Application of Particle Swarm Optimization in BIM Building Modeling

  • Conference paper
  • First Online:
Innovative Computing Vol 1 - Emerging Topics in Artificial Intelligence (IC 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1044))

Included in the following conference series:

  • 517 Accesses

Abstract

BIM technology is defined as a software tool for building models, which is now used by the architecture industry as an extremely important modeling tool for architectural drawings. This tool combines architectural theory and knowledge, as well as many software plug-ins for practical applications. This technology can complete typical 3D drawings, which is one of the basic prerequisites for building construction. BIM technology is a tool with great application value. This tool can carry out 3D stereoscopic of building model and visual design of building structure at the same time. The biggest advantage is that it can ensure the optimal accuracy of each data point of the building, so as to ensure that the building becomes a very accurate building body after the actual completion. This paper studies the application of particle swarm optimization in BIM building modeling and analyzes the application of BIM building modeling. The data test shows that the application of particle swarm optimization algorithm in BIM building modeling has excellent performance in building modeling accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Nabi, S., Ahmed, M.: PSO-RDAL: particle swarm optimization-based resource - and deadline-aware dynamic load balancer for deadline constrained cloud tasks. J. Supercomput. 78(4), 4624–4654 (2021). https://doi.org/10.1007/s11227-021-04062-2

    Article  Google Scholar 

  2. Rodrigues, F., Molina, Y., Silva, C., et al.: Simultaneous tuning of the AVR and PSS parameters using particle swarm optimization with oscillating exponential decay. Int. J. Elec. Power Energy Syst. 133(4), 107–215 (2021)

    Google Scholar 

  3. Alajmi, M.S., Almeshal, A.M.: Least squares boosting ensemble and quantum-behaved particle swarm optimization for predicting the surface roughness in face milling process of aluminum material. Appl. Sci. 11(5), 2126 (2021)

    Article  Google Scholar 

  4. Qt Eat, H., Awad, M.: Using hybrid model of particle swarm optimization and multi-layer perceptron neural networks for classification of diabetes. Int. J. Intell. Eng. Syst. 14(3), 11–22 (2021)

    Google Scholar 

  5. Alshahir, A., Molyet, R.: Improving the reconfiguration of hybrid power networks by combining Genetic Algorithm (GA) with Particle Swarm Optimization (PSO). Amer. J. Elec. Power Energy Syst. 10(1), 6 (2021)

    Article  Google Scholar 

  6. Rezgui, S.E., Benalla, H., Bouhebel, H.: Hybrid bacteria foraging-particle swarm optimization algorithm in DTC performance improving for induction motor drive. Indonesian J. Elec. Eng. Comput. Sci. 22(2), 660 (2021)

    Article  Google Scholar 

  7. Kotla, R.W., Yarlagadda, S.R.: Comparative analysis of photovoltaic generating systems using particle swarm optimization and cuckoo search algorithms under partial shading conditions. J. Européen des Systèmes Automatisés 54(1), 27–33 (2021)

    Google Scholar 

  8. Nabavi, S.R., Eraghi, N.O., Torkestani, J.A.: Wireless sensor networks routing using clustering based on multi-objective particle swarm optimization algorithm. J. Intell. Proced. Elec. Technol. (JIPET) 12(47), 49–67 (2021)

    Google Scholar 

  9. Wijayanti, E.A., Rahmadanti, T., Enri, U.: Perbandingan Algoritma SVM dan SVM Berbasis Particle Swarm Optimization Pada Klasifikasi Beras Mekongga. Gener. J. 5(2), 102–108 (2021)

    Google Scholar 

  10. Malik, G., Upadhyaya, S., Sharma, R.: Particle swarm optimization and maximum entropy results for MX/G/1 retrial G-Queue with delayed repair. Int. J. Math. Eng. Manag. Sci. 6(2), 541–563 (2021)

    Google Scholar 

  11. Ramadhani, B., Garside, A.K.: Particle swarm optimization algorithm to solve vehicle routing problem with fuel consumption minimization. Jurnal Optimasi Sistem Industri 20(1), 1–1 (2021)

    Article  Google Scholar 

  12. Saeed, A.A., Jameel, N.: Intelligent feature selection using particle swarm optimization algorithm with a decision tree for DDoS attack detection. Int. J. Adv. Intell. Inform. 7(1), 37–48 (2021)

    Article  Google Scholar 

  13. Prasetyo, T.A.: Particle swarm optimization and genetic algorithm for big vehicle problem: case study in national pure milk company. Int. J. Comput. Sci. Appl. Math. 7(1), 28 (2021)

    Article  Google Scholar 

  14. Vijayakumar, T., Vinothkanna, R.: Efficient energy load distribution model using modified particle swarm optimization algorithm. J. Artif. Intell. Capsule Netw. 2(4), 226–231 (2021)

    Article  Google Scholar 

  15. Setiami, R., Maulana, A.: Development of E-modules in engineering drawing courses with the BIM system building modeling application. Jurnal PenSil 10(1), 1–7 (2021)

    Article  Google Scholar 

  16. Sheward, H.: BIM Based Analysis of Spatial Properties in Building Layouts. American Journal of Civil Engineering and Architecture 9(4), 142–155 (2021)

    Google Scholar 

  17. Sriyolja, Z., Harwin, N., Yahya, K.: Barriers to implement Building Information Modeling (BIM) in construction industry: a critical review. IOP Conf. Ser. Earth Environ. Sci. 738(1), 012021012021 (2021)

    Article  Google Scholar 

  18. Omayer, H.M.: Building Information Modeling BIM as a development tool for the management of construction projects. Fayoum Univ. J. Eng. 3(2537–0626), 9 (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Guang Yang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yang, G., Guo, X. (2023). Application of Particle Swarm Optimization in BIM Building Modeling. In: Hung, J.C., Chang, JW., Pei, Y. (eds) Innovative Computing Vol 1 - Emerging Topics in Artificial Intelligence. IC 2023. Lecture Notes in Electrical Engineering, vol 1044. Springer, Singapore. https://doi.org/10.1007/978-981-99-2092-1_88

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-2092-1_88

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-2091-4

  • Online ISBN: 978-981-99-2092-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Navigation