A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Digital Twin-Based Automated Green Building Assessment Framework
Accurate green building assessment (GBA) represents one of the best opportunities to understand the holistic sustainability strengths and weaknesses of existing buildings to inform their retrofitting decisions. However, the current process for GBA of existing buildings is very challenging, tedious, complex, time-consuming and costly, and suffers from lack of important data and information. Moreover, most GBA results are not leveraged to retrofit and improve the sustainability performance of existing buildings – they are mostly for just recognition and market edge. To address these limitations, this study aims to develop a framework for using Digital Twin (DT) technology to automate and improve GBA. Although unavailable static building data can be obtained from scan-to-building information modelling (BIM) process, real-time dynamic data cannot. Hence, real-time dynamic data from the internet of things (IoT) sensors and other data should be integrated into the BIM model to create the DT model of the building. A plug-in software can then be deployed to assess the sustainability performance level of the building within the DT environment automatically. The framework is based on the Building Environmental Assessment Method (BEAM) Plus, which is Hong Kong’s leading GBA system. A real DT should feedback into the physical twin after receiving and processing data from it. Therefore, the automated GBA results should inform retrofitting decisions of the physical building. This study contributes to the understanding of how DT can be used to automate and improve GBA, and how the results can be used to improve retrofitting decision-making.
Digital Twin-Based Automated Green Building Assessment Framework
Accurate green building assessment (GBA) represents one of the best opportunities to understand the holistic sustainability strengths and weaknesses of existing buildings to inform their retrofitting decisions. However, the current process for GBA of existing buildings is very challenging, tedious, complex, time-consuming and costly, and suffers from lack of important data and information. Moreover, most GBA results are not leveraged to retrofit and improve the sustainability performance of existing buildings – they are mostly for just recognition and market edge. To address these limitations, this study aims to develop a framework for using Digital Twin (DT) technology to automate and improve GBA. Although unavailable static building data can be obtained from scan-to-building information modelling (BIM) process, real-time dynamic data cannot. Hence, real-time dynamic data from the internet of things (IoT) sensors and other data should be integrated into the BIM model to create the DT model of the building. A plug-in software can then be deployed to assess the sustainability performance level of the building within the DT environment automatically. The framework is based on the Building Environmental Assessment Method (BEAM) Plus, which is Hong Kong’s leading GBA system. A real DT should feedback into the physical twin after receiving and processing data from it. Therefore, the automated GBA results should inform retrofitting decisions of the physical building. This study contributes to the understanding of how DT can be used to automate and improve GBA, and how the results can be used to improve retrofitting decision-making.
Digital Twin-Based Automated Green Building Assessment Framework
Lecture Notes in Civil Engineering
Skatulla, Sebastian (editor) / Beushausen, Hans (editor) / Darko, Amos (author) / Jayasanka, T. A. D. K. (author) / Chan, Albert P. C. (author) / Jalaei, Farzad (author) / Ansah, Mark Kyeredey (author) / Opoku, De-Graft Joe (author)
International Conference on Computing in Civil and Building Engineering ; 2022 ; Cape Town, South Africa
Advances in Information Technology in Civil and Building Engineering ; Chapter: 43 ; 597-613
2023-09-30
17 pages
Article/Chapter (Book)
Electronic Resource
English
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