• Journal of Korean Tunnelling and Underground Space Association
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• v.11 no.2
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• pp.175-188
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• 2009

When a new tunnel is excavated by the drill and blast method near pre-existing underground structures or tunnels due to the region restricted condition such as urban area, the ground will be relaxed by the excavation. In this case, issues can be created in terms of stability of pre-existing underground structures. One of major factors determining the stability of pre-existing underground structures can be a separation distance between pre-existing underground structures and a newly excavated tunnel. The region of ground relaxation defined by the plastic zone due to new excavation can be varied by separation distance. In this study, in other to estimate an influence of new tunnel excavation in terms of separation distance on the stability of pre-existing large pipelines, two-dimensional scaled model tests using plaster were performed for six models which have a different separation distance, The results show that based on the analysis of induced displacement during tunnel construction, the displacement decreases as the separation distance between large pipeline and new tunnel is increased until the distance is 2.5 times of pipeline diameter. Beyond this point, however, the displacement has become stabilized.

• Journal of the Korean Recycled Construction Resources Institute
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• v.11 no.4
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• pp.425-432
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• 2023

In this study, we aim to develop a carbon-reducing concrete technology by incorporating biochar. Performance evaluation experiments were conducted on concrete mixtures containing biochar with insulating and carbon-capturing properties, which are essential for key infrastructure sectors such as construction and tunnels. Concrete mixtures were designed with different biochar incorporation rates of 0 %, 5 %, 10 %, 15 %, and 20 %, as w ell as w ater-to-binder ratios of 0.25, 0.30, 0.35, and 0.40. To assess the physical properties of each mixture, unit weight, total porosity, and permeability were measured, while mechanical properties were determined through the measurement of concrete compressive and flexural strengths. Key factors for enhancing the insulating effect of carbon-reducing concrete containing biochar were identified through regression analysis, indicating a close correlation among biochar incorporation rate, unit weight, concrete strength, and thermal conductivity. It is anticipated that it can be utilized as an insulating material to enhance thermal performance in northern regions with severe winter climates.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.276-283
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• 2023

Garlic (Allium sativum L.) is one of the most important vegetables used in various foods in Korea and many countries. The growth of garlic is influenced by various abiotic factors such as cultivation temperature, humidity, minimum temperature duration, and photoperiod. This study investigated the effects of increasing temperatures on the plant growth of the northern- ecotype garlic 'Uiseong' in a temperature gradient tunnel. As a result, temperature increase led to decreases in the bulb diameter, weight, and clove pieces of garlic. The rise of cultivation temperature increased the occurrence rate of incomplete bolting in the Northern-ecotype garlic 'Uiseong', resulting in decreases in productivity and a decrease in the yield of marketable garlic, indicating that temperature increases affect the development of garlic bulb formation. The findings of this study are expected to contribute as foundational data for understanding the growth responses of the northern-ecotype 'Uiseong' to increasing cultivation temperatures. The results of this study can be used to develop designing garlic growth models. In addition, the results of this study can improve understanding the interaction between increased temperature and garlic growth.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.445-471
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• 2023

Long-term safety over millennia is the top priority consideration in the construction of disposal sites. However, ensuring the mechanical stability of deep geological repositories for spent fuel, a.k.a. radwaste, disposal during construction and operation is also crucial for safe operation of the repository. Imposing restrictions or limitations on tunnel support and lining materials such as shotcrete, concrete, grouting, which might compromise the sealing performance of backfill and buffer materials which are essential elements for the long-term safety of disposal sites, presents a highly challenging task for rock engineers and tunnelling experts. In this study, as part of an extensive exploration to aid in the proper selection of disposal sites, the anticipation of constructing a deep geological repository at a depth of 500 meters in an unknown state has been carried out. Through a review of 2D and 3D numerical analyses, the study aimed to explore the range of properties that ensure stability. Preliminary findings identified the potential range of rock properties that secure the stability of central and disposal tunnels, while the stability of the vertical tunnel network was confirmed through 3D analysis, outlining fundamental rock conditions necessary for the construction of disposal sites.

• Journal of the Society of Disaster Information
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• v.20 no.2
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• pp.302-314
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• 2024

Purpose: This study proposes a fall detection model based on a top-down deep learning pose estimation model to automatically determine falls of multiple workers in an underground utility tunnel, and evaluates the performance of the proposed model. Method: A model is presented that combines fall discrimination rules with the results inferred from YOLOv8-pose, one of the top-down pose estimation models, and metrics of the model are evaluated for images of standing and falling two or fewer workers in the tunnel. The same process is also conducted for a bottom-up type of pose estimation model (OpenPose). In addition, due to dependency of the falling interference of the models on worker detection by YOLOv8-pose and OpenPose, metrics of the models for fall was not only investigated, but also for person. Result: For worker detection, both YOLOv8-pose and OpenPose models have F1-score of 0.88 and 0.71, respectively. However, for fall detection, the metrics were deteriorated to 0.71 and 0.23. The results of the OpenPose based model were due to partially detected worker body, and detected workers but fail to part them correctly. Conclusion: Use of top-down type of pose estimation models would be more effective way to detect fall of workers in the underground utility tunnel, with respect to joint recognition and partition between workers.

• Korean Journal of Construction Engineering and Management
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• v.25 no.1
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• pp.32-41
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• 2024

In the construction industry, not only safety accidents, but also various complex risks such as construction delays, cost increases, and environmental pollution occur, and management technologies are needed to solve them. Among them, process risk management, which directly affects the project, lacks related information compared to its importance. This study tried to develop a MATM tunnel process risk classification system to solve the difficulty of risk information retrieval due to the use of different classification systems for each project. Risk collection used existing literature review and experience mining techniques, and DB construction utilized the concept of natural language processing. For the structure of the classification system, the existing WBS structure was adopted in consideration of compatibility of data, and an RBS linked to the work species of the WBS was established. As a result of the research, a risk classification system was completed that easily identifies risks by work type and intuitively reveals risk characteristics and risk factors linked to risks. As a result of verifying the usability of the established classification system, it was found that the classification system was effective as risks and risk factors for each work type were easily identified by user input of keywords. Through this study, it is expected to contribute to preventing an increase in cost and construction period by identifying risks according to work types in advance when planning and designing NATM tunnels and establishing countermeasures suitable for those factors.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.6
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• pp.541-554
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• 2023

The utilization of underground spaces in relation to tunnels and energy/waste storage is on the rise. To ensure the stability of underground spaces, it is crucial to reinforce rock fractures and discontinuities. Discontinuities, such as joints, can weaken the strength of the rock and lead to groundwater inflow into underground spaces. In order to enhance the strength and stability of the area around these discontinuities, rock grouting techniques are employed. However, during rock grouting, it is impossible to visually confirm whether the grouting material is being smoothly injected as intended. Without proper injection, the expected increases in strength, durability, and degree of consolidation may not be achieved. Therefore, it is necessary to predict in advance whether the grouting material is being injected as designed. In this study, we aimed to assess the injection performance based on injection variables such as the water/cement mixture ratio, injection pressure, and injection flow using UDEC (Universal Distinct Element Code) numerical program. Additionally, numerical results were validated by the lab experiment. The results of this study are expected to help optimize variables such as injection material properties, injection time, and pump pressure in the grouting design in the field.

• 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 the Korean GEO-environmental Society
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• v.25 no.6
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• pp.19-24
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• 2024

Urban densification has necessitated the development of subterranean spaces such as subway networks and underground tunnels to facilitate the dispersal and movement of populations. Development of these underground spaces requires excavation from the ground surface, which can induce groundwater flow and potentially lead to ground subsidence and sinkholes, damaging structures. To mitigate these risks, it is essential to model groundwater flow prior to construction, analyze its characteristics, and predict potential groundwater discharge during excavation. In this study, we collected meteorological, topographical, and soil conditions data for the city of ○○, where tunnel construction was planned. Using the Visual MODFLOW program, we modeled the groundwater flow. Excavation sections were set as drainage points to monitor groundwater discharge during the excavation process, and the effectiveness of seepage control measures was assessed. The model was validated by comparing measured groundwater levels with those predicted by the model, yielding a coefficient of determination of 0.87. Our findings indicate that groundwater discharge is most significant at the beginning of the excavation. Additionally, the presence of seepage barriers was found to reduce groundwater discharge by approximately 59%.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.1
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• pp.39-58
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• 2024

Tunnel Boring Machines (TBM) use multiple disc cutters to excavate tunnels through rock. These cutters wear out due to continuous contact and friction with the rock, leading to decreased cutting efficiency and reduced excavation performance. The rock's abrasivity significantly affects cutter wear, with highly abrasive rocks causing more wear and reducing the cutter's lifespan. The Cerchar Abrasivity Index (CAI) is a key indicator for assessing rock abrasivity, essential for predicting disc cutter life and performance. This study aims to develop a new method for effectively estimating CAI using rock strength, petrological characteristics, linear regression, and machine learning. A database including CAI, uniaxial compressive strength, Brazilian tensile strength, and equivalent quartz content was created, with additional derived variables. Variables for multiple linear regression were selected considering statistical significance and multicollinearity, while machine learning model inputs were chosen based on variable importance. Among the machine learning prediction models, the Gradient Boosting model showed the highest predictive performance. Finally, the predictive performance of the multiple linear regression analysis and the Gradient Boosting model derived in this study were compared with the CAI prediction models of previous studies to validate the results of this research.