Cybersecurity and System Resilience for Deep Learning in Construction and Demolition Waste Classification

Construction and Demolition Waste (CDW) management represents a growing global challenge due to the large volume and heterogeneous nature of materials involved.This study addresses this issue by developing an automated classification system based on computer vision and deep learning, aiming to enhance efficiency and sustainability compared to manual sorting methods.A representative dataset was collected in a recycling facility, and multiple convolutional architectures were evaluated, with ResNet50 employing transfer learning achieving the best performance.The model was integrated into a web-based prototype capable of processing both still images and real-time video, offering visualization and interpretability tools for users.In addition to performance evaluation, the system's cybersecurity and resilience were analyzed, focusing on data integrity, secure model deployment, and robustness against potential cyber threats.Experimental results demonstrate competitive classification accuracy and stable operation under realistic conditions.The study confirms the technical feasibility of the approach and emphasizes the importance of incorporating cybersecurity considerations into AI-driven industrial solutions, establishing a foundation for secure, scalable, and sustainable CDW management systems.