• Title/Summary/Keyword: Soil Classification

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New site classification system and design response spectra in Korean seismic code

  • Kim, Dong-Soo;Manandhar, Satish;Cho, Hyung-Ik
    • Earthquakes and Structures
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    • v.15 no.1
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    • pp.1-8
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    • 2018
  • A new site classification system and site coefficients based on local site conditions in Korea were developed and implemented as a part of minimum design load requirements for general seismic design. The new site classification system adopted bedrock depth and average shear wave velocity of soil above the bedrock as parameters for site classification. These code provisions were passed through a public hearing process before it was enacted. The public hearing process recommended to modify the naming of site classes and adjust the amplification factors so that the level of short-period amplification is suitable for economical seismic design. In this paper, the new code provisions were assessed using dynamic centrifuge tests and by comparing the design response spectra (DRS) with records from 2016 Gyeongju earthquake, the largest earthquake in history of instrumental seismic observation in Korea. The dynamic centrifuge tests were performed to simulate the representative Korean site conditions, such as shallow depth to bedrock and short-period amplification characteristics, and the results corroborated with the new DRS. The Gyeongju earthquake records also showed good agreement with the DRS. In summary, the new code provisions are reliable for representing the site amplification characteristic of shallow bedrock condition in Korea.

Characteristic, Genesis and Classification of Soils Derived from Coarse Grain Granitic Materials (조립질(粗粒質) 화강암(花崗巖) 토양(土壤)의 특성(特性)과 생성(生成)·분류(分類))

  • Jung, Sug-Jae;Hyeon, Geun-Soo;Moon, Yong-Taik;Jo, Young-Kil
    • Korean Journal of Soil Science and Fertilizer
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    • v.27 no.1
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    • pp.3-9
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    • 1994
  • Characterstics, genesis and classification of soils derived from coarse grain granitic materials were discussed with four soil series, such as Samgag, Sangju. Sachon and Yecheon which were distributed over the area of Gangdae-Ri, Nengseo-Myeon, Yeoju-Gun, Gyunggi-Do. The results are as follows. 1. Samgag, Sangju, Sachon and Yecheon had a soil of excessively, well, imperfectly and poorly drained, thus they had a soil drainage sequence. 2. Soil textural class were from sandy loam to loam. Silt and clay content were increased with descending to the local bottom, while sand content was decreased. 3. Soils were very strongly to strongly acid and OM, CEC, exchangeable cation, and available $P_2O_5$ in soils seemed to be increased with ascending to the local boctom. 4. Kaolinite and Quartz were the dominant clay mineral and the other was Vermiculite and Illite. 5. Samgag was classified as Typic Dystrochrepts, Sangju as Dystric-Fluventic Eutrochrepts, Sachon as Aeric-Fluventic Halpaquepts, and Yecheon as Fluventic Haplaquepts.

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Study on Forest Vegetation Classification with Remote Sensing

  • Yuan, Jinguo;Long, Limin
    • Proceedings of the KSRS Conference
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    • 2002.10a
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    • pp.250-255
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    • 2002
  • This paper describes the study methods of identifying forest vegetation types, based on this study, forest vegetation classification method based on vegetation index is proposed. According to reflectance data of vegetation canopy and soil line equation NIR=1.506R+0.0076 in Jingyuetan, Changchun, China, many vegetation index are calculated and analyzed. The relationships between vegetation index and vegetation types are that PVI identifies broadleaf forest and conifer forest the most easily, the next is TSAVI and MSAVI, but their calculation is complex. RVI values of different conifer trees vary obviously, so RVI can classify conifer trees. In a word, combination of PVI and RVI is evaluated to classify different vegetation types.

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A Neuro-Fuzzy Model Approach for the Land Cover Classification

  • Han, Jong-Gyu;Chi, Kwang-Hoon;Suh, Jae-Young
    • Proceedings of the KSRS Conference
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    • 1998.09a
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    • pp.122-127
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    • 1998
  • This paper presents the neuro-fuzzy classifier derived from the generic model of a 3-layer fuzzy perceptron and developed the classification software based on the neuro-fuzzl model. Also, a comparison of the neuro-fuzzy and maximum-likelihood classifiers is presented in this paper. The Airborne Multispectral Scanner(AMS) imagery of Tae-Duk Science Complex Town were used for this comparison. The neuro-fuzzy classifier was more considerably accurate in the mixed composition area like "bare soil" , "dried grass" and "coniferous tree", however, the "cement road" and "asphalt road" classified more correctly with the maximum-likelihood classifier than the neuro-fuzzy classifier. Thus, the neuro-fuzzy model can be used to classify the mixed composition area like the natural environment of korea peninsula. From this research we conclude that the neuro-fuzzy classifier was superior in suppression of mixed pixel classification errors, and more robust to training site heterogeneity and the use of class labels for land use that are mixtures of land cover signatures.

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Application of data mining and statistical measurement of agricultural high-quality development

  • Yan Zhou
    • Advances in nano research
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    • v.14 no.3
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    • pp.225-234
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    • 2023
  • In this study, we aim to use big data resources and statistical analysis to obtain a reliable instruction to reach high-quality and high yield agricultural yields. In this regard, soil type data, raining and temperature data as well as wheat production in each year are collected for a specific region. Using statistical methodology, the acquired data was cleaned to remove incomplete and defective data. Afterwards, using several classification methods in machine learning we tried to distinguish between different factors and their influence on the final crop yields. Comparing the proposed models' prediction using statistical quantities correlation factor and mean squared error between predicted values of the crop yield and actual values the efficacy of machine learning methods is discussed. The results of the analysis show high accuracy of machine learning methods in the prediction of the crop yields. Moreover, it is indicated that the random forest (RF) classification approach provides best results among other classification methods utilized in this study.

Study on the Dynamic Characteristics of Foundation-Soil System for the Seismic Analysis of Structures (구조물 내진설계를 위한 기초지반체계 동특성에 관한 연구)

  • 김용석
    • Journal of the Earthquake Engineering Society of Korea
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    • v.1 no.3
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    • pp.1-10
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    • 1997
  • It is recognized that the dynamic of a structure is affected by the characteristics of the soil layer and foundation. However the design codes for the seismic design of structures are partially reflecting the caharcteristics of the soil layers due to the inherent complexity of them and the lack of systematic study results for the foundation-soil system, and leading to unconservative or too conservative results. In this study, the kinematic interaction effects of foundation-soil system was investigated for the seismic analyses of structures estimating the effects of the shear wave velocity, the depth of the soil layer, the embedment of a foundation and pile foundation, and the modified classification criteria of soil layers are proposed for the reasonable seismic analyses of structures considering the characteristics of soil layers and foundations. For the embedded medium or large foundations (including pile foundations), at least 60m soil layer below the foundation should be considered for the seismic analyses of structures to tate into account the kinematic interaction effects of the foundation-soil system, and also the rocking motion of foundation-soil system with or without piles should be included in the seismic analyses of structures.

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Computer Tomography as a Tool for Physical Analysis in an Anthropogenic Soil

  • Chun, Hyen Chung;Park, Chan Won;Sonn, Yeon Kyu;Cho, Hyun Joon;Hyun, Byung Keun;Song, Kwan Cheol;Zhang, Yong Seon
    • Korean Journal of Soil Science and Fertilizer
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    • v.46 no.6
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    • pp.549-555
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    • 2013
  • Human influence on soil formation has dramatically increased as the development of human civilization and industry. Increase of anthropogenic soils induced research of those soils; classification, chemical and physical characteristics and plant growth of anthropogenic soils. However there have been no reports on soil pore properties from the anthropogenic soils so far. Therefore the objectives of this study were to test computer tomography (CT) to characterize physical properties of an anthropogenic paddy field soil and to find differences between natural and anthropogenic paddy field soils. Soil samples of a natural paddy field were taken from Ansung, Gyeonggi-do (Ansung site), and samples of an anthropogenic paddy field were from Gumi in Gyeongsangnam-do (Gasan) where paddy fields were remodeled in 2011-2012. Samples were taken at three different depths and analyzed for routine physical properties and CT scans. CT scan provided 3 dimensional images to calculate pore size, length and tortuosity of soil pores. Fractal analysis was applied to quantify pore structure within soil images. The results of measured physical properties (bulk density, porosity) did not show differences across depths and sites, but hardness and water content had differences. These differences repeated within the results of pore morphology. Top soil samples from both sites had greater pore numbers and sizes than others. Fractal analyses showed that top soils had more heterogeneous pore structures than others. The bottom layer of the Gasan site showed more degradation of pore properties than ploughpan and bottom layers from the Ansung site. These results concluded that anthropogenic soils may have more degraded pore properties as depth increases. The remodeled paddy fields may need more fundamental remediation to improve physical conditions. This study suggests that pore analyses using CT can provide important information of physical conditions from anthropogenic soils.

Calculation of Thermal Conductivity and Heat Capacity from Physical Data for Some Representative Soils of Korea

  • Aydin, Mehmet;Jung, Yeong-Sang;Lee, Hyun-Il;Kim, Kyung-Dae;Yang, Jae-E.
    • Korean Journal of Soil Science and Fertilizer
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    • v.45 no.1
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    • pp.1-8
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    • 2012
  • The thermal properties including volumetric heat capacity, thermal conductivity, thermal diffusivity, and diurnal and annual damping depths of 10 representative soil series of Korea were calculated using some measurable soil parameters based on the Taxonomical Classification of Korean Soils. The heat capacity of soils demonstrated a linear function of water content and ranged from 0.2 to $0.8cal\;cm^{-3}^{\circ}C^{-1}$ for dry and saturated medium-textured soil, respectively. A small increase in water content of the dry soils caused a sharp increase in thermal conductivity. Upon further increases in water content, the conductivity increased ever more gradually and reached to a maximum value at saturation. The transition from low to high thermal conductivity occurred at low water content in the soils with coarse texture, and at high water content in the other textures. Thermal conductivity ranged between $0.37{\times}10^{-3}cal\;cm^{-1}s^{-1}^{\circ}C^{-1}$ for dry (medium-textured) soil and $4.01{\times}10^{-3}cal\;cm^{-1}s^{-1}^{\circ}C^{-1}$ for saturated (medium/coarse-textured) soil. The thermal diffusivity initially increased rapidly with small increases in water content of the soils, and then decreased upon further increases in the soil-water content. Even in an extreme soil with the highest diffusivity value ($1.1{\times}10^{-2}cm^2s^{-1}$), the daily temperature variation did not penetrate below 70 cm soil depth and the yearly variation not below 13.4 m as four times of damping depths.

GIS Technology for Soil Loss Analysis (금강유역 토양 유실 분석을 위한 GIS응용연구)

  • 김윤종;김원영;유일현;이석민;민경덕
    • Spatial Information Research
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    • v.2 no.2
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    • pp.165-174
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    • 1994
  • Soil loss was estimated by using universal soil loss equation(USLE) through GIS technique in Buyeu area. The expected soil loss is determined from six environmental factors: rainfall, erodibility of selected soil, length and steepness (gradient) of ground slope, crop grown in soil, and land practices used. A scoring system for assessing soil lossrisk has been developed for calculating SLI(Soil Loss Index) by GIS. The scores of six factors multiplied to give a total score which was compared with an chosen classification system to categorize areas of low, moderate and high risk. Finally, a soil loss assessment map was produced by GIS cartographic simulation technique, and this map could be applied in the establishment of regional land use planning.

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Soil Erosion Assessment Using RS/GIS for Watershed Management in Dukchun River Basin, a Tributary of Namgang and Jinyang Lake

  • Cho Byung Jin;Yu Chan
    • Journal of The Korean Society of Agricultural Engineers
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    • v.46 no.7
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    • pp.3-12
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    • 2004
  • The need to predict the rate of soil erosion, both under existing conditions and those expected to occur following soil conservation practice, has been led to the development of various models. In this study Morgan model especially developed for field-sized areas on hill slopes was applied to assess the rate of soil erosion using RS/GIS environment in the Dukchun river basin, one of two tributaries flowing into Jinyang lake. In order to run the model, land cover mapping was made by the supervised classification method with Landsat TM satellite image data, the digital soil map was generated from scanning and screen digitizing from the hard copy of soil maps, digital elevation map (DEM) in order to generate the slope map was made by the digital map (DM) produced by National Geographic Information Institute (NGII). Almost all model parameters were generated to the multiple raster data layers, and the map calculation was made by the raster based GIS software, IL WIS which was developed by ITC, the Netherlands. Model results show that the annual soil loss rates are 5.2, 18.4, 30.3, 58.2 and 60.2 ton/ha/year in forest, paddy fields, built-up area, bare soil, and upland fields respectively. The estimated rates seemed to be high under the normal climatic conditions because of exaggerated land slopes due to DEM generation using 100 m contour interval. However, the results were worthwhile to estimate soil loss in hilly areas and the more precise result could be expected when the more accurate slope data is available.