Minimum weight design of reinforced concrete beams utilizing grey wolf and backtracking search optimization algorithms
DOI: https://doi.org/10.20528/cjcrl.2022.02.003
View Counter: Abstract | 366 times | ‒ Full Article | 143 times |
Full Text:
PDFAbstract
In this study, optimal weight design of a reinforced concrete beam subjected to various loading conditions is investigated. The purpose of the optimization is to attain the minimum weight design of the reinforced concrete beam under distributed and two-point loads. The design problem is handled under three different design load cases. The two-point loads are affected on beam-to-beam connection nodes of reinforced concrete beams. Thus, while the magnitudes of distributed load and two-points load are remained constant, the distances between two-points loads are taken as 2m, 3m and 4m, respectively. The width and height of the rectangular cross-section of the concrete beam, and the diameters of the longitudinal and confinement steel rebars are treated as design variables of the optimum design problem. The design constraints of the optimization problem consist of the geometric constraints and necessities of the Turkish Requirements for Design and Construction of Reinforced Concrete Structures (TS500), and Turkish Building Earthquake Code (TBEC). As two novel metaheuristics, grey wolf (GW) and backtracking search (BS) optimization algorithms are selected as optimizers. Both algorithms are independently operated five times for three different design problems. Thus, the obtained results are examined statistically to compare in accordance with algorithmic performances. The optimal findings from optimization algorithms show that the GW algorithm is a little bit more robust on the exploitation phase, while the BS algorithm is stronger on the exploration phase. Moreover, it can be deducted from optimal beam designs that the GW algorithm is more viable to minimize reinforced concrete beam design.
Keywords
References
Abubakar J, Anum B, Iaren CT (2021). Design optimization of rectangular beams using genetic algorithm. Journal of Engineering Sciences, 4(2), 66–78.
Alimoradi M, Azgomi H, Asghari A (2022). Trees social relations optimization algorithm: A new Swarm-Based metaheuristic technique to solve continuous and discrete optimization problems. Mathematics and Computers in Simulation: 629–664.
Aydogdu I (2016). Cost optimization of reinforced concrete cantilever retaining walls under seismic loading using a biogeography-based optimization algorithm with Levy flights. Engineering Optimization, 49(3), 381–400,
Aydogdu I (2016). New Iterative method to Calculate Base Stress of Footings under Biaxial Bending. International Journal of Engineering and Applied Sciences, 8(4), 40–48.
Carbas S, Toktas A, Ustun D (eds) (2021). Nature-Inspired Metaheuristic Algorithms for Engineering Optimization Applications. Springer Tracts in Nature-Inspired Computing. Springer Singapore, Singapore.
Chen D, Ge Y, Wan Y, Deng Y, Chen Y, Zou F (2022). Poplar optimization algorithm: A new meta-heuristic optimization technique for numerical optimization and image segmentation. Expert Systems with Applications, 117118.
Civicioglu P (2013). Backtracking search optimization algorithm for numerical optimization problems. Applied Mathematics and Computation, 219(15), 8121–8144.
Coello CC, Hernández FS, Farrera FA (1997). Optimal design of reinforced concrete beams using genetic algorithms. Expert Systems with Applications, 12(1), 101–108.
Erdal F, Tas S, Tunca O, Carbas S (2016). Effect of random number sequences on the optimum design of castellated beams with improved harmony search method. International Journal of Engineering and Applied Sciences, 8(3), 25–39.
Fahr S, Mitsos A, Bongartz D (2022). Simultaneous deterministic global flowsheet optimization and heat integration: Comparison of formulations. Computers & Chemical Engineering, 107790.
Geem ZW, Kim JH, Loganathan GV (2016). A new heuristic optimization algorithm: Harmony search. Simulation, 76(2), 60–68.
Goldberg DE, Holland JH (1988). Genetic algorithms and machine learning. Machine Learning, 3(2): 95–99.
Hasan QF, Al-Mamany DA, Fayadh OK (2019) Design of reinforced concrete deep beams using particle swarm optimization technique. Karbala International Journal of Modern Science, 5(4), 255–265.
Jin C, Chung WC, Kwon DS, Kim MH (2021). Optimization of tuned mass damper for seismic control of submerged floating tunnel. Engineering Structures, 112460.
Kazemzadeh Azad S, Aminbakhsh S (2022). ε-constraint guided stochastic search with successive seeding for multi-objective optimization of large-scale steel double-layer grids. Journal of Building Engineering, 103767.
Lu S, Wang C, Fan Y, Lin B (2021). Robustness of building energy optimization with uncertainties using deterministic and stochastic methods: Analysis of two forms. Building and Environment, 108185.
Mirjalili S, Mirjalili SM, Lewis A (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61.
Peng H, Xiao W, Han Y, Jiang A, Xu Z, Li M, Wu Z (2022). Multi-strategy firefly algorithm with selective ensemble for complex engineering optimization problems. Applied Soft Computing, 108634.
Perez RE, Behdinan K (2007). Particle swarm approach for structural design optimization. Computers & Structures, 85(19–20), 1579–1588.
TBEC (2018). Turkish Building Earthquake Code Specifications for Design of Buildings under Seismic Effects, Ankara, Turkey.
TS500 (2000). Requirements for Design and Construction of Reinforced Concrete Structures, Standard TS500, Ankara, Turkey.
Tunca O (2022). Optimum design of a vaulted roof steel structure using grey wolf and backtracking search optimization algorithms through application programming interface. Challenge Journal of Structural Mechanics, 8(1), 1–8.
Verij kazemi M, Fazeli Veysari E (2022). A new optimization algorithm inspired by the quest for the evolution of human society: Human felicity algorithm. Expert Systems with Applications, 116468.
Yang XS (2010). Firefly algorithm, Lévy flights and global optimization. Research and Development in Intelligent Systems XXVI: Incorporating Applications and Innovations in Intelligent Systems, 17, 209–218.
Refbacks
- There are currently no refbacks.