• Geomechanics and Engineering
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• v.19 no.2
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• pp.127-139
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• 2019

Available methods to determine the ultimate bearing capacity of shallow foundations may not be accurate enough owing to the complicated failure mechanism and diversity of the underlying soils. Accordingly, applying new methods of artificial intelligence can improve the prediction of the ultimate bearing capacity. The M5' model tree and the genetic programming are two robust artificial intelligence methods used for prediction purposes. The model tree is able to categorize the data and present linear models while genetic programming can give nonlinear models. In this study, a combination of these methods, called the M5'-GP approach, is employed to predict the ultimate bearing capacity of the shallow foundations, so that the advantages of both methods are exploited, simultaneously. Factors governing the bearing capacity of the shallow foundations, including width of the foundation (B), embedment depth of the foundation (D), length of the foundation (L), effective unit weight of the soil (${\gamma}$) and internal friction angle of the soil (${\varphi}$) are considered for modeling. To develop the new model, experimental data of large and small-scale tests were collected from the literature. Evaluation of the new model by statistical indices reveals its better performance in contrast to both traditional and recent approaches. Moreover, sensitivity analysis of the proposed model indicates the significance of various predictors. Additionally, it is inferred that the new model compares favorably with different models presented by various researchers based on a comprehensive ranking system.

• Geomechanics and Engineering
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• v.37 no.2
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• pp.97-107
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• 2024

The accurate prediction of grouting upward diffusion height is crucial for estimating the bearing capacity of tip-grouted piles. Borehole construction during the installation of bored piles induces soil unloading, resulting in both radial stress loss in the surrounding soil and an impact on grouting fluid diffusion. In this study, a modified model is developed for predicting grout diffusion height. This model incorporates the classical rheological equation of power-law cement grout and the cavity reverse expansion model to account for different degrees of unloading. A series of single-pile tip grouting and static load tests are conducted with varying initial grouting pressures. The test results demonstrate a significant effect of vertical grout diffusion on improving pile lateral friction resistance and bearing capacity. Increasing the grouting pressure leads to an increase in the vertical height of the grout. A comparison between the predicted values using the proposed model and the actual measured results reveals a model error ranging from -12.3% to 8.0%. Parametric analysis shows that grout diffusion height increases with an increase in the degree of unloading, with a more pronounced effect observed at higher grouting pressures. Two case studies are presented to verify the applicability of the proposed model. Field measurements of grout diffusion height correspond to unloading ratios of 0.68 and 0.71, respectively, as predicted by the model. Neglecting the unloading effect would result in a conservative estimate.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.2
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• pp.59-63
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• 2008

The surface runoff is one of the important components for the surface water balance. However, most Land Surface Models(LSMs), coupled to climate models at a large scale for the prediction and prevention of disasters caused by climate changes, simplistically estimate surface runoff from the soil water budget. Ignoring the role of surface flow depth on the infiltration rate causes errors in both surface and subsurface flow calculations. Therefore, for the comprehensive terrestrial water and energy cycle predictions in LSMs, a conjunctive surface-subsurface flow model at a large scale is developed by coupling a 1-D diffusion wave model for surface flow with the 3-D Volume Averaged Soil-moisture Transport(VAST) model for subsurface flow. This paper describes the new conjunctive surface-subsurface flow formulation developed for improvement of the prediction of surface runoff and spatial distribution of soil water by topography, along with basic schemes related to the terrestrial hydrologic system in Common Land Model(CLM), one of the state-of-the-art LSMs.

• Journal of the Korean Geotechnical Society
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• v.16 no.4
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• pp.5-16
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• 2000

THe purpose of this paper is to suggest the analytical method which can predict lateral nonlinear behavior in non-homogeneous soil using the coefficient of soil resistance and ultimate soil resistance. Those parameters are obtained through back analysis on the base of the results of a series of model tests.Analytical method of Chang is more or less difficult to predict nonlinear behavior in non-homogeneous sol. So, in this study, for the prediction of nonlinear behavior the compositive analytical method which apply the p - y curve to Chang model is suggested. Also, the program is developed to predict nonlinear behavior using the compositive analytical method and it can be used to calculated the deflection, bending moment and soil reaction with DFM in non-homogeneous soil. To establish applicability of the suggested analytical method, the results of model tests and field tests and Pentagon2D finite element program are compared with those of the compositive analytical method. In the analysis values of the coefficient of soil reaction and ultimate soil resistance are also applied to the case of non-homogeneous soil. Lateral defection calculated using the compositive analytical method has been found to be in good agreement with values measured in field and model load tests.

• Korean Journal of Agricultural Science
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• v.48 no.4
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• pp.891-897
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• 2021

There is a need for a technology that can quickly and accurately analyze soil carbon contents. Existing soil carbon analysis methods are cumbersome in terms of professional manpower requirements, time, and cost. It is against this background that the present study leverages the soil physical properties of color and water content levels to develop a model capable of predicting the carbon content of soil sample. To predict the total carbon content of soil, the RGB values, water content of the soil, and lux levels were analyzed and used as statistical data. However, when R, G, and B with high correlations were all included in a multiple regression analysis as independent variables, a high level of multicollinearity was noted and G was thus excluded from the model. The estimates showed that the estimation coefficients for all independent variables were statistically significant at a significance level of 1%. The elastic values of R and B for the soil carbon content, which are of major interest in this study, were -2.90 and 1.47, respectively, showing that a 1% increase in the R value was correlated with a 2.90% decrease in the carbon content, whereas a 1% increase in the B value tallied with a 1.47% increase in the carbon content. Coefficient of determination (R²), root mean square error (RMSE), and mean absolute percentage error (MAPE) methods were used for regression verification, and calibration samples showed higher accuracy than the validation samples in terms of R² and MAPE.

• Journal of Korean Society of Forest Science
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• v.103 no.1
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• pp.98-104
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• 2014

This study aims to predict the amount of soil loss from Mt. Palgong's small basin, by using influence factors derived from related models, including RUSLE and MUSLE models, and verify the validity of the model through a comparative analysis of the predicted values and measured values, and the results are as follows: The amount of soil loss were greatly affected by LS factor. In comparison with the measured value of the amount of total soil loss, the predicted values by the two models (RUSLE and MUSLE), appeared to be higher than those of the measured soil loss. Predicted values by RUSLE were closer to values of measured soil loss than those of MUSLE. However, coefficient of variation of MUSLE were lower, but two model's coefficient of variation in similar partial patterns in the prediction of soil loss. RUSLE and MUSLE, prediction soil loss models, proved to be appropriate for use in small mountainous basin. To improve accuracy of prediction of soil loss models, more effort should be directed to collect more data on rainfall-runoff interaction and continuous studies to find more detailed influence factors to be used in soil loss model such as RUSLE and MUSLE.

• Journal of Soil and Groundwater Environment
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• v.17 no.5
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• pp.56-67
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• 2012

In the present study, an enhanced subsurface prediction algorithm based on a non-parametric geostatistical model and a history matching technique through Gibbs sampler is developed and the iterative prediction improvement procedure is proposed. The developed model is applied to a simple two-dimensional synthetic case where domain is composed of three different hydrogeologic media with $500m{\times}40m$ scale. In the application, it is assumed that there are 4 independent pumping tests performed at different vertical interval and the history curves are acquired through numerical modeling. With two hypothetical borehole information and pumping test data, the proposed prediction model is applied iteratively and continuous improvements of the predictions with reduced uncertainties of the media distribution are observed. From the results and the qualitative/quantitative analysis, it is concluded that the proposed model is good for the subsurface prediction improvements where the history data is available as a supportive information. Once the proposed model be a matured technique, it is believed that the model can be applied to many groundwater, geothermal, gas and oil problems with conventional fluid flow simulators. However, the overall development is still in its preliminary step and further considerations needs to be incorporated to be a viable and practical prediction technique including multi-dimensional verifications, global optimization, etc. which have not been resolved in the present study.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.3
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• pp.163-172
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• 1993

A probabilistic approach for evaluation of prediction of the strains using Lade's single surface constitutive model was employed, based on first-order approximate mean and variance. Several experiments such as isotropic compression and drained triaxial compression tests were conducted to examine the variabilities of soil parameters for Lade's model. By taking into account the results of the experimental data such as mean values and standard deviations of soil parameter's, a new probabilistic approach, which explains the uncertainty of computed strains, is applied. The magnitude of the COV for each parameter and the correlation coefficient between the two parameters can be effectively used for reducing the number of the parameters for the model. It is concluded that Lade's single surface constitutive model is surperior model for the prediction of the strain, because the COV of strains is under the "0.51".

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.8 no.1
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• pp.21-31
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• 2012

This paper reviewed and evaluated some of the commonly used prediction models for thermal conductivity of soils with the experimental data. Semi-theoretical models for two-component materials were found inappropriate to estimate the thermal conductivity of dry state soils. It came out that the model developed by Cote and Konrad gave the best overall prediction results for unsaturated soils available in the literature. However, it still needs to be improved to cover a wider range of soil types and degrees of saturation. In the present study, parametric analysis is also conducted to investigate the effect of soil type and moisture content on the horizontal ground heat exchanger design. The analysis shows that horizontal ground heat exchanger pipe length is reduced with the increase of soil thermal conductivity and water content. The calculation results also show that horizontal ground heat exchanger size can be reduced to a certain extent by using backfilling material with a higher thermal conductivity of solid particles.