Details

Autor(en) / Beteiligte

• Wu, Pei-Yu
• Sandels, Claes
• Johansson, Tim
• Mangold, Mikael
• Kristina Mjörnell

Titel

Machine learning models for the prediction of polychlorinated biphenyls and asbestos materials in buildings

Ist Teil von

• Resources, conservation and recycling, 2023-12, Vol.199, p.107253, Article 107253

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2023

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •Detection of hazardous material by building class and region was investigated.•Machine learning offers cost-efficient screening for in-situ hazardous materials.•Several asbestos and PCB materials can be predicted with high AUC and F1 scores.•Building typology, characteristics, and location determine hazardous materials.•The shares and probability distributions of contaminated buildings were estimated. 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.