METHOD FOR CLASSIFYING WASTES BY MAKING-DO USING MACHINE LEARNING

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.