摘要
密实度是衡量粗粒土力学性能的重要指标之一。大量实验表明,颗粒形状对颗粒材料的密实度有显著的影响,但针对特定形状指标对密实度影响的研究较少。选取大石峡堆石坝料场的卵石料颗粒和块石料颗粒作为试验材料,通过三维扫描获取颗粒的真实形状,计算了颗粒的形状指标。基于颗粒的真实形状,构建模拟真实粗粒土颗粒的离散元团簇体模型。采用压缩边界法对颗粒集合体进行制样,并对所有试样孔隙比进行测定。BP神经网络与Olden方法相结合的参数敏感性分析显示:对密实度敏感性较强的形状指标依次为三维球度、凸度、三维圆度和二维圆度,颗粒的三维球度指标能够较好地反映粗粒土密实度随颗粒形状的变化规律。考虑粗粒土颗粒三维球度的正态分布特性,针对三维球度呈不同正态分布的试样进行数值模拟,结果表明:试样的孔隙比随颗粒三维球度分布范围的增大基本保持不变,其分布范围的变化对孔隙比的影响较小。
Compactness is one of the most important criteria to measure the mechanical behaviors of coarse-grained soils.Abundant experiments show that the grain morphology has a significant influence on the compactness of granular materials, but few researchers focus on the effects of specific shape descriptors.In this paper, the pebble grains and block grains of Dashixia rockfill dam were selected as the materials, and the morphology of the grains was obtained by 3 D scanning.Then we calculated the shape descriptors of grains.Based on the morphology, a discrete element cluster model was established to simulate the coarse-grained soil grains.The grain aggregate was prepared by using the compression boundary method, and the void ratios of all samples were measured.Parameter sensitivity analysis combined with BP neural network and Olden method showed that the shape descriptors with strong sensitivity to compactness were three-dimensional sphericity, convex, three-dimensional roundness and two-dimensional roundness in turn.According to normal distribution of the three-dimensional sphericity of coarse-grained soil grains, a numerical simulation was carried out considering different normal distributions of the three-dimensional sphericity.It showed that the void ratios of the samples basically remained unchanged with the increasing of distribution range of three-dimensional sphericity, and the change of the distribution range had little effect on the void ratio.
作者
刘波
刘鹏
马刚
杨平荣
王一涵
冷天培
LIU Bo;LIU Peng;MA Gang;YANG Pingrong;WANG Yihan;LENG Tianpei(Jiangxi Provincial Water Conservancy Planning Design And Research Institute,Nanchang 330000,China;State Key Laboratory of Water Resources and Hydropower Engineering Science,Wuhan University,Wuhan 430072,China)
出处
《人民长江》
北大核心
2022年第4期155-162,170,共9页
Yangtze River
关键词
粗粒土
颗粒形状
密实度
孔隙比
形状指标
离散元
神经网络
coarse-grained soil
grain morphology
compactness
void ratio
shape descriptor
DEM
neural network
