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Digital Twin and Industry 4.0 Enablers in Building and Construction: A Survey

Author(s): ORCID
ORCID

Medium: journal article
Language(s): English
Published in: Buildings, , n. 11, v. 12
Page(s): 2004
DOI: 10.3390/buildings12112004
Abstract:

With increasing interest in automatic and intelligent systems to enhance the building and construction industry, digital twins (DT) are gaining popularity as cost-effective solutions to meet stakeholder requirements. Comprising real-time multi-asset connectivity, simulation, and decision support functionalities, many recent studies have utilised Industry 4.0 technologies with DT systems to fulfil construction-specific applications. However, there is no comprehensive review to our knowledge, holistically examining the benefits of using DT as a platform from the angles of Industry 4.0 technologies, project management, and building lifecycle. To bridge this gap, a systematic literature review of 182 papers on DT-in-construction works over the past 6 years is conducted to address the three perspectives. In this review, a unified framework is first modelled to incorporate Industry 4.0 technologies within the DT structure. Next, a Six M methodology (comprising of Machine, Manpower, Material, Measurement, Milieu, and Method) based on Ishikawa’s Diagram with building lifecycle considerations is proposed to highlight the advantages of DT in ensuring successful construction projects. Lastly, through the identification of 11 future directions, this work aims to serve as a reference for both industry and academia towards the use of DT systems as a fundamental enabler to realise the Construction 4.0 paradigm.

Copyright: © 2022 by the authors; licensee MDPI, Basel, Switzerland.
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10700054
  • Published on:
    10/12/2022
  • Last updated on:
    10/05/2023
 
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