Predicting recoverable material stock in buildings: using machine learning with demolition audit data as a case study
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Date
2023-07
Publication Type
Conference Paper
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yes
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Abstract
Early specification of materials in buildings before their demolition could foster reuse in the construction industry. Studies have already shown the usefulness of machine learning in demolition waste estimation; however, application to real-world datasets is still limited. This study tests the feasibility of predicting recoverable material stock in the local context of the city of Zurich. The results show promise for the overall approach, although training models by using a small and heterogeneous dataset poses challenges. Therefore, we conceptualized an improved demolition data collection, processing, and dissemination. The resulting framework could help researchers and authorities in urban material stock estimation.
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Publication status
published
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Book title
Proceedings of the 2023 European Conference on Computing in Construction and the 40th International CIB W78 Conference
Journal / series
Volume
4
Pages / Article No.
Publisher
European Council on Computing in Construction
Event
European Conference on Computing in Construction and the 40th International CIB W78 Conference (EC³ 2023)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Material; Stock; Prediction; Demolition; Reuse
Organisational unit
09750 - De Wolf, Catherine / De Wolf, Catherine
Notes
Conference lecture held on July 10, 2023.