Young Hwan Son
Seoul National University · Engineering
About the Lab
Professor Young Hwan Son's research lab specializes in geotechnical and environmental engineering, focusing on slope stability under variable rainfall conditions, soil water dynamics, and sustainable construction materials. The lab integrates advanced remote sensing techniques—particularly UAV-based imaging—with digital image processing and machine learning to monitor soil properties such as water content and bulk density with high spatial and temporal resolution. A key research direction involves developing eco-friendly construction materials using industrial by-products, such as oyster shells and biochar, to enhance permeability and structural performance in permeable concrete. The lab also contributes to climate adaptation by improving regional climate model outputs through innovative statistical bias correction methods for hydrological and geotechnical applications.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15The objective of this study is to monitor the water content of soil quickly and accurately using a UAV. Because UAVs have higher spatial and temporal resolution than satellites, they are currently becoming more useful in remote sensing areas. We developed a water content estimation equation using the color of the soil and suggested a calibration method for field application. Since the resolution of the images taken by the UAV is different according to the altitude, the water content estimation f
Rainfall is a major trigger of shallow slope failures, and it is necessary to consider the spatial correlation of soil properties for probabilistic analysis of slope stability in heterogeneous soil. In this study, a case study of a weathered soil slope in Korea was performed to identify the rainfall‐induced landslides considering the spatial variability of the soil properties and the probabilistic rainfall intensity depending on the return period and the rainfall duration. Various laboratory tes
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
In this study, permeable concrete blocks using industrial by-products (Oyster shell, bottom ash, and biochar) were developed, and the recycling suitability of the industrial by-products and engineering performances were evaluated. The flexural strength of permeable concrete blocks using by-products decreased as the bottom ash aggregate replacement ratio increased. When using oyster shell and biochar, the 28 days flexural strength was increased, and the development of initial strength was faster.
Digital image processing (DIP) is used to measure shape properties and settling velocity of soil particles. Particles with diameters of 1 to 10 mm are arbitrarily sampled for the test. The size of each particle is also measured by a Vernier caliper for comparison with the classification results using the shape classification table. The digital images were taken with a digital camera (Canon EOS 100d). Shape properties are calculated by image analysis software. Settling velocity of soil particles
Research Areas
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