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Predicting the presence of hazardous materials in buildings using machine learning
RISE Research Institutes of Sweden, Safety and Transport, Measurement Technology. Lund University, Sweden.ORCID iD: 0000-0002-2178-5391
RISE Research Institutes of Sweden, Safety and Transport, Measurement Technology.ORCID iD: 0000-0002-9860-4472
RISE Research Institutes of Sweden, Built Environment. Lund University, Sweden.ORCID iD: 0000-0002-3863-0740
RISE Research Institutes of Sweden, Built Environment, System Transition and Service Innovation.ORCID iD: 0000-0002-5044-6989
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2022 (English)In: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 213, article id 108894Article in journal (Refereed) Published
Abstract [en]

Identifying in situ hazardous materials can improve demolition waste recyclability and reduce project uncertainties concerning cost overrun and delay. With the attempt to characterize their detection patterns in buildings, the study investigates the prediction potential of machine learning techniques with hazardous waste inventories and building registers as input data. By matching, validating, and assuring the quality of empirical data, a hazardous material dataset for training, testing, and validation was created. The objectives of the explorative study are to highlight the challenges in machine learning pipeline development and verify two prediction hypotheses. Our findings show an average of 74% and 83% accuracy rates in predicting asbestos pipe insulation in multifamily houses and PCB joints or sealants in school buildings in two major Swedish cities Gothenburg and Stockholm. Similarly, 78% and 83% of recall rates were obtained for imbalanced classification. By correlating the training sample size and cross-validation accuracy, the bias and variance issues were assessed in learning curves. In general, the models perform well on the limited dataset, yet collecting more training data can improve the model's generalizability to other building stocks, meanwhile decreasing the chance of overfitting. Furthermore, the average impact on the model output magnitude of each feature was illustrated. The proposed applied machine learning approach is promising for in situ hazardous material management and could support decision-making regarding risk evaluation in selective demolition work. © 2022 The Author(s)

Place, publisher, year, edition, pages
Elsevier Ltd , 2022. Vol. 213, article id 108894
Keywords [en]
Asbestos, Circular economy, Hazardous materials, Machine learning, PCB, Pre-demolition audit, Decision making, Demolition, Forecasting, Hazards, Organic pollutants, Cost-overruns, Demolition wastes, Hazardous wastes, In-buildings, Input datas, Machine learning techniques, Project uncertainty, Recyclability, Polychlorinated biphenyls
National Category
Building Technologies
Identifiers
URN: urn:nbn:se:ri:diva-58771DOI: 10.1016/j.buildenv.2022.108894Scopus ID: 2-s2.0-85124704384OAI: oai:DiVA.org:ri-58771DiVA, id: diva2:1642226
Note

Funding details: Stiftelsen för Strategisk Forskning, SSF, FID18-0021; Funding text 1: This research was funded by the Swedish Foundation for Strategic Research (SSF) , grant number FID18-0021 .

Available from: 2022-03-04 Created: 2022-03-04 Last updated: 2024-02-14Bibliographically approved

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Wu, Pei-YuSandels, ClaesMjörnell, KristinaMangold, MikaelJohansson, Tim

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