Examining the Use of BIM-Based Digital Twins in Construction: Analysis of Key Themes to Achieve a Sustainable Built Environment

Karoline Figueiredo (), Vivian W. Y. Tam () and Assed Haddad
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Karoline Figueiredo: Universidade Federal do Rio de Janeiro
Vivian W. Y. Tam: Western Sydney University
Assed Haddad: Universidade Federal do Rio de Janeiro

A chapter in Proceedings of the 27th International Symposium on Advancement of Construction Management and Real Estate, 2023, pp 1462-1474 from Springer

Abstract: Abstract Pursuing more sustainable construction projects has become a global priority. The construction industry is responsible for the massive use of freshwater resources and fossil fuels and several other environmental impacts, in addition to considerably affecting the gross domestic product (GDP) worldwide. In this vein, it is crucial to find strategies to develop a sustainable built environment based on a triple-bottom-line (TBL) strategy, concurrently considering environmental, social, and economic factors. The application of BIM-based Digital Twins seems to offer a tenable solution for overcoming the challenges related to achieving sustainability in the construction and real estate sectors. This concept is associated with developing a digital counterpart of the facility to assist the decision-making process throughout its life cycle, using real-time data and an actual connection between the 3D digital model and the physical asset. A BIM-based Digital Twin can be advantageous for a single building or an entire city and is, therefore, often related to the development of smart cities. This study’s novelty is presenting a structured literature review that defines the most recent developments in BIM-based Digital Twin applications for the real estate and construction sectors regarding sustainability goals. Based on this literature review, the authors present a discussion of how the knowledge acquired so far can be diffused into the built environment.

Keywords: Sustainable Construction; Real Estate; Building Information Modelling (BIM); Digital Twin (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-981-99-3626-7_113

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