岩土工程学报2025，Vol.47Issue(10)：2096-2105,10.DOI:10.11779/CJGE20240740
基于CT图像人工智能分析的砂砾料几何特征参数提取方法
Method for parameter extraction of geometric features of gravel materials based on artificial intelligence analysis of CT images
摘要
Abstract
Sand and gravel mixture are widely used fill material for earth and rock dams,with its mechanical properties significantly influenced by particle geometric characteristics such as gradation,shape,and spatial arrangement.Accurate acquisition these geometric characteristics is crucial for studying mechanical properties of gravel,which is of great significance for design and construction of earth and rock dams.This study proposes a novel CT image segmentation method for gravel based on a deep learning model,integrating CT image three-dimensional reconstruction and topology principles to create a comprehensive method for extracting geometric feature parameters of gravel.A corresponding program is developed to provide algorithmic flow and parameter settings.Results show that this method achieves a segmentation accuracy over 95%,allowing precise extraction of geometric parameters such as center of mass coordinates,grain size,aspect ratio,and sphericity.The study reveals that gravel specimens exhibit a spatial distribution where sand grains settle at the bottom and gravel grains are uniformly distributed.Additionally,the aspect ratio and sphericity display a skewed distribution in the predicted probability densities.This study is expected to provide new technical means for investigating mechanical properties of gravel,thereby offering new insights for optimizing design and construction of earth and rock dams.关键词
砂砾料/颗粒几何特征/CT图像/深度学习模型/三维模型Key words
sand and gravel mixture/particle geometry features/CT images/deep learning model/three-dimensional model分类
建筑与水利引用本文复制引用
彭家奕,傅中志,沈振中,徐思远..基于CT图像人工智能分析的砂砾料几何特征参数提取方法[J].岩土工程学报,2025,47(10):2096-2105,10.基金项目
国家自然科学基金优秀青年科学基金项目(52222906) （52222906）
国家自然科学基金联合基金重点支持项目(U21A20158) （U21A20158）
广东省水利科技创新项目(2023-02) （2023-02）
南京水利科学研究院青年基金项目(Y323004)This work was supported by the National Science Foundation for Excellent Young Scientists Scholars of China(Grant No.52222906),Joint Funds of the National Natural Science Foundation of China(Grant No.U21A20158),Water Resources Science and Technology Innovation Project in Guangdong Province(Grant No.2023-02),Youth Foundation Project of Nanjing Hydraulic Research Institute(Grant No.Y323004). （Y323004）
