Contained in:
Book Chapter

Digital Twins for Smart Decision Making in Asset Management

  • Chady Elias
  • Raja Issa

This study discusses the classification of Digital Twins (DTs) and their use in the Architecture, Engineering, Construction, and Operations (AECO) industry, the differences between building information modeling (BIM) and DT are emphasized and platforms for implementing DTs are compared. DTs are quickly gaining traction in the AECO industry because they create the ability to interact virtually with all physical smart devices in the built environment. The need for replicas goes all the way back to the 1960s, when NASA created physical replicas of spaceships and connected them to simulators to develop workshop solutions on the ground. DTs are simply building blocks of the metaverse that act as a real-time digital copy of a physical object. Based on data from the physical asset or system, the physical twin (PT), a DT unlocks value in supporting smart decision-making by combining artificial intelligence (AI) with the internet of things (IoT)

  • Keywords:
  • Digital Twins; Internet of Things; Artificial Intelligence; Asset Management,
+ Show More

Chady Elias

University of Florida, United States

Raja Issa

University of Florida, United States - ORCID: 0000-0001-5193-3802

  1. Adamenko, D., Kunnen, S., & Nagarajah, A. (2020a). Comparative Analysis of Platforms for Designing a Digital Twin. In: Ivanov, V., Trojanowska, J., Pavlenko, I., Zajac, & J., Peraković, D. (eds.), Advances in Design, Simulation and Manufacturing III. DSMIE 2020. Lecture Notes in Mechanical Engineering. Springer, Cham. DOI: 10.1007/978-3-030-50794-7_1
  2. American Institute of Architects (AIA) (2022). AIA Document E202TM-2022: BIM Exhibit for Sharing Models with Project Participants.
  3. Asare, K.A.B., Issa, R.R.A., Rui. L. & Anumba, C. (2021). “BIM for Facilities Management: Potential Legal Issues and Opportunities,” Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 2021, 13(4), DOI: 10.1061/(ASCE)LA.1943-4170.0000502.
  4. Attaran, A. & Celik, B. G. (2023). Digital Twin: Benefits, use cases, challenges, and opportunities, Decision Analytics Journal, 6, 100165, DOI: 10.1016/j.dajour.2023.100165
  5. Becerik-Gerber, B., F. Jazizadeh, N. Li, & G. Calis. 2012. “Application Areas and Data Requirements for BIM-Enabled Facilities Management.” Journal of Construction Engineering and Management 138 (3): 431–42. DOI: 10.1061/(ASCE)CO.1943-7862.0000433
  6. Brilakis, I., Pan, Y., Borrmann, A., Mayer, H.-G., Rhein, F., Vos, C., Pettinato, E., & Wagner, S. (2019). “Built Environment Digital Twining”. International Workshop on Built Environment Digital Twinning presented by TUM Institute for Advanced Study and Siemens AG. DOI: 10.17863/CAM.65445
  7. Delgado, J. M. D. and Oyedele, L. (2021). Digital Twins for the built environment: Learning from conceptual and process models in manufacturing. Advanced Engineering Informatics, 49, 101332. DOI: 10.1016/j.aei.2021.101332
  8. Greer, C., Burns, M., Wollman, D., and Griffor, E. (2019). Cyber-physical systems and Internet of Things. National Institute of Standards and Technology (NIST) Special Publication 1900-202. DOI: 10.6028/NIST.SP1900-202
  9. Khajavi, S.H.; Motlagh, N.H.; Jaribion, A.; Werner, L.C.; Holmstrom, J. (2019). Digital Twin: Vision, Benefits, Boundaries, and Creation for Buildings. IEEE Access, 7, 147406-147419.
  10. KPMG (2022). Insight report: Innovation & R&D in construction.
  11. Kritzinger, W., Karner, M., Traar, G., Henjes, J., and Sihn, W. (2018). Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016-1022. DOI: 10.1016/j.ifacol.2018.08.474
  12. Redelinghuys, A.J.H., Basson, A.H. & Kruger, K. (2020). A six-layer architecture for the digital twin: a manufacturing case study implementation. Journal of Intelligent Manufacturing, 31, 1383-1402. DOI: 10.1007/s10845-019-01516-6
  13. Salvador Palau, A., Dhada, M. H., & Parlikad, A. K. (2019). Multiagent system architectures for collaborative prognostics. Journal of Intelligent Manufacturing, 30(8), 2999–3013 DOI: 10.1007/s10845-019-01478-9
  14. Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., and Sui, F. (2018). Digital twin-driven product design, manufacturing and service with big data. International Journal of Advanced Manufacturing Technology, 94(9-12), 3563–3576. DOI: 10.1007/s00170-017-0233-1
PDF
  • Publication Year: 2023
  • Pages: 1255-1260

XML
  • Publication Year: 2023

Chapter Information

Chapter Title

Digital Twins for Smart Decision Making in Asset Management

Authors

Chady Elias, Raja Issa

DOI

10.36253/979-12-215-0289-3.123

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

56

Fulltext
downloads

58

Views

Export Citation

1,310

Open Access Books

in the Catalogue

1,977

Book Chapters

3,390,281

Fulltext
downloads

4,172

Authors

from 873 Research Institutions

of 64 Nations

63

scientific boards

from 340 Research Institutions

of 43 Nations

1,159

Referees

from 345 Research Institutions

of 37 Nations