Abstract
Purpose – At the end of a building’s lifecycle, there are several limitations to the decision-making process. There is the lack of data available from the building’s history, the difficulty in assessing the condition of a building, and the variety of stakeholders’ needs that have to be satisfied. The purpose of this research is to answer the question: how would EOL DMP change when buildings will have been digitally built? The answer will be illustrated through a conceptual framework.
Design/Methodology/Approach – A qualitative analysis of the existing literature has been performed to identify the elements within BIM and advanced digital technologies that could be of support to the decision-making process. The findings have been collected and summarised in a conceptual framework that has been validated and enhanced through online interviews with industry experts.
Findings – The EOL DMP framework has identified that BIM technology would bring the benefit of providing the initial digital data source, from which machine learning and data analytics would then extract the relevant data needed to measure accurately the criteria during the analysis of the end-of-life options put on the table.
Originality – The findings of this research could contribute in developing the software modules making the bridge between BIM and machine learning technologies, to implement them in the end of life decision-making process.
Design/Methodology/Approach – A qualitative analysis of the existing literature has been performed to identify the elements within BIM and advanced digital technologies that could be of support to the decision-making process. The findings have been collected and summarised in a conceptual framework that has been validated and enhanced through online interviews with industry experts.
Findings – The EOL DMP framework has identified that BIM technology would bring the benefit of providing the initial digital data source, from which machine learning and data analytics would then extract the relevant data needed to measure accurately the criteria during the analysis of the end-of-life options put on the table.
Originality – The findings of this research could contribute in developing the software modules making the bridge between BIM and machine learning technologies, to implement them in the end of life decision-making process.
Original language | English |
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Journal | Journal of Engineering, Design and Technology |
Early online date | 4 Aug 2021 |
DOIs | |
Publication status | Early online - 4 Aug 2021 |
Keywords
- BIM
- end of life
- decision-making process
- circular economy
- big data
- machine learning
- sensors
- IoT