The fractal method has recently been applied to a model for determining soil grain size distribution. The objective of this study is to review the applicability of the fractal method for a analysis of submarine sedimentary environments by comparing fractal constants with grain size statistical analysis for the soil samples of Pohang (PH) and Namhae (NH). The y-interception of log (grain size)-log (passing) equation was also used because grain size distribution couldn't be expressed with fractal dimension only. The result of comparison between fractal constants (dimension, y-interception) and grain size statistical indices, the fractal dimension was directly proportional to the mean and the sorting. And the y-interception showed high correlation with the mean. The fractal dimension and y-interception didn't show significant correlation with the skewness and the kurtosis. Thus regression equations between fractal constants and two statistical indices (mean, sorting) were derived. All classifications of the mean and the sorting could be determined using the regression equation based on the fractal dimension and y-interception. Therefore, fractal constants could be used as an alternative index representing the sedimentary environments instead of the mean and sorting.
Weathered soils appear from the rock and its weathering result. In accordance with the degree of weathering the roch may become soft rock, weathered rock and residual soil. In general, classification method for determining the degree of weathering are performed by chemical method and N-value. But these method have some problems. So, this research is to suggest an appropriate physical method to determine the degree of weathering of weathered soils. A new classification method for determining the degree of weathering is suggested, based upon the results from fall cone test. According to the proposed physical method using fall cone apparatus, the measured values of the samples from the same area show distinctive difference of weathering. For the checking, we selected two areas. As a result, the relationship between CWI and water content according to penetration is expressed as an equation in Ilsan and Incheon area. And it proved to be a good method to determine the degree of weathering.
The purpose of this study was to investigate maximum and minimum grain size which eroded by wind according to soil and wind conditions, such as top soil water content, roughness, land slope, wind velocity and proportion of grain size under 0.84mm. For performing this study, portable wind erosion tunnel was designed and utilized during field test, which facilitated measuring actual wind erosions under artificially controlled wind conditions. In the result, maximum, minimum grain size had strong negative correlation with roughness while weak positive correlation with wind velocity. Also, Slope which means the effect of gravity also influence grain size erodible by winds. Based on these results, regression equations were suggested for predicting maximum and minimum grain sizes by using multiple linear regression analysis from SPSS 20.0. The equation for maximum grain size erodible by winds showed a good agreement with the observed data with $R^2$=0.896. Other equation for minimum grain size had $R^2$=0.777.
This study aimed to develop a deep neural network model for predicting the soil water content and bulk density of soil based on features extracted from in situ soil surface images. Soil surface images were acquired using a Canon EOS 100d camera. The camera was installed in the vertical direction above the soil surface layer. To maintain uniform illumination conditions, a dark room and LED lighting were utilized. Following the acquisition of soil surface images, soil samples were collected using a metal cylinder to obtain measurements of soil water content and bulk density. Various features were extracted from the images, including color, texture, and shape features, and used as inputs for both a multiple regression analysis and a deep neural network model. The results show that the deep neural network regression model can predict soil water content and bulk density with root mean squared error of 1.52% and 0.78 kN/m3. The deep neural network model outperformed the multiple regression analysis, achieving a high accuracy for predicting both soil water content and bulk density. These findings suggest that in situ soil surface images, combined with deep learning techniques, can provide a fast and reliable method for predicting important soil properties.
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