ABC, TLBO, TLABC ve FDB-TLABC ALGORİTMALARININ BETONARME SÜREKLİ KİRİŞLERİN OPTİMİZASYONU ÜZERİNDEKİ BAŞARIMI

Yıl 2024, Cilt: 29 Sayı: 1, 205 - 224, 22.04.2024

Hasan Tahsin Öztürk , Sebahat Temiz

https://doi.org/10.17482/uumfd.1312150

Öz

Bu çalışmada ilk olarak, betonarme sürekli kirişlerin detaylı tasarımlarının minimum maliyetle yapılabileceği bir süreç tasarlanmıştır. Ardından bu problem üzerinde FDB-TLABC, TLABC, TLBO ve ABC algoritmalarının performansları değerlendirilmiştir. Bu amaçlarla öncelikle Türk betonarme standardı ve deprem yönetmeliği dikkate alınarak optimizasyon problemi oluşturulmuştur. Bir, iki ve üç açıklıklı kiriş örneklerinden oluşan bir problem takımı hazırlanmıştır. Bu problem takımı üzerinde yapılan testlerden algoritmaların optimum sonuca ulaşma performansları ile belirlenen makul çözümlere ulaşma süreleri ve başarıları belirlenmiştir. Optimum sonuca ulaşmada, TLABC ve FDB-TLABC algoritmalarının en başarılı algoritmalar olduğu görülmüştür. Kararlılık analizinden, TLABC algoritmasının en yüksek kararlılığa ve hıza sahip olduğu görülmüştür.

Anahtar Kelimeler

Betonarme Sürekli Kiriş, Optimizasyon, Sezgisel Algoritmalar

Performance of ABC, TLBO, TLABC and FDB-TLABC Algorithms on Optimization of RC Continuous Beam

Öz

In this study, firstly, a process by which detailed designs of RC continuous beams can be made with minimum cost was designed. Then, the performances of the FDB-TLABC, TLABC, TLBO, and ABC algorithms on this problem were evaluated. For these purposes, first, the optimization problem was created by taking into account the Turkish reinforced concrete standard and earthquake regulations. A problem set consisting of one, two, and three-span beam samples was prepared. From the tests carried out on this problem set, the performance of the algorithms to reach the optimal result and the time to reach feasible solutions and their success were determined. It was seen that the TLABC and FDB-TLABC algorithms are the most successful in reaching the optimum result. From the stability analysis, it was seen that the TLABC algorithm has the highest stability and speed.

Anahtar Kelimeler

RC Continuous Beam, Optimization, Metaheuristic Algorithms

Kaynakça

• 1. Ajayan S. ve Immanuel Selvakumar A. (2022) Metaheuristic optimization techniques to design solar-fuel cell-battery energy system for locomotives, Int J Hydrogen Energy, 47, 1845–62. doi:10.1016/j.ijhydene.2021.10.130
• 2. Ali E.S., Elazim S.M.A, ve Balobaid A.S. (2023) Implementation of coyote optimization algorithm for solving unit commitment problem in power systems, Energy, 263, 125697. doi: 10.1016/j.energy.2022.125697
• 3. Chen X., Xu B., Mei C., Ding Y. ve Li K. (2018) Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation, Applied Energy, 212, 1578-1588. doi: 10.1016/j.apenergy.2017.12.115
• 4. Chutani S. ve Singh J. (2017) Design optimization of reinforced concrete beams, Journal of The Institution of Engineers (India): Series A, 98, 429–35. doi:10.1007/s40030-017-0232-0
• 5. Duan H., Yin X., Kou H., Wang J., Zeng K., Ma F. (2023) Regression prediction of hydrogen enriched compressed natural gas (HCNG) engine performance based on improved particle swarm optimization back propagation neural network method (IMPSO-BPNN), Fuel, 331, 125872. doi: 10.1016/j.fuel.2022.125872
• 6. Duman S., Kahraman H.T., Sonmez Y., Guvenc U., Kati M. ve Aras S. (2022) A powerful meta-heuristic search algorithm for solving global optimization and real-world solar photovoltaic parameter estimation problems. Eng Appl Artif Intell, 111, 104763. doi: 10.1016/j.engappai.2022.104763
• 7. Ferreira C.C., Barros M.H.F.M. ve Barros A.F.M. (2003) Optimal design of reinforced concrete t-sections in bending. Eng Struct, 25, 951–64. doi: 10.1016/S0141-0296(03)00039-7
• 8. Friedman M. (1940) A comparison of alternative tests of significance for the problem of m rankings. Annals of Mathematical Statistics, 11, 86–92.
• 9. Govindaraj V. ve Ramasamy J.V. (2005) Optimum detailed design of reinforced concrete continuous beams using genetic algorithms. Comput Struct, 84, 34–48. doi: 10.1016/j.compstruc.2005.09.001
• 10. Gürgen S., Kahraman H.T., Aras S. ve Altın İ. (2022) A comprehensive performance analysis of meta-heuristic optimization techniques for effective organic rankine cycle design. Appl Therm Eng, 213, 118687. doi: 10.1016/j.applthermaleng.2022.118687
• 11. Jahjouh M.M., Arafa M.H. ve Alqedra M.A. (2013) Artificial bee colony (ABC) algorithm in the design optimization of rc continuous beams. Structural and Multidisciplinary Optimization, 47, 963–79. doi: 10.1007/s00158-013-0884-y
• 12. Kahraman H.T., Aras S. ve Gedikli E. (2019) Fitness-distance balance (FDB): a new selection method for meta-heuristic search algorithms. Knowl Based Syst, 105169. doi: 10.1016/j.knosys.2019.105169
• 13. Karaboga D. ve Akay B. (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput, 214, 108–32. doi: 10.1016/j.amc.2009.03.090
• 14. Koumousis V.K. ve Arsenis S.J. (1998) Genetic algorithms in optimal detailed design of reinforced concrete members. Computer-Aided Civil and Infrastructure Engineering 13, 43–52. doi: 10.1111/0885-9507.00084
• 15. Mardani-Aghabaglou, A., Öztürk, H. T., Kankal, M. ve Ramyar, K. (2021). Assessment and prediction of cement paste flow behavior; Marsh-funnel flow time and mini-slump values. Construction and Building Materials, 301, 124072. doi: 10.1016/j.conbuildmat.2021.124072
• 16. Öztürk H.T., Durmuş A. ve Durmuş A. (2012) Optimum Design of a Reinforced Concrete Beam Using Artificial Bee Colony Algorithm. Computers and Concrete, 10, 295–306. doi: 10.12989/cac.2012.10.3.295
• 17. Öztürk N., Şentürk H.B., Gündoğdu A. ve Duran C. (2018) Modelling of Co(II) Adsorption by Artificial Bee Colony and Genetic Algorithm. Membrane Water Treatment, 9, 363–71. doi: 10.12989/mwt.2018.9.5.363
• 18. Raheem F.S. ve Basil N. (2023) Automation Intelligence Photovoltaic System for Power and Voltage Issues based on Black Hole Optimization Algorithm with FOPID. Measurement: Sensors, 25, 100640. doi: 10.1016/j.measen.2022.100640
• 19. Rahimi Z. ve Maghrebi M. (2023) Minimizing rebar cost using design and construction integration. Autom Constr,147, 104701. doi: 10.1016/j.autcon.2022.104701
• 20. Rao R.V., Savsani V.J. ve Balic J. (2012) Teaching-learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems. Engineering Optimization, 44, 1447–62. doi: 10.1080/0305215X.2011.652103
• 21. Riaz M., Bashir M. ve Younas I. (2022) Metaheuristics based covid-19 detection using medical ımages: a review, Comput Biol Med, 144, 105344. doi: 10.1016/j.compbiomed.2022.105344
• 22. Sahebi M. ve Dehestani M. (2023) Sustainability assessment of reinforced concrete beams under corrosion in life-span utilizing design optimization. Journal of Building Engineering, 65, 105737. doi: 10.1016/j.jobe.2022.105737
• 23. Shaqfa M. ve Orbán Z. (2019) Modified parameter-setting-free harmony search (PSFHS) algorithm for optimizing the design of reinforced concrete beams. Structural and Multidisciplinary Optimization, 60, 999–1019. doi: 10.1007/s00158-019-02252-4
• 24. Shariat M., Shariati M., Madadi A. ve Wakil K. (2018) Computational lagrangian multiplier method by using for optimization and sensitivity analysis of rectangular reinforced concrete beams. Steel and Composite Structures, 29, 243–56. doi: 10.12989/scs.2018.29.2.243
• 25. TBDY (2018) Türkiye Bina Deprem Yönetmeliği, Afet ve Acil Durum Yönetimi Başkanlığı, Ankara.
• 26. TS 500 (2000) Betonarme yapıların tasarım ve yapım kuralları, Türk Standartları Enstitüsü, Ankara.
• 27. Xia X., Ning D., Liu P., Du H. ve Zhang N. (2023) Electrical network optimization for electrically ınterconnected suspension system. Mech Syst Signal Process, 187, 109902. doi: 10.1016/j.ymssp.2022.109902