Informing the information requirements of a digital twin: a rail industry case study. Issue 2 (17th February 2021)

• Record Type: Journal Article
• Authors: Johnson, Aaron; Heaton, James; Yule, Steven; Luke, Sam; Pocock, David; Parlikad, Ajith Kumar; Schooling, Jennifer
• Is Part Of: Proceedings of the Institution of Civil Engineers. Volume 174:Issue 2(2021)
• Page Start: 33
• Page End: 45
• Publication Date: 2021-02-17
• Subjects: Building Information Modelling (BIM) -- data -- digital twin; Construction industry -- Technological innovations -- Periodicals; Construction industry -- Information technology -- Periodicals; Building -- Technological innovations -- Periodicals; Smart structures -- Periodicals; Smart materials in design -- Periodicals; 624.0285
• DOI: 10.1680/jsmic.20.00017
• Languages: English
• ISSNs: 2397-8759

Abstract

Abstract : The right data to the right people at the right time are a crucial component to developing an effective digital twin (DT). A challenge in the industry is that existing legacy data systems have been tailored independently to meet specific technical requirements, leading to disparate information systems that collectively provide a suboptimal foundation needed to support a modern digital environment. To address this challenge, a detailed study was conducted in partnership with Network Rail to test a method that was adapted for the industry to identify appropriate, relevant and effective asset information requirements (AIRs). A robust library of AIRs will determine what data the organisation should be collecting and why these are necessary through design, construction, operation and disposal, hence informing the information requirements of a DT. The methodology was modified with four adaptations to improve its compatibility with the industry and was tested with 24 subject matter experts in the rail sector. It was concluded that integration of the AIR method would ensure that the rail industry is well positioned for digital transformation and provides a solid foundation to develop a DT. However, industry integration of the method would be a significant undertaking due to the amount of information that needs to be considered throughout the process, and so further research is required to improve its scalability.