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Developing a Digital Twin at Building and City Levels: Case Study of West Cambridge Campus

Lookup NU author(s): Dr Xiang XieORCiD

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Abstract

A digital twin (DT) refers to a digital replica of physical assets, processes, and systems. DTs integrate artificial intelligence, machine learning, and data analytics to create living digital simulation models that are able to learn and update from multiple sources as well as represent and predict the current and future conditions of physical counterparts. However, current activities related to DTs are still at an early stage with respect to buildings and other infrastructure assets from an architectural and engineering/construction point of view. Less attention has been paid to the operation and maintenance (O&M) phase, which is the longest time span in the asset life cycle. A systematic and clear architecture verified with practical use cases for constructing a DT would be the foremost step for effective operation and maintenance of buildings and cities. According to current research about multitier architectures, this paper presents a system architecture for DTs that is specifically designed at both the building and city levels. Based on this architecture, a DT demonstrator of the West Cambridge site of the University of Cambridge in the UK was developed that integrates heterogeneous data sources, supports effective data querying and analysis, supports decision-making processes in O&M management, and further bridges the gap between human relationships with buildings/cities. This paper aims at going through the whole process of developing DTs in building and city levels from the technical perspective and sharing lessons learned and challenges involved in developing DTs in real practices. Through developing this DT demonstrator, the results provide a clear roadmap and present particular DT research efforts for asset management practitioners, policymakers, and researchers to promote the implementation and development of DT at the building and city levels.

Publication metadata

Author(s): Lu QC, Parlikad A, Woodall P, Ranasinghe GD, Xie X, Liang ZL, Konstantinou E, Heaton J, Schooling J

Publication type: Article

Publication status: Published

Journal: Journal of Management in Engineering

Year: 2020

Volume: 36

Issue: 3

Print publication date: 01/05/2020

Online publication date: 06/03/2020

Acceptance date: 22/10/2019

ISSN (print): 0742-597X

ISSN (electronic): 1943-5479

Publisher: American Society of Civil Engineers

URL: https://doi.org/10.1061/(ASCE)ME.1943-5479.0000763

DOI: 10.1061/(ASCE)ME.1943-5479.0000763

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