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Lookup NU author(s): Dr Xiang XieORCiD
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With the rising adoption of building information modelling (BIM) for asset management within the architecture, engineering and construction sectors, BIM-enabled asset management during the operation and maintenance phase has been increasingly attracting more and more attention in both research and practice. This paper provides a comprehensive review and analysis of the development of state-of-the-art research and industry standards that impact on BIM and asset management within the operation and maintenance phase. However, in the aspects of both information richness and analytical capability, BIM is not always enough in delivering effective and efficient asset management, particularly in the operation and maintenance phase. Therefore, a framework for future development of smart asset management is proposed, integrating the concept of digital twins. Digital twins integrate artificial intelligence, machine learning and data analytics to create dynamic digital models that are able to learn and update the status of the physical counterpart from multiple information sources. The findings will contribute to inspiring novel research ideas and promote widespread adoption of digital-twin-enabled asset management within the operation and maintenance phase.
Author(s): Lu QC, Xie X, Parlikad A, Schooling J, Konstantinou E
Publication type: Article
Publication status: Published
Journal: Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction
Year: 2020
Volume: 174
Issue: 2
Pages: 46-56
Print publication date: 11/02/2020
Online publication date: 04/03/2022
Acceptance date: 28/01/2020
ISSN (electronic): 2397-8759
Publisher: ICE Publishing
URL: https://doi.org/10.1680/jsmic.19.00011
DOI: 10.1680/jsmic.19.00011
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