• Geomechanics and Engineering
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• v.32 no.4
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• pp.375-385
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• 2023

There are many joint fissures distributed in the engineering rock mass. In the process of geological history, the underground rock mass undergoes strong geological processes, and undergoes complex geological processes such as fracture breeding, expansion, recementation, and re-expansion. In this paper, the damage-stick-slip process (DSSP), an analysis model used for rock mass failure slip, was established to examine the master control and time-dependent mechanical properties of the new and primary fractures of a multi-fractured rock mass under the action of stress loading. The experimental system for the recemented multi-fractured rock mass was developed to validate the above theory. First, a rock mass failure test was conducted. Then, the failure stress state was kept constant, and the fractured rock mass was grouted and cemented. A secondary loading was applied until the grouted mass reached the intended strength to investigate the bearing capacity of the recemented multi-fractured rock mass, and an acoustic emission (AE) system was used to monitor AE events and the update of damage energy. The results show that the initial fracture angle and direction had a significant effect on the re-failure process of the cement rock mass; Compared with the monitoring results of the acoustic emission (AE) measurements, the master control surface, key blocks and other control factors in the multi-fractured rock mass were obtained; The triangular shaped block in rock mass plays an important role in the stress and displacement change of multi-fracture rock mass and the long fissure and the fractures with close fracture tip are easier to activate, and the position where the longer fractures intersect with the smaller fractures is easier to generate new fractures. The results are of great significance to a multi-block structure, which affects the safety of underground coal mining.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.1
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• pp.13-24
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• 2021

Tunnel boring machine (TBM) is widely used for tunnel excavation in hard rock and soft ground. In the perspective of TBM-based tunneling, one of the main challenges is to drive the machine optimally according to varying geological conditions, which could significantly lead to saving highly expensive costs by reducing the total operation time. Generally, drilling investigations are conducted to survey the geological ground before the TBM tunneling. However, it is difficult to provide the precise ground information over the whole tunnel path to operators because it acquires insufficient samples around the path sparsely and irregularly. To overcome this issue, in this study, we proposed a geological type classification system using the TBM operating data recorded in a 5 s sampling rate. We first categorized the various geological conditions (here, we limit to granite) as three geological types (i.e., rock, soil, and mixed type). Then, we applied the preprocessing methods including outlier rejection, normalization, and extracting input features, etc. We adopted a deep neural network (DNN), which has 6 hidden layers, to classify the geological types based on TBM operating data. We evaluated the classification system using the 10-fold cross-validation. Average classification accuracy presents the 75.4% (here, the total number of data were 388,639 samples). Our experimental results still need to improve accuracy but show that geology information classification technique based on TBM operating data could be utilized in the real environment to complement the sparse ground information.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.181-188
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• 2008

In this study, estimation of behavioral characteristics between twin tunnels was performed through the series of laboratory experiment on the small scale tunnel model. In the model test, the experimental parameters were geological conditions, center to center distance between twin tunnels, application of discontinuous inclination. To estimated behavior of pillar and load-displacement relationship by model tests and numerical analyses.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2006.06a
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• pp.195-196
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• 2006

Recently, there are increasing need to make a synthetic assessment about oceanic data which is collected over the various scientific field, in addition to just gathering oceanic data. In this study, we made a basic map using satellite image, aerial photo, multi-beam data, geological stratum data etc. And as well we are producing comprehensive SVT(Scientific Visualization Toolkit) which can visualize various kinds of oceanic data. These oceanic data include both survey data such as tidal height, tide, current, wave, water temperature, salinity, oceanic weather data and numeric modelling results such as ocean hydrodynamic model, wave model, erosion/sediment model, thermal discharged coastal water model, ocean water quality model. In this process, we introduce GIS(Geographic Information System) concepts to reflect time and spatial characteristics of oceanic data.

• The Journal of Engineering Geology
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• v.30 no.3
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• pp.315-325
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• 2020

This study was performed to develop a model to predict landslides and determine the variable importance of landslides susceptibility factors based on the probabilistic prediction of landslides occurring on slopes along the road. Field survey data of 30,615 slopes from 2007 to 2020 in Korea were analyzed to develop a landslide prediction model. Of the total 131 variable factors, 17 topographic factors and 114 geological factors (including 89 bedrocks) were used to predict landslides. Automated machine learning (AutoML) was used to classify landslides and non-landslides. The verification results revealed that the best model, an extremely randomized tree (XRT) with excellent predictive performance, yielded 83.977% of prediction rates on test data. As a result of the analysis to determine the variable importance of the landslide susceptibility factors, it was composed of 10 topographic factors and 9 geological factors, which was presented as a percentage for each factor. This model was evaluated probabilistically and quantitatively for the likelihood of landslide occurrence by deriving the ranking of variable importance using only on-site survey data. It is considered that this model can provide a reliable basis for slope safety assessment through field surveys to decision-makers in the future.

• The Journal of Engineering Geology
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• v.17 no.1 s.50
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• pp.41-47
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• 2007

Geotechnical Phenomena such as landslide, groundwater recharge and groundwater fluctuation due to rainfall can be explain to use a dielectric response and infiltration variation by the movement of a wetting front in the subsurface. The infiltration of a wetting front is infiltrating to the connected pores which are distributed in unsaturated soil. In this study we carried out to laboratory experiment of a vertical infiltration column test using ethanol mix-ing tracer which has same the specific gravity of water. All physical values are detected to use a variation of dielectric constant and calculated to use a dielectric mixing model and tracer test model. This dielectric method measured by each dielectric constant of geological soil porous materials should be of for the geotechnical information and useful a field monitoring technique for detecting the variations of the volumetric water content and the wetting front, which are insignificant the key parameter to understanding the landslide by rainfall.

• Economic and Environmental Geology
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• v.40 no.6
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• pp.739-749
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• 2007

The seismic velocity at the formation varies widely with physical properties in the layers. These features on seismic shot gathers are not capable of reproducing normally by numerical modeling of homogeneous medium, so that we need that of random inhomogeneous medium instead. In this study, we conducted Gaussian autocorrelation function (ACF), exponential autocorrelation function and von Karman autocorrelation function for getting inhomogeneous velocity model and applied a simple geological model. According to the results, von Karman autocorrelation function showed short wavelength to the inhomogeneous velocity medium. For numerical modeling for a gas hydrate, we determined a geological model based on field data set gathered in the East sea. The numerical modeling results showed that the von Karman autocorrelation function could properly describe scattering phenomena in the gas hydrate velocity model which contains an inhomogeneous layer. Besides, bottom-simulating-reflectors and scattered waves which appear at seismic shot gather of the field data showed properly in the inhomogeneous numerical modeling.

• Geomechanics and Engineering
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• v.36 no.5
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• pp.441-453
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• 2024

Sinkhole subsidence and collapse is a common geohazard often formed in karst areas such as the state of Florida, United States of America. To predict the sinkhole occurrence, we need to understand the formation mechanism of sinkhole and its karst hydrogeology. For this purpose, investigating the factors affecting sinkholes is an essential and important step. The main objectives of the presenting study are (1) the development of a machine learning (ML)-based model, namely C5.0 decision tree (C5.0 DT), for the prediction of sinkhole susceptibility, which accounts for sinkhole/subsidence inventory and sinkhole contributing factors (e.g., geological/hydrogeological) and (2) the construction of a regional-scale sinkhole susceptibility map. The study area is east central Florida (ECF) where a cover-collapse type is commonly reported. The C5.0 DT algorithm was used to account for twelve (12) identified hydrogeological factors. In this study, a total of 1,113 sinkholes in ECF were identified and the dataset was then randomly divided into 70% and 30% subsets for training and testing, respectively. The performance of the sinkhole susceptibility model was evaluated using a receiver operating characteristic (ROC) curve, particularly the area under the curve (AUC). The C5.0 model showed a high prediction accuracy of 83.52%. It is concluded that a decision tree is a promising tool and classifier for spatial prediction of karst sinkholes and subsidence in the ECF area.

• Geomechanics and Engineering
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• v.13 no.6
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• pp.1011-1025
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• 2017

The geological conditions surrounding the Jijiapo Tunnel of the Three Gorges Fanba Highway project in Hubei Province are very complex. In this paper, a 3-D physical model was carried out to study the evolution process of filling-type fracture water inrush and mud gush based on the conditions of the section located between 16.040 km and 16.042 km of the Jijiapo Tunnel. The 3-D physical model was conducted to clarify the effect of the self-weight of the groundwater level and tunnel excavation during water inrush and mud gush. The results of the displacement, stress and seepage pressure of fracture and surrounding rock in the physical model were analyzed. In the physical model the results of the model test show that the rock displacement suddenly jumped after sustainable growth, rock stress and rock seepage suddenly decreased after continuous growth before water inrushing. Once water inrush occured, internal displacement of filler increased successively from bottom up, stress and seepage pressure of filler droped successively from bottom up, which presented as water inrush and mud gush of filling-type fracture was a evolving process from bottom up. The numerical study was compared with the model test to demonstrate the effectiveness and accuracy of the results of the model test.

• Journal of Environmental Science International
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• v.24 no.7
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• pp.939-946
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• 2015

Hydraulic conductivity is an important parameter in the analytical model of groundwater. This study analyzed the groundwater movement characteristics by estimating optimal parameters according to hydraulic conductivity input methods with the MODFLOW model which is widely used. It first estimated the optimal parameters by dividing hydraulic conductivity zones by attitude. Next, it estimated optimal parameters by geological characteristic. It analyzed the groundwater movement characteristics by applying the recharge quantity and amount of evapotranspiration of drought periods and flood years with the estimated parameters. As the result was analyzed that there are differences of observation water level values according to hydraulic conductivity input methods but there is no big differences of overall groundwater movement characteristics by hydraulic conductivity input method, the two methods have found to be applicability in analyses of groundwater. So, it is judged that studies on more exact application of hydraulic conductivity and the application methods are needed.