Steel and Composite Structures

  • 제51권4호
  • /
  • Pages.441-456
  • /
  • 2024
  • /
  • 1229-9367(pISSN)
  • /
  • 1598-6233(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Predicting restraining effects in CFS channels: A machine learning approach

  • Seyed Mohammad Mojtabaei (School of Architecture, Building and Civil Engineering, Loughborough University) ;
  • Rasoul Khandan (Faculty of Engineering and Science, University of Greenwich) ;
  • Iman Hajirasouliha (Department of Civil and Structural Engineering, The University of Sheffield)
  • 투고 : 2023.11.28
  • 심사 : 2024.04.18
  • 발행 : 2024.05.25
⟨ 이전 논문 다음 논문 ⟩

초록

This paper aims to develop Machine Learning (ML) algorithms to predict the buckling resistance of cold-formed steel (CFS) channels with restrained flanges, widely used in typical CFS sheathed wall panels, and provide practical design tools for engineers. The effects of cross-sectional restraints were first evaluated on the elastic buckling behaviour of CFS channels subjected to pure axial compressive load or bending moment. Feedforward multi-layer Artificial Neural Networks (ANNs) were then trained on different datasets comprising CFS channels with various dimensions and properties, plate thicknesses, and restraining conditions on one or two flanges, while the elastic distortional buckling resistance of the elements were determined according to the Finite Strip Method (FSM). To develop less biased networks and ensure that every observation from the original dataset has the chance of appearing in the training and test set, a K-fold cross-validation technique was implemented. In addition, the hyperparameters of the ANNs were tuned using a grid search technique to provide ANNs with optimum performances. The results demonstrated that the trained ANNs were able to predict the elastic distortional buckling resistance of CFS flange-restrained elements with an average accuracy of 99% in terms of coefficient of determination. The developed models were then used to propose a simple ANN-based design formula for the prediction of the elastic distortional buckling stress of CFS flange-restrained elements. Finally, the proposed formula was further evaluated on a separate set of unseen data to ensure its accuracy for practical applications.

키워드

참고문헌

  1. Abaqus (2017), Abaqus/CAE User's Manual: Version 6.26. Providence, RI: Abaqus. 
  2. Abeysiriwardena, T. and Mahendran, M. (2022a), "DSM design of LSF walls subject to distortional buckling", Eng. Struct., 272, 115016. https://doi.org/10.1016/j.engstruct.2022.115016. 
  3. Abeysiriwardena, T. and Mahendran, M. (2022b). "Experimental and numerical investigations of LSF walls subject to distortional buckling", Thin-Wall. Struct., 171, 108685. https://doi.org/10.1016/j.tws.2021.108685. 
  4. Adany, S. and Schafer, B.W. (2006). "Buckling mode decomposition of single-branched open cross-section members via finite strip method: Derivation", Thin-Walled Struct., 44, 563-584. https://doi.org/10.1016/j.tws.2006.03.013. 
  5. Arlot, S. and Celisse, A. (2010). "A survey of cross-validation procedures for model selection", Statistics Surveys, 4, 40-79, https://doi.org/10.1214/09-SS054. 
  6. Becque, J., Li, X. and Davison, B. (2019). "Modal decomposition of coupled instabilities: The method of the equivalent nodal forces", Thin-Wall. Struct., 143, 106229. https://doi.org/10.1016/j.tws.2019.106229. 
  7. BS5950 (1987), British Standards Institution, Structural Use of Steelwork in Building, Part 5: Code of Practice for Design of Cold-Formed Sections. BSI, London, UK. 
  8. CEN (2005), Eurocode 3: Design of Steel Structures, Part 1.3: General Rules-Supplementary Rules for Cold Formed Members and Sheeting, in, Brussels: European Comittee for Standardization. 
  9. Cheung, Y.K. and Tham, L.G. (1998), "Finite Strip Method". CRC Press, Boca Raton, USA. 
  10. D'aniello, M., Guneyisi, E.M., Landolfo, R. and Mermerdas, K. (2014). "Analytical prediction of available rotation capacity of cold-formed rectangular and square hollow section beams", Thin-Wall. Struct., 77, 141-152. https://doi.org/10.1016/j.tws.2013.09.015. 
  11. Dai, Y., Roy, K., Fang, Z., Chen, B., Raftery, G.M. and Lim, J.B.P. (2022). "A novel machine learning model to predict the moment capacity of cold-formed steel channel beams with edge-stiffened and un-stiffened web holes", J. of Build. Eng., 53, 104592. https://doi.org/10.1016/j.jobe.2022.104592. 
  12. Dar, M.A., Subramanian, N., Anbarasu, M., Dar, A.R. and Lim J.B.P. (2018), "Structural performance of cold-formed steel composite beams", Steel Comp. Struct., 27, 545-554. https://doi.org/10.12989/scs.2018.27.5.545. 
  13. Degtyarev, V.V. (2021). "Neural networks for predicting shear strength of CFS channels with slotted webs", J. Const. Steel Res., 177, 106443. https://doi.org/10.1016/j.jcsr.2020.106443. 
  14. Degtyarev, V.V. and Naser, M.Z. (2021). "Boosting machines for predicting shear strength of CFS channels with staggered web perforations", Struct., 34, 3391-3403. https://doi.org/10.1016/j.istruc.2021.09.060. 
  15. El-kassas, E.M.A., Mackie, R.I. and El-sheikh, A.I. (2001). "Using neural networks in cold-formed steel design", Comput. Struct., 79, 1687-1696. https://doi.org/10.1016/S0045-7949(01)00099-2. 
  16. El-kassas, E.M.A., Mackie, R.I. and El-sheikh, A.I. (2002). "Using neural networks to predict the design load of cold-formed steel compression members", Adv. in Eng. Soft., 33, 713-719. https://doi.org/10.1016/S0965-9978(02)00051-0. 
  17. Fang, Z., Roy, K., Chen, B., Sham, C.-W., Hajirasouliha, I. and Lim, J.B.P. (2021a). "Deep learning-based procedure for structural design of cold-formed steel channel sections with edge-stiffened and un-stiffened holes under axial compression", Thin-Wall. Struct., 166, 108076. https://doi.org/10.1016/j.tws.2021.108076. 
  18. Fang, Z., Roy, K., Ma, Q., Uzzaman, A. and Lim, J.B.P. (2021b). "Application of deep learning method in web crippling strength prediction of cold-formed stainless steel channel sections under end-two-flange loading", Struct., 33, 2903-2942. https://doi.org/10.1016/j.istruc.2021.05.097. 
  19. Fang, Z., Roy, K., Mares, J., Sham, C.-W., Chen, B. and Lim, J.B.P. (2021c). "Deep learning-based axial capacity prediction for cold-formed steel channel sections using Deep Belief Network", Struct., 33, 2792-2802. https://doi.org/10.1016/j.istruc.2021.05.096. 
  20. Guzelbey, I.H., Cevik, A. and Erklig, A. (2006). "Prediction of web crippling strength of cold-formed steel sheetings using neural networks", J. Const. Steel Res., 62, 962-973. https://doi.org/10.1016/j.jcsr.2006.01.008. 
  21. Hasanali, M., Roy, K., Mojtabaei, S.M., Hajirasouliha, I., Clifton, G.C. and Lim, J.B.P. (2022). "A critical review of cold-formed steel seismic resistant systems: Recent developments, challenges and future directions", Thin-Wall. Struct., 180, 109953. https://doi.org/10.1016/j.tws.2022.109953. 
  22. Kesti, J. (2000), "Local and distortional buckling of perforated steel wall studs", Ph.D. Dissertation, Helsinki University of Technology, Espoo, Finland. 
  23. Mathworks (2021), Matlab R2021b. Mathworks Inc. 
  24. Mojtabaei, S.M., Becque, J. and Hajirasouliha, I. (2020). "Local Buckling in Cold-Formed Steel Moment-Resisting Bolted Connections: Behavior, Capacity, and Design", J. Struct. Eng., 146, 04020167. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002730. 
  25. Mojtabaei, S.M., Becque, J. and Hajirasouliha, I. (2021). "Structural Size Optimization of Single and Built-Up Cold-Formed Steel Beam-Column Members", J. Struct. Eng., 147, 04021030. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002987. 
  26. Mojtabaei, S.M., Becque, J., Hajirasouliha, I. and Khandan, R. (2023). "Predicting the buckling behaviour of thin-walled structural elements using machine learning methods", Thin-Walled Struct., 184, 110518. https://doi.org/10.1016/j.tws.2022.110518. 
  27. Pala, M. (2006). "A new formulation for distortional buckling stress in cold-formed steel members". J. Const. Steel Res., 62, 716-722. https://doi.org/10.1016/j.jcsr.2005.09.011. 
  28. Pan, C.-L. and Peng, J.-L. (2005). "Performance of cold-formed steel wall frames under compression", Steel Comp. Struct., 5, 407-420. https://doi.org/10.12989/scs.2005.5.5.407. 
  29. Parastesh, H., Mojtabaei, S.M., Taji, H., Hajirasouliha, I. and Bagheri Sabbagh, A. (2021). "Constrained optimization of antisymmetric cold-formed steel beam-column sections", Eng. Struct., 228, 111452. https://doi.org/10.1016/j.engstruct.2020.111452. 
  30. Phan, D.T., Mojtabaei, S.M., Hajirasouliha, I., Ye, J. and Lim, J.B.P. (2019). "Coupled element and structural level optimisation framework for cold-formed steel frames", J. Const. Steel Res., 105867. https://doi.org/10.1016/j.jcsr.2019.105867. 
  31. Schafer, B.W. and Adany, S. (2006). "Buckling analysis of cold-formed steel members using CUFSM: conventional and constrained finite strip methods", Eighteenth International Specialty Conference on Cold-Formed Steel Structures, Orland, USA. 
  32. Schmidhuber, J. (2015), "Deep learning in neural networks: An overview", Neural Networks, 61, 85-117, arXiv:1404.7828. https://doi.org/10.48550/arXiv.1404.7828. 
  33. Shifferaw, Y. and Schafer, B.W. (2012), "Inelastic bending capacity of cold-formed steel members", J. Struct. Eng., 138, 468-480. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000469. 
  34. Tohidi, S. and Sharifi, Y. (2015). "Neural networks for inelastic distortional buckling capacity assessment of steel I-beams", Thin-Wall. Struct., 94, 359-371. https://doi.org/10.1016/j.tws.2015.04.023. 
  35. Xiao, L., Li, Q.-Y., Li, H. and Ren, Q. (2022), "Loading capacity prediction and optimization of cold-formed steel built-up section columns based on machine learning methods", ThinWall. Struct., 180, 109826. https://doi.org/10.1016/j.tws.2022.109826. 
  36. Xu, Y., Zheng, B. and Zhang, M. (2021), "Capacity prediction of cold-formed stainless steel tubular columns using machine learning methods", J. Const. Steel Res., 182, 106682. https://doi.org/10.1016/j.jcsr.2021.106682. 
  37. Yilmaz, F., Mojtabaei, S.M., Hajirasouliha, I. and Becque, J. (2023), "Behaviour and performance of OSB-sheathed cold-formed steel stud wall panels under combined vertical and seismic loading", Thin-Wall. Struct., 183, 110419. https://doi.org/10.1016/j.tws.2022.110419. 
  38. Young, B. (2005), "Local buckling and shift of effective centroid of cold-formed steel columns", Steel Comp. Struct., 5, 235-246. https://doi.org/10.12989/scs.2005.5.2_3.235. 
  39. Yousefi, A.M., Lim, J.B.P. and Clifton, C.G. (2019). "Web crippling strength of perforated cold-formed ferritic stainless steel unlipped channels with restrained flanges under one-flange loadings", Thin-Wall. Struct., 137, 94-105. https://doi.org/10.1016/j.tws.2019.01.002. 
  40. Yuan, W.-B., Cheng, S., Li, L.-Y. and Kim, B. (2014). "Web-flange distortional buckling of partially restrained cold-formed steel purlins under uplift loading", Int. J. Mech. Sciences, 89, 476-481. https://doi.org/10.1016/j.ijmecsci.2014.10.011. 
  41. Zarringol, M, Thai, H.-T. and Naser, M.Z. (2021). "Application of machine learning models for designing CFCFST columns", J. Const. Steel Res., 185, 106856. https://doi.org/10.1016/j.jcsr.2021.106856. 
  42. Zhao, X., Wang, G., Sun, X., Wang, X. and Schafer, B.W. (2023), "Modeling of uncertain geometry of cold formed steel members based on laser measurements and machine learning", Eng. Struct., 279, 115578. https://doi.org/10.1016/j.engstruct.2022.115578. 
  43. Zhou, W. and Jiang, L. (2017), "Distortional buckling of cold-formed lipped channel columns subjected to axial compression", Steel Comp. Struct., 23, 331-338. https://doi.org/10.12989/scs.2017.23.3.331.