论文标题
使用透明的沙子和基于深度学习的图像分割来量化地质横向约束
Quantification of geogrid lateral restraint using transparent sand and deep learning-based image segmentation
论文作者
论文摘要
提出了一种实验技术，以量化嵌入在颗粒水平的颗粒土壤中的地理植物所提供的横向约束。重复的载荷三轴测试是在透明的砂样本上进行的，该样本具有模拟地理植入物的地理合成夹杂物。使用基于深度学习的分割算法对激光照明平面上的粒子概述进行了分割。粒子轮廓是根据傅立叶形状描述符的特征，并在依次捕获的图像上跟踪。通过数字图像相关（DIC）测量值验证了粒子位移测量值的准确性。另外，提出了该方法的分辨率和可重复性。根据测得的粒子位移和旋转，为每个测试确定了可能的粒子运动和不可能的粒子运动之间的状态边界线。可能的运动区域的大小可用于量化包含物提供的横向约束。总体而言，测试结果表明，地质夹杂物限制了粒子位移和旋转。但是，发现粒子位移比旋转更明显地受到约束。最后，在标本的永久性菌株的大小与可能运动区域的大小之间发现了独特的关系。
An experimental technique is presented to quantify the lateral restraint provided by a geogrid embedded in granular soil at the particle level. Repeated load triaxial tests were done on transparent sand specimens with geosynthetic inclusions simulating geogrids. Particle outlines on laser illuminated planes through the specimens were segmented using a deep learning-based segmentation algorithm. The particle outlines were characterized in terms of Fourier shape descriptors and tracked across sequentially captured images. The accuracy of the particle displacement measurements was validated against Digital Image Correlation (DIC) measurements. In addition, the method's resolution and repeatability is presented. Based on the measured particle displacements and rotations, a state boundary line between probable and improbable particle motions was identified for each test. The size of the zone of probable motions could be used to quantify the lateral restraint provided by the inclusions. Overall, the tests results revealed that the geosynthetic inclusions restricted both particle displacements and rotations. However, the particle displacements were found to be restrained more significantly than the rotations. Finally, a unique relationship was found between the magnitude of the permanent strains of the specimens and the size of the zone of probable motions.
