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EXPORT DATE: 7 June 2025

@CONFERENCE{Amaral2025,
author={Amaral, Tatiana Gondim do and Maciel, Caio César Medeiros and Junior, Marcos Paulino Roriz and Paula, Gabriella Soares de },
editor={Seppänen, Olli  and Koskela, Lauri  and Murata , Koichi  },
title={Method for Classifying Wastes by Making-do Using Machine Learning}, 
journal={Proceedings of the 33rd Annual Conference of the International Group for Lean Construction (IGLC 33)}, 
booktitle={Proceedings of the 33rd Annual Conference of the International Group for Lean Construction (IGLC 33)}, 
year={2025},
pages={822-833},
url={http://www.iglc.net/papers/details/2419},
doi={10.24928/2025/0274},
affiliation={Full Professor, Environmental and Civil Engineering Department, Federal University of Goiás (UFG), Goiânia, GO, Brazil. tatianagondim@ufg.br, orcid.org/0000-0002-9746-4025 ; Master Student, Program in Production Engineering, Federal University of Goiás, Brazil, caiocesar.eng@hotmail.com, orcid.org/0000-0002-6756-4068 ; Adjunct Professor, Faculty of Science and Technology, Federal University of Goiás (UFG), Aparecida de Goiânia, GO, Brazil, marcosroriz@ufg.br, orcid.org/0000-0003-2795-0009 ; Student, School of Environmental and Civil Engineering, Federal University of Goiás, Brazil, gabriella.soares@discente.ufg.br, orcid.org/0000-0002-7730-2838 },
abstract={The study aims to develop an automated method for classifying making-do wastes using machine learning (ML) techniques. Manual classification of these wastes is prone to inconsistencies, especially in projects with large volumes of data. The automated method makes the process more efficient and accurate. The research is classified as quantitative and empirical, with a descriptive, exploratory and experimental approach. Data was collected using the Melius quality platform and covered six high-end multi-family residential developments from two construction companies in Goiânia. The data was processed in two phases: initially, compliance was checked manually and then the data was adjusted for the ml algorithm. Preliminary results indicate that the main causes of waste are related to lack of labor (67.11%) and problems with materials (15.48%). The highest incidences of waste categories were inadequate, sequencing (26.82%) and lack of equipment (18.21%). In terms of impact, the recurrence of rework (13.37%) and lack of terminality (13.19%) stand out. The neural network model showed unsatisfactory results, with a recall of 55.4% and precision of 53.7%. The study shows the potential of machine learning, but adjustments to the models are necessary to improve their effectiveness. },
author_keywords={Making-do waste, Machine Learning, Predictive models, Automatic classification, Neural Network. },
address={Osaka and Kyoto, Japan },
issn={2789-0015 },
publisher={ },
language={English},
document_type={Conference Paper},
source={IGLC},
}