堆石混凝土坝层面露石分割识别与快速评价方法研究OA

Exposed rocks on the lift joint surface of Rock-filled Concrete(RFC)dams can significantly enhance the shear capacity of the lift joints.While relevant codes and standards stipulate specific requirements regarding exposure of rocks at lift joints,there is currently a lack of rapid and effective detection and evaluation methods.This paper pro-poses a rapid segmentation and recognition algorithm for exposed rocks at lift joints,based on YOLOv11-Seg and incorporating the Slim-Neck attention mechanism and the FASFFHead detection head.Furthermore,by integrating actual engineering data and the latest code specifications,an evaluation method for exposure of rocks on lift joint sur-face is established based on the aforementioned segmentation algorithm.The research results indicate that the improved YOLOv11-Seg model achieves detection precision and recall rates of 91.4％and 89.33％,respectively,rep-resenting increases of 8.6％and 7.8％compared to the initial model,while maintaining high training and recognition efficiency.The measured exposure ratio of rocks at lift joints in actual RFC dam placements exhibits significant varia-tion,and the detection results for individual lift joints conform to a Weibull distribution.In conjunction with the newly revised standard NB/T 10077-2024,Code for Design of Rock-Filled Concrete Dams,the proposed method can effectively evaluate the condition of surface-exposed rock on-site.The horizontal projected area of the exposed rocks shows a hyperbolic relationship with the outropping height of rocks and a linear correlation with the vertical projected area;this relationship can be utilized to further assess the enhancement of the interfacial shear capacity contributed by the exposed rocks.This research provides technical means and a foundation for monitoring and evaluating the qual-ity of lift joints in Rock-filled Concrete dams.