Abstract
In a structural system, the connections (i.e., between the structural elements and the structure to the ground) play an important role in the integrity and stability of the system. So, using the certain pre-defined conventional properties for these systems can stand far away from the expected optimal condition. In this regard, the current study deals with optimal design (i.e., cost and geometry parameters under different loading conditions) of the footing systems applied in the precast industrial buildings and the concrete bracket system as the privilege connection type in the RC frames. To provide a broad perspective about the optimal design of these systems, several distinct optimization models are generated and solved. For solving the proposed optimization problems, a recently developed self-adaptive and non-gradient-based method, so-called Fuzzy Differential Evolution Incorporated Virtual Mutant (FDEVM), is utilized. In the developed models, effect of different loading conditions on the optimum geometry and cost parameters of the proposed systems are investigated. For this aim, sixty three different probable situations are considered and solved, and the attained outcomes are reported through illustrative tables and diagrams. The outcomes indicate that the vertical load and bracket width play important role in the total cost of the system. In addition, provided behavioral diagrams indicate that the FDEVM method shows a dynamic adaptive behavior on during the optimization process.
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References
Mortazavi, A.: Size and layout optimization of truss structures with dynamic constraints using the interactive fuzzy search algorithm. Eng. Optim. (2021). https://doi.org/10.1080/0305215X.2020.1726341
Mortazavi, A.: Large-scale structural optimization using a fuzzy reinforced swarm intelligence algorithm. Adv. Eng. Softw. (2020). https://doi.org/10.1016/j.advengsoft.2020.102790
Moloodpoor, M.; Mortazavi, A.; Özbalta, N.: Thermo-economic optimization of double-pipe heat exchanger using a compound swarm intelligence. Heat Transf. Res. (2021). https://doi.org/10.1615/HEATTRANSRES.2021037293
Mei, L.; Wang, Q.: Structural optimization in civil engineering: a literature review. Science 2, 78 (2021)
Mortazavi, A.: The performance comparison of three Metaheuristic algorithms on the size, layout and topology optimization of truss structures. Mugla J. Sci. Technol. 5, 28–41 (2019)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95 -International Conference on Neural Networks, pp. 1942–1948 (1995)
Rao, R.V.; Savsani, V.J.; Vakharia, D.P.: Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. CAD Comput. Aided Des. (2011). https://doi.org/10.1016/j.cad.2010.12.015
Akay, B.; Karaboga, D.: Artificial bee colony algorithm for large-scale problems and engineering design optimization. J. Intell. Manuf. (2012). https://doi.org/10.1007/s10845-010-0393-4
Zheng, Y.J.: Water wave optimization: a new nature-inspired metaheuristic. Comput. Oper. Res. (2015). https://doi.org/10.1016/j.cor.2014.10.008
Arora, S.; Singh, S.: Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput. (2019). https://doi.org/10.1007/s00500-018-3102-4
Mortazavi, A.: Comparative assessment of five metaheuristic methods on distinct problems. Dicle Univ. J. Eng. 5, 899 (2019). https://doi.org/10.24012/dumf.585790
Mortazavi, A., Togan, V.: Metaheuristic algorithms for optimal design of truss structures. In: Studies in Systems, Decision and Control (2021)
Moloodpoor, M.; Mortazavi, A.: Simultaneous optimization of fuel type and exterior walls insulation attributes for residential buildings using a swarm intelligence. Int. J. Environ. Sci. Technol. (2021). https://doi.org/10.1007/s13762-021-03323-0
Moloodpoor, M.; Mortazavi, A.; Ozbalta, N.: Thermal analysis of parabolic trough collectors via a swarm intelligence optimizer. Sol. Energy. (2019). https://doi.org/10.1016/j.solener.2019.02.008
Mortazavi, A.; Toğan, V.: Triangular units based method for simultaneous optimizations of planar trusses. Adv. Comput. Des. 2, 8 (2017). https://doi.org/10.12989/acd.2017.2.3.195
Sun, L.; Chen, S.; Xu, J.; Tian, Y.; Zhou, Y.: Improved Monarch butterfly optimization algorithm based on opposition-based learning and random local perturbation. Complexity (2019). https://doi.org/10.1155/2019/4182148
Li, G.; Shuang, F.; Zhao, P.; Le, C.: An improved butterfly optimization algorithm for engineering design problems using the cross-entropy method. Symmetry (Basel) (2019). https://doi.org/10.3390/SYM11081049
Nobile, M.S.; Cazzaniga, P.; Besozzi, D.; Colombo, R.; Mauri, G.; Pasi, G.: Fuzzy self-tuning PSO: a settings-free algorithm for global optimization. Swarm Evol. Comput. (2018). https://doi.org/10.1016/j.swevo.2017.09.001
Mortazavi, A.: Bayesian interactive search algorithm: a new probabilistic Swarm intelligence tested on mathematical and structural optimization problems. Adv. Eng. Softw. (2021). https://doi.org/10.1016/j.advengsoft.2021.102994
Saka, M.P.: Optimum design of steel sway frames to BS5950 using harmony search algorithm. J. Constr. Steel Res. 65, 36–43 (2009). https://doi.org/10.1016/j.jcsr.2008.02.005
Fesanghary, M.; Mahdavi, M.; Minary-Jolandan, M.; Alizadeh, Y.: Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems. Comput. Methods Appl. Mech. Eng. 197, 3080–3091 (2008). https://doi.org/10.1016/j.cma.2008.02.006
Daloglu, A.T.; Artar, M.; Özgan, K.; Karakas, A.: Optimum design of steel space frames including soil-structure interaction. Struct. Multidiscip. Optim. (2016). https://doi.org/10.1007/s00158-016-1401-x
Ayvaz, M.T.; Kayhan, A.H.; Ceylan, H.; Gurarslan, G.: Hybridizing the harmony search algorithm with a spreadsheet “Solver” for solving continuous engineering optimization problems. Eng. Optim. 41, 1119–1144 (2009). https://doi.org/10.1080/03052150902926835
Kaveh, A.; Abadi, A.S.M.: Harmony search based algorithms for the optimum cost design of reinforced concrete cantilever retaining walls. Int. J. Civ. Eng. 9, 1–8 (2011)
Aydogdu, I.: Cost optimization of reinforced concrete cantilever retaining walls under seismic loading using a biogeography-based optimization algorithm with Levy flights. Eng. Optim. 49, 381–400 (2017). https://doi.org/10.1080/0305215X.2016.1191837
Molina-Moreno, F.; García-Segura, T.; Martí, J.V.; Yepes, V.: Optimization of buttressed earth-retaining walls using hybrid harmony search algorithms. Eng. Struct. 134, 205–216 (2017). https://doi.org/10.1016/j.engstruct.2016.12.042
Camp, C.V.; Akin, A.: Design of retaining walls using Big Bang-Big crunch optimization. J. Struct. Eng. (2012). https://doi.org/10.1061/(asce)st.1943-541x.0000461
Yücel, M.; Bekdaş, G.; Nigdeli, S.M.; Kayabekir, A.E.: An artificial intelligence-based prediction model for optimum design variables of reinforced concrete retaining walls. Int. J. Geomech. (2021). https://doi.org/10.1061/(asce)gm.1943-5622.0002234
Kaveh, A.; Hamedani, K.B.; Bakhshpoori, T.: Optimal design of reinforced concrete cantilever retaining walls utilizing eleven meta-heuristic algorithms: a comparative study. Period. Polytech. Civ. Eng. (2020). https://doi.org/10.3311/PPci.15217
Martí, J.V.; Gonzalez-Vidosa, F.; Yepes, V.; Alcalá, J.: Design of prestressed concrete precast road bridges with hybrid simulated annealing. Eng. Struct. 48, 342–352 (2013). https://doi.org/10.1016/j.engstruct.2012.09.014
Aydın, Z.; Ayvaz, Y.: Overall cost optimization of prestressed concrete bridge using genetic algorithm. KSCE J. Civ. Eng. 17, 769–776 (2013). https://doi.org/10.1007/s12205-013-0355-4
Camp, C.V.; Assadollahi, A.: CO2 and cost optimization of reinforced concrete footings using a hybrid big bang-big crunch algorithm. Struct. Multidiscip. Optim. 48, 411–426 (2013). https://doi.org/10.1007/s00158-013-0897-6
Nigdeli, S.M.; Bekdaş, G.; Yang, X.S.: Metaheuristic optimization of reinforced concrete footings. KSCE J. Civ. Eng. (2018). https://doi.org/10.1007/s12205-018-2010-6
Turkish Standards (TS500)- Requirements for design and construction of reinforced concrete structures, Ankara, Turkey
Mortazavi, A.; Moloodpoor, M.: Differential evolution method integrated with a fuzzy decision-making mechanism and Virtual Mutant agent: theory and application. Appl. Soft Comput. (2021). https://doi.org/10.1016/j.asoc.2021.107808
Ersoy, U., Ozcebe, G.: Reinforced Concrete I. Istanbul (2012)
Tang, K.; Li, Z.; Luo, L.; Liu, B.: Multi-strategy adaptive particle Swarm optimization for numerical optimization. Eng. Appl. Artif. Intell. (2015). https://doi.org/10.1016/j.engappai.2014.08.002
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Kamal, M., Mortazavi, A. & Cakici, Z. Optimal Design of RC Bracket and Footing Systems of Precast Industrial Buildings Using Fuzzy Differential Evolution Incorporated Virtual Mutant. Arab J Sci Eng 48, 13073–13089 (2023). https://doi.org/10.1007/s13369-023-07650-x
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DOI: https://doi.org/10.1007/s13369-023-07650-x