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Machine Learning in Hazardous Building Material Management : Research Status and Applications
RISE Research Institutes of Sweden, Safety and Transport, Measurement Technology. Lund University, Sweden.ORCID iD: 0000-0002-2178-5391
RISE Research Institutes of Sweden. Lund University, Sweden.ORCID iD: 0000-0002-3863-0740
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: 0000-0002-5044-6989
2021 (English)In: Recent Progress in Materials, E-ISSN 2689-5846, Vol. 3, no 2Article in journal (Refereed) Published
Abstract [en]

Assessment of the presence of hazardous materials in buildings is essential for improving material recyclability, increasing working safety, and lowering the risk of unforeseen cost and delay in demolition. In light of these aspects, machine learning has been viewed as a promising approach to complement environmental investigations and quantify the risk of finding hazardous materials in buildings. In view of the increasing number of related studies, this article aims to review the research status of hazardous material management and identify the potential applications of machine learning. Our exploratory study consists of a two-fold approach: science mapping and critical literature review. By evaluating the references acquired from a literature search and complementary materials, we have been able to pinpoint and discuss the research gaps and opportunities. While pilot research has been conducted in the identification of hazardous materials, source separation and collection, extensive adoption of the available machine learning methods was not found in this field. Our findings show that (1) quantification of asbestos-cement roofing is possible from the combination of remote sensing and machine learning algorithms, (2) characterization of buildings with asbestos-containing materials is progressive by using statistical methods, and (3) separation and collection of asbestos-containing wastes can be addressed with a hybrid of image processing and machine learning algorithms. Analysis from this study demonstrates the method applicability and provides an orientation to the future implementation of the European Union Construction and Demolition Waste Management Protocol. Furthermore, establishing a comprehensive environmental inventory database is a key to facilitating a transition toward hazard-free circular construction

Place, publisher, year, edition, pages
Lidsen Publ. , 2021. Vol. 3, no 2
Keywords [en]
Machine learning technique; hazardous material management; construction and demolition waste; systematic review; asbestos-containing material; polychlorinated biphenyls (PCB)
National Category
Construction Management
Identifiers
URN: urn:nbn:se:ri:diva-53115DOI: 10.21926/rpm.2102017OAI: oai:DiVA.org:ri-53115DiVA, id: diva2:1555642
Available from: 2021-05-19 Created: 2021-05-19 Last updated: 2023-11-01Bibliographically approved

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Wu, Pei-YuMjörnell, KristinaSandels, ClaesMangold, Mikael

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