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Prediction of hazardous materials in the building stock though machine learning

Hazardous materials (e.g. asbestos and PCB) is a health risk and when encountered during renovation processes increases costs and lead times. In this project information about where hazardous materials have been found will be gathered, systematized and connected to comprehensive registers about the building stock. Machine learning makes it possible identify patterns and make predictions of where hazardous materials might be found in the entire Swedish building stock.

Wu, Pei-Yu, Kristina Mjörnell, Mikael Mangold, Claes Sandels, and Tim Johansson. 2021. “A Data-Driven Approach to Assess the Risk of Encountering Hazardous Materials in the Building Stock Based on Environmental Inventories.” Sustainability 13 (14): 7836.

Wu, Pei-Yu, Kristina Mjörnell, Claes Sandels, and Mikael Mangold. 2021. “Machine Learning in Hazardous Building Material Management: Research Status and Applications.” Recent Progress in Materials 3 (2).

Summary

Project name

Prediction of hazardous materials

Status

Active

Region

Västra Götaland Region

RISE role in project

Coordination and execution

Project start

Duration

4 years

Total budget

4.5 MSEK

Project members

Mikael Mangold

Contact person

Mikael Mangold

Forskare

Read more about Mikael

Contact Mikael
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