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PurposePredictive digital twin technology, which amalgamates digital twins (DT), the internet of Things (IoT) and artificial intelligence (AI) for data collection, simulation and predictive purposes, has demonstrated its effectiveness across a wide array of industries. Nonetheless, there is a conspicuous lack of comprehensive research in the built environment domain. This study endeavours to fill this void by exploring and analysing the capabilities of individual technologies to better understand and develop successful integration use cases.Design/methodology/approachThis study uses a mixed literature review approach, which involves using bibliometric techniques as well as thematic and critical assessments of 137 relevant academic papers. Three separate lists were created using the Scopus database, covering AI and IoT, as well as DT, since AI and IoT are crucial in creating predictive DT. Clear criteria were applied to create the three lists, including limiting the results to only Q1 journals and English publications from 2019 to 2023, in order to include the most recent and highest quality publications. The collected data for the three technologies was analysed using the bibliometric package in R Studio.FindingsFindings reveal asymmetric attention to various components of the predictive digital twin’s system. There is a relatively greater body of research on IoT and DT, representing 43 and 47%, respectively. In contrast, direct research on the use of AI for net-zero solutions constitutes only 10%. Similarly, the findings underscore the necessity of integrating these three technologies to develop predictive digital twin solutions for carbon emission prediction.Practical implicationsThe results indicate that there is a clear need for more case studies investigating the use of large-scale IoT networks to collect carbon data from buildings and construction sites. Furthermore, the development of advanced and precise AI models is imperative for predicting the production of renewable energy sources and the demand for housing.Originality/valueThis paper makes a significant contribution to the field by providing a strong theoretical foundation. It also serves as a catalyst for future research within this domain. For practitioners and policymakers, this paper offers a reliable point of reference.
PurposePredictive digital twin technology, which amalgamates digital twins (DT), the internet of Things (IoT) and artificial intelligence (AI) for data collection, simulation and predictive purposes, has demonstrated its effectiveness across a wide array of industries. Nonetheless, there is a conspicuous lack of comprehensive research in the built environment domain. This study endeavours to fill this void by exploring and analysing the capabilities of individual technologies to better understand and develop successful integration use cases.Design/methodology/approachThis study uses a mixed literature review approach, which involves using bibliometric techniques as well as thematic and critical assessments of 137 relevant academic papers. Three separate lists were created using the Scopus database, covering AI and IoT, as well as DT, since AI and IoT are crucial in creating predictive DT. Clear criteria were applied to create the three lists, including limiting the results to only Q1 journals and English publications from 2019 to 2023, in order to include the most recent and highest quality publications. The collected data for the three technologies was analysed using the bibliometric package in R Studio.FindingsFindings reveal asymmetric attention to various components of the predictive digital twin’s system. There is a relatively greater body of research on IoT and DT, representing 43 and 47%, respectively. In contrast, direct research on the use of AI for net-zero solutions constitutes only 10%. Similarly, the findings underscore the necessity of integrating these three technologies to develop predictive digital twin solutions for carbon emission prediction.Practical implicationsThe results indicate that there is a clear need for more case studies investigating the use of large-scale IoT networks to collect carbon data from buildings and construction sites. Furthermore, the development of advanced and precise AI models is imperative for predicting the production of renewable energy sources and the demand for housing.Originality/valueThis paper makes a significant contribution to the field by providing a strong theoretical foundation. It also serves as a catalyst for future research within this domain. For practitioners and policymakers, this paper offers a reliable point of reference.
Digital Twin (DT) developments and applications in the Architectural Engineering Construction (AEC) Industry are emerging. However, insufficient publications synthesised the existing literature on DT of existing buildings, including energy retrofit and challenges as part of Net-zero strategies. When developing DT systems, it is vital to include the existing buildings primarily captured in 2-Dimensions (2-D) static data. To date, the implementation of DT has been minimal in applications in existing buildings in the UK. Despite DT benefits for maintenance (O&M) managers, facilities management (FM) as a comprehensive source of consistent data for predictive maintenance. This study explored the challenges faced by DT adoptions in existing buildings through a systematic review of the extant literature. A systematic approach is adopted to search the Scopus database using relevant keywords such as "Digital Twin.", "Built Environment" and "Existing Buildings.". the study focused on publications from the past five years (2018 to 2023) and prioritised articles in Scopus. The findings of this paper showed that the practitioners, O&M managers, and academics in built environments need more proper knowledge and technical expertise on digital twins as part of Industry 4.0 (I4.0). Evidence from the literature resulted in low empirical case studies and applications. The complexity of real-time data integration and interoperability were highlighted as part of the challenges despite the need for comprehensive knowledge of DT in the built environment. Scarce publication on the study was noted. The directions for comprehensive solutions and future research on digital twin applications in existing buildings towards achieving efficient energy retrofits, cost reductions, and net-zero goals were highlighted
Digital Twin (DT) developments and applications in the Architectural Engineering Construction (AEC) Industry are emerging. However, insufficient publications synthesised the existing literature on DT of existing buildings, including energy retrofit and challenges as part of Net-zero strategies. When developing DT systems, it is vital to include the existing buildings primarily captured in 2-Dimensions (2-D) static data. To date, the implementation of DT has been minimal in applications in existing buildings in the UK. Despite DT benefits for maintenance (O&M) managers, facilities management (FM) as a comprehensive source of consistent data for predictive maintenance. This study explored the challenges faced by DT adoptions in existing buildings through a systematic review of the extant literature. A systematic approach is adopted to search the Scopus database using relevant keywords such as "Digital Twin.", "Built Environment" and "Existing Buildings.". the study focused on publications from the past five years (2018 to 2023) and prioritised articles in Scopus. The findings of this paper showed that the practitioners, O&M managers, and academics in built environments need more proper knowledge and technical expertise on digital twins as part of Industry 4.0 (I4.0). Evidence from the literature resulted in low empirical case studies and applications. The complexity of real-time data integration and interoperability were highlighted as part of the challenges despite the need for comprehensive knowledge of DT in the built environment. Scarce publication on the study was noted. The directions for comprehensive solutions and future research on digital twin applications in existing buildings towards achieving efficient energy retrofits, cost reductions, and net-zero goals were highlighted
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