Steel and Composite Structures

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Improving the axial compression capacity prediction of elliptical CFST columns using a hybrid ANN-IP model

  • Tran, Viet-Linh (Department of Civil and Environmental Engineering, Sejong University) ;
  • Jang, Yun (Department of Computer Engineering, Sejong University) ;
  • Kim, Seung-Eock (Department of Civil and Environmental Engineering, Sejong University)
  • Received : 2020.12.09
  • Accepted : 2021.04.09
  • Published : 2021.05.10
⟨ Previous Next ⟩

Abstract

This study proposes a new and highly-accurate artificial intelligence model, namely ANN-IP, which combines an interior-point (IP) algorithm and artificial neural network (ANN), to improve the axial compression capacity prediction of elliptical concrete-filled steel tubular (CFST) columns. For this purpose, 145 tests of elliptical CFST columns extracted from the literature are used to develop the ANN-IP model. In this regard, axial compression capacity is considered as a function of the column length, the major axis diameter, the minor axis diameter, the thickness of the steel tube, the yield strength of the steel tube, and the compressive strength of concrete. The performance of the ANN-IP model is compared with the ANN-LM model, which uses the robust Levenberg-Marquardt (LM) algorithm to train the ANN model. The comparative results show that the ANN-IP model obtains more magnificent precision (R2 = 0.983, RMSE = 59.963 kN, a20 - index = 0.979) than the ANN-LM model (R2 = 0.938, RMSE = 116.634 kN, a20 - index = 0.890). Finally, a new Graphical User Interface (GUI) tool is developed to use the ANN-IP model for the practical design. In conclusion, this study reveals that the proposed ANN-IP model can properly predict the axial compression capacity of elliptical CFST columns and eliminate the need for conducting costly experiments to some extent.

Keywords

Acknowledgement

This research was supported by the National Research Foundation of Korea (NRF) funded by the Korean government (MSIT) (No. 2018R1A2A2A05018524 and No. 2019R1A4A1021702).

References

  1. ACI 318-14 (2014), ACI 318-14 - Building Code Requirements for Structural Concrete
  2. Ahmed, M. and Liang, Q.Q. (2020), "Computational simulation of elliptical concrete-filled steel tubular short columns including new confinement model", J. Constr. Steel Res., 174, 106294. https://doi.org/10.1016/j.jcsr.2020.106294.
  3. AISC (2016), Specification for Structural Steel Buildings, ANSI / AISC 360-16. Am Inst Steel Constr 676
  4. ACI 318-14 (2014), ACI 318-14 - Building Code Requirements for Structural Concrete.
  5. Ahmed, M. and Liang, Q.Q. (2020), "Computational simulation of elliptical concrete-filled steel tubular short columns including new confinement model", J. Constr. Steel Res., 174, 106294. https://doi.org/10.1016/j.jcsr.2020.106294.
  6. AISC (2016), Specification for Structural Steel Buildings, ANSI / AISC 360-16. Am Inst Steel Constr 676
  7. Albuquerque, J.S., et al. (1997), "Interior point SQP strategies for structured process optimization problems", Comput. Chem. Eng., 21, 853-859. https://doi.org/10.1016/s0098-1354(97)87609-0.
  8. Andrei, N. (2017), Continuous Nonlinear Optimization for Engineering Applications in GAMS Technology.
  9. Apostolopoulou, M., et al. (2020), "On the metaheuristic models for the prediction of cement- metakaolin mortars compressive strength", Metaheuristic Comput. Appl., 1, 63-99.
  10. Ardakani, A., Dinarvand, R. and Namaei, A. (2020), "Ultimate Shear Resistance of Silty Sands Improved by Stone Columns Estimation Using Neural Network and Imperialist Competitive Algorithm", Geotech. Geol. Eng., 38, 1485-1496. https://doi.org/10.1007/s10706-019-01104-8.
  11. Armaghani, D.J. and Asteris, P.G. (2020), "A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength. Springer London.
  12. Armaghani, D.J., et al. (2019), "Soft computing-based techniques for concrete beams shear strength", Procedia Struct. Integr., 17, 924-933. https://doi.org/10.1016/j.prostr.2019.08.123.
  13. Arora, R.K. (2016), Optimization: algorithms and applications.
  14. AS 5100-6 (2004), Australian Standard 5100-6: Bridge Design, Steel and composite construction. 04:269.
  15. Asteris, P.G., Apostolopoulou, M., Skentou, A.D. and Moropoulou, A. (2019), "Application of artificial neural networks for the prediction of the compressive strength of cement-based mortars", Comput. Concrete, 24(4), 329-345. https://doi.org/10.12989/cac.2019.24.4.329.
  16. Asteris, P.G. and Mokos, V.G. (2019), "Concrete compressive strength using artificial neural networks", Neural Comput. Appl., 2. https://doi.org/10.1007/s00521-019-04663-2.
  17. Boggs, P.T. and Tolle, J.W. (1995), "Sequential Quadratic Programming", Acta Numer., 4, 1-51. https://doi.org/10.1017/S0962492900002518.
  18. Chan, T.M., Gardner, L. and Law, K.H. (2010), "Structural design of elliptical hollow sections: A review", Proc. Inst. Civ. Eng. Struct. Build., 163, 391-402. https://doi.org/10.1680/stbu.2010.163.6.391.
  19. Chan, T.M., Huai, Y.M. and Wang, W. (2015), "Experimental investigation on lightweight concrete-filled cold-formed elliptical hollow section stub columns", J. Constr. Steel Res., 115, 434-444. https://doi.org/10.1016/j.jcsr.2015.08.029.
  20. Chen, H., et al. (2019), "Assessing dynamic conditions of the retaining wall: Developing two hybrid intelligent models", Appl. Sci., 9, https://doi.org/10.3390/app9061042.
  21. Chen, X.L., Fu, J.P., Yao, J.L. and Gan, J.F. (2018), "Prediction of shear strength for squat RC walls using a hybrid ANN-PSO model", Eng. Comput., 34, 367-383. https://doi.org/10.1007/s00366-017-0547-5.
  22. Dai, X. and Lam, D. (2010a), "Axial compressive behaviour of stub concrete-filled columns with elliptical stainless steel hollow sections", Steel Compos. Struct., 10(6), 517-539. https://doi.org/10.12989/scs.2010.10.6.517.
  23. Dai, X. and Lam, D. (2010b), "Numerical modelling of the axial compressive behaviour of short concrete-filled elliptical steel columns", J. Constr. Steel Res., 66, 931-942. https://doi.org/10.1016/j.jcsr.2010.02.003.
  24. Dai, X.H., Lam, D., Jamaluddin, N. and Ye, J. (2014), "Numerical analysis of slender elliptical concrete filled columns under axial compression", Thin-Wall. Struct., 77, 26-35. https://doi.org/10.1016/j.tws.2013.11.015.
  25. Degtyarev, V.V. (2020), "Neural networks for predicting shear strength of CFS channels with slotted webs", J. Constr. Steel Res., 106443. https://doi.org/10.1016/j.jcsr.2020.106443.
  26. Devikanniga, D., Vetrivel, K. and Badrinath, N. (2019), "Review of meta-heuristic optimization based artificial neural networks and its applications", J. Phys. Conf. Ser., 1362, https://doi.org/10.1088/1742-6596/1362/1/012074.
  27. Ding, F.X., Yu, Z.W., Bai, Y, and Gong, Y.Z. (2011), "Elastoplastic analysis of circular concrete-filled steel tube stub columns", J. Constr. Steel Res., 67, 1567-1577. https://doi.org/10.1016/j.jcsr.2011.04.001.
  28. Duan, J., Asteris, P/G., Nguyen, H. and Bui, X.N. (2020), A novel artifcial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model.pdf.
  29. Elbaz, K., et al. (2019), "Optimization of EPB shield performance with adaptive neuro-fuzzy inference system and genetic algorithm", Appl. Sci., 9, 1-17. https://doi.org/10.3390/app9040780.
  30. Espinos, A., Gardner, L., Romero, M.L. and Hospitaler, A. (2011), "Fire behaviour of concrete filled elliptical steel columns", Thin-Wall. Struct., 49, 239-255. https://doi.org/10.1016/j.tws.2010.10.008.
  31. Eurocode-4 (2011), Eurocode 4: Design of composite steel and concrete structures - Part 1-1: General rules and rules for buildings. The European Union.
  32. Giorgi, G. and Kjeldsen, T.H. (2014), "Traces and emergence of nonlinear programming", Traces Emerg Nonlinear Program, 1-434. https://doi.org/10.1007/978-3-0348-0439-4.
  33. Guo, K. and Yang, G. (2020), "Load - slip curves of shear connection in composite structures : prediction based on ANNs", Steel Compos. Struct., 36(5), 493-506. https://doi.org/10.12989/scs.2020.36.5.493.
  34. Hasanipanah, M., Noorian-Bidgoli, M., Jahed Armaghani, D. and Khamesi, H. (2016), "Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling", Eng. Comput., 32, 705-715. https://doi.org/10.1007/s00366-016-0447-0.
  35. Hassanein, M.F., et al. (2018), "Structural behaviour and design of elliptical high-strength concrete-filled steel tubular short compression members", Eng. Struct., 173, 495-511. https://doi.org/10.1016/j.engstruct.2018.07.023.
  36. Jamaluddin, N., Lam, D., Dai, X.H. and Ye, J. (2013), "An experimental study on elliptical concrete filled columns under axial compression", J. Constr. Steel Res., 87, 6-16. https://doi.org/10.1016/j.jcsr.2013.04.002.
  37. Karina, C.N.N., Chun, P. and Okubo, K. (2017), "Tensile strength prediction of corroded steel plates by using machine learning approach", Steel Compos. Struct., 24(5), 635-641. https://doi.org/10.12989/scs.2017.24.5.635.
  38. Kojima, M., Megiddo, N. and Mizuno, S. (1993), "A primal-dual infeasible-interior-point algorithm for linear programming", Math. Program, 61, 263-280. https://doi.org/10.1007/BF01582151.
  39. Koopialipoor, M., et al. (2020), "Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance", Eng. Comput., 36, 345-357. https://doi.org/10.1007/s00366-019-00701-8.
  40. Koopialipoor, M., et al. (2019), "Developing a new intelligent technique to predict overbreak in tunnels using an artificial bee colony-based ANN", Environ. Earth Sci., 78, https://doi.org/10.1007/s12665-019-8163-x.
  41. Kwon, S.J. (2011), Artificial neural networks
  42. Lam, D., Gardner, L. and Burdett, M. (2010), "Behaviour of axially loaded concrete filled stainless steel elliptical stub columns", Adv. Struct. Eng., 13, 493-500. https://doi.org/10.1260/1369-4332.13.3.493.
  43. Le, T.T. (2020), "Surrogate neural network model for prediction of load-bearing capacity of cfss members considering loading eccentricity", Appl. Sci., 10. https://doi.org/10.3390/app10103452.
  44. Liu, A., et al. (2020a), "Prediction of mechanical properties of micro-alloyed steels via neural networks learned by water wave optimization", Neural Comput. Appl., 32, 5583-5598. https://doi.org/10.1007/s00521-019-04149-1.
  45. Liu, F., Wang, Y. and Chan, T.M. (2017), "Behaviour of concrete-filled cold-formed elliptical hollow sections with varying aspect ratios", Thin-Wall. Struct., 110, 47-61. https://doi.org/10.1016/j.tws.2016.10.013.
  46. Liu, L., et al. (2020b), "Optimizing an ANN model with genetic algorithm (GA) predicting load-settlement behaviours of eco-friendly raft-pile foundation (ERP) system", Eng. Comput., 36, 421-433. https://doi.org/10.1007/s00366-019-00767-4.
  47. Liu, X. and Zha, X. (2011), Study on Behavior of Elliptical Concrete Filled Steel Tube Members I:Stub and Long Columns under Axial Compression. Prog Steel Build Struct.
  48. Ly, H.B., et al. (2020), "Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models", Neural Comput. Appl., 0123456789: https://doi.org/10.1007/s00521-020-05214-w.
  49. Mahgub, M., Ashour, A., Lam, D. and Dai, X. (2017), "Tests of self-compacting concrete filled elliptical steel tube columns", Thin-Wall. Struct., 110, 27-34. https://doi.org/10.1016/j.tws.2016.10.015.
  50. MATLAB (2018), MATLAB R2018b
  51. McCann, F., et al. (2015), Concrete-filled elliptical section steel columns under concentric and eccentric loading. https://doi.org/10.13140/RG.2.1.1257.5845
  52. Moayedi, H., Mu'azu, M.A. and Kok Foong, L. (2019), "Swarm-based analysis through social behavior of grey wolf optimization and genetic programming to predict friction capacity of driven piles", Eng. Comput., https://doi.org/10.1007/s00366-019-00885-z.
  53. Naderpour, H. and Mirrashid, M. (2020), "Proposed soft computing models for moment capacity prediction of reinforced concrete columns", Soft Comput., 8. https://doi.org/10.1007/s00500-019-04634-8
  54. Nguyen, D.D., et al. (2021), "A machine learning-based formulation for predicting shear capacity of squat flanged RC walls", Structures, 29, 1734-1747. https://doi.org/10.1016/j.istruc.2020.12.054.
  55. Nguyen, H.Q., et al. (2020a), "Optimization of artificial intelligence system by evolutionary algorithm for prediction of axial capacity of rectangular concrete filled steel tubes under compression", Materials (Basel), 13. https://doi.org/10.3390/MA13051205.
  56. Nguyen, M.S.T., Thai, D.K. and Kim, S.E. (2020b), "Predicting the axial compressive capacity of circular concrete filled steel tube columns using an artificial neural network", Steel Compos. Struct., 35(3), 415-437. https://doi.org/10.12989/scs.2020.35.3.415.
  57. Nikbin, I.M., Rahimi, S. and Allahyari, H. (2017), "A new empirical formula for prediction of fracture energy of concrete based on the artificial neural network", Eng. Fract. Mech., 186, 466-482. https://doi.org/10.1016/j.engfracmech.2017.11.010.
  58. Ranganathan, A. (2004), The Levenberg-Marquardt Algorithm 3 LM as a blend of Gradient descent and Gauss-Newton itera. Internet httpexcelsior cs ucsb educoursescs290ipdfL MA pdf 142:1-5.
  59. Ren, Q.X., Han, L.H., Lam, D. and Li, W. (2014), "Tests on elliptical concrete filled steel tubular (CFST) beams and columns", J. Constr. Steel Res., 99, 149-160. https://doi.org/10.1016/j.jcsr.2014.03.010.
  60. Roman, N.D., Bre, F., Fachinotti, V.D. and Lamberts, R. (2020), "Application and characterization of metamodels based on artificial neural networks for building performance simulation: A systematic review", Energy Build., 217, 109972. https://doi.org/10.1016/j.enbuild.2020.109972.
  61. Britto, A.S.F., Raj, R.E. and Mabel, M.C. (2018), "Prediction and optimization of mechanical strength of diffusion bonds using integrated ANN-GA approach with process variables and metallographic characteristics", J. Manuf. Process., 32, 828-838. https://doi.org/10.1016/j.jmapro.2018.04.015.
  62. Salehi, H. and Burgueno, R. (2018), "Emerging artificial intelligence methods in structural engineering", Eng. Struct., 171, 170-189. https://doi.org/10.1016/j.engstruct.2018.05.084.
  63. Shao, Z., et al. (2019), "Estimating the friction angle of black shale core specimens with hybrid-ANN approaches", Meas. J. Int. Meas. Confed., 145, 744-755. https://doi.org/10.1016/j.measurement.2019.06.007.
  64. Shariati, M., et al. (2020), "A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques", Eng. Comput., https://doi.org/10.1007/s00366-019-00930-x
  65. Sheehan, T., Dai, X.H., Chan, T.M. and Lam, D. (2012), "Structural response of concrete-filled elliptical steel hollow sections under eccentric compression", Eng. Struct., 45, 314-323. https://doi.org/10.1016/j.engstruct.2012.06.040.
  66. Shen, Q.H., Wang, J.F., Wang, W. and Wang, J. (2015), "Axial Compressive Behavior and Bearing Capacity Calculation of ECFST Columns Based on Numerical Analysis", Prog. Steel Buid. Struct., https://doi.org/ 10.13969/j.cnki.cn31-1893.2015.06.009.
  67. Shukla, A.K., Janmaijaya, M., Abraham, A. and Muhuri, P.K. (2019), "Engineering applications of artificial intelligence: A bibliometric analysis of 30 years (1988-2018)", Eng. Appl. Artif. Intel., 85, 517-532. https://doi.org/10.1016/j.engappai.2019.06.010.
  68. Solati, A., Hamedi, M. and Safarabadi, M. (2019), "Combined GA-ANN approach for prediction of HAZ and bearing strength in laser drilling of GFRP composite", Opt Laser Technol., 113, 104-115. https://doi.org/10.1016/j.optlastec.2018.12.016.
  69. Tran, V.L., Thai, D.K. and Kim, S.E. (2019a), "Application of ANN in predicting ACC of SCFST column", Compos. Struct., 228. https://doi.org/10.1016/j.compstruct.2019.111332.
  70. Tran, V.L. and Kim, S.E. (2020a), "Efficiency of three advanced data-driven models for predicting axial compression capacity of CFDST columns", Thin-Wall. Struct., 152. https://doi.org/10.1016/j.tws.2020.106744.
  71. Tran, V.L. and Kim, S.E. (2020b), "A practical ANN model for predicting the PSS of two-way reinforced concrete slabs", Eng. Comput., https://doi.org/10.1007/s00366-020-00944-w.
  72. Tran, V.L., Thai, D.K. and Kim, S.E. (2019b), "A new empirical formula for prediction of the axial compression capacity of CCFT columns", Steel Compos. Struct., 33(2), 181-194. https://doi.org/10.12989/scs.2019.33.2.181.
  73. Tran, V.L., Thai, D.K. and Nguyen, D.D. (2020), "Practical artificial neural network tool for predicting the axial compression capacity of circular concrete-filled steel tube columns with ultra-high-strength concrete", Thin-Wall. Struct., 151. https://doi.org/10.1016/j.tws.2020.106720.
  74. Uenaka, K. (2014), "Experimental study on concrete filled elliptical/oval steel tubular stub columns under compression", Thin-Wall. Struct., 78, 131-137. https://doi.org/10.1016/j.tws.2014.01.023.
  75. Wang, J., Shen, Q., Jiang, H. and Pan, X. (2018), "Analysis and Design of Elliptical Concrete-Filled Thin-Walled Steel Stub Columns Under Axial Compression", Int. J. Steel Struct., 18, 365-380. https://doi.org/10.1007/s13296-018-0002-5.
  76. Xu, Y. and Yao, J. (2017), "Axial Bearing Capacity of Elliptical Concrete Filled Steel Tubular Stub Columns", IOP Conf Ser Mater Sci Eng 220. https://doi.org/10.1088/1757-899X/220/1/012002
  77. Yang, H., Lam, D. and Gardner, L. (2008), "Testing and analysis of concrete-filled elliptical hollow sections", Eng. Struct., 30, 3771-3781. https://doi.org/10.1016/j.engstruct.2008.07.004
  78. Yang, H., Liu, F., Chan, T. and Wang, W. (2017), "Behaviours of concrete-filled cold-formed elliptical hollow section beam-columns with varying aspect ratios", Thin-Wall. Struct., 120, 9-28. https://doi.org/10.1016/j.tws.2017.08.018.
  79. Zhang, H., et al. (2020), "Developing a novel artificial intelligence model to estimate the capital cost of mining projects using deep neural network-based ant colony optimization algorithm", Resour. Policy, 66, 101604. https://doi.org/10.1016/j.resourpol.2020.101604.
  80. Zhao, X.L. and Packer, J.A. (2009), "Tests and design of concrete-filled elliptical hollow section stub columns", Thin-Wall. Struct., 47, 617-628. https://doi.org/10.1016/j.tws.2008.11.004.
  81. Zhou, G., Moayedi, H. and Foong, L.K. (2020), "Teaching-learning-based metaheuristic scheme for modifying neural computing in appraising energy performance of building", Eng. Comput., https://doi.org/10.1007/s00366-020-00981-5.
  82. Zorlu, K., et al. (2008), "Prediction of uniaxial compressive strength of sandstones using petrography-based models", Eng. Geol., 96, 141-158. https://doi.org/10.1016/j.enggeo.2007.10.009.

Cited by

  1. Axial compressive behavior of circular concrete-filled double steel tubular short columns vol.25, pp.2, 2021, https://doi.org/10.1177/13694332211046345