• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.

• International Journal of High-Rise Buildings
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• v.12 no.3
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• pp.225-234
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• 2023

Even at this point in the era of digital transformation, we are still facing many problems in the safety sector that cannot prevent the occurrence or spread of human casualties. When you are in an unexpected emergency, it is often difficult to respond only with human physical ability. Human casualties continue to occur at construction sites, manufacturing plants, and multi-use facilities used by many people in everyday life. If you encounter a situation where normal judgment is impossible in the event of an emergency at a life site where there are still many safety blind spots, it is difficult to cope with the existing manual guidance method. New variable guidance technology, which combines artificial intelligence and digital twin, can make it possible to prevent casualties by processing large amounts of data needed to derive appropriate countermeasures in real time beyond identifying what safety accidents occurred in unexpected crisis situations. When a simple control method that divides and monitors several CCTVs is digitally converted and combined with artificial intelligence and 3D digital twin control technology, intelligence augmentation (IA) effect can be achieved that strengthens the safety decision-making ability required in real time. With the enforcement of the Serious Disaster Enterprise Punishment Act, the importance of distributing a smart location guidance system that urgently solves the decision-making delay that occurs in safety accidents at various industrial sites and strengthens the real-time decision-making ability of field workers and managers is highlighted. The smart location guidance system that combines artificial intelligence and digital twin consists of AIoT HW equipment, wireless communication NW equipment, and intelligent SW platform. The intelligent SW platform consists of Builder that supports digital twin modeling, Watch that meets real-time control based on synchronization between real objects and digital twin models, and Simulator that supports the development and verification of various safety management scenarios using intelligent agents. The smart location guidance system provides on-site monitoring using IoT equipment, CCTV-linked intelligent image analysis, intelligent operating procedures that support workflow modeling to immediately reflect the needs of the site, situational location guidance, and digital twin virtual fencing access control technology. This paper examines the limitations of traditional fixed passive guidance methods, analyzes global technology development trends to overcome them, identifies the digital transformation properties required to switch to intelligent variable smart location guidance methods, explains the characteristics and components of AI-based public facility smart fire safety integrated system (ISFS).

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.2
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• pp.81-93
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• 2023

Under the digital economy, companies are facing a new business environment. Previous studies with a traditional perspectives on start-ups explained they are at a disadvantaged compared to large companies in mobilizing resources and building new relationships. However, recent researches on the digital economy and platform ecosystems have suggested that digital platforms can be an efficient means of overcoming the liabilities of smallness and newness for start-up companies. Through platforms start-ups can secure routes to mobilize resources, collect and analyze market information. Especially as various platorms are established within categorized industry or market, unique characteristics and market awareness regarding an individual startup company have been formed. Accordingly there is also an advantage that startup companies have umbrella effect by participating in the platform. From this point of view, this study describes the process by which a start-up company effectively utilizes a platform to overcome the liabilities of newness and smallness through the case the study of 'Genie the Bottle'. The results suggest that platforms play a crucial role for start-ups to secure awareness and legitimacy and grow the market in the beauty industry in which high consumer involvement is dominant.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.51-63
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• 2023

The purpose of this study is to develop and implement a secondary teacher training program based on the TPACK model to strengthen the capacity of teachers of youth entrepreneurship education in the context of the increasing importance of entrepreneurship as a future competency, and to provide theoretical and practical implications based on it. To this end, a teacher training program was developed through the process of analysis, design, development, implementation, and evaluation based on the ADDIE model, and 22 secondary school teachers in Gangwon Province were trained and the effectiveness and validity were analyzed. First, the results of the paired sample t-test of TPACK in entrepreneurship education conducted before and after the program showed statistically significant improvements in all sub-competencies. Second, the satisfaction survey of the training program showed that the overall satisfaction was high with M=4.83. Third, the validity of the program was reviewed by three experts, and it was found to be highly valid with a validity of M=5.0, usefulness of M=4.7, and universality of M=5.0. Based on the results, it is suggested that in order to expand entrepreneurship education, opportunities for teachers' holistic capacity building such as TPACK should be expanded, teachers' understanding and practice of backward design should be promoted, and access to various resources that can be utilized in entrepreneurship education should be improved.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.2
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• pp.35-42
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• 2024

This study evaluated the buildability and mechanical properties of 3DP concrete printed in air and underwater environments. Buildability was evaluated by green strength test on fresh concrete and height and deflection immediately and 1 hour after printing. The green compressive strength of the concrete was 5.0 kPa after 30 minutes and 7.9 kPa after 3 hours, an increase of 1.6 times the initial strength. The total height of the laminated parts met the design height regardless of the printing environment. The amount of deflection in air and under water 1 hour after printing was 1 mm and 0.2 mm, respectively, indicating a small amount of deflection under water. The apparent density of the sample appeared in the order of A-M > A-P > UW-P. This is believed to be because a large amount of air is mixed into the concrete during the printing process, and water infiltrates during the underwater printing process. The compressive strength ratio of UW-P/A-P was 0.86 at 1 day, but the compressive strength of the underwater printed concrete was high from 7 days.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.3
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• pp.1-9
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• 2024

This study proposes a multi-scale template matching technique with image pyramids (TMI) to measure structural dynamic displacement using a vision sensor under atmospheric turbulence conditions and evaluates its displacement measurement performance. To evaluate displacement measurement performance according to distance, the three-story shear structure was designed, and an FHD camera was prepared to measure structural response. The initial measurement distance was set at 10m, and increased with an increment of 10m up to 40m. The atmospheric disturbance was generated using a heating plate under indoor illuminance condition, and the image was distorted by the optical turbulence. Through preliminary experiments, the feasibility of displacement measurement of the feature point-based displacement measurement method and the proposed method during atmospheric disturbances were compared and verified, and the verification results showed a low measurement error rate of the proposed method. As a result of evaluating displacement measurement performance in an atmospheric disturbance environment, there was no significant difference in displacement measurement performance for TMI using an artificial target depending on the presence or absence of atmospheric disturbance. However, when natural targets were used, RMSE increased significantly at shooting distances of 20 m or more, showing the operating limitations of the proposed technique. This indicates that the resolution of the natural target decreases as the shooting distance increases, and image distortion due to atmospheric disturbance causes errors in template image estimation, resulting in a high displacement measurement error.

• Journal of the Korean Geotechnical Society
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• v.40 no.3
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• pp.93-100
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• 2024

Pore water freezes in low-temperature compaction, which leads to different compaction characteristics compared to room temperature conditions. In regions like Alberta, Canada, where organic soils are prevalent, compaction performance is influenced by the high water retention and compressibility of organic soils, as well as their sensitivity to freezing and thawing. Alberta's strict environmental regulations demand the reuse of excavated soil for backfill, and the long winter season creates challenging conditions for civil engineering projects. In this study, a laboratory compaction test was conducted to evaluate the low-temperature compaction characteristics of organic soils with varying organic content. The results indicate that the optimum moisture content increases as the organic content increases, and the maximum dry unit weight decreases by up to 21.9%. In addition, under temperature conditions below -4℃, no optimum moisture content was observed, and the dry unit weight decreased as the moisture content increased.

• Computers and Concrete
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• v.33 no.5
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• pp.497-507
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• 2024

In squat reinforced concrete walls, the displacement capacity for lateral deformation is low and the ability to resist the axial load can quickly be lost, generating collapse. This work consists of testing two squat reinforced concrete walls. One of the specimens is built with conventional detailing of reinforced concrete walls, while the second specimen is built applying an alternative design, including stirrups along the diagonal of the wall to improve its ductility. This solution differs from the detailing of beams or coupling elements that suggest building elements equivalent to columns located diagonally in the element. The dimensions of both specimens correspond to a wall with a low aspect ratio (1:1), where the height and length of the specimen are 1.4 m, with a thickness of 120 mm. The alternative wall included stirrups placed diagonally covering approximately 25% of the diagonal strut of the wall with alternative detailing. The walls were tested under a constant axial load of 0.1f'cAg and a cyclic lateral displacement was applied in the upper part of the wall. The results indicate that the lateral strength is almost identical between both specimens. On the other hand, the lateral displacement capacity increased by 25% with the alternative detailing, but it was also able to maintain the 3 complete hysteretic cycles up to a drift of 2.5%, reaching longitudinal reinforcement fracture, while the base specimen only reached the first cycle of 2% with rapid degradation due to failure of the diagonal compression strut. The alternative design also allows 46% more energy dissipation than the conventional design. A model was used to capture the global response, correctly representing the observed behavior. A parametric study with the model, varying the reinforcement amount and aspect ratio, was performed, indicating that the effectiveness of the alternative detailing can double de drift capacity for the case with a low aspect ratio (1.1) and a large longitudinal steel amount (1% in the web, 5% in the boundary), which decreases with lower amounts of longitudinal reinforcement and with the increment of aspect ratio, indicating that the alternative detailing approach is reasonable for walls with an aspect ratio up to 2, especially if the amount of longitudinal reinforcement is high.

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.125-132
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• 2024

The purpose of this study is to discover a drone utilization model tailored to local characteristics, propose directions for building a drone demonstration city based on demand surveys for drone activation, and suggest ways to utilize and support a drone application system. First, according to the survey results, there was a high understanding of and necessity for drone demonstration projects, particularly in addressing urban issues, which were deemed to have a significant impact. Second, based on the analysis of priorities and short- and long-term approaches, disaster-related tasks were evaluated as a priority, requiring an approach through medium- to long-term strategies. Third, it was noted that budgetary considerations emerged as the most critical issue during project implementation. Practitioners and experts expressed willingness to actively introduce drone-based technologies into their work when budget and technology were ready. Budgetary constraints were identified as the most significant obstacle to proper implementation, emphasizing the need for resolution. Fourth, the necessity of demand surveys during project development was identified in certain areas. Demand surveys were deemed essential for drone-based demonstration city construction, and a survey indicated that public leadership in this regard was also necessary. Fifth, concerning approaches in specific areas, the field of safety and disaster management was highlighted as the most crucial for application.