建筑垃圾再生填料是从建筑垃圾中经过粉碎而获得的，其组成成分具有多样 性，需经过分拣等处理后才可用于路基填筑。目前多采用耗时的人工筛选法进行获取。 本文利用卷积神经网络进行图像分析，可以自动实时地获取再生填料组成。首先创建一 个由 36000 张颗粒图像组成的带标签数据集，对不同 CNN 模型进行训练。其中，使用 40% 的 Dropout 比率自定义 ＲesNet34 模型表现最优，其验证精度可达97% ; 其次基于所 识别的颗粒类别与颗粒形状估计颗粒的质量; 最后将本文提出的方法与人工筛选法进行 了比较，对于大多数再生填料质量差异低于 2% 。本文旨在提高建筑垃圾利用空间，对 建筑垃圾回填路基工程的推广应用具有重要意义。

Construction waste recycling filler is obtained from construction waste by crushing． Due to its diversified composition，It can be used for subgrade filling only after sorting． At present，manual screening method is timeconsuming． In this paper，convolution neural network was used for image analysis and the composition of regenerated packing can be obtained automatically． Firstly，a labeled dataset composed of 36000 granular images is created to train different CNN models． Among them，the user-defined resnet34 model with 40% Dropout rate performs the best，and its verification accuracy can reach 97% ． Secondly，the mass of particles was estimated based on the particle type and shape． Finally，the method proposed in this paper was compared with the manual screening method． For most recycled fillers，the quality difference is less than 2% ． This paper aims to improve the utilization of construction waste and it is of great significance to the popularization and application of construction waste backfill subgrade engineering．