On successive machine learning process for predicting strength and displacement of rectangular reinforced concrete columns subjected to cyclic loading

  • Bu-seog Ju (Department of Civil Engineering, Kyung Hee University) ;
  • Shinyoung Kwag (Department of Civil and Environmental Engineering, Hanbat National University) ;
  • Sangwoo Lee (Department of Civil Engineering, Kyung Hee University)
  • Received : 2023.05.09
  • Accepted : 2023.07.07
  • Published : 2023.11.25


Recently, research on predicting the behavior of reinforced concrete (RC) columns using machine learning methods has been actively conducted. However, most studies have focused on predicting the ultimate strength of RC columns using a regression algorithm. Therefore, this study develops a successive machine learning process for predicting multiple nonlinear behaviors of rectangular RC columns. This process consists of three stages: single machine learning, bagging ensemble, and stacking ensemble. In the case of strength prediction, sufficient prediction accuracy is confirmed even in the first stage. In the case of displacement, although sufficient accuracy is not achieved in the first and second stages, the stacking ensemble model in the third stage performs better than the machine learning models in the first and second stages. In addition, the performance of the final prediction models is verified by comparing the backbone curves and hysteresis loops obtained from predicted outputs with actual experimental data.



This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT). (No.2021R1A2C1010278).


  1. Alpaydin, E. (2020), Introduction to Machine Learning, The MIT Press, Cambridge, MA, USA.
  2. American Society of Civil Engineers (ASCE) (2007), Seismic Rehabilitation of Existing Buildings, American Society of Civil Engineers, Reston, VA, USA.
  3. Armaghani, D.J. and Asteris, P.G. (2021), "A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength", Neural Comput. Appl., 33(9), 4501-4532.
  4. Armaghani, D.J., Hatzigeorgiou, G.D., Karamani, C., Skentou, A., Zoumpoulaki, I. and Asteris, P.G. (2019), "Soft computing-based techniques for concrete beams shear strength", Proc. Struct. Integr., 17, 924-933.
  5. Asteris, P.G. and Mokos, V.G. (2019), "Concrete compressive strength using artificial neural networks", Neural Comput. Appl., 32(15), 11807-11826.
  6. Asteris, P.G., Armaghani, D.J., Hatzigeorgiou, G.D., Karayannis, C.G. and Pilakoutas, K. (2019), "Predicting the shear strength of reinforced concrete beams using artificial neural networks", Comput. Concrete, 24(5), 469-488.
  7. Asteris, P.G., Lemonis, M.E., Le, T.T. and Tsavdaridis, K.D. (2021a), "Evaluation of the ultimate eccentric load of rectangular CFSTs using advanced neural network modeling", Eng. Struct., 248, 113297.
  8. Asteris, P.G., Mamou, A., Hajihassani, M., Hasanipanah, M., Koopialipoor, M., Le, T.T., Kardanin, N. and Armaghani, D.J. (2021b), "Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks", Transp. Geotech., 29, 100588.
  9. Atalay, M.B. and Penzien, J. (1975), The Seismic Behavior of Critical Regions of Reinforced Concrete Components as Influenced by Moment, Shear and Axial Force, Earthquake Engineering Research Center, University of California, Berkeley, CA, USA.
  10. Breiman, L. (1996a), "Bagging predictors", Mach. Learn., 24(2), 123-140.
  11. Breiman, L. (1996b), "Stacked regressions", Mach. Learn., 24(2), 123-140.
  12. Cavaleri, L., Chatzarakis, G.E., Di Trapani, F., Douvika, M.G., Roinos, K., Vaxevanidis, N.M. and Asteris, P.G. (2017), "Modeling of surface roughness in electro-discharge machining using artificial neural networks", Adv. Mater. Res., 6(2), 169-184.
  13. Cortes, C. and Vapnik, V. (1995), "Support-vector networks", Mach. Learn., 20, 273-297.
  14. Elwood, K.J. and Moehle, J.P. (2008), "Dynamic shear and axial-load failure of reinforced concrete columns", J. Struct. Eng., 134(7), 1189-1198.
  15. Feng, D.C., Liu, Z.T., Wang, X.D., Jiang, Z.M. and Liang, S.X. (2020), "Failure mode classification and bearing capacity prediction for reinforced concrete columns based on ensemble machine learning algorithm", Adv. Eng. Informat., 45, 101-126.
  16. Ghosh, J., Padgett, J.E. and Duenas-Osorio, L. (2013), "Surrogate modeling and failure surface visualization for efficient seismic vulnerability assessment of highway bridges", Probab. Eng. Mech., 34, 189-199.
  17. Jeon, J.S., Shafieezadeh, A. and DesRoches, R. (2014), "Statistical models for shear strength of RC beam-column joints using machine-learning techniques", Earthq. Eng. Struct. Dyn., 43(14), 2075-2095.
  18. Keshtegar, B., Nehdi, M.L., Trung, N.T. and Kolahchi, R. (2021), "Predicting load capacity of shear walls using SVR-RSM model", Appl. Soft Comput., 112, 107739.
  19. Lehman, D.E. and Moehle, J.P. (2000), Seismic Performance of Well-Confined Concrete Bridge Columns, Pacific Earthquake Engineering Research Center, Berkeley, CA, USA.
  20. Lu, S., Koopialipoor, M., Asteris, P.G., Bahri, M. and Armaghani, D.J. (2020), "A novel feature selection approach based on tree models for evaluating the punching shear capacity of steel fiber-reinforced concrete flat slabs", Mater., 13(17), 3902.
  21. Luo, H. and Paal, S.G. (2018), "Machine learning-based backbone curve model of reinforced concrete columns subjected to cyclic loading reversals", J. Comput. Civil Eng., 32(5), 04018042.
  22. Lynn, A.C., Moehle, J.P., Mahin, S.A. and Holmes, W.T. (1996), "Seismic evaluation of existing reinforced concrete building columns", Earthq. Spectra., 12(4), 715-739.
  23. Mangalathu, S. and Jeon, J.S. (2018), "Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques", Eng. Struct., 160, 85-94.
  24. Mangalathu, S., Jeon, J.S. and DesRoches, R. (2018), "Critical uncertainty parameters influencing seismic performance of bridges using Lasso regression", Earthq. Eng. Struct. Dyn., 47(3), 784-801.
  25. Mansouri, I., Azmathulla, H.M. and Hu, J.W. (2018), "Gene expression programming application for prediction of ultimate axial strain of FRP-confined concrete", Adv. Civil Arch. Eng., 9(16), 64-76.
  26. Ohno, T. and Nishioka, T. (1984), "An experimental study on energy absorption capacity of columns in reinforced concrete structures", Doboku Gakkai Ronbunshu, 1984(350), 23-33.
  27. Sharifzadeh, M., Sikinioti-Lock, A. and Shah, N. (2019), "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian process regression", Renewab. Sustainab. Energy Rev., 108, 513-538.
  28. Siddique, R., Aggarwal, P., Aggarwal, Y. and Gupta, S.M. (2008), "Modeling properties of self-compacting concrete: Support vector machines approach", Comput. Concrete, 5(5), 461-473.
  29. Sivaramakrishnan, B. (2010), "Non-linear modeling parameters for reinforced concrete columns subjected to seismic loads", Ph.D. Dissertation, University of Texas, Austin, TX, USA.
  30. Tibshirani, R. (1998), "The lasso method for variable selection in the Cox model", Stat. Med., 16(4), 385-395.<385::AID-SIM380>3.0.CO;2-3.
  31. Topaloglu, B., Kaya, G.T., Sutcu, F. and Deger, Z.T. (2021), "Machine learning-based assessment of energy behavior of RC shear walls", arXiv:2111.08295.
  32. Yan, Y., Huang, H. and Sun, L. (2022), "Multivariate structural seismic fragility analysis and comparative study based on moment estimation surrogate model and Gaussian copula function", Eng. Struct., 262, 114324.
  33. Yilmaz, I., Erik, N.Y. and Kaynar, O. (2010), "Different types of learning algorithms of artificial neural network (ANN) models for prediction of gross calorific value (GCV) of coals", Sci. Res. Essays, 5(16), 2242-2249.
  34. Zhang, H., Cheng, X., Li, Y. and Du, X. (2022), "Prediction of failure modes, strength, and deformation capacity of RC shear walls through machine learning", J. Build. Eng., 50, 104145.