• Environmental Engineering Research
• /
• 제23권4호
• /
• pp.462-473
• /
• 2018

The uranium ore residues from the legacies of past uranium mining and milling activities that resulted from the less stringent environmental standards along with the uranium residues from the existing nuclear power plants continue to be a cause of concern as the final uranium residues are not made safe from radiological and general safety point of view. The deposition of uranium in ponds increases the risk of groundwater getting contaminated as these residues essentially leach through the upper unsaturated geological formation. In this context, a numerical model has been developed in order to forecast the $^{238}U$ and its progenies concentration in an unsaturated soil. The developed numerical model is implemented in a hypothetical uranium tailing pond consisting of sandy soil and silty soil types. The numerical results show that the $^{238}U$ and its progenies are migrating up to the depth of 90 m and 800 m after 10 y in silty and sandy soil, respectively. Essentially, silt may reduce the risk of contamination in the groundwater for longer time span and at the deeper depths. In general, a coupled effect of sorption and hydro-geological parameters (soil type, moisture context and hydraulic conductivity) decides the resultant uranium transport in subsurface environment.

• Advances in Computational Design
• /
• 제2권2호
• /
• pp.121-131
• /
• 2017

Collapse was one of the typical common geological hazards during the construction of tunnels. The risk assessment of collapse was an effective way to ensure the safety of tunnels. We established a prediction model of collapse based on Bayesian Network. 76 large or medium collapses in China were analyzed. The variable set and range of the model were determined according to the statistics. A collapse prediction software was developed and its veracity was also evaluated. At last the software was used to predict tunnel collapses. It effectively evaded the disaster. Establishing the platform can be subsequent perfect. The platform can also be applied to the risk assessment of other tunnel engineering.

• The Journal of Engineering Geology
• /
• 제29권4호
• /
• pp.367-381
• /
• 2019

We investigate pore pressure conditions and reservoir compaction associated with oil and gas production using 3 different permeability models, which are all based on one-dimensional radial flow diffusion model, but differ in considering permeability evolution during production. Model 1 assumes the most simplistic constant and invariable permeability regardless of production; Model 2 considers permeability reduction associated with reservoir compaction only due to pore pressure drawdown during production; Model 3 also considers permeability reduction but due to the effects of both pore pressure drawdown and coupled pore pressure-stress process. We first derive a unified stress-permeability relation that can be used for various sandstones. We then apply this equation to calculate pore pressure and permeability changes in the reservoir due to fluid extraction using the three permeability models. All the three models yield pore pressure profiles in the form of pressure funnel with different amounts of drawdown. Model 1, assuming constant permeability, obviously predicts the least amount of drawdown with pore pressure condition highest among the three models investigated. Model 2 estimates the largest amount of drawdown and lowest pore pressure condition. Model 3 shows slightly higher pore pressure condition than Model 2 because stress-pore pressure coupling process reduces the effective stress increase due to pore pressure depletion. We compare field data of production rate with the results of the three models. While models 1 and 2 respectively overestimates and underestimates the production rate, Model 3 estimates the field data fairly well. Our result affirms that coupling process between stress and pore pressure occurs during production, and that it is important to incorporate the coupling process in the permeability modeling, especially for tight reservoir having low permeability.

• Proceedings of the Korean Geotechical Society Conference
• /
• 한국지반공학회 2010년도 추계 학술발표회
• /
• pp.782-789
• /
• 2010

Landslides cause enormous economic losses and casualties. Korea has mountainous regions and heavy slopes in most parts of the land and has consistently built new roads and large-scale housing complexes according to its industrial and urban growth. As a result, the damage from landslides becomes greater every year. In this study, performed a GIS-based landslide hazard analysis by SINMAP(Stability Index MAPping) model in Gyeonggi Icheon area coupling with geomorphological and geological data. SINMAP model has its theoretical basis in the infinite plane slope stability model with wetness obtained from a topographically based steady state model of hydrology. To Gyeonggi Icheon area landslides hazards evaluated, these SINMAP model were analysed results while simultaneously referring to the stability index map, where lines distinguish the zones categorized into the different stability classes and a table giving summary statistics.

• Nuclear Engineering and Technology
• /
• 제53권3호
• /
• pp.894-900
• /
• 2021

This paper examines sorption of Pd(II) onto illite, MX-80 bentonite, and Queenston shale in Na-Ca-Cl solutions of varying ionic strength (IS) from 0.01 to 6.0 mol/L (M) and pH_c ranging from 3 to 9 under atmospheric conditions. A 2-site protolysis non-electrostatic surface complexation and cation exchange model was applied to the Pd sorption onto illite and MX-80 using PHREEQC, and the model results were compared to the experimental ones obtained in this work. Surface complexation and cation exchange constants were estimated for both illite and MX-80 through the optimization process to bring the predicted distribution coefficients from the model into alignment with the experimentally derived values. These optimized surface complexation constants were compared to existing linear free energy relationships (LFER).

• Geomechanics and Engineering
• /
• 제28권4호
• /
• pp.359-374
• /
• 2022

Geology conditions are crucial in decision-making during the planning and design phase of a tunnel project. Estimation of the geology conditions of road tunnels is subject to significant uncertainties. In this work, the effectiveness of a novel regression method in estimating geological or geotechnical parameters of road tunnel projects was explored. This method, called Gaussian process regression (GPR), formulates the learning of the regressor within a Bayesian framework. The GPR model was trained with data of old tunnel projects. To verify its feasibility, the GPR technique was applied to a road tunnel to predict the state of three geological/geomechanical parameters of Rock Mass Rating (RMR), Rock Structure Rating (RSR) and Q-value. Finally, in order to validate the GPR approach, the forecasted results were compared to the field-observed results. From this comparison, it was concluded that, the GPR is presented very good predictions. The R-squared values between the predicted results of the GPR vs. field-observed results for the RMR, RSR and Q-value were obtained equal to 0.8581, 0.8148 and 0.8788, respectively.

• The Journal of Engineering Geology
• /
• 제20권4호
• /
• pp.359-370
• /
• 2010

$CO_2$ leakage from a geological formation utilized for $CO_2$ storage could result in failure of the facility and threaten the environment, as well as human safety and health. A reactive transport model of a $CO_2-H_2O$-cement reaction was constructed to understand chemical changes in the case of $CO_2$ leakage through a cement crack in an injection well, which is the most probable leakage pathway during geological storage. The model results showed the dissolution of portlandite and CSH (calcium silicate hydrate) within the cement paste, and the precipitation of secondary CSH and calcite as the $CO_2$ plume migrated along the crack. Calcite occupied most of the crack after 3 year of reaction, which could be maintained until 30 years after crack development. The present results could be applied in the development of technology to prevent $CO_2$ leakage and to enhance the integrity of wells constructed for $CO_2$ geological storage.

• Tunnel and Underground Space
• /
• 제22권6호
• /
• pp.412-428
• /
• 2012

To achieve mine development, a large amount of data concerned with the geological structure and the ore body had to be investigated and collected through geological survey, drilling and geophysical explorations. In most previous cases, however, the data were usually analyzed two dimensionally and those results showed some limits because of their 2D presentation. Those 2D maps such as geological plane sections or longitudinal sections cause lots of difficulties in understanding the complex geological structure or the feature of ore body in a spatial way. In this study, research area was set on the abandoned Yangyang iron mine in Korea and the Sugaeng ore body within the mine was selected as the research target to design a mine haulage system for reopening. A 3D mine model of this area was tried to be constructed using a 3D modelling software, GEMS. An accurate 3D model including the ore body, the geological structure, the old underground mine drifts and the new mine drifts was constructed under the purpose of reopening of the abandoned iron mine. Especially, mine design for trackless haulage system was conducted. New inclines and drifts were planned and modelled 3 dimensionally considering the utilization of old drifts and shaft. In addition to the 3D modelling, geostatistical technique was adopted to generate a spatial distribution of the ore grade and the rock physical properties. 3D model would be able to contribute in solving problems such as evaluating ore reserves, planning the mine development and additional explorations and changing the development plans, etc.

• 한국지구물리탐사학회:학술대회논문집
• /
• 한국지구물리탐사학회 2007년도 공동학술대회 논문집
• /
• pp.42-47
• /
• 2007

We have studied feasibility of the geostatistical approach to enhance the result of analysis of the sparsely obtained MT(Magnetotelluric) data by combining with gravity data. We have attempted to use geostatistics for integrating the MT data along with gravity data. To evaluate the feasibility of this approach, we have studied about interrelation between geological boundary and density distribution, and corrected density distribution for conversion to more sensitive to geological boundary by minimization of difference between z-directional variogram values of resistivity distribution obtained MT inversion and density distributions. Then, this method has been tested on model and field data. In model test, the results obtained were good agreement with real model. And in a real field data, the result of analysis demonstrate convincingly that our geostatistical approach is effective.

• Computers and Concrete
• /
• 제31권2호
• /
• pp.85-95
• /
• 2023

Natural pozzolans are used as additives in cement to develop more durable and high-performance concrete. Pozzolanic activity index (PAI) is important for assessing the performance of a pozzolan as a binding material and has an important effect on the compressive strength, permeability, and chemical durability of concrete mixtures. However, the determining of the 28 days (short term) and 90 days (long term) PAI of concrete mixtures is a time-consuming process. In this study, to reduce extensive experimental work, it is aimed to predict the short term and long term PAIs as a function of the chemical compositions of various natural pozzolans. For this purpose, the chemical compositions of various natural pozzolans from Central Anatolia were determined with X-ray fluorescence spectroscopy. The mortar samples were prepared with the natural pozzolans and then, the short term and the long term PAIs were calculated based on compressive strength method. The effect of the natural pozzolans' chemical compositions on the short term and the long term PAIs were evaluated and the PAIs were predicted by using multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) model. The prediction model results show that both reactive SiO₂ and SiO₂+Al₂O₃+Fe₂O₃ contents are the most effective parameters on PAI. According to the performance of prediction models determined with metrics such as root mean squared error (RMSE) and coefficient of correlation (R²), ANFIS models are more feasible than the multiple regression model in predicting the 28 days and 90 days pozzolanic activity. Estimation of PAIs based on the chemical component of natural pozzolana with high-performance prediction models is going to make an important contribution to material engineering applications in terms of selection of favorable natural pozzolana and saving time from tedious test processes.