MIXED RECYCLED AGGREGATES CLASSIFICATION USING IMAGING AND DEEP-LEARNING TECHNIQUES FOR EFFECTIVE WASTE MANAGEMENT IN REHABILITATION WORKS

The rehabilitation and renovation of existing buildings generate significant amounts of construction and demolition waste (CDW) [1]. Proper classification of this waste is crucial for effective waste management and resource recovery. However, identifying and classifying the waste can be a tedious, costly, and error-prone process, which can lead to inadequate treatment and have a significant impacton the environment. This study focuses on the development and laboratory testing of advanced classification methods based on image processing and semantic segmentation to classify mixed recycled aggregate fractions. A comprehensive comparison is made between two different approaches: a semantic segmentation without ground truth, i.e. training the neural network without a reference target, and ase mantic segmentation trained after exhaustive labeling of the classes. A dataset of images of different types of CDW, including pure samples of concrete, ceramics, and plaster, as well as prepared mixtures, was collected, labeled, and segmented. The study applies deeplearning techniques and evaluates the performance of the two methods by quantifying the amount ofeach component in the image. The first approach involves training a supervised semantic segmentation network to learn the distinctive features of each pure class of material without mixing and assigning semantic labels to pixels. The second approach involves manual labeling of the classes in mixed samplesas ground truth for the model. The results reveal that both approaches have distinct advantages and disadvantages. The ground truth- based approach provides an accurate and reliable reference but requires considerable effort in manual labeling. On the other hand, the classifier training approach is more efficient in terms of time and resources but may be subject to classification errors. We further show that fine-tunning the ground truth-free model on few labeled samples outperforms both alternatives and represents a data-efficient trade-off. In conclusion, this study demonstrates the potential of deep-learning techniques with image analysis for cost-effective CDW classification. The results obtained in this research can serve as a basis for developing more accurate and reliable methods for CDW identification, contributing to sustainable CDW management for rehabilitation and renovation works.