• Journal of the Korean Geotechnical Society
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• v.34 no.7
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• pp.29-38
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• 2018

Three-dimensional continuum modeling of dynamic soil-pile-structure interaction embedded in a liquefiable sand was carried out. Finn model which can model liquefaction behavior using effective stress method was adopted to simulate development of pore water pressure according to shear deformation of soil directly in real time. Finn model was incorporated into Non-linear elastic, Mohr-Coulomb plastic model. Calibration of proposed modeling method was performed by comparing the results with those of the centrifuge tests performed by Wilson (1998). Excess pore pressure ratio, pile bending moment, pile head displacement-time history according to depth calculated by numerical analysis agreed reasonably well with the test results. Validation of the proposed modeling method was later performed using another test case, and good agreement between the computed and measured values was observed.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.10a
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• pp.358-365
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• 2002

In this paper the model tests have been conducted and the results were compared with those by the theoretical methods to study the behaviors of the piled raft. The size of model box is 2.2m${\times}$2m${\times}$2m. The raft is made of rigid steel plate and piles are made of steel pipes. Generally the bearing capacity of group piles is designed with only the pile capacities, which is Ignored the bearing capacity of raft. But the uncertainty of pile-raft-soil interaction leads to conservative design ignoring the bearing effects of raft. In the case of considering the bearing capacity of raft, the simple sum of bearing capacity of raft and that of each pile cannot be the bearing capacity of piled raft. Because the pile-raft-soil interaction affects the behavior of piled raft. Thus the effects of pile-raft-soil interaction are very important in the optimal design. In this paper, the behaviors of piled raft are studied through model tests of 2${\times}$2, 2${\times}$3, and 3${\times}$3 pile groups. The spacing between piles is changed in the model tests. And the behaviors of free standing and piled raft are also studied.

• Journal of Navigation and Port Research
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• v.36 no.5
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• pp.395-400
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• 2012

The stress-strain relationship of soil is dependent on a number of factors such as soil type, density, stress level and stress path. Th accurate stress-stain relationship can be predict using a constitutive model incorporated all influencing factors. In this study, an isotropic compression-expansion test and a series of drained conventional triaxial tests with several stress paths were performed on Baekma river sand to investigate parameters characteristics of Lade's single work hardening model depending on the stress path.. Based on test results, the parameters of yield function (h, ${\alpha}$) are not much influenced by stress level and stress path, the these parameters do affect a little bit of stress-strain behavior. The parameters h and ${\alpha}$ are closely related to failure criterion ${\eta}_1$, they can be replaced by failure criterion parament. We also observed that predicted values from the Lade's single hardening constitutive model are well matched with the observed data.

• Journal of the Korean Geotechnical Society
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• v.19 no.2
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• pp.247-254
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• 2003

While the interests in the environmental problem during the construction are increasing, the use of low noise-vibration auger-drilled pilling is increasing to solve noise and vibration problem in pilling. Therefore, in Korea, SIP (Soil-Cement Injected Precast Pile) method is mainly used as auger-drilled pilling. However, there is no proper design criteria compatible with the ground condition of Korea, so which is most wanted. To improve and supplement this situation, direct shear tests for the friction between SIP pile skin interface and soil were executed on various conditions. Through the analysis of test results, skin friction characteristics of SIP were investigated thoroughly Also, hyperbolic model parameter fomulas which describe the friction behavior and the new non-linear unit skin friction capacity model with SM, SC soil were suggested.

• Journal of the Korean Geotechnical Society
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• v.18 no.5
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• pp.123-132
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• 2002

The purpose of this study is to predict the shear behavior of the weathered mudstone soil using dynamic neural network which mimics the biological system of human brain. SNN and RNN, which are kinds of the dynamic neural network realizing continuously a pattern recognition as time goes by, are used to predict a nonlinear behavior of soil. After analysis, parameters which have an effect on learning and predicting of neural network, the teaming rate, momentum constant and the optimum neural network model are decided to be 0.5, 0.7, 8$\times$18$\times$2 in SU model and 0.3, 0.9, 8$\times$24$\times$2 in R model. The results of appling both networks showed that both networks predicted the shear behavior of soil in normally consolidated state well, but RNN model which is effective fir input data of irregular patterns predicted more efficiently than SNN model in case of the prediction in overconsolidated state.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.12
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• pp.5963-5973
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• 2011

Current design and analyzing methods about soil nailing structures, developed on the basis of results obtained from experiments in laboratory or in field and numerical analyses, have applied different interaction mechanisms between the reinforced nails and the surrounding ground, and different safety factors against failure have been obtained. They might be proper approaches if the assumptions about rigidity of nails and ground conditions are met with actual conditions occurred in field. Otherwise, they would result in designing on analyzing in inappropriate ways so that it is needed to evaluate the validity of them. Therefore, in this research using the Centrifugal Model Testing, numerical parameters experiments about soil nailing structures' behavior and failure mechanism were performed. In the numerical parameters experiments, transmuted nail's length, setting angle, nail's front panel, stiffness variously, and increased the level of gravity until wall model was destroyed. Based on experimental results, we compared the effect, failure mechanism caused from parameters changes. By reviewing and comparing centrifugal model test results and methods currently in use, verified validity of existing methods.

• International Journal of Highway Engineering
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• v.12 no.4
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• pp.147-155
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• 2010

A prediction model of resilient modulus($E_R$) was developed for recycled crushed-rock-soil mixtures. The evaluation of $E_R$, using the "orthodox" repeated loading tri-axial test, is not feasible for such a large-size gravelly material. An alternative method was proposed hereby using the subtle different modulus called nonlinear dynamic modulus. The prediction model was developed by utilizing in-situ measured shear modulus($G_{max}$) and its reduction curves of modeled materials using the large free-free resonant column test. A pilot evaluation of the model parameters was carried out for recycled crushed-rock-soil-mixture at a highway construction site near Gimcheon, Korea. The values of the model parameters($A_E,\;n_E,\;{\varepsilon}_r\;and\;{\alpha}$) were proposed as 9618, 0.47, 0.0135, and 0.8, respectively.

• Geomechanics and Engineering
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• v.36 no.3
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• pp.259-276
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• 2024

The undrained shear strength is widely acknowledged as a fundamental mechanical property of soil and is considered a critical engineering parameter. In recent years, researchers have employed various methodologies to evaluate the shear strength of soil under undrained conditions. These methods encompass both numerical analyses and empirical techniques, such as the cone penetration test (CPT), to gain insights into the properties and behavior of soil. However, several of these methods rely on correlation assumptions, which can lead to inconsistent accuracy and precision. The study involved the development of innovative methods using extreme gradient boosting (XGB) to predict the pile set-up component "A" based on two distinct data sets. The first data set includes average modified cone point bearing capacity (q_t), average wall friction (f_s), and effective vertical stress (σ_vo), while the second data set comprises plasticity index (PI), soil undrained shear cohesion (S_u), and the over consolidation ratio (OCR). These data sets were utilized to develop XGBoost-based methods for predicting the pile set-up component "A". To optimize the internal hyperparameters of the XGBoost model, four optimization algorithms were employed: Particle Swarm Optimization (PSO), Social Spider Optimization (SSO), Arithmetic Optimization Algorithm (AOA), and Sine Cosine Optimization Algorithm (SCOA). The results from the first data set indicate that the XGBoost model optimized using the Arithmetic Optimization Algorithm (XGB - AOA) achieved the highest accuracy, with R2 values of 0.9962 for the training part and 0.9807 for the testing part. The performance of the developed models was further evaluated using the RMSE, MAE, and VAF indices. The results revealed that the XGBoost model optimized using XGBoost - AOA outperformed other models in terms of accuracy, with RMSE, MAE, and VAF values of 0.0078, 0.0015, and 99.6189 for the training part and 0.0141, 0.0112, and 98.0394 for the testing part, respectively. These findings suggest that XGBoost - AOA is the most accurate model for predicting the pile set-up component.

• Journal of the Korean Society of Industry Convergence
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• v.20 no.5
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• pp.395-404
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• 2017

Cultivating soybeans in rice paddy field reduces labor costs and increases the yield. Soybeans, however, are highly susceptible to excessive soil water in paddy field. Controlled drainage system can adjust groundwater level (GWL) and control soil moisture content, resulting in improvement soil environments for optimum crop growth. The objective of this study was to fit the soybean growth data (canopy height and stem diameter) using Gompertz model and Logistic model at different GWL and validate those models. The soybean, Daewon cultivar, was grown on the lysimeters controlled GWL (20cm and 40cm). The soil textures were silt loam and sandy loam. The canopy height and stem diameter were measured from the 20th days after seeding until harvest. The Gompertz and Logistic models were fitted with the growth data and each growth rate and maximum growth value was estimated. At the canopy height, the $R_2$ and RMSE were 0.99 and 1.58 in Gompertz model and 0.99 and 1.33 in Logistic model, respectively. The large discrepancy was shown in full maturity stage (R8), where plants have shed substantial amount of leaves. Regardless of soil texture, the maximum growth values at 40cm GWL were greater than the value at 20cm GWL. The growth rates were larger at silt loam. At the stem diameter, the $R_2$ and RMSE were 0.96 and 0.27 in Gompertz model and 0.96 and 0.26 in Logistic model, respectively. Unlike the canopy height, the stem diameter in R8 stage didn't decrease significantly. At both GWLs, the maximum growth values and the growth rates at silt loam were all larger than the values at sandy loam. In conclusion, Gompertz model and Logistic model both well fit the canopy heights and stem diameters of soybeans. These growth models can provide invaluable information for the development of precision water management system.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.449-456
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• 2022

This study proposes a deep learning algorithm to predict crop yield using GIS (Geographic Information System) to extract soil properties from Soilgrids and soil suitability class maps. The proposed model modified the structure of a published CNN-RNN (Convolutional Neural Network-Recurrent Neural Network) based crop yield prediction model suitable for the domestic crop environment. The existing model has two characteristics. The first is that it replaces the original yield with the average yield of the year, and the second is that it trains the data of the predicted year. The new model uses the original field value to ensure accuracy, and the network structure has been improved so that it can train only with data prior to the year to be predicted. The proposed model predicted the yield per unit area of autumn cabbage for kimchi by region based on weather, soil, soil suitability classes, and yield data from 1980 to 2020. As a result of computing and predicting data for each of the four years from 2018 to 2021, the error amount for the test data set was about 10%, enabling accurate yield prediction, especially in regions with a large proportion of total yield. In addition, both the proposed model and the existing model show that the error gradually decreases as the number of years of training data increases, resulting in improved general-purpose performance as the number of training data increases.