• Proceedings of the Korean Geotechical Society Conference
• /
• 2008.03a
• /
• pp.1466-1483
• /
• 2008

This paper discusses the development of an up-to-date computerized CPT (Cone Penetration Test) based soil engineering classification system to provide geotechnical engineers with a handy tool for their daily design activities. Five CPT soil engineering classification systems are incorporated in this effort. They include the probabilistic region estimation and fuzzy classification methods, both developed by Zhang and Tumay, the Schmertmann, the Douglas and Olsen, and the Robertson et al. methods. In the probabilistic region estimation method, a conformal transformation is used to determine the soil classification index, U, from CPT cone tip resistance and friction ratio. A statistical correlation is established between U and the compositional soil type given by the Unified Soil Classification System (USCS). The soil classification index, U, provides a soil profile over depth with the probability of belonging to different soil types, which more realistically and continuously reflects the in-situ soil characterization, which includes the spatial variation of soil types. The CPT fuzzy classification on the other hand emphasizes the certainty of soil behavior. The advantage of combining these two classification methods is realized through implementing them into visual basic software with three other CPT soil classification methods for friendly use by geotechnical engineers. Three sites in Louisiana were selected for this study. For each site, CPT tests and the corresponding soil boring results were correlated. The soil classification results obtained using the probabilistic region estimation and fuzzy classification methods are cross-correlated with conventional soil classification from borings logs and three other established CPT soil classification methods.

• Korean Journal of Soil Science and Fertilizer
• /
• v.49 no.1
• /
• pp.93-114
• /
• 2016

As a means of improving Korean Soil Classification System, we have reviewed Australian Soil Classification System by comparing Soil Taxonomy and FAO/WRB Classification System. Australian Soil Classification System is composed of 14 of Order, 87 of Sub-order, 556 of Great-group, 2,451 of Sub-group, and 7,276 of Family. Interestingly, soil order has the Anthroposols which is not classified with Soil Taxonomy, and the classification for some of soils is based on soil texture abruption horizon and soil structure. Seven of 14 soil orders are classified with an old version based on soil color rather than morphological characteristics. The distribution scale of Australian soil order is the largest in Tenosols, and followed by Kandosols, Rudosols, Sodosols and Vertisols in Australia.

• Korean Journal of Soil Science and Fertilizer
• /
• v.48 no.5
• /
• pp.535-541
• /
• 2015

Korean soil classification system is based on the US soil taxonomy. This study aimed to understand and inform the soil taxonomy of 2010 and 2014 US soil classification systems. Ingwan series belonged to anthropogenic soil was classified to coarse loamy over sandy, mixed, mesic family of Aquic Udorthents based on the soil taxonomy of 2010 and coarse loamy over sandy, mixed, mesic family of Anthroportic Udorthents based on the soil taxonomy of 2014. An anthropogenic soil is increasing in Korea. Considering the domestic relationship depending on US soil taxonomy, it is important to be well-informed of the taxonomy and apply the updated taxonomy system properly to the domestic soil classification. This study of defining the anthropogenic soil will provide useful information on soil management and its utilization.

• Geomechanics and Engineering
• /
• v.37 no.3
• /
• pp.263-277
• /
• 2024

Artificial Neural Networks (ANNs) are artificial learning algorithms that provide successful results in solving many machine learning problems such as classification, prediction, object detection, object segmentation, image and video classification. There is an increasing number of studies that use ANNs as a prediction tool in soil classification. The aim of this research was to understand the role of hyperparameter optimization in enhancing the accuracy of ANNs for soil type classification. The research results has shown that the hyperparameter optimization and hyperparamter optimized ANNs can be utilized as an efficient mechanism for increasing the estimation accuracy for this problem. It is observed that the developed hyperparameter tool (HyperNetExplorer) that is utilizing the Covariance Matrix Adaptation Evolution Strategy (CMAES), Genetic Algorithm (GA) and Jaya Algorithm (JA) optimization techniques can be successfully used for the discovery of hyperparameter optimized ANNs, which can accomplish soil classification with 100% accuracy.

• Korean Journal of Soil Science and Fertilizer
• /
• v.50 no.6
• /
• pp.520-529
• /
• 2017

In Korea, the largest agricultural lands are paddy fields which have poor infiltration and drainage properties. Recently, Korean government pursuits cultivating upland crops in paddy fields to reduce overproduced rice in Korea. In order to succeed this policy, it is critical to set criteria suitability classification for upland crops cultivating in paddy field soils. The objective of this study was developing guideline of suitability classification for sesame cultivation in paddy field soils. Yields of sesame cultivated in paddy field soils and soil properties were investigated at 40 locations at nationwide scale. Soil properties such as topography, soil texture, soil moisture contents, slope, and drainage level were investigated. The guideline of suitability classification for sesame was determined by multi-regression method. As a result, sesame yields had the greatest correlation with topography, soil moisture content, and slope. Since sesame is sensitive to excessive soil moisture content, paddy fields with well drained, slope of 7-15% and mountain foot or hill were best suit for cultivating sesame. Sesame yields were greater with less soil moisture contents. Based on these results, area of best suitable paddy field land for sesame was 161,400 ha, suitable land was 62,600 ha, possible land was 331,600 ha, and low productive land was 1,075,500 ha. Compared to existing suitability classification, the new guideline of classification recommended smaller area of best or suitable areas to cultivate sesame. This result may suggest that sesame cultivation in paddy field can be very susceptible to soil moisture contents.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.5C
• /
• pp.293-301
• /
• 2008

Soil profiling is one of the most important work among geotehnical engineering practice. Generally, soil profile is estimated from the observation of soil samples during subsurface exploration but such estimation also includes some experiencing aspects such as flushed water from the borehole, slime colour, boring speed and so on. In addition, since the capacity of hydraulic drill rig is significantly increased, thin layers might be easily missed. So, continuous soil profile is almost impossible over all depth to be bored from conventional subsurface exploration. While CPT or CPTu can serve continuous soil profile information over all depth generally in 5cm interval. Many charts or methods for soil profile from CPT result have been proposed during last several decades over the world. However they have not been verified in local ground condition in Korea. In this research, CPT results and soil classification results based on USCS were compiled from 17 sites over the Korea. Soil classification results by using 7 CPT soil classification charts were compared with those of USCS for the compiled database. Most proper CPT soil classification chart for Korean soil characteristics was evaluated and effective parameters for the soil classification from CPT were discussed. Finally interrelationship between CPT soil classification chart and USCS soil classification was evaluated.

• Progress in Superconductivity and Cryogenics
• /
• v.23 no.3
• /
• pp.26-31
• /
• 2021

Advanced classification of Cs contaminated soil by using a magnetic force-assisted selection pipe was investigated. A selection pipe is a device that sort particles depending on their particle size, based on the relationship between buoyancy, drag, and gravity force acting on the particles. Radioactive cesium is concentrated in small-particle size soil components with a large specific surface area. Hence, the volume of the Cs contaminated soil can be reduced by recycling the large-particle size soil components with low radioactive concentration. One of the problems of the selection pipe was that the radioactive concentration of the stayed soil in the selection pipe exceeds 8000 Bq/kg, which is the standard value of recycling of Cs contaminated soil, due to low classification accuracy. In this study, magnetic fields were applied to the lab-scale selection pipe from upper side to improve the classification accuracy and to reduce the radioactive concentration of the stayed soil.

• Journal of Soil and Groundwater Environment
• /
• v.15 no.6
• /
• pp.122-127
• /
• 2010

Constructing the national inventory that can be used as a tool to identify and assess existing or potential contamination is necessary for efficiently managing the soil and groundwater contamination. In order to start this construction, the first step is how we define and classify potential contamination sources of soil and groundwater. After selecting the basic classification model of contamination sources from developed countries, we suggested the classification and list of the potential contamination sources of soil and groundwater which are appropriate for specific conditions of South Korea. In addition, we investigated several databases to confirm the existence of available data sources and then examined established attribute data through chemical accident response information system (CARIS) and water information system (WIS) in National Institute of Environmental Research and mine geographic information system (MGIS) in Mine Reclamation Corporation. All sorts of attribute data in the existing databases can be utilized as significant assessment factors for determining the management priority of potential contamination sources in the future. Therefore, it is required the expanded investigation of additional database sources and the continual modification so that the classification system of potential contamination sources can be improved.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2002.03a
• /
• pp.133-138
• /
• 2002

Several well-known soil classification charts, like made by Robertson(1986,1990), Olsen(1981), existed already. In Korea, Lee Sun-Jae(1997) made new classification chart based on Unified Soil Classification System with locality In this study, 6 classification charts were applied respective area. Even exact decision is impossible which one is correct, this study can give useful guideline.

• Korean Journal of Soil Science and Fertilizer
• /
• v.46 no.6
• /
• pp.536-541
• /
• 2013

In Soil Taxonomy system, anthropogenic soils are still classified as Entisols since the International Classification Committee for Anthropogenic Soils is in the process of classifying anthropogenic soils as new orders. In reality, it is difficult to characterize anthropogenic soils because Soil Taxonomy (ST) system does not distinguish between natural and anthropogenic Entisols. On the other hand, World Reference Base for soil resources (WRB) considers human impacts on soils and contains an independent category of anthropogenic soils, which makes easier to understand anthropogenic soil characteristics than Soil Taxonomy system. A remodeled paddy field (Gasan) was selected to classify by ST and WRB. Soil samples were taken to analyze chemical and physical properties. Based on the results of the analyses, the ST system classified Gasan as coarse loamy, mixed, mesic, Aquic Udorthents while the WRB did as Stagnic Urbic Technosols (Oxyaquic, Arenic). As a conclusion, the WRB classification information of the anthropogenic provides more detail characteristics of the anthropogenic soils.