Machine learning models for the prediction of polychlorinated biphenyls and asbestos materials in buildings
(2023) In Resources, Conservation and Recycling 199.- Abstract
- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4375ecb4-67d7-4558-b9dc-caef0f357620
- author
- Wu, Pei-Yu LU ; Sandels, Claes ; Johansson, Tim ; Mangold, Mikael and Mjörnell, Kristina LU
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Resources, Conservation and Recycling
- volume
- 199
- article number
- 107253
- pages
- 16 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:85174186956
- ISSN
- 0921-3449
- DOI
- 10.1016/j.resconrec.2023.107253
- language
- English
- LU publication?
- yes
- id
- 4375ecb4-67d7-4558-b9dc-caef0f357620
- date added to LUP
- 2023-10-17 15:37:45
- date last changed
- 2023-12-07 14:45:58
@article{4375ecb4-67d7-4558-b9dc-caef0f357620,
abstract = {{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.}},
author = {{Wu, Pei-Yu and Sandels, Claes and Johansson, Tim and Mangold, Mikael and Mjörnell, Kristina}},
issn = {{0921-3449}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Resources, Conservation and Recycling}},
title = {{Machine learning models for the prediction of polychlorinated biphenyls and asbestos materials in buildings}},
url = {{http://dx.doi.org/10.1016/j.resconrec.2023.107253}},
doi = {{10.1016/j.resconrec.2023.107253}},
volume = {{199}},
year = {{2023}},
}