Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Modeling Artificial Neural Networks to Predict Asbestos-containing Materials in Residential Buildings

Wu, Pei-Yu LU ; Mangold, Mikael ; Sandels, Claes ; Johansson, Tim and Mjörnell, Kristina LU (2022)
Abstract

The presence of hazardous materials inhibits material
circularity. The existing residential buildings are exposed to the risk of the
unforeseen presence of asbestos-containing materials during the demolition or renovation
process. Estimating the potential occurrence of contaminated building
components can therefore facilitate semi-selective demolition and decontamination
planning. The study aims to investigate the prediction possibility of seven frequently
detected asbestos-containing materials by using artificial neural networks
based on a hazardous material dataset from pre-demolition audit inventories and
national building registers. Through iterative model evaluation and... (More)

The presence of hazardous materials inhibits material
circularity. The existing residential buildings are exposed to the risk of the
unforeseen presence of asbestos-containing materials during the demolition or renovation
process. Estimating the potential occurrence of contaminated building
components can therefore facilitate semi-selective demolition and decontamination
planning. The study aims to investigate the prediction possibility of seven frequently
detected asbestos-containing materials by using artificial neural networks
based on a hazardous material dataset from pre-demolition audit inventories and
national building registers. Through iterative model evaluation and careful
hyperparameter tuning, the prediction performance for each asbestos-containing
material was benchmarked. A high level of accuracy was obtained for asbestos
pipe insulation and ventilation channel, yet barely any patterns were found for
asbestos floor mats. Artificial neural networks show potential for classifying
specific asbestos components and can enhance the knowledge of their detection
patterns. However, more quality data are needed to bring the models into
practice for risk assessment for not yet inventoried residential buildings. The
proposed screening approach for in situ asbestoscontaining materials has high
applicability for the quality assurance of recycled materials in circular value
chains.

(Less)
Please use this url to cite or link to this publication:
author
; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
IOP Conference Series: Earth and Environmental Science
pages
8 pages
external identifiers
  • scopus:85146696463
DOI
10.1088/1755-1315/1122/1/012050
language
English
LU publication?
yes
id
5f0ff195-fc22-4225-a4a6-5b9cb7531baf
date added to LUP
2023-01-13 14:28:31
date last changed
2023-04-09 16:24:24
@inproceedings{5f0ff195-fc22-4225-a4a6-5b9cb7531baf,
  abstract     = {{<p class="MsoNoSpacing">The presence of hazardous materials inhibits material<br>
circularity. The existing residential buildings are exposed to the risk of the<br>
unforeseen presence of asbestos-containing materials during the demolition or renovation<br>
process. Estimating the potential occurrence of contaminated building<br>
components can therefore facilitate semi-selective demolition and decontamination<br>
planning. The study aims to investigate the prediction possibility of seven frequently<br>
detected asbestos-containing materials by using artificial neural networks<br>
based on a hazardous material dataset from pre-demolition audit inventories and<br>
national building registers. Through iterative model evaluation and careful<br>
hyperparameter tuning, the prediction performance for each asbestos-containing<br>
material was benchmarked. A high level of accuracy was obtained for asbestos<br>
pipe insulation and ventilation channel, yet barely any patterns were found for<br>
asbestos floor mats. Artificial neural networks show potential for classifying<br>
specific asbestos components and can enhance the knowledge of their detection<br>
patterns. However, more quality data are needed to bring the models into<br>
practice for risk assessment for not yet inventoried residential buildings. The<br>
proposed screening approach for in situ asbestoscontaining materials has high<br>
applicability for the quality assurance of recycled materials in circular value<br>
chains.</p>}},
  author       = {{Wu, Pei-Yu and Mangold, Mikael and Sandels, Claes and Johansson, Tim and Mjörnell, Kristina}},
  booktitle    = {{IOP Conference Series: Earth and Environmental Science}},
  language     = {{eng}},
  title        = {{Modeling Artificial Neural Networks to Predict Asbestos-containing Materials in Residential Buildings}},
  url          = {{http://dx.doi.org/10.1088/1755-1315/1122/1/012050}},
  doi          = {{10.1088/1755-1315/1122/1/012050}},
  year         = {{2022}},
}