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.

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.

Construction sites with deep soft deposits usually experience significant consolidation settlement that can compromise structural integrity if not properly monitored. Conventional methods, such as settlement plates, are limited by high costs and sparse spatial coverage, which leaves areas unmonitored and vulnerable to unexpected settlement. Therefore, this study develops an integrated UAV LiDAR monitoring framework that optimizes data preprocessing and introduces a novel timeseries settlement correction and interpolation technique for staged surcharge loading. Using UAV LiDAR data acquired at biweekly intervals from May 2021 to March 2022 at Busan Newport, high-quality digital elevation models were generated through optimal preprocessing. We are the first to evaluate the spatial representativeness of consolidation settlement at multiple section sizes (10 m × 10 m, 50 m × 50 m, and 100 m × 100 m) using high-resolution LiDAR, revealing that larger section sizes produce greater spatial variability and prediction error. Moreover, we demonstrate that at least seven biweekly UAV LiDAR surveys are essential to reliably capture early-stage settlement behaviors, providing a practical guideline for monitoring campaigns. These findings show that the proposed UAV LiDAR framework can deliver valuable insights for managing settlement in marine and soft ground construction projects.