Sizing optimization of the protected steel components at elevated temperature
DOI:
https://doi.org/10.7764/RDLC.22.2.277Keywords:
Eurocode 3, fire design, metaheuristic algorithms, optimization, steel structures.Abstract
In this paper, the metaheuristic algorithms such as Flower pollination and Harmony search algorithms are proposed to optimize the sizes of the steel components at the elevated temperature dealing with EN 1993 1-2. The purpose of these algorithms inspired by nature is to obtain the appropriate cross-section properties of the welded I sections. Numerical examples from the literature consisting of the protected steel structural components have been resized under different fire situations such as 30-, 60- and 90-minutes fire time. Based on the results from the numerical examples, the effect of the fire protection materials on the objective function (total weight of the steel structures) is quite high and the reduction of the total cost is almost 30% compared with the other studies. In addition, one of the most important duties of civil engineers, ensuring the balance between economic efficiency and safety, is fulfilled in a short time with the aid of the metaheuristic algorithms.
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Akcay, C., & Işikyildiz, S. (2020). Multi-objective optimization of time-cost-quality in construction projects using genetic algorithm. Revista De La Cons-trucción. Journal of Construction, 19(3), 335–346. https://doi.org/10.7764/rdlc.19.3.335-346
Atashpaz-Gargari, E., & Lucas, C. (2007). Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. 2007 IEEE Congress on Evolutionary Computation. doi:10.1109/cec.2007.4425083
Ayyarao, T. S., Ramakrishna, N. S. S., Elavarasan, R. M., Polumahanthi, N., Rambabu, M., Saini, G., ... & Alatas, B. (2022). War strategy optimization algorithm: a new effective metaheuristic algorithm for global optimization. IEEE Access, 10, 25073-25105.
Bekdaş, G., Nigdeli, S. M., & Yang, X.-S. (2015). Sizing optimization of truss structures using flower pollination algorithm. Applied Soft Computing, 37, 322–331. doi:10.1016/j.asoc.2015.08.037
Daloglu, A. T., Artar, M., Özgan, K., & Karakas, A. İ. (2016). Optimum design of steel space frames including soil-structure interaction. Structural and Multidisciplinary Optimization, 54(1), 117–131. doi:10.1007/s00158-016-1401-x
Degertekin, S. O., Tutar, H., & Lamberti, L. (2021). School-based optimization for performance-based optimum seismic design of steel frames. Engineer-ing with Computers, 37(4), 3283-3297.
Dietmar , H., 2012. Brandschutz in Europa- Bemessung nach Eurocodes.
Eurocode. Basis of structural design. (2002). doi:10.3403/02612036
Eurocode 3: Design of Steel Structures-Part 1-2: General Rules-Structural Fire Design. London: European Committee for Standardisation.
Farshchin, M., Maniat, M., Camp, C. V., & Pezeshk, S. (2018). School based optimization algorithm for design of steel frames. Engineering Structures, 171, 326–335. doi:10.1016/j.engstruct.2018.05.085
Fister, I., Fister, I., Yang, X.-S., & Brest, J. (2013). A comprehensive review of firefly algorithms. Swarm and Evolutionary Computation, 13, 34–46. doi:10.1016/j.swevo.2013.06.001
Gardner, L. (2007). Stainless steel structures in fire. Proceedings of the Institution of Civil Engineers - Structures and Buildings, 160(3), 129–138. doi:10.1680/stbu.2007.160.3.129
Gardner, L., & Baddoo, N. R. (2006). Fire testing and design of stainless steel structures. Journal of Constructional Steel Research, 62(6), 532–543. doi:10.1016/j.jcsr.2005.09.009
Gardner, L., & Nethercot, D. A. (2004). Experiments on stainless steel hollow sections—Part 1: Material and cross-sectional behaviour. Journal of Con-structional Steel Research, 60(9), 1291–1318. doi:10.1016/j.jcsr.2003.11.006
Geem, Z.W., Kim, J.H., Loganathan, G.V., 2001. A new heuristic optimization algorithm: harmony search. Simulation 76, 60–68. doi:10.1177/003754970107600201
Gholizadeh, S. (2015). Performance-based optimum seismic design of steel structures by a modified firefly algorithm and a new neural network. Advances in Engineering Software, 81, 50–65. doi:10.1016/j.advengsoft.2014.11.003
Gholizadeh, S., & Salajegheh, E. (2009). Optimal design of structures subjected to time history loading by swarm intelligence and an advanced metamod-el. Computer Methods in Applied Mechanics and Engineering, 198(37-40), 2936–2949. doi:10.1016/j.cma.2009.04.010
Gholizadeh, S., Danesh, M., & Gheyratmand, C. (2020). A new Newton metaheuristic algorithm for discrete performance-based design optimization of steel moment frames. Computers & Structures, 234, 106250.
Hasançebi, O., & Carbas, S. (2014). Bat inspired algorithm for discrete size optimization of steel frames. Advances in Engineering Software, 67, 173–185. doi: 10.1016/j.advengsoft.2013.10.003
Hashim, F. A., Hussain, K., Houssein, E. H., Mabrouk, M. S., & Al-Atabany, W. (2021). Archimedes optimization algorithm: a new metaheuristic algo-rithm for solving optimization problems. Applied Intelligence, 51, 1531-1551.
Jármai, K., Farkas, J., & Kurobane, Y. (2006). Optimum seismic design of a multi-storey steel frame. Engineering Structures, 28(7), 1038–1048. doi: 10.1016/j.engstruct.2005.11.011
Kameshki, E. S., & Saka, M. P. (n.d.). Optimum Design of Nonlinear Steel Frames with Semi-Rigid Connections using a Genetic Algorithm. Optimization and Control in Civil and Structural Engineering. doi:10.4203/ccp.60.4.5
Karaboga, D., & Basturk, B., 2008. On the performance of artificial bee colony (ABC) algorithm. Applied soft computing, 8(1), 687-697. doi: 10.1016/j.asoc.2007.05.007
Kaveh, A., & Talatahari, S. (2012). A hybrid CSS and PSO algorithm for optimal design of structures. Structural Engineering and Mechanics, 42(6), 783–797. doi:10.12989/sem.2012.42.6.783
Kennedy, J., Eberhart, R.C., 1995. Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks No. IV, November 27-December 1, pp. 1942–1948, Perth Australia.
Kucukler, M. (2020). Compressive resistance of high-strength and normal-strength steel CHS members at elevated temperatures. Thin-Walled Structures, 152, 106753. doi: 10.1016/j.tws.2020.106753
Ky, V. S., Lenwari, A., & Thepchatri, T. (2015). Optimum Design of Steel Structures in Accordance with AISC 2010 Specification Using Heuristic Algo-rithm. Engineering Journal, 19(4), 71–81. doi:10.4186/ej.2015.19.4.71
Miguel, L. F. F., & Fadel Miguel, L. F. (2012). Shape and size optimization of truss structures considering dynamic constraints through modern metaheuris-tic algorithms. Expert Systems with Applications, 39(10), 9458–9467. doi: 10.1016/j.eswa.2012.02.113
Mirjalili, S., & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51–67. doi: 10.1016/j.advengsoft.2016.01.008
Pan, J. S., Zhang, L. G., Wang, R. B., Snášel, V., & Chu, S. C. (2022). Gannet optimization algorithm: A new metaheuristic algorithm for solving engineer-ing optimization problems. Mathematics and Computers in Simulation, 202, 343-373.
Pezeshk, S., Camp, C. V., & Chen, D. (2000). Design of Nonlinear Framed Structures Using Genetic Optimization. Journal of Structural Engineering, 126(3), 382–388. doi:10.1061/(asce)0733-9445(2000)126:3(382)
Rao, R., (2016). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), 19-34. doi: 10.5267/j.ijiec.2015.8.004
Rao, R. V., Savsani, V. J., & Vakharia, D. P., 2011. Teaching–learning-based optimization: a novel method for constrained mechanical design optimiza-tion problems. Computer-Aided Design, 43(3), 303-315. doi: 10.1016/j.cad.2010.12.015.
Shayanfar, H., & Gharehchopogh, F. S. (2018). Farmland fertility: A new metaheuristic algorithm for solving continuous optimization problems. Applied Soft Computing, 71, 728-746.
Toğan, V. (2012). Design of planar steel frames using Teaching–Learning Based Optimization. Engineering Structures, 34, 225–232. doi: 10.1016/j.engstruct.2011.08.035
Truong, V. H., & Kim, S. E. (2018). A robust method for optimization of semi-rigid steel frames subject to seismic loading. Journal of Constructional Steel Research, 145, 184-195.
Van Laarhoven, P. J. M., & Aarts, E. H. L. (1987). Simulated annealing. Simulated Annealing: Theory and Applications, 7–15. doi:10.1007/978-94-015-7744-1_2
Vikhar, P. A. (2016). Evolutionary algorithms: A critical review and its future prospects. 2016 International Conference on Global Trends in Signal Pro-cessing, Information Computing and Communication (ICGTSPICC). doi:10.1109/icgtspicc.2016.7955308
Xin-She Yang, 2012. Flower pollination algorithm for global optimization, in: Unconventional Computation and Natural Computation 2012, Lecture Notes in Computer Science, Vol. 7445, pp. 240-249.
Xing, Z., Kucukler, M., & Gardner, L. (2021). Local buckling of stainless steel I-sections in fire: Finite element modelling and design. Thin-Walled Struc-tures, 161, 107486. doi: 10.1016/j.tws.2021.107486
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