The construction industry is a major contributor to global carbon emissions and inefficiencies, underscoring the need for transformative waste reduction strategies. This study presents a method based on Lean-AI Autonomation for Construction and Demolition (C&D) waste management that repurposes a computer vision (CV) model to error-proof waste segregation. By evaluating existing tools and their limitations, we adopted a practical approach of using YOLOv8, a commercially available, user-friendly model, which was trained on Construction and Demolition waste data in a setting which makes its application more feasible. An operational simulation demonstrates the proposed concept and how integrating lean principles can overcome common tool constraints, thereby enhancing the reuse and recycling process in construction waste management.
Khan et al. (Thu,) studied this question.