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Modeling Artificial Neural Networks to Predict Asbestos-containing Materials in Residential Buildings
RISE Research Institutes of Sweden, Safety and Transport, Measurement Technology. Lund University, Sweden.ORCID iD: 0000-0002-2178-5391
RISE Research Institutes of Sweden, Built Environment, System Transition and Service Innovation.ORCID iD: 0000-0002-5044-6989
RISE Research Institutes of Sweden, Safety and Transport, Measurement Technology.ORCID iD: 0000-0002-9860-4472
RISE Research Institutes of Sweden, Built Environment, System Transition and Service Innovation.ORCID iD: 0000-0002-8107-7768
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2022 (English)In: IOP Conference Series: Earth and Environmental Science, Vol. 1122, article id 012050Article in journal (Refereed) Published
Abstract [en]

The presence of hazardous materials inhibits material circularity. The existing residential buildings are exposed to the risk of the unforeseen presence of asbestos-containing materials during the demolition or renovation process. Estimating the potential occurrence of contaminated building components can therefore facilitate semi-selective demolition and decontamination planning. The study aims to investigate the prediction possibility of seven frequently detected asbestos-containing materials by using artificial neural networks based on a hazardous material dataset from pre-demolition audit inventories and national building registers. Through iterative model evaluation and careful hyperparameter tuning, the prediction performance for each asbestos-containing material was benchmarked. A high level of accuracy was obtained for asbestos pipe insulation and ventilation channel, yet barely any patterns were found for asbestos floor mats. Artificial neural networks show potential for classifying specific asbestos components and can enhance the knowledge of their detection patterns. However, more quality data are needed to bring the models into practice for risk assessment for not yet inventoried residential buildings. The proposed screening approach for in situ asbestos-containing materials has high applicability for the quality assurance of recycled materials in circular value chains.

Place, publisher, year, edition, pages
2022. Vol. 1122, article id 012050
National Category
Building Technologies
Identifiers
URN: urn:nbn:se:ri:diva-63227DOI: 10.1088/1755-1315/1122/1/012050OAI: oai:DiVA.org:ri-63227DiVA, id: diva2:1732174
Conference
SBEfin2022 Emerging Concepts for Sustainable Built Environment (SBEfin2022) 23/11/2022 - 25/11/2022
Note

The work is part of the PhD project “Prediction of Hazardous Materials in Buildings using Machine Learning” supported by RISE Research Institutes of Sweden. The research fund comes from the Swedish Foundation for Strategic Research (SSF) with grant number FID18-0021, and from the EU BuiltHub project with the grant agreement ID 957026. 

Available from: 2023-01-30 Created: 2023-01-30 Last updated: 2024-02-14Bibliographically approved

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Wu, Pei-YuMangold, MikaelSandels, ClaesJohansson, TimMjörnell, Kristina

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