• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2004.03a
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• pp.634-643
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• 2004

In this paper are presented a concept of a new supersonic air inlet, which is designated a Multi-Row Disk (MRD) inlet, aiming at performance improvement under off-design conditions, and results of wind tunnel tests examined performance characteristics of the MRD inlet. The MRD inlet is frequently called ‘a skeleton inlet’ because of its appearance. The performance of a conventional axisymmetric inlet with a solid center body (spike) deteriorates under off-design Mach number conditions. It is due to the fact that total pressure recovery (TPR) governed by the throat area of inlet and mass capture ratio (MCR) governed by an incidence position of an oblique shock from the spike tip into the cowl can not be controlled independently in such air inlet. The MRD inlet has the spike that is composed of a tip cone and several disks arranged downstream of it, based on the experimental fact that several deep cavities on a conical surface have little negative effect on the boundary layer growth. The overall spike length of the MRD inlet is adjustable to the given flight speed by changing space between disks so that a spillage flow can be controlled independently from controlling the throat area. It could be made clear from the result of wind tunnel tests that the MRD inlet improves TPR by 10% compared with a conventional inlet with a solid spike under off-design conditions.

• Journal of the Korean Geotechnical Society
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• v.18 no.5
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• pp.99-108
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• 2002

The nonlinear-brittle-plastic model derived from experiments as well as elastic and elasto-plastic models was applied to the analysis of the rock damaged zone around a highly stressed circular tunnel. The depths of stress redistribution and disturbed zone as well as the characteristic behaviors predicted from each numerical model were compared, As the magnitudes and stress differences of in situ stresses increased, influences of stress redistribution and stress disturbance on un(tiled region of rock mass also intensified. As a result, larger stress redistribution and disturbed zone as well as greater deviatoric stress and displacement were obtained by the nonlinear-brittle-plastic model rather than other conventional models such as elasto-plastic and elastic models. from such results, it was concluded that as the magnitudes and stress differences of in situ stresses increased, larger rock damaged zone might be predicted by the nonlinear-brittle-plastic model. Therefore, it is thought that the damage analysis may be indispensable far highly stressed tunnels.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.4B
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• pp.178-185
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• 2005

NEMO basic support is a solution that provides network mobility in the Internet topology. Yet, when multiple mobile networks are nested, this basic solution suffers ken pinball-routing and a severe routing overhead. Therefore, several solutions for route optimization in a nested mobile network have already been suggested by the IETF NEMO WG. However, the current paper proposes Regional Information-based Route Optimization (RIRO) in which mobile routers maintain a Nested Router List (NRL) to obtain next-hop information, and packets are transmitted with a new routing header called an RIRO Routing Header (RIRO-RH). We showed that RIRO had the minimum packet overhead that remained constant, irrespective of how deep the mobile network was nested, in comparison with two earlier proposed schemes - Reverse Routing Header (RRH) and Bi-directional tunnel between HA and Top-Level mobile router (BHT).

• Tunnel and Underground Space
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• v.31 no.1
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• pp.25-40
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• 2021

As the era of Industry 4.0 arrives, the researches using artificial intelligence in the field of rock engineering as well have increased. For a better understanding and availability of AI, this paper analyzed the types of algorithms and how to apply them to the research papers where AI is applied among domestic and international studies related to tunnels, blasting and mines that are major objects in which rock engineering techniques are applied. The analysis results show that the main specific fields in which AI is applied are rock mass classification and prediction of TBM advance rate as well as geological condition ahead of TBM in a tunnel field, prediction of fragmentation and flyrock in a blasting field, and the evaluation of subsidence risk in abandoned mines. Of various AI algorithms, an artificial neural network is overwhelmingly applied among investigated fields. To enhance the credibility and accuracy of a study result, an accurate and thorough understanding on AI algorithms that a researcher wants to use is essential, and it is expected that to solve various problems in the rock engineering fields which have difficulty in approaching or analyzing at present, research ideas using not only machine learning but also deep learning such as CNN or RNN will increase.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.223-241
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• 2024

Evaluating the performance of Tunnel Boring Machines (TBMs) stands as a pivotal juncture in the domain of hard rock mechanized tunneling, essential for achieving both a dependable construction timeline and utilization rate. In this investigation, three advanced artificial neural networks namely, gated recurrent unit (GRU), back propagation neural network (BPNN), and simple recurrent neural network (SRNN) were crafted to prognosticate TBM-rate of penetration (ROP). Drawing from a dataset comprising 1125 data points amassed during the construction of the Alborze Service Tunnel, the study commenced. Initially, five geomechanical parameters were scrutinized for their impact on TBM-ROP efficiency. Subsequent statistical analyses narrowed down the effective parameters to three, including uniaxial compressive strength (UCS), peak slope index (PSI), and Brazilian tensile strength (BTS). Among the methodologies employed, GRU emerged as the most robust model, demonstrating exceptional predictive prowess for TBM-ROP with staggering accuracy metrics on the testing subset (R² = 0.87, NRMSE = 6.76E-04, MAD = 2.85E-05). The proposed models present viable solutions for analogous ground and TBM tunneling scenarios, particularly beneficial in routes predominantly composed of volcanic and sedimentary rock formations. Leveraging forecasted parameters holds the promise of enhancing both machine efficiency and construction safety within TBM tunneling endeavors.

• Tunnel and Underground Space
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• v.29 no.6
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• pp.468-479
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• 2019

It is essential to comprehend coupled hydro-mechanical behavior to utilize subsurface for the recent demand for underground space usage. In this study, we developed a new simulator for numerical simulation as a tool for researching to consider the various domestic field and subsurface conditions. To develop the new module, we combined OpenGeoSys, one of the scientific software package that handles fluid mechanics (H), thermodynamics (T), and rock and soil mechanics (M) in the subsurface with FLAC3D, one of the commercial software for geotechnical engineering problems reinforced. In this simulator development, we design OpenGeoSys as a master and FLAC3D as a slave via a file-based sequential coupling. We have chosen Terzaghi's consolidation problem related to single-phase fluid flow at a saturated condition as a benchmark model to verify the proposed module. The comparative results between the analytical solution and numerical analysis showed a good agreement.

• Tunnel and Underground Space
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• v.29 no.3
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• pp.184-196
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• 2019

This study aims to extract a 3D image of micro-cracks generated by hydraulic fracturing tests, using the deep learning method and X-ray computed tomography images. The pixel-level cracks are difficult to be detected via conventional image processing methods, such as global thresholding, canny edge detection, and the region growing method. Thus, the convolutional neural network-based encoder-decoder network is adapted to extract and analyze the micro-crack quantitatively. The number of training data can be acquired by dividing, rotating, and flipping images and the optimum combination for the image augmentation method is verified. Application of the optimal image augmentation method shows enhanced performance for not only the validation dataset but also the test dataset. In addition, the influence of the original number of training data to the performance of the deep learning-based neural network is confirmed, and it leads to succeed the pixel-level crack detection.

• Tunnel and Underground Space
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• v.34 no.1
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• pp.28-53
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• 2024

In general, high-level radioactive waste (HLW) generated as a result of nuclear power generation should be disposed within the country. Determination of the disposal site and host rock for HLW deep geological repository is an important issue not only scientifically but also politically, economically, and socially. Considered host rock types worldwide for geological disposal include crystalline rocks, sedimentary rocks, volcanic rocks, and salt dome. However, South Korea consists of various rock types except salt dome. This paper not only analyzed the geological and rock mechanical characteristics on a nationwide scale with the preliminary results on various rock type studies for the disposal host rock, but also reviewed the characteristics and possibility of various rock types as a host rock through deep drilling surveys. Based on the nationwide screening for host rock types resulted from literature review, rock distributions, and detailed case studies, Jurassic granites and Cretaceous sedimentary rocks (Jinju and Jindong formations) were derived as a possible candidate host rock types for the geological disposal. However, since the analyzed data for candidate rock types from this study is not enough, it is suggested that the disposal rock type should be carefully determined from additional and detailed analysis on disposal depth, regional characteristics, multidisciplinary investigations, etc.

• Journal of Korean Tunnelling and Underground Space Association
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• v.9 no.3
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• pp.263-273
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• 2007

When a tunnel or an underground structure is excavated in deep geological environments, the failure process is affected and eventually dominated by stress-induced fractures growing preferentially parallel to the excavation boundary. This fracturing is generally referred to as brittle failure by spatting and slabbing. Continuum models with traditional failure criteria such as Hoek-Brown or Mohr-Coulomb criteria have not been successful in prediction of the extent and depth of brittle failure. Instead cohesion weakening and frictional strengthening (CWFS) model is known to predict brittle failure well. In this study, CWFS model was applied to predict the brittle failure around a circular opening observed in physical model experiments. To obtain the input parameters for CWFS model, damage-controlled tests were carried out. The predicted depth and extent of brittle failure using CWFS model were compared to the results of the physical model experiment and numerical simulation using traditional model.

• Geomechanics and Engineering
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• v.20 no.1
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• pp.67-73
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• 2020

With the increasing number of underground projects, the problem of rock-water coupling catastrophe has increasingly become the focus of safety. Grouting reinforcement is gradually applied in subway, tunnel, bridge reinforcement, coal mine floor and other construction projects. At present, cement-based grouting materials are easy to shrink and have low strength after solidification. In order to overcome the special problems of high water pressure and high in-situ stress in deep part and improve the reinforcement effect. In view of the mining conditions of deep surrounding rock, a new type of cement-based reinforcement material was developed. We analyses the principle and main indexes of floor strengthening, and tests and optimizes the indexes and proportions of the two materials through laboratory tests. Then, observes and compares the microstructures of the optimized floor strengthening materials with those of the traditional strengthening materials through scanning electron microscopy. The test results show that 42.5 Portland cement-based grouting reinforcement material has the advantages of slight expansion, anti-dry-shrinkage, high compressive strength and high density when the water-cement ratio is 0.4, the content of bentonite is 4%, and the content of Nano Silica is 2.5%. The reinforcement effect is better than other traditional grouting reinforcement materials.