Contenuto in:
Capitolo

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,
+ Mostra di più

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
  • Anno di pubblicazione: 2023
  • Pagine: 1255-1260

XML
  • Anno di pubblicazione: 2023

Informazioni sul capitolo

Titolo del capitolo

Digital Twins for Smart Decision Making in Asset Management

Autori

Chady Elias, Raja Issa

DOI

10.36253/979-12-215-0289-3.123

Opera sottoposta a peer review

Anno di pubblicazione

2023

Copyright

© 2023 Author(s)

Licenza d'uso

CC BY-NC 4.0

Licenza dei metadati

CC0 1.0

Informazioni bibliografiche

Titolo del libro

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

Sottotitolo del libro

Managing the Digital Transformation of Construction Industry

Curatori

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

Opera sottoposta a peer review

Anno di pubblicazione

2023

Copyright

© 2023 Author(s)

Licenza d'uso

CC BY-NC 4.0

Licenza dei metadati

CC0 1.0

Editore

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

Collana

Proceedings e report

ISSN della collana

2704-601X

e-ISSN della collana

2704-5846

139

Download dei libri

125

Visualizzazioni

Salva la citazione

1.347

Libri in accesso aperto

in catalogo

2.262

Capitoli di Libri

3.790.127

Download dei libri

4.421

Autori

da 923 Istituzioni e centri di ricerca

di 65 Nazioni

65

scientific boards

da 348 Istituzioni e centri di ricerca

di 43 Nazioni

1.248

I referee

da 380 Istituzioni e centri di ricerca

di 38 Nazioni