Solving Multi-Objective Rational Placement of Load-Bearing Walls Problem via Genetic Algorithm

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Abstract

The rational placement of load-bearing walls remains a complex and poorly studied problem, despite the existence of numerous algorithms and models for solving the similar problem of column placement. The main complexity factors are the large number of alternative solutions, the significant time required to calculate deformations for a given wall placement, and the multi-objective nature of the problem. In addition to the nonlinear criterion for estimating deformations, it is necessary to minimize the length of load-bearing walls and the number of their unique lengths. A model for the rational placement of load-bearing walls is proposed, which divides the walls into functional parts with a specific step and considers all the required target criteria. Adjacent wall parts with the same functionality are combined into segments. The combinatorial formulation applied in the model of the problem allows the use of genetic algorithms as a solution tool. Therefore, a new approach to multi-objective genetic algorithm is proposed, containing metrics for calculating population diversity at the phenotype and genotype levels. Modifications of crossover, mutation, and selection operators, considering the segmental structure of the wall's genotype, are presented. A comparative analysis of the developed algorithm with other known multi-objective genetic algorithms showed that the developed algorithm finds, on average, three times more non-dominated solutions, particularly more plans with a lower deformation estimates, despite the twice-longer execution time. The proposed model differs significantly from previous models in terms of handling deformations in slab-support systems, comparing placement plans with each other rather than calculating precise reinforcement estimates, which is often unnecessary at the early stages. The proposed genetic algorithm scheme increases the number of found nondominated solutions without losing their diversity, and can be used to solve other multi-objective problems, taking into account the specified features. The developed algorithm was easily integrated into the CAD-based decision support software and can be used in practice by building designers.

About the authors

V. I Zinov

Ufa University of Science and Technology

Email: zinovvladislavufa@gmail.com
Z. Validi St. 32

V. M Kartak

Ufa University of Science and Technology

Email: kvmail@mail.ru
Z. Validi St. 32

Y. I Valiakhmetova

Ufa University of Science and Technology

Email: julikas@inbox.ru
Z. Validi St. 32

References

  1. Petprakob W. Beam-slab floor optimization using genetic and particle swarm optimization algorithms // A Thesis for degree of master of science in engineering and technology. Thailand: Thammasat University, Sirindhorn International Institute of Technology. 2014. 90 p. doi: 10.14457/TU.the.2014.490.
  2. Nimtawat A., Nanakorn P. Automated layout design of beam-slab floors using a genetic algorithm // Computers & Structures. 2009. vol. 87(21-22). pp. 1308–1330. doi: 10.1016/j.compstruc.2009.06.007.
  3. Sharafi P. Cost optimization of the preliminary design layout of reinforced concrete framed buildings // A Thesis for degree of Doctor of Philosophy in civil engineering. Australia: University of Wollongong, School of Civil, Mining and Environmental Engineering. 2013. 286 p.
  4. Hadi M.H., Sharafi P., Teh L.H. A new formulation for the geometric layout optimisation of flat slab floor systems // Proceeding of the Australasian Structural Engineering Conference 2012: The Past, Present and Future of Structural Engineering. Perth, Western Australia: Engineers Australia, 2012. pp. 122–129. doi: 10.3316/informit.026275045488938.
  5. Sahab M.G., Ashour A.F., Toropov V.V. Cost optimisation of reinforced concrete flat slab buildings // Engineering Structures. 2005. vol. 27(3). pp. 313–322. doi: 10.1016/j.engstruct.2004.10.002.
  6. Meng X., Lee T.U., Xiong Y., Huang X., Xie Y.M. Optimizing support locations in the roof–column structural system // Applied Science. 2021. vol. 11(6). doi: 10.3390/app11062775.
  7. Zelickman Y., Amir O. Optimization of column layouts in buildings considering structural and architectural constraints // Engineering Archive (engrXiv). 2024. 45 p. doi: 10.31224/2723.
  8. Steiner B., Mousavian E., Mehdizadeh Saradj F., Wimmer M., Musialski P. Integrated structural-architectural design for interactive planning // Computer Graphics Forum. 2016. vol. 36(8). pp. 80–94. doi: 10.1111/cgf.12996.
  9. Валиахметова Ю.И., Васильева Л.И., Зинов В.И. Алгоритм решения задачи определения рационального плана размещения несущих конструкций при строительстве многоэтажных зданий // Современные проблемы и перспективы развития естествознания: Сб. научн. тр. Национальной научно-практической конференции (г. Уфа, 8–9 июня 2020 г.). Т. 2. Уфа: БГПУ. 2020. С. 46–54.
  10. Eleftheriadis S., Duffour P., Stephenson B., Mumovic D. Automated specification of steel reinforcement to support the optimisation of RC floors // Automation in Construction. 2018. vol. 96. pp. 366–377. doi: 10.1016/j.autcon.2018.10.005.
  11. Wang J., Chen K., Yang H., Zhang L. Ensemble deep learning enabled multi-condition generative design of aerial building machine considering uncertainties // Automation in Construction. 2024. vol. 157. doi: 10.1016/j.autcon.2023.105134.
  12. Лешкевич О.Н. Использование искусственных нейронных сетей для оценки армирования железобетонных плит перекрытия // Проблемы современного бетона и железобетона: Сб. научн. тр. № 11. Минск: Ин-т БелНИИС, 2019. С. 51–62. doi: 10.35579/2076-6033-2019-11-04.
  13. Liao W.J., Lu X.Z., Fei Y.F., Gu Y., Huang Y.L. Generative AI design for building structures // Automation in Construction. 2024. vol. 157. doi: 10.1016/j.autcon.2023.105187.
  14. Зинов В.И., Картак В.М., Валиахметова Ю.И. Алгоритм оценки деформации плит перекрытий по пролетно-опорным схемам здания // Системы анализа и обработки данных. 2023. № 4(92). С. 35–54. doi: 10.17212/2782-2001-2023-4-35-54.
  15. Long Q., Wu C.Z., Wang X.Y., Jiang L., Li J. A multiobjective genetic algorithm based on a discrete selection procedure // Mathematical Problems in Engineering. 2015. vol. 2015. 17 p. doi: 10.1155/2015/349781.
  16. Валиахметова Ю.И., Прокудина Е.И., Зинов В.И. Многокритериальный генетический алгоритм решения задачи рационального размещения опорных конструкций на плане зданий // Современные физика, математика, цифровые и нанотехнологии в науке и образовании: Сб. научн. тр. II Всероссийской молодежной школы-конференции (г. Уфа, 18–20 апреля 2023 г.). Уфа: БГПУ, 2023. С. 95–100.
  17. Rahimi I., Gandomi A.H., Nikoo M.R., Chen F. A comparative study on evolutionary multi-objective algorithms for next release problem // Applied Soft Computing. 2023. vol. 144. doi: 10.1016/j.asoc.2023.110472.
  18. Srinivas N., Deb K. Muiltiobjective optimization using nondominated sorting in genetic algorithms // Evolutionary Computation. 1994. vol. 2(3). pp. 221–248. doi: 10.1162/evco.1994.2.3.221.
  19. Deb K., Pratap A., Agarwal S., Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGAII // IEEE Transactions on Evolutionary Computation. 2002. vol. 6(2). pp. 182–197. doi: 10.1109/4235.996017.
  20. Zitzler E., Laumanns M., Thiele L. SPEA2: Improving the strength Pareto evolutionary algorithm // TIK-Report. 2001. vol. 103. doi: 10.3929/ethz-a-004284029.
  21. Liu M., Zou X., Chen Y., Wu Z. Performance assessment of DMOEA-DD with CEC 2009 MOEA Competition Test Instances // Proceeding of the IEEE Congress on Evolutionary Computation (CEC 2009). Norway: Trondheim, 2009. pp. 2913–2918. doi: 10.1109/CEC.2009.4983309.
  22. He M., Wang X., Chen H., Li X. A Knee Point-Driven Many-Objective Evolutionary Algorithm with Adaptive Switching Mechanism // Journal of Applied Mathematics. 2024. vol. 2024(1). 43 p. doi: 10.1155/2024/4737604.
  23. Ester M., Kriegel H.-P., Sander J., Xu X. A density-base algorithm for discovering clusters in large spatial databases with noise // Proceeding of 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96). Germany: Munich, 1996. pp. 226–231.

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