Self-Supervised Learning Framework for Automated Material Grouping of Demolition Waste: Advancing Sustainable Waste Management

The construction and demolition (C&D) sector generates significant waste, necessitating efficient sorting and recycling to promote sustainable waste management. Traditional sorting methods in C&D waste management often depend on manual processes, which are labor-intensive and prone to errors, thus limiting recycling efficiency. While automated sorting systems have been introduced, they face challenges with the complex, heterogeneous nature of C&D debris and require large datasets that are manually labeled—meaning every image must be tagged with material types by experts—for training. This study addresses this gap by developing a self-supervised learning framework that reduces reliance on labeled data for effective feature extraction and material grouping. We implemented contrastive learning and autoencoder models, enhancing model performance through a dual approach of fine-tuning and parameter optimization, including edge detection, temperature, and batch size adjustments. The contrastive learning model, when optimized with lower temperatures and smaller batch sizes, exhibited superior feature differentiation and minimized loss. Clustering results highlighted that agglomerative hierarchical clustering provided the most coherent material groupings, outperforming other methods in adaptability to diverse debris characteristics. This framework provides a scalable, autonomous method for material grouping, which can streamline the sorting process by categorizing materials based on shared characteristics. By facilitating the initial grouping of materials, it reduces the manual effort required for detailed sorting and enhances recycling efficiency. Our findings demonstrate the effectiveness of self-supervised learning models in identifying distinct material features and clustering similar materials, contributing to sustainable waste management by enabling more efficient material recovery and recycling processes.