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Machine learning models for the prediction of polychlorinated biphenyls and asbestos materials in buildings
RISE Research Institutes of Sweden, Säkerhet och transport, Mätteknik. Lund University, Sweden.ORCID-id: 0000-0002-2178-5391
RISE Research Institutes of Sweden, Säkerhet och transport, Mätteknik.ORCID-id: 0000-0002-9860-4472
RISE Research Institutes of Sweden, Samhällsbyggnad, Systemomställning och tjänsteinnovation.ORCID-id: 0000-0002-8107-7768
RISE Research Institutes of Sweden, Samhällsbyggnad, Systemomställning och tjänsteinnovation.ORCID-id: 0000-0002-5044-6989
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2023 (Engelska)Ingår i: Resources, Conservation and Recycling, ISSN 0921-3449, E-ISSN 1879-0658, Vol. 199, artikel-id 107253Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Hazardous materials in buildings cause project uncertainty concerning schedule and cost estimation, and hinder material recovery in renovation and demolition. The study aims to identify patterns and extent of polychlorinated biphenyls (PCBs) and asbestos materials in the Swedish building stock to assess their potential presence in pre-demolition audits. Statistics and machine learning pipelines were generated for four PCB and twelve asbestos components based on environmental inventories. The models succeeded in predicting most hazardous materials in residential buildings with a minimum average performance of 0.79, and 0.78 for some hazardous components in non-residential buildings. By employing the leader models to regional building registers, the probability of hazardous materials was estimated for non-inspected building stocks. The geospatial distribution of buildings prone to contamination was further predicted for Stockholm public housing to demonstrate the models’ application. The research outcomes contribute to a cost-efficient data-driven approach to evaluating comprehensive hazardous materials in existing buildings.

Ort, förlag, år, upplaga, sidor
Elsevier B.V. , 2023. Vol. 199, artikel-id 107253
Nyckelord [en]
Demolition; Forecasting; Hazardous materials; Hazards; Housing; Machine learning; Polychlorinated biphenyls; Building stocks; Cost estimations; In-buildings; Machine learning models; Machine-learning; Material recovery; Pre-demolition audit; Probability: distributions; Project uncertainty; Residential building; asbestos; building; demolition; machine learning; modeling; PCB; prediction; probability; Probability distributions
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URN: urn:nbn:se:ri:diva-67646DOI: 10.1016/j.resconrec.2023.107253Scopus ID: 2-s2.0-85174186956OAI: oai:DiVA.org:ri-67646DiVA, id: diva2:1809403
Tillgänglig från: 2023-11-03 Skapad: 2023-11-03 Senast uppdaterad: 2024-02-14Bibliografiskt granskad

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Wu, Pei-YuSandels, ClaesJohansson, TimMangold, MikaelMjörnell, Kristina

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Wu, Pei-YuSandels, ClaesJohansson, TimMangold, MikaelMjörnell, Kristina
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