• 한국지반신소재학회논문집
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• 제16권4호
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• pp.129-137
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• 2017

본 연구에서는 Decision Tree model을 기반으로 개발된 산사태 예측프로그램 SHAPP ver 1.0을 이용하여 전라남도 무안군 ${\bigcirc}{\bigcirc}$지역의 호남선 철도 주변에 대한 산사태 발생예측을 실시하였다. 이를 위하여 먼저 대상지역의 총 8개소에서 토층시료를 채취하고, 이에 대한 토질시험을 실시하였다. 대상지역에 대한 토질시험결과를 토대로 투수계수와 간극비에 대한 주제도를 작성하고 수치지형도를 이용하여 지형의 경사분석을 실시하였다. 이를 이용하여 산사태 발생예측을 실시한 결과 총 15,552개의 해석셀 가운데 435개의 셀에서 산사태가 발생될 것으로 예측되었다. 이때 해석셀의 크기는 $10m{\times}10m$이므로 산사태 발생예상 면적은 $43,500m^2$으로 나타났다.

• Journal of Biosystems Engineering
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• 제23권3호
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• pp.219-228
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• 1998

This study was conducted to develop the mathematical model and computer simulation program(TPPMTV98) for predicting the tractive performance of tracked vehicles. It takes into account major design parameters of the vehicle as well as the pressure-sinkage and shearing characteristics of the soil, and the response of the soil to repetitive loading. Structural analysis and numerical iterative method were used for the derivation of mathematical model. The simulatiom model TPPMTV98 can predict the ground pressure distribution and the shear stress under a track, the motion resistance, the tractive effort and the drawbar pull of the vehicles as functions of slip. Predicted tractive performance results obtained by the simulation model were validated by comparing the results firm the Wong's model, the offectiveness of Wong's model validated by many of the experiment. It was found that there is fairy close agreement between the prediction by TPPMTV98 and the results from Wong's model. The computer simulation model TPPMTV98 can be used for the optimization of tracked vehicle design or for the evaluation of vehicle candidates for a given mission and environment.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2003년도 추계 학술발표회논문집
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• pp.107-114
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• 2003

There are many problems in the prediction of dynamic behaviors of saturated soils because undrained excess pore water pressure builds up and then the strain softening behavior is occurred simultaneously. A few analytical constitutive models based on the effective stress concept have been proposed but most models hardly predict the excess pore water pressure and strain softening behaviors correctly In this study, the disturbed state concept (DSC) model proposed by Dr, Desai was modified to predict the saturated soil behaviors under the dynamic loads. Also, back-prediction program was developed for verification of modified DSC model. Cyclic triaxial tests were carried out to determine DSC parameters and test result was compared with the result of back-prediction. Through this research, it is proved that the proposed model based on the modified disturbed state concept can predict the realistic soil dynamic characteristics such as stress degradation and strain softening behavior according to dynamic process of excess pore water pressure.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2009년도 세계 도시지반공학 심포지엄
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• pp.233-242
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• 2009

Based on the data obtained from field investigation and soil testing to slope hazards occurrence section and non-occurrence section in crystalline rocks like gneiss, granite, and so on, a prediction model was developed by the use of a decision tree model. The classification standard of the selected prediction model is composed of the slope angle, the coefficient of permeability and the void ratio in the order. The computer program, SHAPP ver. 1.0 for prediction of slope hazards around an important national facilities using GIS technique and the developed model. To prove the developed prediction model and the computer program, the field data surveyed from Jumunjin, Gangneung city were compared with the prediction result in the same site. As the result of comparison, the real occurrence location of slope hazards was similar to the predicted section. Through the continuous study, the accuracy about prediction result of slope hazards will be upgraded and the computer program will be commonly used in practical.

• Geomechanics and Engineering
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• 제15권2호
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• pp.783-791
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• 2018

In consideration of the mesoscopic structure of soil-rock mixtures in which the rock aggregates are wrapped by soil at normal temperatures, a two-layer embedded model of single-inclusion composite material was built to calculate the shear modulus of soil-rock mixtures. At a freezing temperature, an interface ice interlayer was placed between the soil and rock interface in the mesoscopic structure of the soil-rock mixtures. Considering that, a three-layer embedded model of double-inclusion composite materials and a multi-step multiphase micromechanics model were then built to calculate the shear modulus of the frozen soil-rock mixtures. Given the effect of pore structure of soil-rock mixtures at normal temperatures, its shear modulus was also calculated by using of the three-layer embedded model. Experimental comparison showed that compared with the two-layer embedded model, the effect predicted by the three-layer embedded model of the soil-rock mixtures was better. The shear modulus of the soil-rock mixtures gradually increased with the increase in rock regardless of temperature, and the increment rate of the shear modulus increased rapidly particularly when the rock content ranged from 50% to 70%. The shear modulus of the frozen soil-rock mixtures was nearly 3.7 times higher than that of the soil-rock mixtures at a normal temperature.

• International Journal of Naval Architecture and Ocean Engineering
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• 제8권2호
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• pp.177-187
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• 2016

To evaluate the tractive performance of tracked trencher on seafloor surface, a new shear stress-displacement empirical model was proposed for saturated soft-plastic soil (SSP model). To validate the SSP model, a test platform, where track segment shear test can be performed in seafloor soil simulacrum (bentonite water mixture), was built. Series shear tests were carried out. Test results indicate that the SSP model can describe the mechanical behavior of track segment with good approximation in seafloor soil simulacrum. Through analyzing the main external forces applied to seafloor tracked trencher during the uniform linear trenching process, a drawbar pull prediction model was deduced with the SSP model. A tracked walking mechanism of the seafloor tracked trencher prototype was built, and verification tests were carried out. Test results indicate that this prediction model was feasible and effective; moreover, from another side, this conclusion also proved that the SSP model was effective.

• 한국지반공학회논문집
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• 제21권1호
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• pp.15-27
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• 2005

이 논문에서는 앞서 수행된 국내의 화강풍화토의 실험 결과를 토대로 하여 화강풍화토의 함수특성곡선을 합리적으로 예측하기 위한 방법이 제안되었다. 이 방법은 인공신경망기법을 이용해서 Fredlund와 Xing의 함수특성곡선식의 계수들을 추정하도록 제안되었다. 이러한 계수들을 추정하기 위한 신경망 모델의 입력 자료로는 실험결과에서 함수특성곡선의 계수에 큰 영향을 미치는 것으로 확인된 입도분포곡선, 다짐함수비, 그리고 간극비가 사용되었다. Fredlund 와 Xing의 함수특성곡선식의 계수를 구하기 위해 본 연구에서 제안된 신경망 모델은 신뢰성 있는 예측결과를 보였으며 그 예측결과의 정확도가 이전의 다른 방법들에 비해 높게 나타났다.

• 한국지하수토양환경학회지:지하수토양환경
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• 제19권4호
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• pp.23-30
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• 2014

Coastal aquifer in Muan, Jeonnam, has experienced heavy seawater intrusion caused by the extraction of a substantial amount of groundwater for the agricultural purpose throughout the year. It was observed that groundwater level dropped below sea level due to heavy pumping during a dry season, which could accelerate seawater intrusion. Therefore, water level needs to be monitored and managed to prevent further seawater intrusion. The purpose of this study is to develop the autoregressive-cross-regressive (ARCR) models that can predict the present or future groundwater level using its own previous values and pumping events. The ARCR model with pumping and water level data of the proceeding five hours (i.e., the model order of five) predicted groundwater level better than that of the model orders of ten and twenty. This was contrary to expectation that higher orders do increase the coefficient of determination ($R^2$) as a measure of the model's goodness. It was found that the ARCR model with order five was found to make a good prediction of next 48 hour groundwater levels after the start of pumping with $R^2$ higher than 0.9.

• 한국농공학회논문집
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• 제55권4호
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• pp.1-11
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• 2013

Unsaturated hydraulic conductivity function is one of key parameters to solve the flow phenomena in problems of landslide. Prediction models for hydraulic conductivity function related to soil-water retention curve equations in many geotechnical applications have been still used instead of direct measurement of the hydraulic conductivity function since prediction models from soil-water retention curve equations are attractive for their fast and easy use and low cost. However, many researchers found that prediction models for the hydraulic conductivity function can not predict the hydraulic conductivity exactly in comparison with experimental outputs. This research introduced an inverse analysis to evaluate the hydraulic conductivity function corresponding to experimental output from the flow pump system. Optimisation process was carried out to obtain the hydraulic conductivity function. This research showed that the inverse analysis with flow pump system was suitable to assess the hydraulic conductivity in unsaturated soil, and the prediction models for the hydraulic conductivity were led to the significant discrepancy from actual experimental outputs.

• Geomechanics and Engineering
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• 제33권1호
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• pp.1-9
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• 2023

The unconfined compression strength (UCS) of soils is commonly used either before or during the construction of geo-structures. In the pre-design stage, UCS as a mechanical property is obtained through a laboratory test that requires cumbersome procedures and high costs from in-situ sampling and sample preparation. As an alternative way, the empirical model established from limited testing cases is used to economically estimate the UCS. However, many parameters affecting the 1D soil compression response hinder employing the traditional statistical analysis. In this study, gene expression programming (GEP) is adopted to develop a prediction model of UCS with common affecting soil properties. A total of 79 undisturbed soil samples are collected, of which 54 samples are utilized for the generation of a predictive model and 25 samples are used to validate the proposed model. Experimental studies are conducted to measure the unconfined compression strength and basic soil index properties. A performance assessment of the prediction model is carried out using statistical checks including the correlation coefficient (R), the root mean square error (RMSE), the mean absolute error (MAE), the relatively squared error (RSE), and external criteria checks. The prediction model has achieved excellent accuracy with values of R, RMSE, MAE, and RSE of 0.98, 10.01, 7.94, and 0.03, respectively for the training data and 0.92, 19.82, 14.56, and 0.15, respectively for the testing data. From the sensitivity analysis and parametric study, the liquid limit and fine content are found to be the most sensitive parameters whereas the sand content is the least critical parameter.