Real-Time Sensor-Based Characterization of Recycled Coarse Aggregates (RCA): Advancing Sustainable Construction Through Automated Quality Assessment

The integration of recycled coarse aggregates (RCA) into construction projects has encountered industry resistance, primarily attributable to apprehensions about variable quality. This paper underscores the imperative need for reliable material quality assessments of RCA to ensure compliance with industry standards. Addressing this concern, we introduce a novel solution: a mobile, containerized sensor-based quality inspection system. This system is furnished with a 3D scanner Gocator, which, through optimized point cloud processing and streamlined segmentation algorithms, ensures rapid extrapolation of particle size distribution (PSD) from the RCA's surface point cloud data, producing outcomes closely aligned with conventional manual sieving techniques. Additionally, the application of laser-induced breakdown spectroscopy (LIBS) within this system has proven effective, consistently producing stable spectral data indicative of the material composition. The effectiveness of LIBS is further enhanced through the adoption of a cluster-based identification algorithm, which provides exceptional accuracy and precision in the spectral analysis. The system also includes conveyor belts capable of processing more than 100 tons of RCA per hour. This synergistic integration of technologies underpins a paradigm shift in RCA assessment, offering a scalable and adaptable model for enhancing the efficiency and reliability of End-of-Life material processing, aligning with global aspirations for sustainable infrastructural development.