• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.5
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• pp.30-42
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• 2022

Due to the increase in construction of tunnels, the burdens of maintenance works for tunnel structures have been increasing in Korea. In addition, the increase of traffic volume and aging of materials also threatens the safety of tunnel facilities, therefore, maintenance costs are expected to increase significantly in the future. Accordingly, automated condition assessment technologies like image-based tunnel scanning system for inspection and diagnosis of tunnel facilities have been proposed. For image-based tunnel scanning system, it is key to create a planar image through stitching of multiple images captured by tunnel scanning system. In this study, performance of feature-based stitching algorithms suitable for stitching tunnel scanning images was evaluated. In order to find a suitable algorithm SIFT, ORB, and BRISK are compared. The performance of the proposed algorithm was determined by the number of feature extraction, calculation speed, accuracy of feature matching, and image stitching result. As for stitching performance, SIFT algorithm was the best in all parts of tunnel image. ORB and BRISK also showed satisfactory performance and short calculation time. SIFT can be used to generate precise planar images. ORB and BRISK also showed satisfactory stitching results, confirming the possibility of being used when real-time stitching is required.

• Journal of the Korean Geotechnical Society
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• v.24 no.1
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• pp.81-89
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• 2008

The purpose of this study is to develop a new technique to quickly assess the quantitative stability of a tunnel by using measured displacement at the tunnel construction site. To achieve this purpose, in this study, a critical strain concept was introduced for the first time and applied to an assessment of a tunnel under construction. The new technique calculates numerically the strains of the surrounding ground by using displacements measured during tunnel excavation. The techniques considering the relative displacement, shotcrete, and anisotropic characteristics of ground were newly introduced after reinvestigating the existing analysis technique. In addition, an analysis module was developed based on the proposed analysis technique in this study, and the applicability of the developed module was verified. To verify the module, first of all, the calculated excavation displacements of a cylindrical tunnel by analytic method and commercial programs (Pentagon-3D, Flac-2D) were compared for the confirmation of applicability of commercial programs. Then, the calculated excavation displacements under the same initial condition, both with and without a shotcrete lining, by two commercial programs were compared. finally, we assess the load condition and material properties of in-situ ground by inputting tunnel excavation displacement, which was calculated by a commercial program, into the developed analysis module (FAST-Ver. 1.2, feedback Analysis System for Tunneling), and checked whether the assessed results conform to the originally assumed values.

• Journal of the Korean Geotechnical Society
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• v.22 no.8
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• pp.107-118
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• 2006

Among a variety of influencing components, time-variant seepage and long-term underground motion are important to understand the abnormal behavior of tunnels. Excessiveness of these two components could be the direct cause of severe damage on tunnels, however, it is not easy to quantify the effect of these on the behavior of tunnels. These parameters can be estimated by using inverse methods once the appropriate relationship between inputs and results is clarified. Various inverse methods or parameter estimation techniques such as artificial neural network and least square method can be used depending on the characteristics of given problems. Numerical analyses, experiments, or monitoring results are frequently used to prepare a set of inputs and results to establish the back analysis models. In this study, a back analysis method has been developed to estimate geotechnically hard-to-known parameters such as permeability of tunnel filter, underground water table, long-term rock mass load, size of damaged zone associated with seepage and long-term underground motion. The artificial neural network technique is adopted and the numerical models developed in the first part are used to prepare a set of data for learning process. Tunnel behavior, especially the displacements of the lining, has been exclusively investigated for the back analysis.

• Journal of the Korean Geotechnical Society
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• v.22 no.10
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• pp.77-91
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• 2006

Domestic practices in shotcrete use have developed in many respects even now, but it still has issues about material, construction, quality standard and so on. In overseas, the construction using high strength shotcrete with $39.2{\sim}58.8 MPa$ of compressive strength is becoming common based on the shotcrete technology of high strength and durability. However, domestic shotcrete design strength is low at around 20.6 MPa of compressive strength and a long term durability is also insufficient. In this paper, field tests using high-quality additives and accelerators were performed to obtain the improvement of shotcrete strength and EFNARC standard was used to evaluate the field test results. In addition, deterioration test combined with the freezing-thawing and carbonation was also performed in order to investigate a long-term durability of high-strength shotcrete. As a result of the field test, the promotion ratio of early strength was $90{\sim}97%$ in case of using alkali-free accelerators. And the compressive strength of the shotcrete using Micro-silica fume was $45.2{\sim}55.8MPa$ and flexible strength was $5.01{\sim}6.66MPa$, so the promotion ratio of strength was $37{\sim}79%$ and $17{\sim}61%$ respectively. The promotion effect of strength by silica fine additives ratio of $7.5{\sim}10%$ for cement mass was much superior to the other cases. It was especially examined that using Micro-silica fume reduced deterioration due to mixed steel fiber and improved a long-term durability of shotcrete.

• Journal of Korean Tunnelling and Underground Space Association
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• v.11 no.1
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• pp.1-9
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• 2009

When a rock slope is excavated adjacent to a existing tunnel, the behavior of the existing tunnel in the jointed rock masses is greatly influenced by the joint conditions and slope status. In this study, the effects of joint dip and slope angle close to a tunnel are investigated through a large scale model using a biaxial test equipment ($3.1\;m\;{\times}\;3.1\;m\;{\times}\;0.50\;m$ (width $\times$ height $\times$ length)). The jointed rock masses were built by concrete blocks. The diameter of the modeled tunnel is 0.6 m and the dip angles of joint vary in the range of $0-90^{\circ}$. In addition, the excavated slope angle varies within $30{\sim}90^{\circ}$. Deformational behaviors of the tunnel were analyzed in consideration of joint dip and slope angle. With increase of the joint dip and slope angle, the magnitude of tunnel distortion and the moment of tunnel lining were increased. Rock mass displacement in horizontal was also dependent on the joint dip and the excavated slope angle, which indicated the optimal slope reinforcement for a specific rock mass conditions.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.6
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• pp.555-567
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• 2023

The cracks in the tunnel are currently determined through visual inspections conducted by inspectors based on images acquired using tunnel imaging acquisition systems. This labor-intensive approach, relying on inspectors, has inherent limitations as it is subject to their subjective judgments. Recently research efforts have actively explored the use of deep learning to automatically detect tunnel cracks. However, most studies utilize public datasets or lack sufficient objectivity in the analysis process, making it challenging to apply them effectively in practical operations. In this study, we selected test datasets consisting of images in the same format as those obtained from the actual inspection system to perform an objective evaluation of deep learning models. Additionally, we introduced ensemble techniques to complement the strengths and weaknesses of the deep learning models, thereby improving the accuracy of crack detection. As a result, we achieved high recall rates of 80%, 88%, and 89% for cracks with sizes of 0.2 mm, 0.3 mm, and 0.5 mm, respectively, in the test images. In addition, the crack detection result of deep learning included numerous cracks that the inspector could not find. if cracks are detected with sufficient accuracy in a more objective evaluation by selecting images from other tunnels that were not used in this study, it is judged that deep learning will be able to be introduced to facility safety inspection.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.8 no.2
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• pp.242-253
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• 1998

South Korea has been producing asbestos over 60 years. The use of asbestos was over 50 years for production of asbestos slate and 27 years for asbestos friction materials including asbestos textile and brake-lining. Thus, it can be supposed that asbestos related diseases such as asbestosis, lung cancer and mesothelioma could be found in the vulnerable workers exposed to asbestos in 1955-1975, given the average latency period of 10-30 years. Asbestos was produced primarily by Japanese during World War II In Korea. The production of chrysotile peaked to 4,815 tons in 1944. From 1978 to 1984, 10,000 tons of asbestos were produced annually. However, the production was interrupted by raising labor costs and extinction of mine reserves, and finally they had to depend on import for the need of asbestos. In 1945, there were 16 asbestos mines, in total, with the addition of new asbestos mines in South Korea. Imports of asbestos was increased from 74,000 tons to 95,000 tons during the period of 1976 - 1992. But the imports was reduced to 88,000 tons in 1995. Since, in addition to the import of asbestos itself, the imports of asbestos products were increased as well and the accumulation of asbestos reached to 30,000 tons during the period of 1964 to 1993. In 1965, there was only one asbestos company with 207 employees. But the size of asbestos industry has been expanded so much that 118 asbestos companies could be found in 1993 with 1,476 workers. However, there was no record on the survey of asbestos concentration to which workers were exposed in any companies in 1983. The record of the air-borne concentration of the asbestos in textile working places in 1984 showed 6.7 fibers/cc by geometric mean(GM), but it was reduced to 1.2 fibers/cc in 1993. GMs of asbestos in working places for construction materials and asbestos textiles were also decreased from 1.7 fibers/cc to 0.55 fibers/cc during the period of 1984 - 1996.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.617-628
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• 2022

A ground support pattern should be designed by properly integrating various support materials in accordance with the rock mass grade when constructing a tunnel, and a technical decision must be made in this process by professionals with vast construction experiences. However, designing supports at the early stage of tunnel design, such as feasibility study or basic design, may be very challenging due to the short timeline, insufficient budget, and deficiency of field data. Meanwhile, the design of the support pattern can be performed more quickly and reliably by utilizing the machine learning technique and the accumulated design data with the rapid increase in tunnel construction in South Korea. Therefore, in this study, the design data and ground exploration data of 48 road tunnels in South Korea were inspected, and data about 19 items, including eight input items (rock type, resistivity, depth, tunnel length, safety index by tunnel length, safety index by rick index, tunnel type, tunnel area) and 11 output items (rock mass grade, two items for shotcrete, three items for rock bolt, three items for steel support, two items for concrete lining), were collected to automatically determine the rock mass class and the support pattern. Three machine learning models (S1, A1, A2) were developed using two machine learning algorithms (SVR, ANN) and organized data. As a result, the A2 model, which applied different loss functions according to the output data format, showed the best performance. This study confirms the potential of support pattern design using machine learning, and it is expected that it will be able to improve the design model by continuously using the model in the actual design, compensating for its shortcomings, and improving its usability.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.2
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• pp.279-299
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• 2019

Recently occurred earthquake Gyeongju and Pohang served as a momentum to remind that Korean peninsular is not a safety zone from earthquake anymore. The importance of seismic design, therefore, have been realized and researches regarding design response spectrum have been actively carried out by many researchers and engineers. Current tunnel seismic design method is conducted to check safety of tunnel structure by dynamic numerical analysis with condition of completed lining installation, so, it is impossible to consider safety of tunnel behavior under construction. In this study, therefore, dynamic numerical analysis considering seismic wave propagations has been performed after back analysis using results from field monitoring of tunnel under construction in fractured zone and 1st reinforcement (shotcrete, rockbolt) behaviour are analyzed. Waves are classified by period characteristic (short and long). As a result, the difference depending on period characteristic is minor, and increasements of displacement are obtained at crown displacement due to seismic wave is 28~31%, 14~16% at left side of tunnel in the fractured zone, 13~27% at right side of tunnel in the bed rock, respectively. In case of shotcrete axial force is increased 113~115% at tunnel crown, 102% at left side, 106~110% at right side, respectively. Displacement and axial force of rockbolts which are selected by type of anchored grounds (only fractured zone, fractured zone and bed rock, only bedrock) are analyzed, as a result, rockbolt which is anchored to fractured zone and bed rock at the same time are weaker than any other case.