• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.4
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• pp.337-346
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• 2020

Traffic jam and congestion in urban areas has caused the need to improve the utility of underground space. In response, research on underground structures is increasingly being conducted. Notably, a double-deck tunnel is one of the most widely used of all those underground structures. This double-deck tunnel is separated by the middle slab into the upper and lower roadways. Both vehicle load and earthquake load cause the middle slab to exhibit dynamic behavior. Earthquake-related response characteristics, in particular, are highly complex and difficult to interpret in a theoretical context, and thus experimental research is required. The aim of the present study is to assess the stability of a double-deck tunnel's middle slab of the Collapse Prevention Level and Seismic Category 1 with regard to the presence of vibration-damping Rubber Bearings. In vibration table tests, the ratio of similitude was set to 1/4. Linings and vibrating platforms were fixed during scaled model tests to represent the integrated behavior of the ground and the applied models. In doing so, it was possible to minimize relative behavior. The standard TBM cross-section for the virtual double-deck tunnel was selected as a test subject. The level of ground motion exerted on the bedrock was set to 0.154 g (artificial seismic wave, Collapse Prevention Level and Seismic Category 1). A seismic wave with the maximum acceleration of 0.154 g was applied to the vibration table input (bedrock) to analyze resultant amplification in the models. As a result, the seismic stability of the middle slab was evaluated and analyzed with respect to the presence of vibration-damping rubber bearings. It was confirmed that the presence of vibration-damping rubber bearings improved its earthquake acceleration damping performance by up to 40%.

• Journal of Korean Tunnelling and Underground Space Association
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• v.16 no.3
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• pp.321-339
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• 2014

This paper concerns the development of a knowledge-based tunnel design system within the framework of artifical neural networks(ANNs). The system is aimed at expediting a routine tunnel design works such as computation of segment lining body forces and stability analysis of selected cross section. A number of sub-modules for computation of segment lining body forces and stability analysis were developed and implemented to the system. It is shown that the ANNs trained with the results of 3D numerical analyses can be generalized with a reasonable accuracy, and that the ANN based tunnel design concept is a robust tool for tunnel design optimization. The details of the system architecture and the ANNs development are discussed in this paper.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.517-534
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• 2021

Hydrogen fuel is emerging as an new energy source to replace fossil fuels in that it can solve environmental pollution problems and reduce energy imbalance and cost. Since hydrogen is eco-friendly but highly explosive, there is a high concern about fire and explosion accidents of hydrogen fueled vehicles. In particular, in semi-enclosed spaces such as tunnels, the risk is predicted to increase. Therefore, this study was conducted on the applicability of the equivalent TNT model and the numerical analysis method to evaluate the hydrogen explosion pressure in the tunnel. In comparison and review of the explosion pressure of 6 equivalent TNT models and Weyandt's experimental results, the Henrych equation was found to be the closest with a deviation of 13.6%. As a result of examining the effect of hydrogen tank capacity (52, 72, 156 L) and tunnel cross-section (40.5, 54, 72, 95 m²) on the explosion pressure using numerical analysis, the explosion pressure wave in the tunnel initially it propagates in a hemispherical shape as in open space. Furthermore, when it passes the certain distance it is transformed a plane wave and propagates at a very gradual decay rate. The Henrych equation agrees well with the numerical analysis results in the section where the explosion pressure is rapidly decreasing, but it is significantly underestimated after the explosion pressure wave is transformed into a plane wave. In case of same hydrogen tank capacity, an explosion pressure decreases as the tunnel cross-sectional area increases, and in case of the same cross-sectional area, the explosion pressure increases by about 2.5 times if the hydrogen tank capacity increases from 52 L to 156 L. As a result of the evaluation of the limiting distance affecting the human body, when a 52 L hydrogen tank explodes, the limiting distance to death was estimated to be about 3 m, and the limiting distance to serious injury was estimated to be 28.5~35.8 m.

• Economic and Environmental Geology
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• v.39 no.2 s.177
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• pp.163-171
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• 2006

This study is the case of groundwater development based on the geometrical fracture model of target area established only through geological fracture mapping technique. A fracture mapping of $9km^2$, eastern Munsan, has been conducted to determine geological and hydrological factors for new water well placement in the Gyeonggi gneiss complex. Geophysical exploration was not applicable because of small restricted area and dense underground utilities at the site. Form line mapping on the basis of foliation orientation and rock type revealed a synform of NS fold axis bearing to the south. An EW geological cross-section passed through the site area shows a F2 synform as a double-wall ice cream spoon shape. Three regional faults of $N20^{\circ}E,\;N30^{\circ}W$, and NS have been dragged into the site to help understand extensional fault paths. The $N20^{\circ}E$ fault with dextral sense is geometrically interpreted as a western fault of two flexural conjugate type-P shear faults in the F2 synformal fold. The NE cross-section reveals that a possible groundwater belt in the western limb of super-posed fold area is formed as a trigonal prism within 100 m depth of the intersectional space between the $N20^{\circ}E$ fault plane and the weakly sheared plane of transposed foliation. Another possible fault for water resource strikes $N40^{\circ}E$. Recommended sites for new water well placement are along the $N20^{\circ}E\;and\;N40^{\circ}E$ faults. As a result of fracture mapping, 145 ton/day of water can be produced at one well along the $N20^{\circ}E$ fault line. Exploration of groundwater in the area is succeeded only using with geological fracture mapping and interpretation of geological cross-section, without any geophysical survey. Intersection of fault generated with the F2 synformal fold and foliation supply space of groundwater reserver.

• 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.