Drivers for Digital Twin Adoption in the Construction Industry: A Systematic Literature Review
Digital twin (DT) is gaining increasing attention due to its ability to present digital replicas of existing assets, processes and systems. DT can integrate artificial intelligence, machine learning, and data analytics to create real-time simulation models. These models learn and update from multipl...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/12/2/113 |
| _version_ | 1797482120877178880 |
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| author | De-Graft Joe Opoku Srinath Perera Robert Osei-Kyei Maria Rashidi Tosin Famakinwa Keivan Bamdad |
| author_facet | De-Graft Joe Opoku Srinath Perera Robert Osei-Kyei Maria Rashidi Tosin Famakinwa Keivan Bamdad |
| author_sort | De-Graft Joe Opoku |
| collection | DOAJ |
| description | Digital twin (DT) is gaining increasing attention due to its ability to present digital replicas of existing assets, processes and systems. DT can integrate artificial intelligence, machine learning, and data analytics to create real-time simulation models. These models learn and update from multiple data sources to predict their physical counterparts’ current and future conditions. This has promoted its relevance in various industries, including the construction industry (CI). However, recognising the existence of a distinct set of factors driving its adoption has not been established. Therefore, this study aims to identify the drivers and integrate them into a classification framework to enhance its understanding. Utilising popular databases, including Scopus, Web of Science, and ScienceDirect, a systematic literature review of 58 relevant DT adoptions in the CI research was conducted. From the review, the drivers for DT adoption in the CI were identified and classified. The results show that developed countries such as the UK, US, Australia, and Italy have been the top countries in advancing DT adoption in the CI, while developing countries have made commendable contributions. A conceptual framework has been developed to enhance the successful adoption of DT in the CI based on 50 identified drivers. The major categories of the framework include concept-oriented drivers, production-driven drivers, operational success drivers, and preservation-driven drivers. The developed framework serves as a guide to propel DT adoption in the CI. Furthermore, this study contributes to the body of knowledge about DT adoption drivers, which is essential for DT promotion in the CI. |
| first_indexed | 2024-03-09T22:25:44Z |
| format | Article |
| id | doaj.art-1cffe6be9e8941c899d3f1119c98590f |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-09T22:25:44Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-1cffe6be9e8941c899d3f1119c98590f2023-11-23T19:05:08ZengMDPI AGBuildings2075-53092022-01-0112211310.3390/buildings12020113Drivers for Digital Twin Adoption in the Construction Industry: A Systematic Literature ReviewDe-Graft Joe Opoku0Srinath Perera1Robert Osei-Kyei2Maria Rashidi3Tosin Famakinwa4Keivan Bamdad5Centre for Smart Modern Construction (c4SMC), School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, AustraliaCentre for Smart Modern Construction (c4SMC), School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, AustraliaCentre for Smart Modern Construction (c4SMC), School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, AustraliaCentre for Infrastructure Engineering (CIE), School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, AustraliaTeaching and Research Technical Services (TRTS), Built Environment and Engineering Cluster, Western Sydney University, Penrith, NSW 2751, AustraliaCentre for Smart Modern Construction (c4SMC), School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, AustraliaDigital twin (DT) is gaining increasing attention due to its ability to present digital replicas of existing assets, processes and systems. DT can integrate artificial intelligence, machine learning, and data analytics to create real-time simulation models. These models learn and update from multiple data sources to predict their physical counterparts’ current and future conditions. This has promoted its relevance in various industries, including the construction industry (CI). However, recognising the existence of a distinct set of factors driving its adoption has not been established. Therefore, this study aims to identify the drivers and integrate them into a classification framework to enhance its understanding. Utilising popular databases, including Scopus, Web of Science, and ScienceDirect, a systematic literature review of 58 relevant DT adoptions in the CI research was conducted. From the review, the drivers for DT adoption in the CI were identified and classified. The results show that developed countries such as the UK, US, Australia, and Italy have been the top countries in advancing DT adoption in the CI, while developing countries have made commendable contributions. A conceptual framework has been developed to enhance the successful adoption of DT in the CI based on 50 identified drivers. The major categories of the framework include concept-oriented drivers, production-driven drivers, operational success drivers, and preservation-driven drivers. The developed framework serves as a guide to propel DT adoption in the CI. Furthermore, this study contributes to the body of knowledge about DT adoption drivers, which is essential for DT promotion in the CI.https://www.mdpi.com/2075-5309/12/2/113digital twindriversconstruction industryreviewtechnology |
| spellingShingle | De-Graft Joe Opoku Srinath Perera Robert Osei-Kyei Maria Rashidi Tosin Famakinwa Keivan Bamdad Drivers for Digital Twin Adoption in the Construction Industry: A Systematic Literature Review Buildings digital twin drivers construction industry review technology |
| title | Drivers for Digital Twin Adoption in the Construction Industry: A Systematic Literature Review |
| title_full | Drivers for Digital Twin Adoption in the Construction Industry: A Systematic Literature Review |
| title_fullStr | Drivers for Digital Twin Adoption in the Construction Industry: A Systematic Literature Review |
| title_full_unstemmed | Drivers for Digital Twin Adoption in the Construction Industry: A Systematic Literature Review |
| title_short | Drivers for Digital Twin Adoption in the Construction Industry: A Systematic Literature Review |
| title_sort | drivers for digital twin adoption in the construction industry a systematic literature review |
| topic | digital twin drivers construction industry review technology |
| url | https://www.mdpi.com/2075-5309/12/2/113 |
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