Lean Practices Using Building Information Modeling (BIM) and Digital Twinning for Sustainable Construction
1
Faculty of Built Environment, The University of New South Wales, Sydney, NSW 2052, Australia
2
Engineering Management Group, Department of Infrastructure Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Victoria, VIC 3010, Australia
3
Faculty of Architecture and Art, University of Tehran, Tehran 14174, Iran
4
Renewable Energy and Energy Efficiency Group, Department of Infrastructure Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Victoria, VIC 3010, Australia
*
Author to whom correspondence should be addressed.
Sustainability 2021, 13(1), 161; https://doi.org/10.3390/su13010161
Received: 1 December 2020 / Revised: 22 December 2020 / Accepted: 22 December 2020 / Published: 25 December 2020
(This article belongs to the Special Issue Spatial Big Data, BIM and GIS Visualization: Sustainable, Resilience and Smart Cities)
There is a need to apply lean approaches in construction projects. Both BIM and IoT are increasingly being used in the construction industry. However, using BIM in conjunction with IoT for sustainability purposes has not received enough attention in construction. In particular, the capability created from the combination of both technologies has not been exploited. There is a growing consensus that the future of construction operation tends to be smart and intelligent, which would be possible by a combination of both information systems and sensors. This investigation aims to find out the recent efforts of utilizing BIM for lean purposes in the last decade by critically reviewing the published literature and identifying dominant clusters of research topics. More specifically, the investigation is further developed by identifying the gaps in the literature to utilize IoT in conjunction with BIM in construction projects to facilitate applying lean techniques in a more efficient way in construction projects. A systematic review method was designed to identify scholarly papers covering both concepts “lean” and “BIM” in construction and possibilities of using IoT. A total of 48 scholarly articles selected from 26 construction journals were carefully reviewed thorough perusal. The key findings were discussed with industry practitioners. The transcriptions were analyzed employing two coding and cluster analysis techniques. The results of the cluster analysis show two main directions, including the recent practice of lean and BIM interactions and issues of lean and BIM adoption. Findings revealed a large synergy between lean and BIM in control interactions and reduction in variations, and surprisingly there are many uncovered areas in this field. The results also show that the capability of IoT is also largely not considered in recent developments. The number of papers covering both lean and BIM is very limited, and there is a large clear gap in understanding synergetic interactions of lean concepts applying in BIM and IoT in specific fields of construction such as sustainable infrastructure projects.
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Keywords:
digital construction; lean; lean construction; Building Information Modeling (BIM); Internet of Things (IoT); Digital Twin; computer-aided engineering (CAD)
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MDPI and ACS Style
Sepasgozar, S.M.E.; Hui, F.K.P.; Shirowzhan, S.; Foroozanfar, M.; Yang, L.; Aye, L. Lean Practices Using Building Information Modeling (BIM) and Digital Twinning for Sustainable Construction. Sustainability 2021, 13, 161. https://doi.org/10.3390/su13010161
AMA Style
Sepasgozar SME, Hui FKP, Shirowzhan S, Foroozanfar M, Yang L, Aye L. Lean Practices Using Building Information Modeling (BIM) and Digital Twinning for Sustainable Construction. Sustainability. 2021; 13(1):161. https://doi.org/10.3390/su13010161
Chicago/Turabian StyleSepasgozar, Samad M.E.; Hui, Felix K.P.; Shirowzhan, Sara; Foroozanfar, Mona; Yang, Liming; Aye, Lu. 2021. "Lean Practices Using Building Information Modeling (BIM) and Digital Twinning for Sustainable Construction" Sustainability 13, no. 1: 161. https://doi.org/10.3390/su13010161
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