Machine learning models for the prediction of polychlorinated biphenyls and asbestos materials in buildings
This article, featured in the December 2023 edition of the journal, Resources, Conservation and Recycling, focuses on a cost-efficient data-driven approach to evaluating comprehensive hazardous materials in existing buildings. Access an abstract of the article here.
Publication Date: 11 Dec 2023
Author: P. Wu, T. Johansson, M. Mangold, C. Sandels, K. Mjornell