33rd Annual Conference of the International Group for Lean Construction (IGLC 33) - Osaka and Kyoto, Japan - 2025

https://doi.org/10.24928/2025/0274

Method for Classifying Wastes by Making-do Using Machine Learning

Tatiana Gondim do Amaral1, Caio César Medeiros Maciel2, Marcos Paulino Roriz Junior3 & Gabriella Soares de Paula4

1Full Professor, Environmental and Civil Engineering Department, Federal University of Goiás (UFG), Goiânia, GO, Brazil. [email protected], orcid.org/0000-0002-9746-4025
2Master Student, Program in Production Engineering, Federal University of Goiás, Brazil, [email protected], orcid.org/0000-0002-6756-4068
3Adjunct Professor, Faculty of Science and Technology, Federal University of Goiás (UFG), Aparecida de Goiânia, GO, Brazil, [email protected], orcid.org/0000-0003-2795-0009
4Student, School of Environmental and Civil Engineering, Federal University of Goiás, Brazil, [email protected], 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.

Keywords

Making-do waste, Machine Learning, Predictive models, Automatic classification, Neural Network.

Files


Full Text
 

Reference

Download: BibTeX | RIS Format

Reference in APA 7th edition format:

Amaral, T. G., Maciel, C. C. M., Junior, M. P. R. & Paula, G. S.. (2025). Method for Classifying Wastes by Making-do Using Machine Learning. In Seppänen, O., Koskela, L., & Murata , K. (Eds.), Proceedings of the 33rd Annual Conference of the International Group for Lean Construction (IGLC 33) (pp. 822–833). https://doi.org/10.24928/2025/0274

Shortened reference for use in IGLC papers:

Amaral, T. G., Maciel, C. C. M., Junior, M. P. R. & Paula, G. S.. (2025). Method for Classifying Wastes by Making-do Using Machine Learning. IGLC33. https://doi.org/10.24928/2025/0274

 

Founded in 1993, the IGLC is an international network of researchers from practice and academia in architecture, engineering, and construction (AEC) who feel that the practice, education, and research of the AEC industry have to be radically renewed in order to respond to the global challenges ahead.

2015 © All Rights Reserved.