Warianty tytułu
Języki publikacji
Abstrakty
In response to the current issue of poor modeling performance of Building Information Modeling for building models, a new Building Information Modeling based on an improved region growth algorithm is proposed. This method improves the region growth algorithm by introducing machine learning technology, and utilizes the improved algorithm to perfect the building model, thereby improving the efficiency of Building Information Modeling. The performance comparison experiment of the improved algorithm shows that its accuracy is 92.3%, respectively, which are lower than the comparison algorithm. Subsequent empirical analysis found that the robustness rating of the renovated building with the new Building Information Modeling was 94.06, significantly higher than the traditional model. The above results indicate that the new Building Information Modeling proposed in the study has high efficiency and accuracy in building reinforcement and renovation. This method can provide a new solution and idea for the field of building reinforcement and renovation.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
445--457
Opis fizyczny
Bibliogr. 21 poz., il., tab.
Twórcy
autor
- School of Civil Engineering, Inner Mongolia University of Technology, Hohhot, China, ducong@imut.edu.cn
autor
- College of Architecture, Inner Mongolia University of Technology, Hohhot, China, wenyichina@126.com
autor
- School of Civil Engineering, Inner Mongolia University of Technology, Hohhot, China, renlijian0423@163.com
Bibliografia
- [1] N. Shakeel and S. Shakeel, “Context-free word importance scores for attacking neural networks”, Journal of Computational and Cognitive Engineering, vol. 1, no. 4, pp. 187-192, 2022, doi: 10.47852/bonviewJCCE2202406.
- [2] A. Miatto, C. Sartori, M. Bianchi, P. Borin, A. Giordano, and S. Saxe, “Tracking the material cycle of Italian bricks with the aid of building information modeling”, Journal of Industrial Ecology, vol. 26, no. 2, pp. 609-626, 2022, doi: 10.1111/jiec.13208.
- [3] W. Trochmiak, A. Krygier, M Stachura and J. Jaworski, “The BIM 5D model of the bridge built usingthe incremental launching method”, Archives of Civil Engineering, vol. 69, no. 3, pp. 157-172, 2023, doi: 10.24425/ace.2023.146073.
- [4] M. Najjar, K. Figueiredo, A. Hammad, and A. Haddad, “Integrated optimization with building information modeling and life cycle assessment for generating energy efficient buildings”, Applied Energy, vol. 250, no. 15, pp. 1366-1382, 2019, doi: 10.1016/j.apenergy.2019.05.101.
- [5] H. Liu, J. Song, and G. Wang, “Development of a tool for measuring building information modeling (BIM) user satisfaction-method selection, scale development and case study”, Engineering, Construction and Architectural Management, vol. 27, no. 9, pp. 2409-2427, 2020, doi: 10.1108/ECAM-08-2019-0448.
- [6] B. Koo, S. La, N.W. Cho, and Y. Yu, “Using support vector machines to classify building elements for checking the semantic integrity of building information models”, Automation in Construction, vol. 98, pp. 183-194, 2019, doi: 10.1016/j.autcon.2018.11.015.
- [7] H.J. Pan and J.D.Ward, “Computationally efficient algorithm for solving population balances with size-dependent growth, nucleation, and growth-dissolution cycles”, Industrial and Engineering Chemistry Research, vol. 60, no. 34, pp. 12614-12628, 2021, doi: 10.1021/acs.iecr.1c01947.
- [8] S.R. Anderson, V.P. Debattista, P. Erwin, et al., “The secular growth of bars revealed by flat (peak + shoulders) density profiles”, Monthly Notices of the Royal Astronomical Society, vol. 513, no. 2, pp. 1642-1661, 2022, doi: 10.1093/mnras/stac913.
- [9] X. Wang, H. Dai, W. Wang, J. Zheng, N. Yu, G. Chen, W. Dou, and X. Xu, “Practical heterogeneous wireless charger placement with obstacles”, IEEE Transactions on Mobile Computing, vol. 19, no. 8. pp. 1910-1927, 2020, doi: 10.1109/TMC.2019.2916384.
- [10] G.L. Sciuto, G. Capizzi, R. Shikler, and C. Napoli, “Organic solar cells defects classification by using a new feature extraction algorithm and an EBNN with an innovative pruning algorithm”, International Journal of Intelligent Systems, vol. 36, no. 6, pp. 2443-2464, 2021, doi: 10.1002/int.22386.
- [11] Y. Liu, S. Du, W. Cui, et al., “Precise point set registration based on feature fusion”, The Computer Journal, vol. 64, no. 7, pp. 1039-1055, 2021, doi: 10.1093/comjnl/bxab114.
- [12] M. Tavakolan, S. Mohammadi, and B. Zahraie, “Construction and resource short-term planning using a BIM-based ontological decision support system”, Canadian Journal of Civil Engineering, vol. 48, no. 1, pp. 75-88, 2021, doi: 10.1139/cjce-2019-0439.
- [13] O.I. Olanrewaju, N. Chileshe, S.A. Babarinde, and M. Sandanayake, “Investigating the barriers to building information modeling (BIM) implementation within the Nigerian construction industry”, Engineering, Construction and Architectural Management, vol. 27, no. 10, pp. 2931-2958, 2020, doi: 10.1108/ECAM-01-2020-0042.
- [14] M. Meisaroh, A.E. Husin, and B. Susetyo, “Analysis of key success factors using RII method on the implementation building information modeling (BIM)-based quantity take-off to improve cost performance hospital structure”, Solid State Technology, vol. 64, no. 2, pp. 3179-3188, 2021.
- [15] J.Walter, T. Obermeier, and J. Díaz, “BIM in der brandschutzplanung: praxisrelevante attribute und klassen/BIM in fire protection planning: Attributes and classes relevant to practice”, Bauingenieur, vol. 96, no. 5, pp. 182-190, 2021, doi: 10.37544/0005-6650-2021-05-66.
- [16] K. Lawson, “Validating BIM load calculations”, ASHRAE Journal, vol. 7, no. 64, pp. 30-35, 2022.
- [17] H.J. Pan and J.D.Ward, “Computationally efficient algorithm for solving population balances with size-dependent growth, nucleation, and growth-dissolution cycles”, Industrial and Engineering Chemistry Research, vol. 60, no. 34, pp. 12614-12618, 2021, doi: 10.1021/acs.iecr.1c01947.
- [18] F. Tang, “An improved intelligent bionic optimization algorithm based on the growth characteristics of tree branches”, Journal of Intelligent and Fuzzy Systems, vol. 40, no. 3, pp. 3821-3829, 2021, doi: 10.3233/JIFS-190487.
- [19] S.R. Anderson, V.P. Debattista, P. Erwin, et al., “The secular growth of bars revealed by flat (peak + shoulders) density profiles”, Monthly Notices of the Royal Astronomical Society, vol. 512, no. 2, pp. 1642-1661, 2022, doi: 10.1093/mnras/stac913.
- [20] S. Sun, M. Jiang, D. He, Y. Long, and H. Song, “Recognition of green apples in an orchard environment by combining the GrabCut model and Ncut algorithm”, Biosystems Engineering, vol. 187, pp. 201-213, 2019, doi: 10.1016/j.biosystemseng.2019.09.006.
- [21] C. Kampen, B.N. Mckinley, and J.R. Dan, “Building information modeling project coordination: The all-in approach”, Pci Journal, vol. 66, no. 1, pp. 22-27, 2021, doi: 10.15554/pcij66.1-03.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-123b1240-014d-4495-b48f-3d468bf4ddb3