• Geomechanics and Engineering
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• v.10 no.3
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• pp.269-284
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• 2016

Simplified techniques based on in situ testing methods are commonly used to assess seismic liquefaction potential. Many of these simplified methods were developed by analyzing liquefaction case histories from which the liquefaction boundary (limit state) separating two categories (the occurrence or non-occurrence of liquefaction) is determined. As the liquefaction classification problem is highly nonlinear in nature, it is difficult to develop a comprehensive model using conventional modeling techniques that take into consideration all the independent variables, such as the seismic and soil properties. In this study, a modification of the Multivariate Adaptive Regression Splines (MARS) approach based on Logistic Regression (LR) LR_MARS is used to evaluate seismic liquefaction potential based on actual field records. Three different LR_MARS models were used to analyze three different field liquefaction databases and the results are compared with the neural network approaches. The developed spline functions and the limit state functions obtained reveal that the LR_MARS models can capture and describe the intrinsic, complex relationship between seismic parameters, soil parameters, and the liquefaction potential without having to make any assumptions about the underlying relationship between the various variables. Considering its computational efficiency, simplicity of interpretation, predictive accuracy, its data-driven and adaptive nature and its ability to map the interaction between variables, the use of LR_MARS model in assessing seismic liquefaction potential is promising.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.09a
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• pp.660-670
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• 2009

Neighboring construction becomes mainstream of Ground excavation in downtown area. This causes the displacement, deformation, stress condition, etc of the ground surroundings. Therefore Neighboring construction have an effect on Neighboring structure. All these years a lot of Neighboring construction carried out, and the accumulation of technology also get accomplished. But earth retaining structure collapse happens yet. Types of earth retaining structure collapse are 12. 1. Failure of anchor or strut system, 2. Insufficiency of penetration, 3. H-pile Failure on excessive bending moment, 4. Slope sliding failure, 5. Excessive settlement of the back, 6. Deflection of H-pile, 7. Joint failure of coupled H-pile, 8. Rock failure when H-pile penetration is rock mass, 9. Plane arrangement of support systems are mechanically weak, 10. Boiling, 11. Heaving, 12. Over excavation. But field collapses are difficult for classification according to the type, because collapse process are complex with various types. When we consider the 12 collapse field, insufficient recognition of ground condition is 4 case. Thorough construction management prevents from fault construction. For limitations of soil survey, It is difficult to estimate ground condition exactly. Therefore, it should estimate the safety of earth retaining system, plan for necessary reinforcement, according to measurement and observation continuously.

• Journal of Life Science
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• v.10 no.2
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• pp.169-176
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• 2000

At Koppl area, undamaged natural habitat of sundew, I have analysed interspecific affinities and community composition through mathematical method and important environmental factors. Interspecific affinities of the species with frequency of more than 5% in vegetation table were analysed through chi-square test and showed obvious group of Drosera rotundifolia, Vaccinium uliginosum, Calluna vulgaris, Eriophorum angustifolium and 21 species. The result of ordination anlysis using DECORANA of VESPAN II showed eigenvalue of 0.6047 for axis I, 0.2024 for axis II and 0.0763 for axis Ⅲ. And it divided into 4 groups of quadrat number 1-5 for Sphagnum squarrosum-community, 6-10 for Crepis paludosa-community. 11-25 for Carex panicea-community and 26-35 for Scorpidium scorpioides-community. By the classification using TWINSPAN, the 7 areas divided into 2 groups of 1-10 and 11-35 at first level of division with high eigenvalue of 0.588 and indicator was Sphagnum squarrosum. At second level of division it divided into 4 groups as the results of DECORANA with eigenvalues of 0.268 and 0.423 and indicators were Pinguicula vulgaris and Scorpidium scorpioides. Microclimatic environment of studied area was low in temperature and high in humidity and soil environment showed high in field moisture capacity, acid, high organic matter content, low NO3 and K2O content, compare to normal soil, and high ground water level.

• Geomechanics and Engineering
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• v.18 no.5
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• pp.493-502
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• 2019

Internal stability is an important safety issue for levees, embankments, and other earthen structures. Since a large part of the world's population lives near oceans, lakes and rivers, floods resulting from breaching of dams can lead to devastating disasters with tremendous loss of life and property, especially in densely populated areas. There are some main factors that affect the internal stability of dams, levees and other earthen structures, such as the erodibility of the soil, the water velocity inside the soil mass and the geometry of the earthen structure, etc. Thus, the mechanism of internal erosion and stability of soils is very complicated and it is vital to investigate the assessment methods of internal stability of soils in embankment dams and their foundations. This paper presents an improved support vector machine (SVM) model to predict the internal stability of soils. The grid search algorithm (GSA) is employed to find the optimal parameters of SVM firstly, and then the cross - validation (CV) method is employed to estimate the classification accuracy of the GSA-SVM model. Two examples of internal stability of soils are presented to validate the predictive capability of the proposed GSA-SVM model. In addition to verify the effectiveness of the proposed GSA-SVM model, the predictions from the proposed GSA-SVM model were compared with those from the traditional back propagation neural network (BPNN) model. The results showed that the proposed GSA-SVM model is a feasible and efficient tool for assessing the internal stability of soils with high accuracy.

• Membrane and Water Treatment
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• v.10 no.4
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• pp.313-320
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• 2019

Increasing the concentration of nitrate ions in the soil solution and then leaching it to underground aquifers increases the concentration of nitrate in the water, and can cause many health and ecological problems. This study was conducted to evaluate the vulnerability of Meymeh aquifer to nitrate pollution. In this research, sampling of 10 wells was performed according to standard sampling principles and analyzed in the laboratory by spectrophotometric method, then; the nitrate concentration zonation map was drawn by using intermediate models. In the drastic model, the effective parameters for assessing the vulnerability of groundwater aquifers, including the depth of ground water, pure feeding, aquifer environment, soil type, topography slope, non-saturated area and hydraulic conductivity. Which were prepared in the form of seven layers in the ARC GIS software, and by weighting and ranking and integrating these seven layers, the final map of groundwater vulnerability to contamination was prepared. Drastic index estimated for the region between 75-128. For verification of the model, nitrate concentration data in groundwater of the region were used, which showed a relative correlation between the concentration of nitrate and the prepared version of the model. A combination of two vulnerability map and nitrate concentration zonation was provided a qualitative aquifer classification map. According to this map, most of the study areas are within safe and low risk, and only a small portion of the Meymeh Aquifer, which has a nitrate concentration of more than 50 mg / L in groundwater, is classified in a hazardous area.

• Journal of The Korean Society of Grassland and Forage Science
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• v.42 no.3
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• pp.137-145
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• 2022

Aluminum (Al) is one of the major factors adversely affects crop growth and productivity in acidic soils. In this study, the effect of Al on plants in soil was investigated by comparing the protein expression profiles of alfalfa roots exposed to Al stress treatment. Two-week-old alfalfa seedlings were exposed to Al stress treatment at pH 4.0. Total protein was extracted from alfalfa root tissue and analyzed by two-dimensional gel electrophoresis combined with MALDI-TOF/TOF mass spectrometry. A total of 45 proteins differentially expressed in Al stress-treated alfalfa root tissues were identified, of which 28 were up-regulated and 17 were down-regulated. Of the differentially expressed proteins, 7 representative proteins were further confirmed for transcript accumulation by RT-PCR analysis. The identified proteins were involved in several functional categories including disease/defense (24%), energy (22%), protein destination (9%), metabolism (7%), transcription (5%), secondary metabolism (4%), and ambiguous classification (29%). The identification of key candidate genes induced by Al in alfalfa roots will be useful to elucidate the molecular mechanisms of Al stress tolerance in alfalfa plants.

• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.4
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• pp.205-213
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• 2024

In the seismic evaluation of underground utility tunnels, selecting an analytical method is critical to estimating reasonable seismic responses. In simplified pseudo-static analysis methods widely applied to typical seismic design and evaluation of underground tunnels in practice, it is essential to check whether the methods provide valid results for cut-and-cover tunnels buried in shallow to medium depth. The differences between the two simplified pseudo-static methods are discussed in this study, and the analysis results are compared to those obtained from FLAC models. In addition to the analysis methods, seismic site classification, overburden soil depth, and sectional configuration are considered variables to examine their effects on the seismic response of underground utility tunnels. Based on the analysis results, the characteristics derived from the concepts and details of each simplified model are discussed. Also, general observations are made for the application of simplified analysis methods.

• Korean Journal of Environment and Ecology
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• v.12 no.2
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• pp.119-130
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• 1998

The purposes of this study were to investigate vegetation structure and to present management plan of mountain type green space in Kangwhado, Inchon. The actual vegetation in survey sites(11,331ha) was divided into 19 community types. It was consisted of secondary forest(92.32%) which was Quercus acutissima, Pinus densiflora-Q. acutissima, and Q. mongolica community so on. Artificial planting forest area, such as Robinia pseudoacacia and Pinus rigida forest and others, was 5.40%(612ha) and it was less than cases in other cities in the Metropolitan area. According to the classification by TWINSPAN, 57 survey plots were divided into seven community types; P rigida(community A), Q. acutissima(community B) P. densiflora-Q. acutissima(community C), Q. acutissima-P. densiflora (community D), P. densiflora-Carpinus laxiflora-Q. serrata-Q. acutissima(community E), Q. serrata-Q. mongolica(community F), and Zelkova serrata-Acer mono(community G). From this result, ecological succession trend of vegetation in this area seems to be change from P. densiflora forest through Q. acutissima forest to Q. mongolica, Q. serrata, and C. laxiflora forest. It was similar to the ordinary successional trend of temperate deciduous forest in middle area, Korea. In addition, this study area was showed acid soil(pH 4.17). Therefore, there is a need for managing the soil environment for effective vegetation management.

• Korean Journal of Soil Science and Fertilizer
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• v.37 no.4
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• pp.199-206
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• 2004

Factors of universal soil loss equation, USLE, and its revised version, RUSLE for Korean soils were reevaluated to estimate the national scale of soil loss based on digital soil maps. Rainfall erosivity factor, R, of 158 locations of cities and counties were spacially interpolated by the inverse distance weight method. Soil erodibility factor, K, of 1321 soil phases of 390 soil series were calculated using the data of soil survey and agri-environmental quality monitoring. Topographic factor, LS, was estimated using soil map of 1:25,000 scale with soil phase and land use type. Cover management factor, C, of major crops and support practice factor, P, were summarized by analyzing the data of lysimeter and field experiments for 27 years (1975-2001) in the National Institute of Agricultural Science and Technology. R factor varied between 2322 and 6408 MJ mm $ha^{-1}$ $yr^{-1}$ $hr^{-1}$ and the average value was 4276 MJ mm $ha^{-1}$ $yr^{-1}$ $hr^{-1}$. The average K value was evaluated as 0.027 MT hr $MJ^{-1}$ $mm^{-1}$. The highest K factor was found in paddy rice fields, 0.034 MT hr $MJ^{-1}$ $mm^{-1}$, and K factors in upland fields, grassland, and forest were 0.026, 0.019, and 0.020 MT hr $MJ^{-1}$ $mm^{-1}$, respectively. C factors of upland crops ranged from 0.06 to 0.45 and that of grassland was 0.003. P factor varied between 0.01 and 0.85.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1269-1276
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• 2023

Crop estimation is essential for the multinational meal and powerful demand due to its numerous aspects like soil, rain, climate, atmosphere, and their relations. The consequence of climate shift impacts the farming yield products. We operate the dataset with temperature, rainfall, humidity, etc. The current research focuses on feature selection with multifarious classifiers to assist farmers and agriculturalists. The crop yield estimation utilizing the feature selection approach is 96% accuracy. Feature selection affects a machine learning model's performance. Additionally, the performance of the current graph classifier accepts 81.5%. Eventually, the random forest regressor without feature selections owns 78% accuracy and the decision tree regressor without feature selections retains 67% accuracy. Our research merit is to reveal the experimental results of with and without feature selection significance for the proposed ten algorithms. These findings support learners and students in choosing the appropriate models for crop classification studies.