• Journal of Korean Tunnelling and Underground Space Association
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• v.16 no.1
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• pp.105-115
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• 2014

A numerical analysis on the smoke behavior and evacuee safety has been performed with computational fluid dynamics. The purpose of this study is to build computational processes for an evacuation and prevention of a fire disaster of a 3 km-length tunnel in Korea. To save computational cost, 1.5 km of the tunnel that can include a few cross-passing tunnels is considered. We are going to assess the fire safety in a road tunnel according to the smoke level, which consists of the smoke density and the height from the floor. The smoke density is obtained in detail from three-dimensional unsteady CFD analysis. To obtain proper temperature distributions on the tunnel wall, one-dimensional conduction equation is considered instead of an adiabatic wall boundary or a constant heat flux. The tunnel considered in this study equips the cross passing tunnels for evacuees every 250 m. The distance is critical in both safety and economy. The more cross passing tunnels, the more safe but the more expensive. Three different jet fan operations can be considered in this study; under- and over-critical velocities for normal traffic condition and 0-velocoty operation for the traffic congestion. The SE (smoke environment) level maps show a smoke environment and an evacuating behavior every moment.

• Journal of the Mineralogical Society of Korea
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• v.22 no.1
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• pp.13-22
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• 2009

The study area is a tunnelling section passing through the Yangsan Fault zone. Kyungbu express highway and national road 35 are located above the tunnel. Previous study showed that fault gouge and fault breccia were widely distributed in the tunnelling section with a maximum width of 100 m. From the present study, it is found that sedimentary rocks consisting mainly of shale are distributed at the eastern block of the Yangsan Fault and these rocks are not subject to mechanical fracturing and hydrothermal alteration. On the other hand, dacitic tuff distributed at the western block of the Yangsan Fault is largely affected by mechanical fracturing and hydrothermal alteration. The large fault zone of $50{\sim}130m$ width was formed by complex processes of mechanical fracturing and hydrothermal alterations such as chloritization, sericitization, and kaolinization. Based on the characteristics of mechanical fracturing and hydrothermal alterations, the Yangsan fault zone in the study area is geotechnically classified as four zones: unaltered zone, altered zone, altered fractured zone, and fault gouge zone. These zones show different degrees and aspects in mechanical fracturing and hydrothermal alterations, resulting in different engineering properties.

• Journal of Korean Tunnelling and Underground Space Association
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• v.9 no.2
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• pp.133-141
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• 2007

The excavation damaged zone (EDZ) is an area around an excavation where in situ rock mass properties, stress condition, displacement, groundwater flow conditions have been altered due to the processes induced by the excavation. Various studies have been carried out on EDZ, but most studies have focused on the mechanical bahavior of EDZ by in situ experiment. Even though the EDZ could potentially form a high permeable pathway of groundwater flow, only a few studies were performed on the analysis of groundwater flow in EDZ. In this study, the 'hydraulic EDZ' was defined as the rock zone adjacent to the excavation where the hydraulic aperture has been changed due to the excavation by using H-M coupling analysis. Fundamental principles of distinct element method (DEM) were used in the analysis. In the same groundwater level, the behavior of hydraulic aperture near the cavern was analyzed for different stress ratios, initial apertures, fracture angles and fracture spacings by using a two-dimensional DEM program. We evaluate the excavation induced hydraulic aperture change. Using the results of the study, hydraulic EDZ was defined as an elliptical shape model perpendicular to the joint.

• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.2
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• pp.155-170
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• 2020

In this paper, a risk management system applicable to NATM tunneling projects is proposed. After investigating case histories in NATM tunnel collapse, this paper analyzes the potential risk factors and their corresponding risk events during NATM tunnel construction. The risk factors are categorized into three groups: geological, design and construction risk factors. The risk events are also categorized into four types: excessive deformation, excessive deformation with subsidence, collapse inside tunnels, and collapse inside tunnels with subsidence. The paper identifies risk scenarios in consideration of the risk factors and proposes a risk analysis/evaluation method for the NATM tunnel risk scenarios. Based on the evaluation results, the optimal mitigation measure to handle the risk events is suggested. In order to effectively facilitate a series of risk management processes, it is necessary to develop a risk register and a management ledger for risk mitigation measures that are customized to NATM tunnels. Lastly, the risk management for an actual NATM tunnel construction site is performed to verify the validity of the proposed system.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.637-658
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• 2022

TBM tunnel is simple with the iterative process of excavating the ground, building a segment ring-build, and backfilling. Drill & Blast, a conventional tunnel construction method, is more complicated than the TBM tunnel and has some restrictions because it repeats the inspection, drilling, charging, blasting, ventilation, muck treatment, and installation of support materials. However, the preparation work for excavation requires time and cost based on a very detailed plan compared to Drill & Blasting, which reinforces the ground and forms a tunnel after the formation of tunnel portal. This is because the TBM equipment for excavating the target ground determines the success or failure of the construction. If the TBM, an expensive order-made equipment, is incorrectly configured at the assembly stage, it becomes difficult to excavate from the initial stage as well as the main excavation stage. When the assembled shield TBM equipment is dismantled again, and a situation of re-assembly occurs, it is difficult throughout the construction period due to economic loss as well as time. Therefore, in this study, the layout and plan of the site and the assembly process for each major part of the TBM equipment were reviewed for the assembly of slurry shield TBM to construct the largest diameter road tunnel in domestic passing through the Han River and minimized interference with other processes and the efficiency of cutter head assembly and transport were analyzed and improved to suit the site conditions.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.2
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• pp.267-278
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• 2019

Variety of tunnel ahead prediction methods have been performed for safe tunnel construction during tunnel excavation. Pole-pole array among the electrical resistivity survey, which is one of the tunnel ahead prediction method, has been utilized to predict water-bearing sediments or weak zone located within 5 times of tunnel diameter. One of the most important processes is the estimation of virgin ground resistivity and it can be obtained from the following process: 1) calculation of contact area between the electrodes and the medium, and 2) assumption of the electrodes as equivalent spherical electrodes which have a same surface area with the electrodes. This assumption is valid in a small contact area and sufficient distance between the electrodes. Since the measured resistance, in general, varies with the electrode size, shape, and distance between the electrodes, it is necessary to evaluate the influence of these factors. In this study, theoretical equations were derived and experimental tests were conducted considering the electrode size, shape, and distance of cylindrical electrodes which is the most commonly utilized electrode shape. Through this theoretical and experimental study, it is known that one should be careful to use the assumption of the equivalent half-spherical electrode with large ratio between the penetrated depth and radius of the cylindrical electrode, as the error may get larger.

• Journal of Korean Tunnelling and Underground Space Association
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• v.15 no.5
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• pp.505-522
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• 2013

Previously, a new concept of indexing methodology has been proposed for quantitative assessment of tunnel collapse hazard level at each tunnel face with respect to the given geological data, design condition and the corresponding construction activity (Shin et al, 2009a). In this paper, 'linear' model, in which weights of influence factors are invariable, and 'non-linear' model, in which weights of influence factors are variable, are taken into account with some examples. Then, the 'non-linear' model is validated by using 100 tunnel collapse cases. It appears that 'non-linear' model allows us to have adapted weight values of influence factors to characteristics of given tunnel site. In order to make a better understanding and help for an effective use of the system, a series of operating processes of the system are built up. Then, by following the processes, the system is applied to a real-life tunnel project in very weak and varying ground conditions. Through this approach, it would be quite apparent that the tunnel collapse hazard indices are determined by well interlinked consideration of face mapping data as well as design/construction data. The calculated indices seem to be in good agreement with available electric resistivity distribution and design/construction status. In addition, This approach could enhance effective usage of face mapping data and lead timely and well corresponding field reactions to situation of weak tunnel faces.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.5
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• pp.431-449
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• 2022

As many tunnels generally have been constructed, various experiences and techniques have been accumulated for tunnel design as well as tunnel construction. Hence, there are not a few cases that, for some usual tunnel design works, it is sufficient to perform the design by only modifying or supplementing previous similar design cases unless a tunnel has a unique structure or in geological conditions. In particular, for a tunnel blast design, it is reasonable to refer to previous similar design cases because the blast design in the stage of design is a preliminary design, considering that it is general to perform additional blast design through test blasts prior to the start of tunnel excavation. Meanwhile, entering the industry 4.0 era, artificial intelligence (AI) of which availability is surging across whole industry sector is broadly utilized to tunnel and blasting. For a drill and blast tunnel, AI is mainly applied for the estimation of blast vibration and rock mass classification, etc. however, there are few cases where it is applied to blast pattern design. Thus, this study attempts to automate tunnel blast design by means of machine learning, a branch of artificial intelligence. For this, the data related to a blast design was collected from 25 tunnel design reports for learning as well as 2 additional reports for the test, and from which 4 design parameters, i.e., rock mass class, road type and cross sectional area of upper section as well as bench section as input data as well as16 design elements, i.e., blast cut type, specific charge, the number of drill holes, and spacing and burden for each blast hole group, etc. as output. Based on this design data, three machine learning models, i.e., XGBoost, ANN, SVM, were tested and XGBoost was chosen as the best model and the results show a generally similar trend to an actual design when assumed design parameters were input. It is not enough yet to perform the whole blast design using the results from this study, however, it is planned that additional studies will be carried out to make it possible to put it to practical use after collecting more sufficient blast design data and supplementing detailed machine learning processes.