Research on segmentation, identification, and statistical evaluation methods for rockfill placement in rock-filled concrete

Abstract

[Objective] The construction quality of rock-filled concrete placement is closely related to the particle size and shape parameters of the rockfill inside the placement areas. However, current on-site evaluation and control of rockfill placement heavily rely on workers' subjective experience and judgment. Therefore, it is necessary to develop a rapid and effective method for segmentation, identification, and statistical evaluation of the rockfill placement used in rock-filled concrete. [Methods] Based on this, an improved rockfill segmentation and identification method was proposed within the YOLO(You Only Look Once) algorithm framework. Furthermore, a statistical calculation method for particle size and shape parameters was presented that accounted for the influence of boundary rockfill in images. A discrimination method for identifying concentration zones of undersized rockfill materials was proposed using the DBSCAN(Density-Based Spatial Clustering of Applications with Noise) algorithm. Then, based on relevant standards and engineering practices, a zoning evaluation method combining hard and soft indicators for the particle size and shape parameters of rockfill placement was developed. [Results] The result showed that the mean average precision(mAP50) for the segmentation and identification models of rockfill, reference markers, and safety helmets were 92.3%, 99.4%, and 97.5%, respectively, demonstrating high recognition accuracy. The proposed method for particle size and shape parameter estimation and the identification of concentration zones of undersized rockfill materials were validated through laboratory and field case studies, demonstrating reliable estimation and discrimination performance. The zoning statistical evaluation method was verified in typical engineering applications, with result consistent with actual engineering conditions. [Conclusion] The segmentation, identification, and statistical evaluation method proposed in this study enables effective field analysis and evaluation of rockfill placement quality, providing an essential technical solution for evaluating rockfill placement in rock-filled concrete engineering.

Keywords

rock-filled concrete / rockfill segmentation and identification / improved YOLO algorithm / concentration zones of undersized rockfill materials / statistical evaluation / influencing factors