Hydraulic properties of bentonite buffer material beyond 100 °C.

Bentonite buffer materials are important components of engineered barrier systems for the disposal of high-level radioactive waste produced during nuclear power generation. The design temperature of the buffer material is < 100 °C, and increasing the design temperature can reduce the required disposal area. This characteristic necessitates the evaluation of the thermal-hydraulic-mechanical properties of the buffer at temperatures above 100 °C to increase its target temperature. Therefore, the hydraulic properties of Gyeongju (KJ) bentonite buffer material were evaluated in this study, including the soil-water characteristic curve (SWCC) and hydraulic conductivity. An experimental system was manufactured to measure the suction and saturated hydraulic conductivity of KJ bentonite buffer material above 100 °C; the relative humidity of KJ bentonite buffer material was measured at 25-149 °C with an initial water content of 0, 0.06, and 0.12 under constant saturation conditions. The suction decreased as the temperature increased (10%-25% reduction at 99 °C-149 °C). The Van-Genuchten SWCC fitting parameters were also derived at 25 °C-149 °C using previously reported and newly generated experimental results, and the applicability of the modified Van-Genuchten SWCC model in this temperature range was verified. The hydraulic conductivity was proportional to temperature up to 100 °C, in agreement with the theoretical model results. Between 100 °C and 150 °C, the hydraulic conductivity increased nonlinearly because of molecular motion and structural changes inside the sample.

A Novel Method for Calibration of Digital Soil Images Captured under Irregular Lighting Conditions.

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.

Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging.

Soil water content is one of the most important physical indicators of landslide hazards. Therefore, quickly and non-destructively classifying soils and determining or predicting water content are essential tasks for the detection of landslide hazards. We investigated hyperspectral information in the visible and near-infrared regions (400-1000 nm) of 162 granite soil samples collected from Seoul (Republic of Korea). First, effective wavelengths were extracted from pre-processed spectral data using the successive projection algorithm to develop a classification model. A gray-level co-occurrence matrix was employed to extract textural variables, and a support vector machine was used to establish calibration models and the prediction model. The results show that an optimal correct classification rate of 89.8% could be achieved by combining data sets of effective wavelengths and texture features for modeling. Using the developed classification model, an artificial neural network (ANN) model for the prediction of soil water content was constructed. The input parameter was composed of Munsell soil color, area of reflectance (near-infrared), and dry unit weight. The accuracy in water content prediction of the developed ANN model was verified by a coefficient of determination and mean absolute percentage error of 0.91 and 10.1%, respectively.