Evaluation of the particle characteristics of aggregates from construction spoils treatment through a real-time...

Construction spoils are generated during construction activities and typically contain aggregates along with mud, requiring size distribution (gradation) assessment for reuse. Conventional methods using the square opening sieves are inefficient and labor-intensive. This study introduced an intelligent multi-modal module primarily for gradation detection based on 3D scanning technology to replace traditional sieve techniques. The proposed Particle Point Cloud Clustering algorithm achieved nearly 100% segmentation accuracy for multi-particle point clouds within 2 s through adaptive point-spacing optimization. A Particle Sieving Size Determination method ensured particle size classification accuracy exceeding 93.0%. A particle surface reconstruction algorithm was integrated into the Particle Characteristics Extraction (PCE) method to address the challenge of volume calculation for unscanned particle bottom surfaces, providing a novel strategy for computing particle geometry that encompasses traditional analysis. To streamline volume calculation and bypass individual particle reconstruction, we developed a volume prediction approach that combines the Oriented Bounding Box volume with the particle morphological parameter (λ) obtained through the PCE method. Furthermore, the Particle Mass Modification model determined aggregate mass by multiplying the predicted volume with the established density. This model significantly reduced gradation errors to less than 1.2% on average, which was experimentally validated. Experimental results also confirmed that the proposed method achieves real-time, second-level detection and fulfills the typical application needs in a construction site. This study is expected to benefit other industrial processes, such as particle screening in the mining industry, since information on particle characteristics is equally crucial for this sector.