Identifying the spatial distribution of deformation and shear band characteristics is important for accurately modeling soil behavior and ensuring the safety of nearby geotechnical structures. However, most research on the shear behavior of soils has focused on granular soil and clay-rich rocks, with little focus on clayey soil, and the entire shearing process from the initial state to failure has not been observed. This study evaluated the spatial distribution and evolution of deformation in clayey soils from the initial state to the post-failure state and the shear band characteristics. Plane strain tests were performed on normally consolidated and over-consolidated clay specimens, and digital images were captured through a transparent side wall for particle image velocimetry (PIV) analysis. PIV was performed to evaluate the displacement and deformation of soil particles. The results show that the shear-strain behaviors of two clays during the shearing process could be divided into four stages: initial, peak, softening, and steady state. Shear bands were observed to form in the softening stage, and the shear band slopes were compared to values in the literature. These results can be used to characterize shear bands in clay as well as predict failure behavior and guide reinforcement at actual sites with soft ground.

Traditional inverse distance weighting (IDW) interpolation is a process employed to estimate unknown values based on neighborhoods in 2D space. Proposed in this study is an improved IDW interpolation method that uses 3D search neighborhoods for effective interpolation on vertically connected observation data, such as water level, depth, and altitude. Borehole data are the data collected by subsurface boring activities and exhibit heterogeneous spatial distribution as they are densely populated near civil engineering or construction sites. In addition, they are 3D spatial data that show different subsurface characteristics by depth. The subsurface characteristics observed as such are used as core data in spatial modeling in fields, such as geology modeling, estimation of groundwater table distribution, global warming assessment, and seismic liquefaction assessment, among others. Therefore, this study proposed a seismic liquefaction assessment and mapping workflow using an improved IDW application by combining geographic information system (GIS) (ArcGIS (Esri, Redlands, CA, USA)), NURBS-based 3D CAD system (Rhino/Grasshopper (Robert McNeel & Associates, Seattle, WA, USA)), and numerical analysis system (MATLAB (MathWorks, Natick, MA, USA)). The 3D neighborhood search was conducted by the B-rep-based 3D topology analysis, and the mapping was done under the 2.5D environment by combining the voxel layer, DEM, and aerial images. The experiment was performed by collecting data in Songpa-gu, Seoul, which has the highest population density among the OECD countries. The results of the experiment showed between 7 and 105 areas with liquefaction potentials according to the search distance and the method of the approach. Finally, this study improved users’ accessibility to interpolation results by producing a 3D web app that used REST API based on OGC I3S Standards. Such an approach can be applied effectively in spatial modeling that uses 3D observation data, and in the future, it can contribute to the expansion of 3D GIS application.

Soil color is commonly used as an indicator to classify soil and identify its properties. However, color-based soil assessments are susceptible to variations in light conditions and the subjectivity of visual evaluations. This study proposes a novel method of calibrating digital images of soil, regardless of lighting conditions, to ensure accurate identification. Two different color space models, RGB and CIELAB, were assessed in terms of their potential utility in calibrating changes to soil color in digital images. The latter system was determined to be suitable, as a result of its ability to accurately reflect illuminance and color temperature. Linear regression equations relating soil color and light conditions were developed based on digital images of four different types of soil samples, each photographed under 15 different light conditions. The proposed method can be applied to calibrate variations in the soil color obtained by digital images, thus allowing for more standardized, objective, and accurate classification and evaluation of soil based on its color.

This paper explains a newly developed technique for identifying particles belonging to the contact force chains in a model particle assembly. This was accomplished through digital image analysis of the color images obtained from photoelastic measurements. Descriptive statistics for RGB color intensities of pixels in a central square region inside the particle were digitally measured to determine the particle force, as opposed to the traditional method of counting fringe orders. The relationships between the descriptive statistics of the RGB color intensities and the applied forces were analyzed. The developed image analysis technique was applied to digital photoelastic images of an assembly of stacked particles. This technique could embed the numeric data of the particle forces in the digital photoelastic images which provide comprehensive information for a quantitative analysis of the progressive evolution of the soil arching phenomena in a particulate media.