Steel and Composite Structures

  • 제47권6호
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  • Pages.759-779
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  • 2023
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  • 1229-9367(pISSN)
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  • 1598-6233(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Hybrid GA-ANN and PSO-ANN methods for accurate prediction of uniaxial compression capacity of CFDST columns

  • Quang-Viet Vu (Laboratory for Computational Civil Engineering, Institute for Computational Science and Artificial Intelligence, Van Lang University) ;
  • Sawekchai Tangaramvong (Center of Excellence in Applied Mechanics and Structures, Department of Civil Engineering, Chulalongkorn University) ;
  • Thu Huynh Van (Center of Excellence in Applied Mechanics and Structures, Department of Civil Engineering, Chulalongkorn University) ;
  • George Papazafeiropoulos (Department of Structural Engineering, National Technical University of Athens)
  • 투고 : 2023.01.20
  • 심사 : 2023.04.27
  • 발행 : 2023.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

The paper proposes two hybrid metaheuristic optimization and artificial neural network (ANN) methods for the close prediction of the ultimate axial compressive capacity of concentrically loaded concrete filled double skin steel tube (CFDST) columns. Two metaheuristic optimization, namely genetic algorithm (GA) and particle swarm optimization (PSO), approaches enable the dynamic training architecture underlying an ANN model by optimizing the number and sizes of hidden layers as well as the weights and biases of the neurons, simultaneously. The former is termed as GA-ANN, and the latter as PSO-ANN. These techniques utilize the gradient-based optimization with Bayesian regularization that enhances the optimization process. The proposed GA-ANN and PSO-ANN methods construct the predictive ANNs from 125 available experimental datasets and present the superior performance over standard ANNs. Both the hybrid GA-ANN and PSO-ANN methods are encoded within a user-friendly graphical interface that can reliably map out the accurate ultimate axial compressive capacity of CFDST columns with various geometry and material parameters.

키워드

과제정보

This research was supported by Thailand Science research and Innovation Fund Chulalongkorn University (IND66210025). The support from Ratchadapisek Somphot Fund for Postdoctoral Fellowship and Second Century Fund under Chulalongkorn University are also acknowledged.

참고문헌

  1. ACI Committee 318 (2011), Building code requirements for structural concrete and commentary, American Concrete Institute; Farmington Hills, MI, United States of America.
  2. AISC 360-16 (2016), Specification for structural steel buildings, American Institute of Steel Construction; Chicago, IL, United States of America.
  3. Ahmed, M., Ci, J., Yan, X.F., Lin, S. and Chen, S. (2021), "Numerical modeling of axially loaded circular concrete-filled double-skin steel tubular short columns incorporating a new concrete confinement model", Struct., 30, 611-627. https://doi.org/10.1016/j.istruc.2021.01.044.
  4. Asteris, P.G., Lemonis, M. E., Le, T.T. and Tsavdaridis, K.D. (2021), "Evaluation of the ultimate eccentric load of rectangular CFSTs using advanced neural network modeling", Eng. Struct., 248, 113297. https://doi.org/10.1016/j.engstruct.2021.113297.
  5. Chen, J. and Hai, Y. (2011), "Research on bearing capacity of short concrete filled double skin steel tubes columns under axial compression", Adv. Mater. Res., 168-170, 2154-2157. https://doi.org/10.4028/www.scientific.net/AMR.168-170.2154
  6. Chen, J., Ni, Y.Y. and Jin, W.L. (2015), "Column tests of dodecagonal section double skin concrete-filled steel tubes", Thin-Wall. Struct., 88, 28-40. https://doi.org/10.1016/j.tws.2014.11.013.
  7. Ci, J., Ahmed, M., Tran, V.L., Jia, H., Chen, S. and Nguyen, T. N. (2022), "Buckling resistance of axially loaded square concrete-filled double steel tubular columns", Steel Compos. Struct., 43(6), 689-706. https://doi.org/10.12989/scs.2022.43.6.689.
  8. Duan, Z.K. and Wang, R. (2015), "Research of the stainless steel-concrete-carbon steel circular concrete-filled double skin steel tubes under axial compression", Adv. Mater. Res., 1065-1069, 1349-1353. https://doi.org/10.4028/www.scientific.net/AMR.1065-1069.1349.
  9. Ding, Z.W. Yu, Bai, Y. and Gong, Y.Z. (2011), "Elasto-plastic analysis of circular concrete-filled steel tube stub columns", J. Construct. Steel Res., 67, 1567-1577. https://doi.org/10.1016/j.jcsr.2011.04.001.
  10. Elchalakani, M., Zhao, X.L. and Grzebieta, R. (2002), "Tests on concrete filled double-skin (CHS outer and SHS inner) composite short columns under axial compression", Thin-Wall. Struct., 40, 415-441. https://doi.org/10.1016/S0263-8231(02)00009-5.
  11. Essopjee, Y. and Dundu, M. (2015), "Performance of concrete-filled double-skin circular tubes in compression", Compos. Struct., 133, 1276-1283. https://doi.org/10.1016/j.compstruct.2015.08.033.
  12. Eurocode 4 (2004), Design of Composite Steel and Concrete Structures-Part 1-1: General Rules and Rules for Buildings, European Committee for Standardization; London, United Kingdom.
  13. Goldberg, D.E. (1989), Genetic Algorithms in Search, Optimization & Machine Learning, Addison-Wesley Publishing Company, Inc., Boston, MA, United States of America.
  14. Han, L.H., Ren, Q.X. and Li, W. (2011), "Tests on stub stainless steel concrete carbon steel double-skin tubular (DST) columns", J. Construct. Steel Res., 67, 437-452. https://doi.org/10.1016/j.jcsr.2010.09.010.
  15. Han, L.H., Li, Y.J. and Liao, F.Y. (2011), "Concrete-filled double skin steel tubular (CFDST) columns subjected to long-term sustained loading", Thin-Wall. Struct., 49, 1534-1543. https://doi.org/10.1016/j.tws.2011.08.001.
  16. Harrell, F.E. Jr., Lee, K.L., Califf, R.M., Pryor, D.B. and Rosati, R.A. (1984). "Regression modelling strategies for improved prognostic prediction", Stat. Med., 3(2), 143-152. https://doi.org/10.1002/sim.4780030207.
  17. Hassanein, M.F. and Kharoob, O.F. (2014), "Compressive strength of circular concrete-filled double skin tubular short columns", Thin-Wall. Struct., 77, 165-173. https://doi.org/10.1016/j.tws.2013.10.004.
  18. Ipek, S. and Guneyisi, E.M. (2019), "Ultimate axial strength of concrete-filled double skin steel tubular column sections", Adv. Civil Eng., 2019, 6493037. https://doi.org/10.1155/2019/6493037
  19. Kennedy, J. and Eberhart, R. (1995), "Particle Swarm Optimization", IEEE International Conference on Neural Networks, Perth, Australia, November-December. https://doi.org/10.1109/ICNN.1995.488968
  20. Kim, S. E., Vu, Q.V., Papazafeiropoulos, G., Kong, Z. and Truong, V. H. (2020), "Comparison of machine learning algorithms for regression and classification of ultimate load-carrying capacity of steel frames", Steel Compos. Struct., 37(2), 193-209. https://doi.org/10.12989/scs.2020.37.2.193.
  21. Li, Z., Lin, S. and Zhao, Y. G. (2022), "Analytical model for concrete-filled double skin tube columns with different cross-sectional shapes under axial compression", Struct., 43, 316-337. https://doi.org/10.1016/j.istruc.2022.06.016.
  22. Liang, Q.Q. (2017), "Nonlinear analysis of circular double-skin concrete-filled steel tubular columns under axial compression", Eng. Struct., 131, 639-650. https://doi.org/10.1016/j.engstruct.2016.10.019.
  23. Luat, N.V., Shin, J., Han, S. W., Nguyen, N.V. and Lee, K. (2021), "Ultimate axial capacity prediction of CCFST columns using hybrid intelligence models-a new approach", Steel Compos. Struct., 40(3), 461-479. https://doi.org/10.12989/scs.2021.40.3.461.
  24. Luat, N.V., Han, S.W. and Lee, K. (2021), "Genetic algorithm hybridized with extreme gradient boosting to predict axial compressive capacity of CCFST columns", Compos. Struct., 278, 114733. https://doi.org/10.1016/j.compstruct.2021.114733.
  25. Lyu, F., Fan, X., Ding, F. and Chen, Z. (2021), "Prediction of the axial compressive strength of circular concrete-filled steel tube columns using sine cosine algorithm-support vector regression", Compos. Struct., 273, 114282. https://doi.org/10.1016/j.compstruct.2021.114282.
  26. Naderpour, H., Rafiean, A.H. and Fakharian, P. (2018), "Compressive strength prediction of environmentally friendly concrete using artificial neural networks", J. Build. Eng., 16, 213-219. https://doi.org/10.1016/j.jobe.2018.01.007.
  27. Ngo, N.T., Pham, T.P.T., Le, H.A., Nguyen, Q.T. and Nguyen, T.T.N. (2022), "Axial strength prediction of steel tube confined concrete columns using a hybrid machine learning model", Struct., 36, 765-780. https://doi.org/10.1016/j.istruc.2021.12.054.
  28. Nguyen, M.S., Thai, D.K. and Kim, S.E. (2020), "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.
  29. Peduzzi, P., Concato, J., Kemper, E., Holford, T.R. and Feinstein, A.R. (1996). "A simulation study of the number of events per variable in logistic regression analysis", J. Clinic. Epidem., 49(12), 1373-1379. https://doi.org/10.1016/s0895-4356(96)00236-3.
  30. Tao, Z., Han, L.H. and Zhao, X.L. (2004), "Behaviour of concrete-filled double skin (CHS inner and CHS outer) steel tubular stub columns and beam-columns", J. Construct. Steel Res., 60, 1129-1158. https://doi.org/10.1016/j.jcsr.2003.11.008.
  31. Tran, V.L., Jang, Y. and Kim, S.E. (2021), "Improving the axial compression capacity prediction of elliptical CFST columns using a hybrid ANN-IP model", Steel Compos. Struct, 39(3), 319-335. https://doi.org/10.12989/scs.2021.39.3.319.
  32. Tran, V.L. and Kim, S.E. (2020), "Efficiency of three advanced data-driven models for predicting axial compression capacity of CFDST columns", Thin-Wall. Struct., 152, 106744. https://doi.org/10.1016/j.tws.2020.106744.
  33. Tran, V.L., Thai, D.K. and Kim, S.E. (2019), "A new empirical formula for prediction of the axial compression capacity of CCFT columns", Steel Compos. Struct., 33, 181-194. https://doi.org/10.12989/scs.2019.33.2.181.
  34. Truong, V.H., Vu, Q.V., Thai, H.T. and Ha, M.H. (2020), "A robust method for safety evaluation of steel trusses using Gradient Tree Boosting algorithm", Adv. Eng. Softw., 147, 102825. https://doi.org/10.1016/j.advengsoft.2020.102825.
  35. Truong, V.H., Papazafeiropoulos, G., Vu, Q.V., Pham, V.T. and Kong, Z. (2021), "Predicting the patch load resistance of stiffened plate girders using machine learning algorithms", Ocean Eng., 240, 109886. https://doi.org/10.1016/j.oceaneng.2021.109886.
  36. Uenaka, K., Kitoh, H. and Sonoda, K. (2010), "Concrete filled double skin circular stub columns under compression", ThinWall. Struct., 48, 19-24. https://doi.org/10.1016/j.tws.2009.08.001.
  37. Vu, Q.V., Truong, V.H. and Thai, H.T. (2021), "Machine learning-based prediction of CFST columns using gradient tree boosting algorithm", Compos. Struct., 259, 113505. https://doi.org/10.1016/j.compstruct.2020.113505.
  38. Wang, J., Liu, W., Zhou, D., Zhu, L. and Fang, H. (2014), "Mechanical behaviour of concrete filled double skin steel tubular stub columns confined by FRP under axial compression", Steel Compos. Struct., 17, 431-452. https://doi.org/10.12989/scs.2014.17.4.431.
  39. Wei, S., Mau, S.T., Vipulanandan, C. and Mantrala, S.K. (1995), "Performance of new sandwich tube under axial loading: Experiment", J. Struct. Eng., 121, 1806-1814. https://doi.org/10.1061/(ASCE)0733-9445(1995)121:12(1806).
  40. Yu, M., Zha, X., Ye, J. and Li, Y. (2013), "A unified formulation for circle and polygon concrete filled steel tube columns under axial compression", Eng. Struct., 49, 1-10. https://doi.org/10.1016/j.engstruct.2012.10.018.
  41. Zarringol, M. and Thai, H.T. (2022), "Prediction of the load-shortening curve of CFST columns using ANN-based models", J. Build. Eng., 51, 104279. https://doi.org/10.1016/j.jobe.2022.104279.
  42. Zhao, X.L., Tong, L.W. and Wang, X.Y. (2010), "CFDST stub columns subjected to large deformation axial loading", Eng. Struct., 32, 692-703. https://doi.org/10.1016/j.engstruct.2009.11.015.