Journal of Steel & Structure

Journal of Steel & Structure

Optimization of Shear Capacity in Cold-Formed Steel Double Channel Beams with Web Openings

Document Type : Original Article

Authors
1 Department of Civil Engineering, As.C. Islamic Azad University, Astara, Iran
2 Associate Professor, Civil Engineering Department, University of Science and Culture, Tehran, Iran
Abstract
The use of lightweight cold-formed steel members with versatile applications is increasing. Implementing web openings for building services passage, especially in support zones, is particularly important. This research aims to evaluate the ultimate shear capacity of cold-formed steel beams with web openings. I-shaped beams, 1000 mm in length, composed of back-to-back C-sections were modeled for nonlinear analysis under three-point loading using finite element software, considering various parameters and material non-elastic behavior. The finite element model was validated based on existing experimental results from reliable sources. Key parameters in this modeling and design constraints for optimizing hole dimensions and shape included section thickness, the presence or absence of lips on the sections, and the distance of the openings from the support. This study employed a hybrid Neural Network-Genetic Algorithm (NSGA-II), a multi-objective optimization algorithm, to optimize the two objectives of maximum load and minimum mass. The outputs from the finite element models (maximum load) served as input for neural network training and design, and the neural network outputs were selected as the objective function. Results show a maximum difference of 4.18% between the ultimate load from finite element analysis and the genetic algorithm. In this research presented hybrid framework (FEM-ANN-GA) enables the selection of the optimal web opening specifications from the Pareto solutions, while simultaneously considering both strength and weight. The shear resistance investigation reveals that rectangular openings perform approximately 4% better, and circular openings about 7% better than square openings.
Keywords
Subjects

[1] Adeli, H., and Karim, A. (1997), “Neural network model for optimization of cold-formed steel beams”, Journal of Structural Engineering, 123(11), pp.1535–1543.
[2] Wanniarachchi, K.S., Mahendran, M., and Keerthan, P. (2017), “Shear behavior and design of lipped channel beams with non-circular web openings”, Thin-Walled Structures, 119, pp.83–102.
[3] Pham, S.H., Pham, C.H., and Hancock, G.J. (2017), “Review of direct strength method of design for cold-formed steel structures with holes with a focus on shear”, Congrès International de Géotechnique–Ouvrages–Structures, pp.954-963.
[4] Mojtabaei, S.M., Kabir, M.Z., Hajirasouliha, I., and Kargar, M. (2018), “Analytical and experimental study on the seismic performance of cold-formed steel frames”, Journal of Constructional Steel Research, 143, pp.18–31.
[5] Zhao, J., Sun, K., Yu, C., and Wang, J. (2019), “Tests and direct strength design on cold-formed steel channel beams with web holes”, Engineering Structures, 184, pp.434–446.
[6] Chen, B., Roy, K., Uzzaman, A., Raftery, G., Nash, D., Clifton, G., Pouladi, P., and Lim, J. (2019), “Effects of edge-stiffened web openings on the behaviour of cold-formed steel channel sections under compression”, Thin-Walled Structures, 144, pp.106307.
[7] Yu, N., Kim, B., Yuan, W., Li, L., and Yu, F. (2019), “An analytical solution of distortional buckling resistance of cold-formed steel channel-section beams with web openings”, Thin-Walled Structures, 135, pp.446–452.
[8] Horacek, M., Melcher, J., Balazs, I., and Pesek, O. (2019), “On problem of torsional characteristics of thin-walled steel beams with web openings”, Materials Science and Engineering, 471, pp.052040.
[9] Yu, N., Kim, B., Huang, X., Yuan, W., Ye, R., Wu, L., and Lea, J. (2021), “Analytical solution for flange/web distortional buckling of cold-formed steel beams with circular web perforations”, Mechanics of Advanced Materials and Structures, 29(6), pp.3463–3473.
[10] Shaker, F.M.F., Mamdooh, Z., Deifalla, A., and Yehia, M.M. (2022), “Experimental investigations of the behavior of stiffened perforated cold-formed steel sections subjected to axial compression”, Buildings, 12(6), pp.812.
[11] Živaljević, V., Jovanović, Ð., Kovačević, D., and Džolev, I. (2022), “The influence of web holes on the behavior of cold-formed steel members: A review”, Buildings, 12(8), pp.1091.
[12] Zhao, J., Liu, J., Yu, C., and Zhang, W. (2022), “Test investigation and direct strength method on cold-formed steel compression members with web holes of different widths”, Engineering Structures, 272, pp.114979.
[13] Zhong, Y., Liu, Y., and Feng, R. (2022), “A two-level optimization framework for new family of CFS sections”, Journal of Constructional Steel Research, 197, pp.107460.
[14] Chen, B., Roy, K., Fang, Z., Uzzaman, A., Pham, C.H., Raftery, G.M., and Lim, J.B.P. (2022), “Shear capacity of cold-formed steel channels with edge-stiffened web holes, unstiffened web holes, and plain webs”, Journal of Structural Engineering, 148(9), pp.04022129.
[15] Sönmez, M., and Komur, M.A. (2010), “Using FEM and artificial networks to predict elastic buckling load of perforated rectangular plates under linearly varying in-plane normal load”, Structural Engineering and Mechanics, 34(2), pp.159–174.
[16] Keerthan, P., and Mahendran, M. (2011), “Shear behavior and strength of Lite steel beams with web openings”, Advances in Structural Engineering, 14(2), pp.171–184.
[17] Li, Z., Leng, J., Guest, J.K., and Schafer, B.W. (2016), “Two-level optimization for a new family of cold-formed steel lipped channel sections against local and distortional buckling”, Thin-Walled Structures, 108, pp.64–74.
[18] Yousefi, A.M., Lim, J.B.P., and Clifton, G.C. (2017), “Cold-formed ferritic stainless steel unlipped channels with web openings subjected to web crippling under interior-two-flange loading condition–Part I: Tests and finite element model validation”, Thin-Walled Structures, 116, pp.333–341.
[19] Yousefi, A.M., Lim, J.B.P., Uzzaman, A., Lian, Y., Clifton, G.C., and Young, B. (2017), Design of cold-formed stainless steel lipped channel sections with web openings subjected to web crippling under end-one-flange loading condition”, Advances in Structural Engineering, 20(7), pp.1024–1045.
[20] Pham, S.H., Pham, C.H., and Hancock, G.J. (2017), “On the design of cold-formed steel beams with holes in shear using the direct strength method”, In EUROSTEEL, pp.1590–1599.
[21] Gatheeshgar, P., Poologanathan, K., Gunalan, S., Shyha, I., Tsavdaridis, K.D., and Corradi, M. (2020), “Optimal design of cold-formed steel lipped channel beams: Combined bending, shear, and web crippling”, Structures, 28, pp.825–836.
[22] Gatheeshgar, P., Poologanathan, K., Gunalan, S., Tsavdaridis, K.D., Degtyareva, N., and Nagaratnam, B. (2019), “Optimised and slotted cold-formed steel channels: A solution for modular building systems”, 10th International Conference on Structural Engineering and Construction Management.
[23] Degtyareva, N., Gatheeshgar, P., Poologanathan, K., Gunalan, S., Shyha, I., and McIntosh, A. (2020), “Local buckling strength and design of cold-formed steel beams with slotted perforations”, Thin-Walled Structures, 156, pp.106951.
[24] Pham, C.H., and Hancock, G.J. (2020), “Shear tests and design of cold-formed steel channels with central square holes”, Journal of Structural Engineering, 146(4), pp.04019173.
[25] Taheri, E., Fard, S.E., Zandi, Y., and Samali, B. (2021), “Experimental and numerical investigation of an innovative method for strengthening cold-formed steel profiles in bending”, Applied Sciences, 11(11), pp.5242.
[26] Zhong, Y., Liu, Y., and Feng, R. (2022), “A two-level optimization framework for new family of CFS sections”, Journal of Constructional Steel Research, 197, pp.107460.
[27] Neves, M., Basaglia, C., and Camotim, D. (2022), “Stiffening optimization of conventional cold-formed steel cross sections based on a multi-objective genetic algorithm and using generalized beam theory”, Thin-Walled Structures, 179, pp.109713.
[28] Qadir, S.J., Nguyen, V.B., and Hajirasouliha, I. (2024), “Design optimisation for CFS beam sections with web and flange stiffeners”, Journal of Constructional Steel Research, 213, pp.108375.
[29] Hosseinjani, N., Parastesh, H., Haji Rasouliha, I., and Mojtabaei, S.M., (2024), “Parametric Study of Shear Capacity of Double Sections of Cold Formed Steel with Hole”, Journal of Structural and Construction Engineering, 11(12), pp.108-126.
[30] Pham, N.H., (2023), “Investigation of Web Hole Effects on Capacities of Cold-Formed Steel Channel Members”, Shuren Wang Jingan Li Kui Hu, pp.161.
[31] Gatheeshgar, P., Poologanathan, K., Gunalan, S., Shyha, I., Tsavdaridis, K.D., and Corradi, M. (2020), “Optimal design of cold-formed steel lipped channel beams: Combined bending, shear, and web crippling”, Structures, 28, pp.825–836.
[32] Yu, C. (2012), “Cold-formed steel flexural member with edge stiffened holes: Behavior, optimization, and design”, Journal of Constructional Steel Research, 71, pp.210–218.
[33] Dai, Y., Fang, Z., Roy, K., Raftery, G.M., and Lim, J.B.P. (2023), “Optimal design of cold-formed steel face-to-face built-up columns through deep belief network and genetic algorithm”, Structures, 56, pp.104906.
[34] Yin, H., Xiao, Y., Wen, G., Qing, Q., and Deng, Y. (2015), “Multiobjective optimization for foam-filled multi-cell thin-walled structures under lateral impact”, Thin-Walled Structures, 94, pp.1–12.
[35] Madeira, J.F.A., Dias, J., and Silvestre, N. (2015), “Multiobjective optimization of cold-formed steel columns”, Thin-Walled Structures, 96, pp.29–38.
[36] Srinivas, N., and Deb, K. (1994), “Multiobjective optimization using nondominated sorting in genetic algorithms”, Evolutionary Computation, 2(3), pp.221–248.
[37] Atashkari, K., Nariman-Zadeh, N., Jamali, A., and Pilechi, A. (2005), “Thermodynamic Pareto optimization of turbojet using multi-objective genetic algorithm”, International Journal of Thermal Sciences, 44(11), pp.1061–1071.
[38] Qazani, M.R.C., Bidabadi, B.S., Asadi, H., Nahavandi, S., and Bidabadi, F.S. (2023), “Multiobjective optimization of roll-forming procedure using NSGA-II and type-2 fuzzy neural network”, IEEE Transactions on Automation Science and Engineering, 21(3), pp.3842-3851.
[39] Gao, H., Pan, Z., Zhu, W., Li, X., Chen, Y., and Wang, Q. (2025), “Seismic performance of cold-formed thin-walled steel-composite shear wall with double-layer inclined slots and stiffeners”, International Journal of Civil Engineering, 23(8), pp.1075–1094.
[40] El-Taly, B., and El-Shami, M. (2021), “Structural performance of cold-formed steel face-to-face and back-to-back beams”, International Journal of Civil Engineering, 19(12), pp.1427–1444.
[41] Yadav, N., Yadav, A., and Kumar, M. (2015), “History of neural networks”, In An Introduction to Neural Network Methods for Differential Equations, pp.13–15.
[42] Schmidhuber, J. (2015), “Deep learning in neural networks: An overview”, Neural Networks, 61, pp.85–117.
[43] El-Kassas, E.M.A., Mackie, R.I., and El-Sheikh, A.I. (2001), “Using neural networks in cold-formed steel design”, Computers and Structures, 79(18), pp.1687–1696.
[44] Tashakori, A., and Adeli, H. (2002), “Optimum design of cold-formed steel space structures using neural dynamics model”, Journal of Constructional Steel Research, 58(12), pp.1545–1566.
[45] Guzelbey, I.H., Cevik, A., and Erklig, A. (2006), “Prediction of web crippling strength of cold-formed steel sheetings using neural networks”, Journal of Constructional Steel Research, 62(10), pp.962–973.
[46] Pala, M., and Caglar, N. (2007), “A parametric study for distortional buckling stress on cold-formed steel using a neural network”, Journal of Constructional Steel Research, 63(5), pp.686–691.
[47] Moradi, M.S.S., Azadi, M., and Jahanian, H. (2022), “Multi-objective optimization of tunnel parameters inside a liquefied sand lens under seismic loads”, Geotechnical Research, 9(4), pp.196–211.
[48] Toffolo, A., and Benini, E. (2003), “Genetic diversity as an objective in multi-objective evolutionary algorithms”, Evolutionary Computation, 11(2), pp.151–167.
[49] Pareto, V. (1964), “Cours d'économie politique”, 1, Librairie Droz.
[50] Singh, R., and Samanta, A. (2022), “Numerical finite element simulation and structural behaviour of cold-formed steel members”, Materials Today: Proceedings, 65(8), pp.3300–3305.
[51] Yu, W.W., LaBoube, R.A., and Chen, H., (2019), “Cold-formed steel design”, John Wiley and Sons.
[52] American Iron and Steel Institute (AISI). (2022), North American specification for the design of cold-formed steel structural members (AISI S100-16).
[53] Gurney, K. (2018), “An introduction to neural networks”, CRC Press.
[54] Demuth, H.B., Beale, M.H., De Jess, O., and Hagan, M.T. (2014), “Neural network design”, Martin Hagan.
[55] Kaveh, A., and Khavaninzadeh, N. (2023), “Hybrid ECBO–ANN algorithm for shear strength of partially grouted masonry walls”, Periodica Polytechnica Civil Engineering, 67(4), pp.1176–1187.
[56] Lagaros, N.D. (2023), “Artificial neural networks applied in civil engineering”, Applied Sciences, 13(3), pp.1131.
[57] Waszczyszyn, Z. (2011), “Artificial neural networks in civil engineering: Another five years of research in Poland”, Computer Assisted Mechanics and Engineering Sciences, 18, pp.131–146.
[58] Guenin, B., Könemann, J., and Tuncel, L. (2022), “Practical optimization: A gentle introduction”, Cambridge University Press.
[59] Leng, J. (2024), “Optimization techniques for structural design of cold-formed steel structures”, In Recent Trends in Cold-formed Steel Construction, pp.215-238.
[60] André, J., Siarry, P., and Dognon, T. (2001), “An improvement of the standard genetic algorithm fighting premature convergence in continuous optimization”, Advances in Engineering Software, 32, pp.49-60.
[61] Nariman-Zadeh, N., Atashkari, K., Jamali, A., Pilechi, A., and Yao, X. (2005), “Inverse modeling of multi-objective thermodynamically optimized turbojet engine using GMDH-type neural networks and evolutionary algorithms”, Engineering Optimization, 37(2), pp.437–462.
[62] Liu, B., Yu, M., Liu, Y., Chen, W., Fang, Z., and Lim, J.B.P. (2024), “Fire resistance time prediction and optimization of cold-formed steel walls based on machine learning”, Thin-Walled Structures, 203, pp.112207.
[63] Hou, S., Li, Q., Long, S., Yang, X., and Li, W. (2009), “Crashworthiness design for foam filled thin-wall structures”, Materials and Design, 30(6), pp.2024–2032.

  • Receive Date 16 November 2025
  • Revise Date 26 December 2025
  • Accept Date 07 January 2026
  • First Publish Date 08 January 2026
  • Publish Date 08 January 2026