Email this article Optimum design of reinforced concrete beam sections with JAYA algorithm
DOI: https://doi.org/10.20528/cjcrl.2024.04.003
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Section design is an important process for designing reinforced concrete structures because of the existence of factors such as bearing capacity and cost. After defining the initial cross sections for reinforced concrete elements, reinforcement amounts are calculated within the framework of certain rules and regulations. In the classical method, optimum cost and reliable structure can be designed by trial and error. Designing with the classical method is quite time-consuming. As in different fields, metaheuristic algorithms are employed to civil engineering applications to reach the optimum solution. In this study, unlike other examples from the literature, the optimum cost design of reinforced concrete beam cross-section was done, adding torsional moment to bending moment and effects of shearing force through the use of the JAYA algorithm. The optimum cost design of a reinforced concrete beam section under the effects of torsional moment, bending moment and shear force is performed. The design is done according to the rules specified in ACI 318 (Building code requirements for structural concrete), as it is well-known and used in many international projects. For the purpose of this study, cross-sections of 10 different reinforced concrete beam cross-sections were designed under 5 different loads for 2 different concrete classes through MATLAB software, with the aim of finding the optimum beam cross-section. A reinforced concrete beam is designed under different torsional loads and for different concrete classes and it is aimed to find the optimum beam cross-section. It shows the effect of torsional force on reinforced concrete beam cross section and reinforcement by using different torsional loads. It was observed that the algorithm used tends to approach the optimum result, increasing the area of concrete with lower unit cost or reducing the distance between stirrups. It was concluded that Jaya algorithm performs effectively with respect to optimizing reinforced concrete beams. The algorithm used can be applied to different reinforced concrete elements.
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References
ACI 318-05 (2008). Building code requirements for structural concrete (ACI 318-05) and commentary. ACI Committee, American Concrete Institute, USA.
Alqedra M, Arafa M, İsmail M (2011). Optimum cost of prestressed and reinforced concrete beams using genetic algorithms, Journal of Artificial Intelligence, 4(1), 76-88.
Bekdaş G, Çakıroğlu C, İslam K, Kim S, Geem ZW (2022a). Optimum design of cylindrical walls using ensemble learning methods. Applied Sciences, 12, 2165.
Bekdaş G, Çakıroğlu C, Kim S, Geem ZW (2022b). Optimization and predictive modeling of reinforced concrete circular columns. Materials, 15, 6624.
Bekdaş G, Niğdeli SM (2016). Optimum design of reinforced concrete columns employing teaching-learning based optimization. Challenge Journal of Structural Mechanics, 2(4), 216-219.
Bekdaş G, Temür R (2017). Metaheuristic approaches for optimum design of cantilever reinforced concrete retaining walls. Challenge Journal of Structural Mechanics, 3(1), 23-30.
Çakıroğlu C, Bekdaş G, Kim S, Geem ZW (2020a). Optimisation of shear and lateral–torsional buckling of steel plate girders using meta-heuristic algorithms. Applied Sciences, 10, 3639.
Çakıroğlu C, Bekdaş G, Geem ZW (2020b). Harmony search optimisation of dispersed laminated composite plates. Materials, 13, 2862.
Çakıroğlu C, İslam K, Bekdaş G, Kim S, Geem ZW (2021a). Metaheuristic optimization of laminated composite plates with cut-outs. Coatings, 11, 1235.
Çakıroğlu C, İslam K, Bekdaş G, Kim S, Geem ZW (2021b). CO2 emission optimization of concrete-filled steel tubular rectangular stub columns using metaheuristic algorithms. Sustainability, 13, 10981.
Çakıroğlu C, İslam K, Bekdaş G, Nehdi ML (2023a). Data-driven ensemble learning approach for optimal design of cantilever soldier pile retaining walls. Structures, 51, 1268-1280.
Çakıroğlu C, İslam K, Bekdaş G (2023b). Manta Ray Foraging and Jaya Hybrid Optimization of concrete filled steel tubular stub columns based on CO2 emission. Hybrid Metaheuristics in Structural Engineering, 480, 111-125.
Dede T (2018). Jaya algorithm to solve single objective size optimization problem for steel grillage structures. Steel and Composite Structures, 26(2), 163-170.
Dede T, Ayvaz Y (2015). Combined size and shape optimization of structures with a new meta-heuristic algorithm. Applied Soft Computing, 28, 250-258.
Deliktaş B, Bikçe M, Coşkun H, Türker HT (2009). Betonarme kirişlerin optimum tasarımında genetik algoritma parametrelerinin etkisinin belirlenmesi. Fırat University Journal of Engineering Science, 21(2), 125-132.
Dos Santos NR, Alves EC, Kripka M (2023). Towards sustainable reinforced concrete beams: multi-objective optimization for cost, CO2 emission, and crack prevention. Asian Journal of Civil Engineering, 25, 575-582.
Du DC, Vinh HH, Trung VD, Quyen NTH, Trung NT (2018). Efficiency of Jaya algorithm for solving the optimization-based structural damage identification problem based on a hybrid objective function. Engineering Optimization, 50(8), 1233-1251.
El-Sayed TA (2019). Flexural behavior of RC beams containing recycled industrial wastes as steel fibers. Construction and Building Materials, 212, 27-38.
El-Sayed TA, Shahen YBI (2020). Flexural performance of recycled wheat straw ash-based geopolymer RC beams and containing recycled steel fiber. Structures, 28, 1713-1728.
Ghali MKN, El-Sayed TA, Salah A, Khater N (2024). Performance of RC beams under shear loads strengthened with metallic and non-metallic fibers. Buildings, 14, 1869.
Goldberg DE, Samtani MP (1986). Engineering optimization via genetic algorithm. Proceedings of Ninth Conference on Electronic Computation, ASCE, New York, United States of America, 471-482.
Govindaraj V, Ramasamy JV (2005). Optimum detailed design of reinforced concrete continuous beams using Genetic Algorithms. Computers & Structures, 84(1-2), 34-48.
Guimarães SA, Klein D, Calenzani AFG, Alves EC (2022). Optimum design of steel columns filled with concrete via genetic algorithm: environmental impact and cost analysis. International Engineering Journal, 75(2), 117-128.
Jahjouh MM, Arafa MH, Alqedra MA (2013). Artificial Bee Colony (ABC) algorithm in the design optimization of RC continuous beams. Structural and Multidisciplinary Optimization, 47, 963-979.
Kaveh A, Hamedani KB (2023). Discrete structural optimization with set theoretical Jaya algorithm. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 47, 79-103.
Kayabekir AC, Yücel M, Bekdaş G, Nigdeli SM (2020). Comparative study of optimum cost design of reinforced concrete retaining wall via metaheuristics. Challenge Journal of Concrete Research Letters, 11(3), 75-81.
Nigdeli SM, Bekdaş G, Yang X-S (2018). Metaheuristic optimization of reinforced concrete footings. KSCE Journal of Civil Engineering, 22, 4555-4563.
Nimtawat A, Nanakorn P (2010). A genetic algorithm for beam–slab layout design of rectilinear floors. Engineering Structures, 32(11), 3488-3500.
Özturk HT, Durmus A (2013). Optimum cost design of RC columns using artificial bee colony algorithm. Structural Engineering and Mechanics, 45(5), 643-654.
Öztürk HT, Dede T, Türker E (2020). Optimum design of reinforced concrete counterfort retaining walls using TLBO, Jaya algorithm. Structures, 25, 285-296.
Pham HA, Nguyen B D (2024). Fuzzy structural analysis using ımproved Jaya-based optimization approach. Periodica Polytechnica Civil Engineering, 68(1), 1-7.
Pierott R, Hammad AWA, Haddad A, Garcia S, Falcon G (2021). A mathematical optimisation model for the design and detailing of reinforced concrete beams. Engineering Structures, 245, 112861.
Rao HS, Babu BR (2006) Optimized column design using genetic algorithm based neural networks. Indian Journal of Engineering and Materials Sciences, 13 (6), 503-511.
Rao RV, Savsani VJ, Vakharia DP (2011). Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303-315.
Rao RV (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.
Sahab MG, Ashour AF, Toropov VV (2005). A hybrid genetic algorithm for reinforced concrete flat slab buildings. Computers & Structures, 83(8-9), 551-559.
Shooli AR, Vosoughi AR, Banan MR (2019). A mixed GA-PSO-based approach for performance-based design optimization of 2D reinforced concrete special moment-resisting frames. Applied Soft Computing Journal, 85, 105843.
Sönmez M (2011). Artificial Bee Colony algorithm for optimization of truss structures. Applied Soft Computing, 11(2), 2406-2418.
Tapao A, Cheerarot R (2017). Optimal parameters and performance of artificial bee colony algorithm for minimum cost design of reinforced concrete frames. Engineering Structures, 151, 802-820.
Temur R, Bekdaş G (2016). Teaching learning-based optimization for design of cantilever retaining walls. Structural Engineering and Mechanics, 57(4), 763-783.
Yousif ST, Najem RN (2013). Optimum cost design of reinforced concrete continuous beams using Genetic Algorithms. International Journal of Applied Sciences and Engineering Research, 2(1), 79-92.
Yücel M, Niğdeli SM, Bekdaş G (2023). Optimization of truss structures by using a hybrid population-based metaheuristic algorithm. Arabian Journal for Science and Engineering, 49, 5011-5026.
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