{"title":"粘土-砂土混合物的粒子跟踪辅助数字体积相关性","authors":"Mengmeng Wu, Jianfeng Wang, Bing Pan, Zhenyu Yin","doi":"10.1680/jgeot.24.00036","DOIUrl":null,"url":null,"abstract":"This study introduces a novel, interdisciplinary method that merges fundamental geomechanics with computer vision to develop an advanced hybrid feature-aided Digital Volume Correlation (DVC) technique. This technique is specifically engineered to measure and compute the full-field strain distribution in fine-grained soil mixtures. A clay-sand mixture specimen composed of quartz sand particles and kaolinite was created. Its mechanical properties and deformation behaviour were then tested using a mini-triaxial apparatus, combined with micro-focus X-ray Computed Tomography (μCT). The CT slices underwent image processing for denoising, segmentation of distinct phases, reconstruction of sand particles, and feature extraction within the soil specimen. The proposed approach incorporated a two-step particle tracking method, which initially uses particle volume and surface area features to establish a preliminary matching list for a reference particle and then use the Iterative Closest Point (ICP) method for precise target particle matching. The soil specimen's initial displacement field was then mapped onto the DVC method's grid, and further refined through subvoxel registration via a three-dimensional inverse compositional Gauss-Newton algorithm. The proposed method's effectiveness and efficiency were validated by accurately calculating the displacement and strain fields of the soil mixture sample, and comparing the results with those from a traditional DVC method. Given the soil's compositional and microstructural characteristics, these image-matching techniques can be integrated to create a versatile, efficient, and robust DVC system, suitable for a variety of soil mixture types.","PeriodicalId":508398,"journal":{"name":"Géotechnique","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Particle tracking–aided digital volume correlation for clay-sand soil mixtures\",\"authors\":\"Mengmeng Wu, Jianfeng Wang, Bing Pan, Zhenyu Yin\",\"doi\":\"10.1680/jgeot.24.00036\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study introduces a novel, interdisciplinary method that merges fundamental geomechanics with computer vision to develop an advanced hybrid feature-aided Digital Volume Correlation (DVC) technique. This technique is specifically engineered to measure and compute the full-field strain distribution in fine-grained soil mixtures. A clay-sand mixture specimen composed of quartz sand particles and kaolinite was created. Its mechanical properties and deformation behaviour were then tested using a mini-triaxial apparatus, combined with micro-focus X-ray Computed Tomography (μCT). The CT slices underwent image processing for denoising, segmentation of distinct phases, reconstruction of sand particles, and feature extraction within the soil specimen. The proposed approach incorporated a two-step particle tracking method, which initially uses particle volume and surface area features to establish a preliminary matching list for a reference particle and then use the Iterative Closest Point (ICP) method for precise target particle matching. The soil specimen's initial displacement field was then mapped onto the DVC method's grid, and further refined through subvoxel registration via a three-dimensional inverse compositional Gauss-Newton algorithm. The proposed method's effectiveness and efficiency were validated by accurately calculating the displacement and strain fields of the soil mixture sample, and comparing the results with those from a traditional DVC method. Given the soil's compositional and microstructural characteristics, these image-matching techniques can be integrated to create a versatile, efficient, and robust DVC system, suitable for a variety of soil mixture types.\",\"PeriodicalId\":508398,\"journal\":{\"name\":\"Géotechnique\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Géotechnique\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1680/jgeot.24.00036\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Géotechnique","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1680/jgeot.24.00036","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
本研究介绍了一种新颖的跨学科方法,它将基础地质力学与计算机视觉相结合,开发出一种先进的混合特征辅助数字体积相关(DVC)技术。该技术专门用于测量和计算细粒土混合物的全场应变分布。我们制作了一个由石英砂颗粒和高岭石组成的粘砂混合物试样。然后使用微型三轴仪器结合微聚焦 X 射线计算机断层扫描(μCT)对其机械性能和变形行为进行了测试。CT 切片经过图像处理,以进行去噪、不同阶段的分割、沙粒的重建以及土壤试样内的特征提取。所提出的方法采用了两步颗粒跟踪法,首先使用颗粒体积和表面积特征建立参考颗粒的初步匹配列表,然后使用迭代最邻近点(ICP)法进行精确的目标颗粒匹配。然后将土壤试样的初始位移场映射到 DVC 方法的网格上,并通过三维逆合成高斯-牛顿算法进一步细化子体素配准。通过精确计算土壤混合物样本的位移场和应变场,并将结果与传统的 DVC 方法进行比较,验证了所提出方法的有效性和效率。考虑到土壤的成分和微观结构特征,这些图像匹配技术可以集成到一个多功能、高效和稳健的 DVC 系统中,适用于各种类型的土壤混合物。
Particle tracking–aided digital volume correlation for clay-sand soil mixtures
This study introduces a novel, interdisciplinary method that merges fundamental geomechanics with computer vision to develop an advanced hybrid feature-aided Digital Volume Correlation (DVC) technique. This technique is specifically engineered to measure and compute the full-field strain distribution in fine-grained soil mixtures. A clay-sand mixture specimen composed of quartz sand particles and kaolinite was created. Its mechanical properties and deformation behaviour were then tested using a mini-triaxial apparatus, combined with micro-focus X-ray Computed Tomography (μCT). The CT slices underwent image processing for denoising, segmentation of distinct phases, reconstruction of sand particles, and feature extraction within the soil specimen. The proposed approach incorporated a two-step particle tracking method, which initially uses particle volume and surface area features to establish a preliminary matching list for a reference particle and then use the Iterative Closest Point (ICP) method for precise target particle matching. The soil specimen's initial displacement field was then mapped onto the DVC method's grid, and further refined through subvoxel registration via a three-dimensional inverse compositional Gauss-Newton algorithm. The proposed method's effectiveness and efficiency were validated by accurately calculating the displacement and strain fields of the soil mixture sample, and comparing the results with those from a traditional DVC method. Given the soil's compositional and microstructural characteristics, these image-matching techniques can be integrated to create a versatile, efficient, and robust DVC system, suitable for a variety of soil mixture types.