Skip to main content

Advertisement

SpringerLink
Log in

A hybrid model for predicting the axial compression capacity of square concrete-filled steel tubular columns

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. 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

    Article  Google Scholar 

  2. 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

    Article  Google Scholar 

  3. 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

    Article  Google Scholar 

  4. 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

    Article  Google Scholar 

  5. 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

    Article  Google Scholar 

  6. 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.

  7. 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

    Article  Google Scholar 

  8. 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

    Article  Google Scholar 

  9. 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

    Article  Google Scholar 

  10. 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

    Article  Google Scholar 

  11. ACI Committee, International Organization for Standardization (2008). Building code requirements for structural concrete (ACI 318-08) and commentary. American Concrete Institute

  12. 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

    Google Scholar 

  13. Committee A (2010) Specification for structural steel buildings (ANSI/AISC 360–10). Am. Inst. Steel Constr, Chicago-Illinois

    Google Scholar 

  14. 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

    Article  Google Scholar 

  15. 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

    Article  Google Scholar 

  16. 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

    Article  Google Scholar 

  17. 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

    Article  Google Scholar 

  18. 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

    Article  Google Scholar 

  19. 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

    Article  Google Scholar 

  20. 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

    Article  Google Scholar 

  21. 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

    Article  Google Scholar 

  22. 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

    Article  Google Scholar 

  23. 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

    Article  Google Scholar 

  24. 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

    Article  Google Scholar 

  25. 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

    Article  Google Scholar 

  26. Gupta R, Kewalramani MA, Goel A (2006) Prediction of concrete strength using neural-expert system. J Mater civil Eng 18(3):462–466

    Article  Google Scholar 

  27. Nikoo M, Moghadam FT, Sadowski A (2015) Prediction of concrete compressive strength by evolutionary artificial neural networks 2015.

  28. Lee S (2003) Prediction of concrete strength using artificial neural networks 25:849–57. https://doi.org/10.1016/S0141-0296(03)00004-X

  29. 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

  30. 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

    Article  Google Scholar 

  31. Nair AM (2016) Urban hydrology. Watershed Manag Soc Econ Aspects. https://doi.org/10.1007/978-3-319-40195-9

    Article  Google Scholar 

  32. 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

    Article  Google Scholar 

  33. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

  34. Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press

  35. Kumar M, Husian M, Upreti N, Gupta D (2010) Genetic Algorithm: review and application. Int J Inf Technol Knowl Manag 2:451–454

    Google Scholar 

  36. 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

    Article  Google Scholar 

  37. Yang X (2010) Firefly algorithm, stochastic test functions and design optimization. Int J Bio Insp Comput 2:78–84

    Article  Google Scholar 

  38. 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

    Article  Google Scholar 

  39. 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

    Article  Google Scholar 

  40. 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.

  41. 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

    Article  Google Scholar 

  42. 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

    Article  Google Scholar 

Download references

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

  1. Department of Hydraulic Engineering and Construction, National University of Civil Engineering (NUCE), Hanoi, Vietnam

    Sy Hung Mai & Phuong Lam Nguyen

  2. Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Mohamed El Amine Ben Seghier

  3. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Mohamed El Amine Ben Seghier

  4. Department of Civil Engineering, Faculty of Engineering, University of Sistan and Baluchestan, Zahedan, Iran

    Jafar Jafari-Asl

  5. Department of Civil and Environmental Engineering, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul, 143-747, South Korea

    Duc-Kien Thai

Authors
  1. Sy Hung Mai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed El Amine Ben Seghier
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Phuong Lam Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jafar Jafari-Asl
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Duc-Kien Thai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Mohamed El Amine Ben Seghier or Duc-Kien Thai.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-020-01104-w

Keywords

Search

Navigation