• Tunnel and Underground Space
• /
• v.33 no.1
• /
• pp.57-69
• /
• 2023

In this study, we simulated induced seismicity in the Groningen natural gas reservoir using 2D hydro-mechanical coupled discrete element modelling (DEM). The code used is PFC2D (Particle Flow Code 2D), a commercial software developed by Itasca, and in order to apply to this study we further developed 1)initialization of inhomogeneous reservoir pressure distribution, 2)a non-linear pressure-time history boundary condition, 3)local stress field monitoring logic. We generated a 2D reservoir model with a size of 40 × 50 km² and a complex fault system, and simulated years of pressure depletion with a time range between 1960 and 2020. We simulated fault system failure induced by pressure depletion and reproduced the spatiotemporal distribution of induced seismicity and assessed its failure mechanism. Also, we estimated the ground subsidence distribution and confirmed its similarity to the field measurements in the Groningen region. Through this study, we confirm the feasibility of the presented 2D hydro-mechanical coupled DEM in simulating the deformation of a complex fault system by hydro-mechanical coupled processes.

• Tunnel and Underground Space
• /
• v.33 no.2
• /
• pp.95-107
• /
• 2023

In this study, the effect of repeated traffic vibration on the long-term stability of mine openings is analyzed for re-utilization of abandoned mine galleries. The research mine in this study is an underground limestone mine which is developed by room-and-pillar mining method, and a dynamic numerical analysis is performed assuming that the research mine will be utilized as a logistics warehouse. The actual traffic vibration generated by the mining vehicles is measured directly, and its waveform is used as input data for dynamic numerical analysis, As a results of dynamic numerical analysis, after 20,000 repetitions of traffic vibration, the mine openings is analyzed to be stable, but an increase in the maximum principal stress and an additional area of plastic zone are observed in the analysis section. As shown in the changes of displacement, volumetric strain, and maximum principal stress which are measured at the mine opening walls. It is confirmed that if the repeated traffic vibration is continuously applied, the instability of the mine openings can be increased. Authors expect that the results of this study can be used as a reference for basic study on utilization of abandoned mine.

• The Journal of Engineering Geology
• /
• v.33 no.4
• /
• pp.519-530
• /
• 2023

Moye's analytical solution was examined as a method for constant-head tests under steady-state conditions, and results were compared with transient-state analyses in in situ hydraulic tests. The sensitivity of hydraulic conductivities calculated using Moye's method increased with the length of the test section, which should be as large as possible under test conditions. Particularly in low-permeability media with less than 10^-8 m/sec of hydraulic conductivity, hydraulic conductivity is lower than that under transient-state conditions and can be recalculated by adjusting the boundary between radial and spherical flow assumed in Moye's equation. Constant-head tests performed in the research borehole at the KAERI Underground Research Tunnel (KURT) indicated that transmissivities derived from the constant-head withdrawal test under transient-state conditions in low-permeability media were higher than those derived from steady-state tests, likely because the groundwater flow boundary was smaller than the "half of the test-section length"assumed by Moye's equation. When interpreting constant-head test results for crystalline rock, the hydrogeological properties of the medium may be better understood by considering assumed conditions accompanying analysis of the steady-state condition and comparing them with results for the transient-state analysis, rather than simply assuming properties based on steady-state analyses.

• Explosives and Blasting
• /
• v.41 no.3
• /
• pp.26-37
• /
• 2023

In order to determine the amount of explosives that can minimize the vibration generated during tunnel construction using the blasting method, it is necessary to derive the blasting vibration coefficients, K and n, by analyzing the vibration records of trial blasting in the field or under similar conditions. In this study, we aimed to develop a technique that can derive reasonable K and n when trial blasting cannot be performed. To this end, we collected full-scale trial blast data and studied how to predict the blast vibration coefficient (K, n) according to the type of explosive, center cut blasting method, rock origin and type, and rock grade using deep learning (DL). In addition, the correction value between full-scale and borehole trial blasting results was calculated to compensate for the limitations of the borehole trial blasting results and to carry out a design that aligns more closely with reality. In this study, when comparing the available explosive amount according to the borehole trial blasting result equation, the predictions from deep learning (DL) exceed 50%, and the result with the correction value is similar to other blast vibration estimation equations or about 20% more, enabling more economical design.

• Tunnel and Underground Space
• /
• v.33 no.5
• /
• pp.388-398
• /
• 2023

The changes in groundwater flow due to mining development act as a contributing factor to major issues such as ground subsidence, strength reduction and collapse. For the sustainable mining development, measures for dealing with fluctuations in seasonal underground water inflow, power losses, pump damage, and unexpected increases in inflow must be put in place. In this study, the aim is to identify the causes of underground seepage through the examination of hydrological connectivity between the study area and nearby limestone mine. A tracer tes for assessing subsurface connectivity has been planned. A variety of tracers, such as dyes and ions, were applied in lab test to select the optimal tracer material, and a hydrological model of the study area was implemented through field test. Finally, the hydrological connectivity between the external stream and underground water in the mine was analyzed.

• Tunnel and Underground Space
• /
• v.33 no.5
• /
• pp.399-413
• /
• 2023

The present study conducted a numerical modeling of CO₂ injection at the Janggi Basin using the TOUGH-FLAC simulator, and examined the hydro-mechanical stability of the aquifer and the fault. Based on the site investigations and a 3D geological model of the target area, we simulated the injection of 32,850 tons of CO₂ over a 3-year period. The analysis of CO₂ plume with different values of the aquifer permeability revealed that assuming a permeability of 10^-14 m² the CO₂ plume exhibited a radial flow and reached the fault after 2 years and 9 months. Conversely, a higher permeability of 10^-13 m² resulted in predominant westward flow along the reservoir, with negligible impact on the fault. The pressure changes around the injection well remained below 0.6 MPa over the period, and the influence on the hydro-mechanical stability of the reservoir and fault was found to be insignificant.

• Tunnel and Underground Space
• /
• v.33 no.6
• /
• pp.534-546
• /
• 2023

If the disc cutter is excessively worn or damaged, it becomes incapable of rotating and efficiently cutting rockmass. Therefore, it is important to appropriately manage the replacement cycle of the disc cutter based on its degree of wear. In general, the replacement cycle is determined based on the results of manual inspection. However, the manual measurements has issues related to worker safety and may lead to inaccurate measurement results. For these reasons, some foreign countries are developing the real-time measurement system of disc cutter wear by using different sensors. The ultrasonic sensors, eddy current sensors, magnetic sensors, and others are utilized for measuring the wear amount of disc cutters. In this study, the applicability of eddy current sensors for real-time measurement of wear amount in TBM disc cutters was evaluated. The distance measurement accuracy of the eddy current sensor was assessed through laboratory tests. In particular, the accuracy of eddy-current sensor was evaluated in various environmental conditions within the cutterhead chamber. In addition, the measurement accuracy of the eddy current sensor was validated using a 17-inch disc cutter.

• Tunnel and Underground Space
• /
• v.33 no.6
• /
• pp.547-560
• /
• 2023

Recently, research on predicting ground classification using machine learning techniques, TBM excavation data, and ground data is increasing. In this study, a multi-classification prediction study for uniaxial compressive strength (UCS) was conducted by applying random forest model based on a decision tree among machine learning techniques widely used in various fields to machine data and ground data acquired at three slurry shield TBM sites. For the classification prediction, the training and test data were divided into 7:3, and a grid search including 5-fold cross-validation was used to select the optimal parameter. As a result of classification learning for UCS using a random forest, the accuracy of the multi-classification prediction model was found to be high at both 0.983 and 0.982 in the training set and the test set, respectively. However, due to the imbalance in data distribution between classes, the recall was evaluated low in class 4. It is judged that additional research is needed to increase the amount of measured data of UCS acquired in various sites.

• Tunnel and Underground Space
• /
• v.34 no.3
• /
• pp.208-217
• /
• 2024

There is so much research effort for developing and implementing deep learning-based surveillance systems to manage health and safety issues in construction sites. Especially, the development of deep learning-based object detection in various environmental changes has been progressing because those affect decreasing searching performance of the model. Among the various environmental variables, the accuracy of the object detection model is significantly dropped under low illuminance, and consistent object detection accuracy cannot be secured even the model is trained using low-light images. Accordingly, there is a need of low-light enhancement to keep the performance under low illuminance. Therefore, this paper conducts a comparative study of various deep learning-based low-light image enhancement models (GLADNet, KinD, LLFlow, Zero-DCE) using the acquired construction site image data. The low-light enhanced image was visually verified, and it was quantitatively analyzed by adopting image quality evaluation metrics such as PSNR, SSIM, Delta-E. As a result of the experiment, the low-light image enhancement performance of GLADNet showed excellent results in quantitative and qualitative evaluation, and it was analyzed to be suitable as a low-light image enhancement model. If the low-light image enhancement technique is applied as an image preprocessing to the deep learning-based object detection model in the future, it is expected to secure consistent object detection performance in a low-light environment.

• Tunnel and Underground Space
• /
• v.34 no.3
• /
• pp.218-230
• /
• 2024

Recently, many studies have been conducted for safety management in construction sites by incorporating computer vision. Anchor box parameters are used in state-of-the-art deep learning-based object detection and segmentation, and the optimized parameters are critical in the training process to ensure consistent accuracy. Those parameters are generally tuned by fixing the shape and size by the user's heuristic method, and a single parameter controls the training rate in the model. However, the anchor box parameters are sensitive depending on the type of object and the size of the object, and as the number of training data increases. There is a limit to reflecting all the characteristics of the training data with a single parameter. Therefore, this paper suggests a method of applying multiple parameters optimized through data split to solve the above-mentioned problem. Criteria for efficiently segmenting integrated training data according to object size, number of objects, and shape of objects were established, and the effectiveness of the proposed data split method was verified through a comparative study of conventional scheme and proposed methods.