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Optimisation of decision making for construction projects of assembly buildings based on improved PSO algorithm

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Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A cutting-edge construction method called fitted construction allows for several parallel lines of work to speed up construction and enhance building quality. However, achieving optimal project decisions for global construction projects demands a high level of objective decision-making. To enhance the decision-making process, this research utilizes particle swarm algorithms to optimize construction project decisions in assembled buildings. To tackle the issue of early convergence in particle swarm algorithms, three swarm enhanced particle swarm algorithms are proposed by merging the variational mechanism of the differential evolution algorithm and quantifying the decision making tasks for assembly building construction projects to be solved by the enhanced particle swarm algorithm. Regarding the research results, the upgraded particle swarm algorithm achieved a fundamental convergence in 20 iterations whilst resolving the Sphere, Rosebrock, Rastrigin, and Griewank functions. The improved particle swarm algorithm converges to an optimal solution of –19.208 within 20 iterations on the Holder function, with an optimal domain of [8.055, –9.665]. The results of the optimization study for the decision-making problem of the assembly building project demonstrate that implementing Sigmoid smoothing yields a minimum duration problem of 0.755 and a minimum duration of 45 days. The optimal cost and time required to solve the problem of economic maximisation strategy using the enhanced particle swarm method are 500,000 and 52 days, respectively. The results indicate that the improved particle swarm approach outperforms conventional algorithms in the decision-making process for assembly building projects, maintaining computational accuracy throughout.
Rocznik
Strony
113--125
Opis fizyczny
Bibliogr. 15 poz., il., tab.
Twórcy
autor
  • Faculty of Engineering and Economics, Henan Finance University, Zhengzhou, China
Bibliografia
  • [1] S. Durdyev, S. R. Mohandes, S. Tokbolat, H. Sadeghi, and T. Zayed, “Examining the OHS of green building construction projects: A hybrid fuzzy-based approach”, Journal of Cleaner Production, vol. 338, no. 1, pp. 590-602, 2022, doi: 10.1016/j.jclepro.2022.130590.
  • [2] Y. Liu, J. Li, W. Q. Chen, L. Song, and S. Dai, “Quantifying urban mass gain and loss by a GIS-based material stocks and flows analysis”, Journal of Industrial Ecology, vol. 26, no. 3, pp. 1051-1060, 2022, doi: 10.1111/jiec.13252.
  • [3] A. M. Wierzbicka, A. Orchowska, and E. Nagiel, “Prefabrication in Władysław Pienkowski’s work as an example of the author’s signature approach to architectural design”, Archives of Civil Engineering, vol. 68, no. 2, pp. 355-375, 2022, doi: 10.24425/ace.2022.140647.
  • [4] M. Tomczak and P. Jaskowski, “Harmonizing construction processes in repetitive construction projects with multiple buildings”, Automation in Construction, vol. 139, no. 7, pp. 266-288, 2022, doi: 10.1016/j.autcon.2022.104266.
  • [5] V. N. Hartmann, A. Orthey, D. Driess, O. S. Oguz, and M. Toussaint, “Long-horizon multi-robot rearrangement planning for construction assembly”, IEEE Transactions on Robotics, vol. 39, no. 1, pp. 239-252, 2022, doi: 10.1109/TRO.2022.3198020.
  • [6] Z. A. B. Ismail, “Thermal comfort practices for precast concrete building construction projects: Towards BIM and IOT integration”, Engineering Construction and Architectural Management, vol. 29, no. 3, pp. 1504-1521,2022, doi: 10.1108/ECAM-09-2020-0767.
  • [7] J. Peng, Y. Feng, Q. Zhang, and X. Liu, “Multi-objective integrated optimization study of prefabricated building projects introducing sustainable levels”, Scientific Reports, vol. 13, art. no. 2821, 2023, doi: 10.1038/s41598-023-29881-6.
  • [8] B. Yang, T. Fang, X. Luo, B. Liu, and M. Dong, “A bim-based approach to automated prefabricated building construction site layout planning”, KSCE Journal of Civil Engineering, vol. 26, no. 4, pp. 1535-1552, 2023, doi: 10.1007/s12205-021-0746-x.
  • [9] H. Ezaldeen, S. K. Bisoy, R. Misra, and R. Alatrash, “Semantics-aware context-based learner modelling using normalized PSO for personalized E-learning”, Journal of Web Engineering, vol. 21, no. 4, pp. 1187-1224, 2022, doi: 10.13052/jwe1540-9589.2148.
  • [10] L. Cao, W. Y. Zhang, X. Kan, and W. Yao, “A novel adaptive mutation PSO optimized SVM algorithm for sEMG-based gesture recognition”, Scientific Programming, vol. 2021, art. no. 9988823, 2021, doi: 10.1155/2021/9988823.
  • [11] B. Stojanović, S. Gajević, N. Kostić, S. Miladinović, and A. Vencl, “Optimization of parameters that affect wear of A356/Al2O3 nanocomposites using RSM, ANN, GA and PSO methods”, Industrial Lubrication and Tribology, vol. 74, no. 3, pp. 350-359, 2022, doi: 10.1108/ILT-07-2021-0262.
  • [12] A. Jarndal, S. Husain, M. Hashmi, and F. M. Ghannouchi, “Large-signal modeling of GaN HEMTs using hybrid GA-ANN, PSO-SVR, and GPR-Based approaches”, IEEE Journal of the Electron Devices Society, vol. 9, pp. 195-208, 2021, doi: 10.1109/JEDS.2020.3035628.
  • [13] J. H. Song, S. W. Kang, and Y. J. Kim, “Optimal design of the disc vents for high-speed railway vehicles using thermal-structural coupled analysis with genetic algorithm”, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 236, no. 10, pp. 5154-5164, 2022, doi: 10.1177/09544062211059112.
  • [14] Y. Xiao and J. Bhola, “Design and optimization of prefabricated building system based on BIM technology”, International Journal of System Assurance Engineering and Management, vol. 13, no. 1, pp. 111-120, 2022, doi: 10.1007/s13198-021-01288-4.
  • [15] Y. Han, X. Yan, and P. Piroozfar, “An overall review of research on prefabricated construction supply chain management”, Engineering, Construction and Architectural Management, vol. 30, no. 10, pp. 5160-5195, 2023, doi: 10.1108/ECAM-07-2021-0668.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4764cf2b-29ee-4962-aa15-55369a3f8d36
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