Digital Twins for the built environment: Learning from conceptual and process models in manufacturing

Davila Delgado, Juan Manuel; Oyedele, Lukumon

Abstract

The overall aim of this paper is to contribute to a better understanding of the Digital Twin (DT) paradigm in the built environment by drawing inspiration from existing DT research in manufacturing. The DT is a Product Life Management information construct that has migrated to the built environment while research on the subject has grown intensely in recent years. Common to early research phases, DT research in the built environment has developed organically, setting the basis for mature definitions and robust research frameworks. As DT research in manufacturing is the most developed, this paper seeks to advance the understanding of DTs in the built environment by analysing how the DT systems reported in manufacturing literature are structured and how they function. Firstly, this paper presents a thorough review and a comparison of DT, cyber-physical systems (CPS), and building information modelling (BIM). Then, the results of the review and categorisation of DT structural and functional descriptions are presented. Fifty-four academic publications and industry reports were reviewed, and their structural and functional descriptions were analysed in detail. Three types of structural models (i.e. conceptual models, system architectures, and data models) and three types of functional models (process and communication models) were identified. DT maturity models were reviewed as well. From the reviewed descriptions, four categories of DT conceptual models (prototypical, model-based, interface-oriented, and service-based) and six categories of DT process models (DT creation, DT synchronisation, asset monitoring, prognosis and simulation, optimal operations, and optimised design) were defined and its applicability to the AECO assessed. While model-based and service-based models are the most applicable to the built environment, amendments are still required. Prognosis and simulation process models are the most widely applicable for AECO use-cases. The main contribution to knowledge of this study is that it compiles the DT’s structural and functional descriptions used in manufacturing and it provides the basis to develop DT conceptual and process models specific to requirements of the built environment sectors.

• Journal Article Type: Article
• Acceptance Date: May 26, 2021
• Online Publication Date: Jun 15, 2021
• Publication Date: Aug 1, 2021
• Deposit Date: Aug 30, 2021
• Publicly Available Date: Aug 31, 2021
• Journal: Advanced Engineering Informatics
• Print ISSN: 1474-0346
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 49
• Article Number: 101332
• DOI: https://doi.org/10.1016/j.aei.2021.101332
• Keywords: Artificial Intelligence; Information Systems
• Public URL: https://uwe-repository.worktribe.com/output/7473301
• Additional Information: This article is maintained by: Elsevier; Article Title: Digital Twins for the built environment: learning from conceptual and process models in manufacturing; Journal Title: Advanced Engineering Informatics; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.aei.2021.101332; Content Type: article; Copyright: © 2021 Published by Elsevier Ltd.