Selective Cost Optimization of Singly/Doubly Reinforced Beam Design with Limited Cross-section
Source abstract
Abstract
Extensive research on optimizing singly and doubly reinforced concrete beams has not considerably addressed the selection of the reinforcement type when the cross-sectional dimension is constrained by factors such as story height. This study developed a genetic algorithm model to optimize the design of either singly or doubly reinforced concrete beams under restricted cross-sectional depths, thereby ensuring compliance with design standards. We constructed a dataset comprising 168 test cases, each characterized by the beam length, dead load, live load, and cross-sectional depth limitation. For validation, the actual optimal values for each case were calculated using a brute force algorithm. To enhance model stability, the best out of three optimization processes performed per data point was selected. The findings highlighted an R 2 value of 0.9996, thereby confirming the effectiveness of the developed model in addressing the optimization problem. This study highlights the significance of selecting an appropriate design approach based on specific conditions and recommends that future research should investigate more robust models for more complex structures.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Topology Optimization in Engineering · Structural Behavior of Reinforced Concrete · Advanced Multi-Objective Optimization Algorithms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (33)
Show source-provided references
1. Afzal, M., Liu, Y., Cheng, J. C., & Gan, V. J. (2020). Reinforced concrete structural design optimization: A critical review. Journal of Cleaner Production, 260, Article 120623. https://doi.org/10.1016/j.jclepro.2020.12062310.1016/j.jclepro.2020.120623 ↗
2. Aga, A. A., & Adam, F. M. (2015). Design optimization of reinforced concrete frames. Open Journal of Civil Engineering, 05(1), 74–83. https://doi.org/10.4236/ojce.2015.5100810.4236/ojce.2015.51008 ↗
3. Akin, A., & Saka, M. P. (2015). Harmony search algorithm based optimum detailed design of reinforced concrete plane frames subject to ACI 318–05 provisions. Computers and Structures, 147, 79–95. https://doi.org/10.1016/j.compstruc.2014.10.00310.1016/j.compstruc.2014.10.003 ↗
4. Babaei, M., & Mollayi, M. (2021). Multiobjective optimal design of reinforced concrete frames using two metaheuristic algorithms. Journal of Engineering Research, 9(4B), 166–192. https://doi.org/10.36909/jer.997310.36909/jer.9973 ↗
5. Camp, C. V., Pezeshk, S., & Hansson, H. (2003). Flexural design of reinforced concrete frames using a genetic algorithm. Journal of Structural Engineering, 129(1), 105–115. https://doi.org/10.1061/(ASCE)0733-9445(2003)129:1(105)10.1061/(ASCE)0733-9445(2003)129:1(105) ↗
6. Chutani, S., & Singh, J. (2017). Design optimization of reinforced concrete beams. Journal of the Institution of Engineers (India). Series A, 98(4), 429–435. https://doi.org/10.1007/s40030-017-0232-010.1007/s40030-017-0232-0 ↗
7. De Falco, I., Della Cioppa, A., & Tarantino, E. (2002). Mutation-based genetic algorithm: Performance evaluation. Applied Soft Computing, 1(4), 285–299. https://doi.org/10.1016/S1568-4946(02)00021-210.1016/S1568-4946(02)00021-2 ↗
8. dos Santos, N. R., Alves, E. C., & Kripka, M. (2024). Towards sustainable reinforced concrete beams: Multi-objective optimization for cost, CO2 emissions, and crack prevention. Asian Journal of Civil Engineering, 25(1), 575–582. https://doi.org/10.1007/s42107-023-00795-y10.1007/s42107-023-00795-y ↗
9. Esfandiary, M. J., Sheikholarefin, S., & Bondarabadi, H. R. (2016). A combination of particle swarm optimization and multi-criterion decision-making for optimum design of reinforced concrete frames. International Journal of Optimization in Civil Engineering, 6(2), 245–268.
10. Fakhimi, A. H., Masoomeh Jahanshahi, M., & Cengiz, T. Y. (2017). The most applicable meta-heuristic optimization methods on concrete structures. 3rd Iran International Conference on Structural Engineering
11. Galeb, A. (2018). Optimum design of doubly reinforced concrete beams using simulated annealing. International Journal of Civil Engineering and Technology, 9(10), 61–70.
12. Ghoreishi, S. N., Clausen, A., & Joergensen, B. N. (2017). Termination criteria in evolutionary algorithms: A survey. Proceeding of the 9th International Joint Conference on Computational Intelligence, pp. 373–384. https://doi.org/10.5220/000657790373038410.5220/0006577903730384 ↗
13. Gilli, M., & Winker, P. (2008). A review of heuristic optimization methods in econometrics. Swiss Finance Institute Research Paper, pp. 08–12, http://ssrn.com/abstract=1140655
14. Global Cement and Concrete Association (2021). Cement and concrete around the world. https://gccassociation.org/concretefuture/cement-concrete-around-the-world/, Accessed 7 Oct 2024
15. Govindaraj, V., & Ramasamy, J. V. (2005). Optimum detailed design of reinforced concrete continuous beams using genetic algorithms. Computers and Structures, 84(1–2), 34–48. https://doi.org/10.1016/j.compstruc.2005.09.00110.1016/j.compstruc.2005.09.001 ↗
16. Gupta, S., Abderazek, H., Yıldız, B. S., Yildiz, A. R., Mirjalili, S., & Sait, S. M. (2021). Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems. Expert Systems with Applications, 183, Article 115351. https://doi.org/10.1016/j.eswa.2021.11535110.1016/j.eswa.2021.115351 ↗
17. Haupt, R. L., & Haupt, S. E. (2004). Practical genetic algorithms. (second edn.). Wiley10.1002/0471671746 ↗
18. Jeong, J. H., & Jo, H. (2021). Deep reinforcement learning for automated design of reinforced concrete structures. Computer-Aided Civil and Infrastructure Engineering, 36(12), 1508–1529. https://doi.org/10.1111/mice.1277310.1111/mice.12773 ↗
19. Kim, A., Cunningham, P. R., Kamau-Devers, K., & Miller, S. A. (2022). OpenConcrete: A tool for estimating the environmental impacts from concrete production. Environmental Research: Infrastructure and Sustainability, 2(4), Article 041001. https://doi.org/10.1088/2634-4505/ac8a6d10.1088/2634-4505/ac8a6d ↗
20. Kim, B. I., & Kwon, J. H. (2009). Optimum design of reinforced concrete beam using genetic algorithms. Journal of Ocean Engineering and Technology, 23(6), 131–135.
21. Lambora, A., Gupta, K., & Chopra, K. (2019). Genetic algorithm- A literature review. International Conference on Machine Learning, Big Data, Cloud and Parallel Computing. 380–384. https://doi.org/10.1109/COMITCon.2019.886225510.1109/COMITCon.2019.8862255 ↗
22. Li, H., Deng, Q., Zhang, J., Xia, B., & Skitmore, M. (2019). Assessing the life cycle CO2 emissions of reinforced concrete structures: Four cases from China. Journal of Cleaner Production, 210, 1496–1506. https://doi.org/10.1016/j.jclepro.2018.11.10210.1016/j.jclepro.2018.11.102 ↗
23. Miller, S. A., Horvath, A., & Monteiro, P. J. (2016). Readily implementable techniques can cut annual CO2 emissions from the production of concrete by over 20%. Environmental Research Letters, 11(7), Article 074029. https://doi.org/10.1088/1748-9326/11/7/07402910.1088/1748-9326/11/7/074029 ↗
24. Mohammad, F. A., & Ahmed, H. G. (2018). Optimum design of reinforced concrete cantilever retaining walls according Eurocode 2 (EC2). Athens Journal of Τechnology & Engineering, 5(3), 277–296. https://doi.org/10.30958/ajte.5-3-410.30958/ajte.5-3-4 ↗
25. Parejo, J. A., Ruiz-Cortés, A., Lozano, S., & Fernandez, P. (2012). Metaheuristic optimization frameworks: A survey and benchmarking. Soft Computing, 16(3), 527–561. https://doi.org/10.1007/s00500-011-0754-810.1007/s00500-011-0754-8 ↗
26. Paya, I., Yepes, V., González-Vidosa, F., & Hospitaler, A. (2008). Multiobjective optimization of concrete frames by simulated annealing. Computer-Aided Civil and Infrastructure Engineering, 23(8), 596–610. https://doi.org/10.1111/j.1467-8667.2008.00561.x10.1111/j.1467-8667.2008.00561.x ↗
27. Pourrajabian, A., Dehghan, M., & Rahgozar, S. (2021). Genetic algorithms for the design and optimization of horizontal axis wind turbine (HAWT) blades: A continuous approach or a binary one? Sustainable Energy Technologies and Assessments, 44, Article 101022. https://doi.org/10.1016/j.seta.2021.10102210.1016/j.seta.2021.101022 ↗
28. Rajeev, S., & Krishnamoorthy, C. S. (1998). Genetic algorithm-based methodology for design optimization of reinforced concrete frames. Computer-Aided Civil and Infrastructure Engineering, 13(1), 63–74. https://doi.org/10.1111/0885-9507.0008610.1111/0885-9507.00086 ↗
29. Sánchez-Olivares, G., & Tomás, A. (2017). Improvements in meta-heuristic algorithms for minimum cost design of reinforced concrete rectangular sections under compression and biaxial bending. Engineering Structures, 130, 162–179. https://doi.org/10.1016/j.engstruct.2016.10.01010.1016/j.engstruct.2016.10.010 ↗
30. Shukla, A., Pandey, H. M., & Mehrotra, D. (2015). Comparative review of selection techniques in genetic algorithm. International Conference on Futuristic Trends on Computational Analysis and Knowledge Management. 515–519. https://doi.org/10.1109/ABLAZE.2015.715491610.1109/ABLAZE.2015.7154916 ↗
31. Tamrazyan, A., & Alekseytsev, A. V. (2023). Optimization of reinforced concrete beams under local mechanical and corrosive damage. Engineering Optimization, 55(11), 1905–1922. https://doi.org/10.1080/0305215X.2022.213435610.1080/0305215X.2022.2134356 ↗
32. Van Damme, H. (2018). Concrete material science: Past, present, and future innovations. Cement and Concrete Research, 112, 5–24. https://doi.org/10.1016/j.cemconres.2018.05.00210.1016/j.cemconres.2018.05.002 ↗
33. Wong, W. K., & Ming, C. I. (2019). A review on metaheuristic algorithms: Recent trends, benchmarking and applications. 7th International Conference on Smart Computing & Communications. https://doi.org/10.1109/ICSCC.2019.884362410.1109/ICSCC.2019.8843624 ↗