columns using combined artificial neural network.” Eng. Struct. 42 (Sep): 23–32. https://doi.org/10.1016/j.engstruct.2012.04.013.
Lefevre, L., P. Whittle, E. Lascaris, and A. Finkelstein. 1997. “Feeding innovations and forebrain size in birds.” Anim. Behav. 53 (3): 549–560. https://doi.org/10.1006/anbe.1996.0330.
Liu, B., Y. Zhang, R. Li, G. Zhou, and Y. Zhao. 2020. “Essential stressing state features of spirally reinforced concrete short columns revealed by modeling experimental strain data.” Structures 25 (Feb): 1–7. https://doi.org/10.1016/j.istruc.2020.02.006.
Madani, H., M. Kooshafar, and M. Emadi. 2020. “Compressive strength prediction of nanosilica-incorporated cement mixtures using adaptive neuro-fuzzy inference system and artificial neural network models.” Pract. Period. Struct. Des. Constr. 25 (3): 04020021. https://doi.org/10.1061/(ASCE)SC.1943-5576.0000499.
Mander, J. B., M. J. N. Priestley, and R. Park. 1988. “Observed stress-strain behavior of confined concrete.” J. Struct. Eng. 114 (8): 1827–1849. https://doi.org/10.1061/(ASCE)0733-9445(1988)114:8(1827).
McCulloch, W. S., and W. Pitts. 1943. “A logical calculus of the ideas immanent in nervous activity.” Bull. Math. Biophys. 5 (4): 115–133. https://doi.org/10.1007/BF02478259.
Naderpour, H., A. Kheyroddin, and G. G. Amiri. 2010. “Prediction of FRP-confined compressive strength of concrete using artificial neural networks.” Compos. Struct. 92 (12): 2817–2829. https://doi.org/10.1016/j.compstruct.2010.04.008.
Naderpour, H., and M. Mirrashid. 2018. “An innovative approach for compressive strength estimation of mortars having calcium inosilicate minerals.” J. Build. Eng. 19 (Sep): 205–215. https://doi.org/10.1016/j.jobe.2018.05.012.
Naderpour, H., and M. Mirrashid. 2020. “Moment capacity estimation of spirally reinforced concrete columns using ANFIS.” Complex Intell. Syst. 6 (1): 97–107. https://doi.org/10.1007/s40747-019-00118-2.
Onwunalu, J. E., and L. J. Durlofsky. 2010. “Application of a particle swarm optimization algorithm for determining optimum well location and type.” Comput. Geosci. 14 (1): 183–198. https://doi.org/10.1007/s10596-009-9142-1.
Oreta, A. W. C., and J. M. C. Ongpeng. 2011. “Modeling the confined compressive strength of hybrid circular concrete columns using neural networks.” Comput. Concr. 8 (5): 597–616. https://doi.org/10.12989/cac.2011.8.5.597.
Panagiotakos, T. B., and M. N. Fardis. 1998. “Effect of column capacity design on earthquake response of reinforced concrete buildings.” J. Earthquake Eng. 2 (1): 113–145. https://doi.org/10.1080/13632469809350316.
Parsa, P., and H. Naderpour. 2021. “Shear strength estimation of reinforced concrete walls using support vector regression improved by teaching–learning-based optimization, particle swarm optimization, and Harris hawks optimization algorithms.” J. Build. Eng. 44 (Dec): 102593. https://doi.org/10.1016/j.jobe.2021.102593.
Pham, T. M., and M. N. S. Hadi. 2014. “Predicting stress and strain of FRP-confined square/rectangular columns using artificial neural networks.” J. Compos. Constr. 18 (6): 04014019. https://doi.org/10.1061/(ASCE)CC.1943-5614.0000477.
Razvi, S., and M. Saatcioglu. 1999. “Confinement model for high-strength concrete.” J. Struct. Eng. 125 (3): 281–289. https://doi.org/10.1061/(ASCE)0733-9445(1999)125:3(281).
Richart, F., A. Brandtzaeg, and R. L. Brown. 1928. A study of the failure of concrete under combined compressive stresses. Champaign, IL: Univ. of Illinois at Urbana Champaign.
Roy, H. E., and M. A. Sozen. 1963. A model to simulate the response of concrete to multi-axial loading. Champaign, IL: College of Engineering, Univ. of Illinois at Urbana–Champaign.
Saatcioglu, M., and S. R. Razvi. 1992. “Strength and ductility of confined concrete.” J. Struct. Eng. 118 (6): 1590–1607. https://doi.org/10.1061/(ASCE)0733-9445(1992)118:6(1590).
Sheikh, S. A., and S. M. Uzumeri. 1980. “Strength and ductility of tied concrete columns.” J. Struct. Div. 106 (5): 1079–1102. https://doi.org/10.1061/JSDEAG.0005416.
Sheikh, S. A., and S. M. Uzumeri. 1982. “Analytical model for concrete confined in tied piers.” J. Struct. Div. 108 (12): 2703–2722. https://doi.org/10.1061/JSDEAG.0006100.
Soltani, M., A. Abu-Abaileh, and B. Scott Rowe. 2021. “Statistical approach to modeling reduced shear capacity of corrosion-damaged reinforced concrete beams.” Pract. Period. Struct. Des. Constr. 26 (2): 04020073. https://doi.org/10.1061/(ASCE)SC.1943-5576.0000564.
Tan, T. H., and N. Nguyen. 2005. “Flexural behavior of confined high-strength concrete columns.” ACI Struct. J. 113 (2): 198. https://doi.org/10.14359/51688196.
Werbos, P. 1974. “Beyond regression: New tools for prediction and analysis in the behavioral sciences.” Ph.D. dissertation, Dept. of Applied Mathematics, Harvard Univ.
Xie, Y., Z. Li, L. Jia, H. Zhou, W. Bai, and Y. Li. 2018. “Flexural behavior and size effect of normal-strength RC columns under monotonic horizontal loading.” Eng. Struct. 166 (Apr): 251–262. https://doi.org/10.1016/j.engstruct.2018.03.082.
Information & Authors
Information
Published In
Practice Periodical on Structural Design and Construction
Volume 26 • Issue 4 • November 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Dec 31, 2020
Accepted: May 5, 2021
Published online: Aug 10, 2021
Published in print: Nov 1, 2021
Discussion open until: Jan 10, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Van-Tien Phan, Viet-Linh Tran, Van-Quang Nguyen, Duy-Duan Nguyen, Machine Learning Models for Predicting Shear Strength and Identifying Failure Modes of Rectangular RC Columns, Buildings, 10.3390/buildings12101493, 12, 10, (1493), (2022).
- Hosein Naderpour, Mahdi Akbari, Masoomeh Mirrashid, Denise-Penelope N. Kontoni, Compressive Capacity Prediction of Stirrup-Confined Concrete Columns Using Neuro-Fuzzy System, Buildings, 10.3390/buildings12091386, 12, 9, (1386), (2022).
- Masoomeh Mirrashid, Hosein Naderpour, Transit search: An optimization algorithm based on exoplanet exploration, Results in Control and Optimization, 10.1016/j.rico.2022.100127, 7, (100127), (2022).
- Mohammad Ali Irandegani, Daxu Zhang, Mahdi Shadabfar, Compressive strength of concrete cylindrical columns confined with fabric-reinforced cementitious matrix composites under monotonic loading: Application of machine learning techniques, Structures, 10.1016/j.istruc.2022.05.111, 42, (205-220), (2022).