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There are no standard dimensions or shapes for cold-formed sections (CFS), making it difficult for a designer to choose the optimal section dimensions in order to obtain the most cost-effective section. A great number of researchers have utilized various optimization strategies in order to obtain the optimal section dimensions. Multi-objective optimization of CFS C-channel beams using a non-dominated sorting genetic algorithm II was performed using a Microsoft Excel macro to determine the optimal cross-section dimensions. The beam was optimized according to its flexural capacity and cross-sectional area. The flexural capacity was computed utilizing the effective width method (EWM) in accordance with the Egyptian code. The constraints were selected so that the optimal dimensions derived from optimization would be production and construction-friendly. A Pareto optimal solution was obtained for 91 sections. The Pareto curve demonstrates that the solution possesses both diversity and convergence in the objective space. The solution demonstrates that there is no optimal solution between 1 and 1.5 millimeters in thickness. The solutions were validated by conducting a comprehensive parametric analysis of the change in section dimensions and the corresponding local buckling capacity. In addition, performing a single-objective optimization based on section flexural capacity at various thicknesses The parametric analysis and single optimization indicate that increasing the dimensions of the elements, excluding the lip depth, will increase the section’s carrying capacity. However, this increase will depend on the coil’s wall thickness. The increase is more rapid in thicker coils than in thinner ones.
Rocznik
Tom
Strony
52--65
Opis fizyczny
Bibliogr. 22 poz., rys., wykr.
Twórcy
autor
- Construction and Building Engineering Department Arab Academy for Science and Technology and Maritime Transport, EGYPT
autor
- Construction and Building Engineering Department Arab Academy for Science and Technology and Maritime Transport, EGYPT
autor
- Construction and Building Engineering Department Arab Academy for Science and Technology and Maritime Transport, EGYPT
Bibliografia
- [1] Tran T. and Li L. (2006): Global optimization of cold-formed steel channel sections.– Thin-Walled Struct., vol.44, pp.399-406.
- [2] Lu W. (2003): Optimum Design of Cold-Formed Steel Purlins Using Genetic Algorithms.– PhD thesis, Helsinki University of Technology, p198.
- [3] Ye J., Hajirasouliha I., Becque J. and Eslami A. (2016): Optimum design of cold-formed steel beams using particle swarm optimization method.– J. Constr. Steel. Res., vol.122, pp.80-93.
- [4] Leng J., Guest J.K. and Schafer B.W. (2011): Shape optimization of cold-formed steel columns.– Thin-Walled Struct., vol.49, pp.1492-1503.
- [5] Madeira J.F.A., Dias J. and Silvestre N. (2015): Multiobjective optimization of cold-formed steel columns.– Thin-Walled Struct., vol.96, pp.29-38.
- [6] Lee J., Kim S.-M., Park H.-S. and Woo B.-H. (2005): Optimum design of cold-formed steel channel beams using micro Genetic Algorithm.– Eng. Struct., vol.27, pp.17-24.
- [7] Lee J., Kim S.-M. and Park H.S. (2006): Optimum design of cold-formed steel columns by using micro genetic algorithms.– Thin-Walled Struct., vol.44, pp.952-960.
- [8] Seaburg P.A. and Salmon C.G. (1971): Minimum weight design of light gage steel members.– J. Struct. Div., vol.97, pp.203-222.
- [9] Tian Y.S. and Lu T.J. (2004): Minimum weight of cold-formed steel sections under compression.– Thin-Walled Struct., vol.42, pp.515-532.
- [10] Liu H., Igusa T. and Schafer B.W. (2004): Knowledge-based global optimization of cold-formed steel columns.– Thin-Walled Struct., vol.42, pp.785-801.
- [11] El Hady A.M., El Aghoury M.A., Ibrahim S.M. and Amoush E.A. (2022): Experimental investigation of steel built-up beam-columns composed of tracks and channels cold-formed sections.– J. Build. Eng., vol.51, pp.104295.
- [12] Adeli H. and Karim A. (1997): Neural network model for optimization of cold-formed steel beams.– J. Struct. Eng., vol.123, pp.1535-1543.
- [13] Magnucki K. and Paczos P. (2009): Theoretical shape optimization of cold-formed thin-walled channel beams with drop flanges in pure bending.– J. Constr. Steel Res., vol.65, pp.1731-1737.
- [14] Moharrami M., Louhghalam A. and Tootkaboni M. (2014): Optimal folding of cold formed steel cross sections under compression.– Thin-Walled Struct., vol.76, pp.145-156.
- [15] Ma W., Becque J., Hajirasouliha I. and Ye J. (2015): Cross-sectional optimization of cold-formed steel channels to Eurocode 3.– Eng. Struct., vol.101, pp.641-651.
- [16] Yu W.-W. (1973): Cold-Formed Steel Structures.– McGraw-Hill New York, NY.
- [17] Mojtabaei S.M., Hajirasouliha I. and Becque J. (2021): Optimized design of cold-formed steel elements for serviceability and ultimate limit states.– Special Issue: EUROSTEEL 2021 Sheffield, Steel's coming home, vol.4, No.2-4, pp.481-486, https://doi.org/10.1002/cepa.1319.
- [18] Utilities and Urban Development ECP (ASD) (2016): Egyptian Code of Practice for Steel Construction and Bridges (Allowable Stress Design).– 2016th ed., Arab Republic of Egypt Ministry of Housing.
- [19] Utilities and Urban Development ECP (LRFD) (2016): Egyptian Code of Practice for Steel Construction (Load and resistance factor design).– 2016th ed., Arab Republic of Egypt Ministry of Housing.
- [20] Rao S.S. (2019): Engineering Optimization: Theory and Practice.– John Wiley & Sons.
- [21] Konak A., Coit D.W. and Smith A.E. (2006): Multi-objective optimization using genetic algorithms: A tutorial.– Reliab. Eng. Syst. Saf., vol.91, pp.992-1007.
- [22] Raghuwanshi M.M. and Kakde O.G. (2004): Survey on multiobjective evolutionary and real coded genetic algorithms.– in Proceedings of the 8th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Cairns, 6-7 December 2004, vol.11, pp.150-161.
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7b2c91f7-306e-46bf-9757-3f13511de08b