Laboratory, Field and In-Situ Soil Characterization Through Image Processing

通过图像处理进行实验室、现场和原位土壤表征

• 批准号: 0900105
• 负责人: Roman Hryciw
• 金额: $ 39.01万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0900105&HistoricalAwards=false
• 关键词: Laboratory; Field; Situ; Soil; Characterization

Soil classification is the first and foremost task of geotechnical, geoenvironmental and geotechnical earthquake engineering site characterization. Assessment of grain size distribution is essential to the classification. Despite significant shortcomings, grain size analysis is ubiquitously performed by sieve and hydrometer tests. A research program is therefore being undertaken to develop the hardware, analytical tools and software for characterization of soils from images of grain assemblies collected in the laboratory; above ground in the field; and from below the ground surface. The effort may result in cleaner, quicker, more efficient, less costly and more accurate grain size determination and stratigraphic delineation. Earlier image processing efforts were successful at determining the dominant grain size when particles are relatively uniform in size, or if the particles can be quickly segregated by size prior to image capture. The assessment of grain size distribution from images becomes orders of magnitude more complex when a soil contains a range of grain sizes. Previous techniques using image textural features and wavelet mathematics have paved the way for new methods including edge pixel density (EPD), edge segment size (ESS) and mathematical morphology that will yield more complex grain size distributions. The newer techniques may necessitate image capture at a range of magnifications.For the laboratory, a system is being developed for rapidly segregating a soil specimen by size using a sedimentation column. This permits piecewise image collection and assemblage of a traditional grain size distribution curve. The hardware and image collection system is being designed to facilitate size recognition down to 0.005 mm diameter, the commonly cited size threshold between ?silt? and ?clay?. The hardware development will be performed as part of an interdisciplinary undergraduate design project with students from several disciplines including civil/geotechnical engineering, mechanical design, image processing and mathematical statistics.For field applications, the research will develop a rapid response system to determine the grain size distribution of soils at the ground surface using two different approaches: mathematical morphology and edge detection to determine edge pixel density (EPD) and edge segment size (ESS). Preliminary evidence suggests these methods applied to images taken at multiple scales may provide the grain size distribution of even well-graded soils without the need for particle segregation. The research will also develop a rapidly deployable field portable computer and camera system. For subsurface grain size assessment, the vision cone penetrometer (VisCPT) will be used for image collection. Earlier studies have shown that textural indices can only detect changes in stratigraphy, but grain size determination has been too complex a problem. With the newly proposed image processing methods, in-situ grain size determination becomes tractable. VisCPT data will be collected at an exceptionally well characterized sand and gravel quarry in southwestern Indiana. Existing available date data includes continuous soil sampling and grain size distributions to a depth of 20 meters. The glacial outwash site includes a large assortment of soil gradations ranging from sandy gravels to clayey silts.The imaging methods developed herein for soil grain size characterization have direct application to other disciplines and industries where the sizing of manufactured components at various scales, ingredients in preparations, naturally occurring biologic or geologic matter, and interpretation of remotely sensed objects through image processing are essential to quality control, inspection, diagnosis, state assessment, and prediction of future behavior. Such industries include pharmaceuticals, food processing, materials science, powder metallurgy, microbiology and others.

土的分类是岩土工程、地质环境和岩土地震工程场地特征描述的首要任务。 粒度分布的评估是分类的关键。 尽管存在明显的缺点，粒度分析普遍采用筛分和比重计测试。 因此，正在开展一项研究方案，以开发硬件、分析工具和软件，用于根据实验室收集的颗粒集图像、实地收集的地面图像和地面以下图像对土壤进行定性。这一努力可能导致更清洁、更快速、更有效、成本更低和更准确的粒度确定和地层划分。 早期的图像处理工作在确定颗粒尺寸相对均匀时，或者如果颗粒可以在图像捕获之前通过尺寸快速分离，则成功地确定了主要颗粒尺寸。 当土壤含有一定范围的颗粒大小时，从图像中评估颗粒大小分布变得更加复杂。 以前的技术，使用图像纹理特征和小波数学铺平了道路，新的方法，包括边缘像素密度（EPD），边缘段大小（ESS）和数学形态学，将产生更复杂的粒度分布。 新的技术可能需要在一定范围内放大的图像捕获。对于实验室，正在开发一种系统，用于使用沉降柱快速分离土壤样品的大小。 这允许传统粒度分布曲线的分段图像收集和组合。 硬件和图像采集系统的设计，以促进大小识别到0.005毫米直径，通常引用的大小阈值之间？淤泥？然后呢？克莱？ 硬件开发将作为跨学科本科生设计项目的一部分进行，学生来自多个学科，包括土木/岩土工程，机械设计，图像处理和数学计算学。对于现场应用，研究将开发一个快速响应系统，使用两种不同的方法来确定地面土壤的粒度分布： 数学形态学和边缘检测来确定边缘像素密度（EPD）和边缘段尺寸（ESS）。 初步证据表明，这些方法适用于在多个尺度下拍摄的图像，可以提供均匀级配土壤的粒度分布，而不需要颗粒分离。 该研究还将开发一种可快速部署的现场便携式计算机和照相机系统。 对于表面下粒度评估，将使用视锥显微镜（VisCPT）进行图像采集。 早期的研究表明，纹理指数只能检测地层的变化，但粒度测定一直是一个过于复杂的问题。 随着新提出的图像处理方法，原位粒度测定变得容易。 VisCPT数据将在印第安纳州西南部的一个非常好的砂石采石场收集。 现有的数据包括连续的土壤采样和20米深度的粒度分布。 冰川沉积物遗址包括从桑迪到粘土质粉土的各种土壤级配。本文开发的用于土壤粒度表征的成像方法可直接应用于其他学科和行业，其中对各种规模的制造组件、制剂中的成分、天然存在的生物或地质物质、通过图像处理对遥感物体进行分析和解释，对于质量控制、检查、诊断、状态评估和未来行为预测至关重要。 这些工业包括制药、食品加工、材料科学、粉末冶金、微生物学等。