Asset information requirements for blockchain-based digital twins: a data-driven predictive analytics perspective
2024 (English)In: Smart and Sustainable Built Environment, ISSN 2046-6099, E-ISSN 2046-6102, Vol. 13, no 1, p. 22-41Article in journal (Refereed) Published
Abstract [en]
Purpose
The purpose of this study is to identify the key data categories and characteristics defined by asset information requirements (AIR) and how this affects the development and maintenance of an asset information model (AIM) for a blockchain-based digital twin (DT).
Design/methodology/approach
A mixed-method approach involving qualitative and quantitative analysis was used to gather empirical data through semistructured interviews and a digital questionnaire survey with an emphasis on AIR for blockchain-based DTs from a data-driven predictive analytics perspective.
Findings
Based on the analysis of results three key data categories were identified, core data, static operation and maintenance (OM) data, and dynamic OM data, along with the data characteristics required to perform data-driven predictive analytics through artificial intelligence (AI) in a blockchain-based DT platform. The findings also include how the creation and maintenance of an AIM is affected in this context.
Practical implications
The key data categories and characteristics specified through AIR to support predictive data-driven analytics through AI in a blockchain-based DT will contribute to the development and maintenance of an AIM.
Originality/value
The research explores the process of defining, delivering and maintaining the AIM and the potential use of blockchain technology (BCT) as a facilitator for data trust, integrity and security.
Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2024. Vol. 13, no 1, p. 22-41
Keywords [en]
Asset management, Asset information requirements, Asset information model, Digital twins, Blockchain, Artificial intelligence and machine learning
National Category
Construction Management Information Systems
Identifiers
URN: urn:nbn:se:hj:diva-59436DOI: 10.1108/SASBE-08-2022-0183ISI: 000913702700001Scopus ID: 2-s2.0-85146335947Local ID: HOA;;857743OAI: oai:DiVA.org:hj-59436DiVA, id: diva2:1730622
Note
This study is funded by Smart Built Environment (Sweden) (Grant No. 2021-00296). The authors would like to acknowledge the project industrial partners Pythagoras AB and Plan B BIM AB for their contribution to the project.
2023-01-252023-01-252024-01-15Bibliographically approved