Waste auditing is one of the tools used to quantify waste generation in construction processes, especially in industrialized building construction facilities that aim to reduce waste. These audits are organized following a regular schedule to monitor manufacturing activities with respect to the waste generated. However, the identification and quantification of waste through occasional audits of activities at any particular workstation remains a biased, manual, error-prone, and monotonous task. This paper proposes the automation of waste auditing in industrialized construction facilities, using as a case study a cutting station on a window manufacturing line. The waste generated during the cutting process is quantified using contour-based image processing algorithms, and the identification of the material is determined by optimized deep learning classification models. This approach allows the continuous acquisition of waste generation data at the workstation level and enables data-driven waste management decision-making that has the potential to support the reduction of waste in industrialized building construction facilities.
|Number of pages||15|
|Journal||International Journal of Advanced Manufacturing Technology|
|State||Published - Apr 2022|
Bibliographical noteFunding Information:
The authors gratefully acknowledge the financial support from the Natural Sciences and Engineering Research Council of Canada (File No. IRCPJ 419145–15).
© 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
- Construction waste
- Deep learning
- Machine vision
- Waste management
- Window manufacturing
ASJC Scopus subject areas
- Control and Systems Engineering
- Mechanical Engineering
- Computer Science Applications
- Industrial and Manufacturing Engineering