TY - GEN
T1 - Digital Twin-Based Automated Green Building Assessment Framework
AU - Darko, Amos
AU - THANAWEERA ACHCHIGE, Jayasanka
AU - Chan, Albert P.C.
AU - Jalaei, Farzad
AU - Ansah, Mark Kyeredey
AU - Opoku, De Graft Joe
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2024/1
Y1 - 2024/1
N2 - 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.
AB - 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.
KW - Accurate Green Building Assessment
KW - BEAM Plus
KW - Digital Twin
KW - Existing Buildings
KW - Plug-in
KW - Retrofitting
UR - http://www.scopus.com/inward/record.url?scp=85174718370&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-35399-4_43
DO - 10.1007/978-3-031-35399-4_43
M3 - Conference article published in proceeding or book
AN - SCOPUS:85174718370
SN - 9783031353987
T3 - Lecture Notes in Civil Engineering
SP - 597
EP - 613
BT - Advances in Information Technology in Civil and Building Engineering - Proceedings of ICCCBE 2022 - Volume 1
A2 - Skatulla, Sebastian
A2 - Beushausen, Hans
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th International Conference on Computing in Civil and Building Engineering, ICCCBE 2022
Y2 - 26 October 2022 through 28 October 2022
ER -