Development of Digital Twin for Intelligent Maintenance of Civil Infrastructure

Mojtaba Mahmoodian, Farham Shahrivar, Sujeeva Setunge and Sam Mazaheri
Additional contact information
Mojtaba Mahmoodian: School of Engineering, RMIT University, Melbourne 3000, Australia
Farham Shahrivar: School of Engineering, RMIT University, Melbourne 3000, Australia
Sujeeva Setunge: STEM College, RMIT University, Melbourne 3000, Australia
Sam Mazaheri: Beta International Associates Pty Ltd., Melbourne 3000, Australia

Sustainability, 2022, vol. 14, issue 14, 1-25

Abstract: Over the life cycle of a civil infrastructure (a bridge as an example), 0.4–2% of the construction cost is spent annually on its maintenance. Utilising new technologies including the internet of things (IoT) and digital twin (DT) can significantly reduce the infrastructure maintenance costs. An infrastructure DT involves its digital replica and must include data on geometric, geospatial reference, performance, attributes (material, environment etc.) and management. Then, the acquired data need to be analysed and visualised to inform maintenance decision making. To develop this DT, the first step is the study of the infrastructure life cycle to design DT architecture. Using data semantics, this paper presents a novel DT architecture design for an intelligent infrastructure maintenance system. Semantic modelling is used as a powerful tool to structure and organize data. This approach provides an industry context through capturing knowledge about infrastructures in the structure of semantic model graph. Using new technologies, DT approach derives and presents meaningful data on infrastructure real-time performance and maintenance requirements, and in a more expressible and interpretable manner. The data semantic model will guide when and what data to collect for feeding into the infrastructure DT. The proposed DT concept was applied on one of the conveyors of Dalrymple Bay Coal Terminal in Queensland Australia to monitor the structural performance in real-time, which enables predictive maintenance to avoid breakdowns and disruptions in operation and consequential financial impacts.

Keywords: digital twin; internet of things; architecture design; data semantic modelling; intelligent infrastructure maintenance (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/14/8664/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/14/8664/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:14:p:8664-:d:863451

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().