Abstract
Accurate prediction of axial compression capacity (ACC) of concrete-filled steel tubular (CFST) columns is an important issue to maintain the safety levels of related structures and avoiding failure consequences. This paper aims to develop a new framework for accurate estimation of the ACC for square concrete-filled steel tubular (SCFST) columns based on a novel hybrid artificial intelligence technique. Therefore, the radial basis function neural network (RBFNN) was used as a predictive model to solve this problem, whereas for optimum generalization and accurate prediction, a new optimization algorithm inspired by the firefly movement was proposed, namely the firefly algorithm (FFA). Besides that, other well-known optimization algorithms were used to compare the accuracy of the new-developed predictive model, namely Differential Evolution (DE) and Genetic algorithm (GA). In addition, a large database of 300 experimental tests was collected from the open published literature to train the new hybrid proposed models in terms of RBFNN-GA, RBFNN-DE, and RBFNN-FFA. Several comparative criteria were used to evaluate the robustness and accuracy of the new proposed model. The obtained performances were compared with the ones given from the artificial neural network (ANN) method based on the trial and error method. Results showed that the novel predictive model based on the hybrid RBFNN with FFA provides the highest efficiency and accuracy in terms of predictive estimations of the ACC for SCFST columns compared to ANN, whereas the novel RBFNN-FFA model enhances the prediction results by 28%, 37%, and 52% compared to RBFNN-GA, RBFNN-DE, and ANN, respectively.
Similar content being viewed by others
References
Han L, Li W, Bjorhovde R (2014) Developments and advanced applications of concrete- filled steel tubular ( CFST ) structures : members concrete cracks. JCSR 100:211–228. https://doi.org/10.1016/j.jcsr.2014.04.016
Ding F, Fang C, Bai Y, Gong Y (2014) Mechanical performance of stirrup-con fi ned concrete- fi lled steel tubular stub columns under axial loading. JCSR 98:146–157. https://doi.org/10.1016/j.jcsr.2014.03.005
Roeder CW, Asce M, Lehman DE, Asce M, Bishop E (2010) Strength and stiffness of circular concrete-filled tubes. J Struct Eng 136(12):1545–1553
Wang Z, Tao Z, Han L, Uy B, Lam D, Kang W (2017) Strength, stiffness and ductility of concrete-filled steel columns under axial compression. Eng Struct 135:209–221. https://doi.org/10.1016/j.engstruct.2016.12.049
Han LH, Zhao XL, Tao Z (2001) Tests and mechanics model for concrete-filled SHS stub columns, columns and beam-columns. Steel Compos Struct 1:51–74
Han L, Yao G, Zhao X. Tests and calculations for hollow structural steel ( HSS ) stub columns filled with self-consolidating concrete (SCC) 2005;61:1241–69. https://doi.org/10.1016/j.jcsr.2005.01.004.
Aslani F, Uy B, Tao Z, Mashiri F (2015) Behaviour and design of composite columns incorporating compact high-strength steel plates. JCSR 107:94–110. https://doi.org/10.1016/j.jcsr.2015.01.005
Uy B, Tao Z, Han L (2011) Behaviour of short and slender concrete-filled stainless steel tubular columns. J Constr Steel Res 67:360–378. https://doi.org/10.1016/j.jcsr.2010.10.004
Yang YF, Han LH (2012) Thin-Walled Structures Concrete filled steel tube ( CFST ) columns subjected to concentrically partial compression. Thin Walled Struct 50:147–156. https://doi.org/10.1016/j.tws.2011.09.007
Liu D (2005) Tests on high-strength rectangular concrete-filled steel hollow section stub columns. J Constr Steel Res 61:902–11. https://doi.org/10.1016/j.jcsr.2005.01.001
ACI Committee, International Organization for Standardization (2008). Building code requirements for structural concrete (ACI 318-08) and commentary. American Concrete Institute
Johnson RPAD (2004) Designers’ guide to EN 1994-1-1: eurocode 4: design of composite steel and concrete structures. Gen. Rules Rules Build, Thomas Telford
Committee A (2010) Specification for structural steel buildings (ANSI/AISC 360–10). Am. Inst. Steel Constr, Chicago-Illinois
Bagheri M, Peng Z-P, Mohamed El Amine BS, Ben KB (2020) Hybrid intelligent method for fuzzy reliability analysis of corroded X100 steel pipelines. Eng Comput. https://doi.org/10.1007/s00366-020-00969-1
el Amine M, Seghier B, Keshtegar B, Correia JAFO, Lesiuk G, De JAMP (2019) Reliability analysis based on hybrid algorithm of M5 model tree and Monte Carlo simulation for corroded pipelines : case of study X60 Steel grade pipes. Eng Fail Anal 97:793–803. https://doi.org/10.1016/j.engfailanal.2019.01.061
Yaseen ZM, Tran MT, Kim S, Bakhshpoori T, Deo RC (2018) Shear strength prediction of steel fiber reinforced concrete beam using hybrid intelligence models: a new approach. Eng Struct 177:244–255. https://doi.org/10.1016/j.engstruct.2018.09.074
El-Abbasy MS, Senouci A, Zayed T, Mirahadi F, Parvizsedghy L (2014) Artificial neural network models for predicting condition of offshore oil and gas pipelines. Autom Constr 45:50–65. https://doi.org/10.1016/j.autcon.2014.05.003
Sebaaly H, Varma S, Maina JW (2018) Optimizing asphalt mix design process using artificial neural network and genetic algorithm. Constr Build Mater 168:660–670. https://doi.org/10.1016/j.conbuildmat.2018.02.118
Tohidi S, Shari Y (2015) Thin-Walled Structures Neural networks for inelastic distortional buckling capacity assessment of steel I-beams 94:359–371. https://doi.org/10.1016/j.tws.2015.04.023
Tran V, Thai D, Kim S (2019) Application of ANN in predicting ACC of SCFST column. Compos Struct 228:111332. https://doi.org/10.1016/j.compstruct.2019.111332
Khan M, Uy B, Tao Z, Mashiri F (2017) Behaviour and design of short high-strength steel welded box and concrete-filled tube (CFT) sections. Eng Struct 147:458–72. https://doi.org/10.1016/j.engstruct.2017.06.016
Khan M, Uy B, Tao Z, Mashiri F (2016) Concentrically loaded slender square hollow and composite columns incorporating high strength properties. Eng Struct 131:69–89. https://doi.org/10.1016/j.engstruct.2016.10.015
Shen W, Guo X, Wu C, Wu D (2011) Knowledge-based systems forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm. Knowl Based Syst 24(3):378–385. https://doi.org/10.1016/j.knosys.2010.11.001
Le LT, Nguyen H, Dou J, Zhou J (2019) A comparative study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in estimating the heating load of buildings’ energy efficiency for smart city planning. Appl Sci 9(13):2630
Haque ME, Sudhakar KV (2001) ANN based prediction model for fatigue crack growth in DP steel. Fatigue & Fracture Eng Mater Struct 24(1):63–68
Gupta R, Kewalramani MA, Goel A (2006) Prediction of concrete strength using neural-expert system. J Mater civil Eng 18(3):462–466
Nikoo M, Moghadam FT, Sadowski A (2015) Prediction of concrete compressive strength by evolutionary artificial neural networks 2015.
Lee S (2003) Prediction of concrete strength using artificial neural networks 25:849–57. https://doi.org/10.1016/S0141-0296(03)00004-X
Robles-velasco A, Cort P, Onieva L (2020) Prediction of pipe failures in water supply networks using logistic regression and support vector classification 196:106754. https://doi.org/10.1016/j.ress.2019.106754
El M, Ben A, Keshtegar B, Fah K, Zayed T, Abbassi R et al (2020) Prediction of maximum pitting corrosion depth in oil and gas pipelines. Eng Fail Anal 112:104505. https://doi.org/10.1016/j.engfailanal.2020.104505
Nair AM (2016) Urban hydrology. Watershed Manag Soc Econ Aspects. https://doi.org/10.1007/978-3-319-40195-9
Sonmez M (2018) Performance comparison of metaheuristic algorithms for the optimal design of space trusses performance comparison of metaheuristic algorithms for the optimal. Arab J Sci Eng. https://doi.org/10.1007/s13369-018-3080-y
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82
Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press
Kumar M, Husian M, Upreti N, Gupta D (2010) Genetic Algorithm: review and application. Int J Inf Technol Knowl Manag 2:451–454
Hancer E, Xue B, Zhang M (2017) Differential evolution for filter feature selection based on information theory and feature ranking. Knowl Based Syst 0:1–17. https://doi.org/10.1016/j.knosys.2017.10.028
Yang X (2010) Firefly algorithm, stochastic test functions and design optimization. Int J Bio Insp Comput 2:78–84
Ghorbani MA, Deo RC, Yaseen ZM, Kashani MH, Mohammadi B (2017) Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran. Theor Appl Climatol 133(3–4):1119–1131. https://doi.org/10.1007/s00704-017-2244-0
Ghorbani MA, Deo RC, Karimi V, Yaseen ZM, Terzi O (2017) Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir. Turkey. Stoch Environ Res Risk Assess 32(6):1–15. https://doi.org/10.1007/s00477-017-1474-0
Keshtegara B, Seghier M el A Ben (2018) Modified response surface method basis harmony search to predict the burst pressure of corroded pipelines. Eng Fail Anal 89:177–99. https://doi.org/10.1016/j.engfailanal.2018.02.016.
Despotovic M, Nedic V, Despotovic D, Cvetanovic S (2015) Review and statistical analysis of different global solar radiation sunshine models. Renew Sustain Energy Rev 52:1869–1880. https://doi.org/10.1016/j.rser.2015.08.035
Amar MN, Ghriga MA, Ouaer H, Seghier MEAB, Pham BT, Andersen PØ (2020) Modeling viscosity of CO2 at high temperature and pressure conditions. J Nat Gas Sci Eng. https://doi.org/10.1016/j.jngse.2020.103271
Acknowledgements
This research is funded by the National University of Civil Engineering (NUCE), Hanoi, Vietnam, under grant number 33–2019/KHXD-TĐ.
Author information
Authors and Affiliations
Corresponding authors
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Mai, S.H., Ben Seghier, M.E.A., Nguyen, P.L. et al. A hybrid model for predicting the axial compression capacity of square concrete-filled steel tubular columns. Engineering with Computers 38, 1205–1222 (2022). https://doi.org/10.1007/s00366-020-01104-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00366-020-01104-w