• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6C
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• pp.385-393
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• 2006

Soil nailing is a reinforcement method used for stabilizing excavated walls or slopes. Due to its much advantages such as ease of construction and economical efficiency, use of soil nailing is increased. However, the soil nail has much disadvantages for use in urban area. The soil nail needs to be installed inevitably beyond private land boundary, which causes rent for use. For this reason, removable soil nailing system was developed. However, the removal rate of this system is just about 50￠¦70%. To resolve this problem, the Fiber Reinforced Plastic (FRP) soil nailing system which does not need to be removed and allows for the installation beyond private land, is developed. In this paper, through theoretical and experimental studies in laboratory and field such as prototype tests, pullout tests, we evaluate the stability and behavior characteristics of the FRP soil nailing system. And, numerical analyses using FLAC2D were performed with respect to various soil conditions, where prototype test for excavation wall and pullout tests were carried out. As a result of this study, the FRP soil nailing systems show similar behavior characteristics with those of removable soil nailing system. Finally, considering the serviceability and mechanical stability of FRP soil nailing systems, it is enough to be used as a good alternative of general soil nailing system.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.391-393
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• 2022

In recent years, technologies forecasting demand based on deep learning and big data have accelerated the smartification of the field of e-commerce, logistics and distribution areas. In particular, ports, which are the center of global transportation networks and modern intelligent logistics, are rapidly responding to changes in the global economy and port environment caused by the 4th industrial revolution. Port traffic forecasting will have an important impact in various fields such as new port construction, port expansion, and terminal operation. Therefore, the purpose of this study is to compare the time series analysis and deep learning analysis, which are often used for port traffic prediction, and to derive a prediction model suitable for the future container prediction of Busan Port. In addition, external variables related to trade volume changes were selected as correlations and applied to the multivariate deep learning prediction model. As a result, it was found that the LSTM error was low in the single-variable prediction model using only Busan Port container freight volume, and the LSTM error was also low in the multivariate prediction model using external variables.

• Korean Journal of Ecology and Environment
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• v.56 no.4
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• pp.363-374
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• 2023

The limitation in deriving the species richness representing the entire country of South Korea lies in its relatively short history of species field observations and the scattered observation data, which has been collected by various organizations in different fields. In this study, a comprehensive compilation of the observation data for plants held by agencies under the Ministry of Environment was conducted, enabling the construction of a time series dataset spanning over 100 years. The data integration was carried out using minimal criteria such as species name, observed location, and time (year) followed by data verification and correction processes. Based on the integrated plant species data, the comprehensive collection of plant species in South Korea has occurred predominantly since 2000, and the number of plant species explored through these surveys appears to be converging recently. The collection of species survey data necessary for deriving national-level biodiversity information has recently begun to meet the necessary conditions. Applying the Chao 2 method, the species richness of indigenous plants estimated at 3,182.6 for the 70-year period since 1951. A minimum cumulative period of 7 years is required for this estimation. This plant species richness from this study can be a baseline to study future changes in species richness in South Korea. Moreover, the integrated data with the estimation method for species richness used in this study appears to be applicable to derive regional biodiversity indices such as for local government units as well.

• Journal of Trauma and Injury
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• v.36 no.4
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• pp.441-446
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• 2023

Inferior vena cava (IVC) injuries, while accounting for fewer than 0.5% of blunt abdominal trauma cases, are among the most difficult to manage. Despite advancements in prehospital care, transportation, operative techniques, and perioperative management, the mortality rate for IVC injuries has remained at 20% to 66% for several decades. Furthermore, 30% to 50% of patients with IVC injuries succumb during the prehospital phase. A 65-year-old male patient, who had been struck in the back by a 500-kg excavator shovel at a construction site, was transported to a regional trauma center. Injuries to the right side of the infrarenal IVC and the right external iliac vein (EIV) were suspected, along with fractures to the right iliac bone and sacrum. The injury to the right side of the infrarenal IVC wall was repaired, and the right internal iliac artery was ligated. However, persistent bleeding around the right EIV was observed, and we were unable to achieve proximal and distal control of the right EIV. Attempts at prolonged manual compression were unsuccessful. To decrease venous return, we ligated the right superficial femoral vein. This reduced the amount of bleeding, enabling us to secure the surgical field. We ultimately controlled the bleeding, and approximately 5 L of blood products were infused intraoperatively. A second-look operation was performed 2 days later, by which time most of the bleeding sites had ceased. Orthopedic surgeons then took over the operation, performing closed reduction and external fixation. Five days later, the patient underwent definitive fixation and was transferred for rehabilitation on postoperative day 22.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.93-98
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• 2023

In paddy rice farming, water management is a critical task. To suppress weed emergence during the early stages of growth, fields are deeply flooded, and after transplantation, the water level is reduced to promote rooting and stimulate stem generation. Later, water is drained to prevent the production of sterile tillers. The adequacy of water supply is influenced by various factors such as field location, irrigation channels, soil conditions, and weather, requiring farmers to frequently check water levels and control the ingress and egress of water. This effort increases if the fields are scattered in remote locations. Automated irrigation systems have been considered to reduce labor and improve productivity. However, the net income from rice production in 2022 was about KRW 320,000/10a on average, making it financially unfeasible to implement high-cost devices or construct new infrastructure. This study focused on developing an IoT-Based irrigation valve that can be easily integrated into existing agricultural infrastructure without additional construction. The research was carried out in three main areas: Firstly, an irrigation valve was designed for quick and easy installation on existing agricultural pipes. Secondly, a power circuit was developed to connect a low-power Cat M1 communication modem with an Arduino Nano board for remote operation. Thirdly, a cloud-based platform was used to set up a server and database environment and create a web interface that users can easily access.

• Explosives and Blasting
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• v.41 no.3
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• pp.26-37
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• 2023

In order to determine the amount of explosives that can minimize the vibration generated during tunnel construction using the blasting method, it is necessary to derive the blasting vibration coefficients, K and n, by analyzing the vibration records of trial blasting in the field or under similar conditions. In this study, we aimed to develop a technique that can derive reasonable K and n when trial blasting cannot be performed. To this end, we collected full-scale trial blast data and studied how to predict the blast vibration coefficient (K, n) according to the type of explosive, center cut blasting method, rock origin and type, and rock grade using deep learning (DL). In addition, the correction value between full-scale and borehole trial blasting results was calculated to compensate for the limitations of the borehole trial blasting results and to carry out a design that aligns more closely with reality. In this study, when comparing the available explosive amount according to the borehole trial blasting result equation, the predictions from deep learning (DL) exceed 50%, and the result with the correction value is similar to other blast vibration estimation equations or about 20% more, enabling more economical design.

• 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).

• Journal of the Korean Chemical Society
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• v.67 no.6
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• pp.441-461
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• 2023

This study explore characteristics of teaching orientation and pck of science teachers in online-offline mixed learning environment. Data consisted of open-ended survey, semi-structured interview, class observation, field notes from 12 science teachers. We categorized teaching orientation considering both science education goals and science teaching·learning orientation. There were 8 different teaching orientations such as 'understanding science concepts-lecture centered' 'constructing science concepts-inquiry based' 'applying science concepts and inquiry-inquiry based' 'applying science concepts and inquiry-lectured centered' 'analyzing and judging science information-inquiry based' 'developing scientific attitude-inquiry based' 'developing scientific attitude-lecture centered' and 'developing perception of interrelationships among science, technology, and society-inquiry based'. Teachers with inquiry based teaching·learning orientation seemed to have knowledge of science curriculum specific to online learning environment for student inquiry. While teachers with 'understanding science concepts-lecture centered' teaching orientation appeared to have questioning strategy of checking student understanding and strategy of repeating a lecture, teachers with 'constructing science concepts-inquiry based' teaching orientation appeared to have knowledge of instructional strategies to perform online group activities targeting student construction of knowledge and to replace face-to-face group activities with virtual experiments and individual experiments. While teachers with 'understanding science concepts-lecture centered' teaching orientation did not show knowledge of student science learning, teachers with 'constructing science concepts-inquiry based' teaching orientation appeared to have knowledge of student difficulties in inquiry based learning.

• Journal of the Korean Geotechnical Society
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• v.39 no.12
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• pp.75-91
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• 2023

In this study, we explore the use of ground penetrating radar (GPR) for the nondestructive survey of subsurface conduits, focusing on the challenges posed by multichannel environments. A key concern is the shadow regions created by conduits, which significantly impact survey results. The shadow regions, which are influenced by conduit position and diameter, hinder signal propagation, thereby making detection within these regions challenging. Using finite-difference time-domain numerical analysis, we examined the characteristics of conduit signals, which typically manifest in hyperbolic patterns. Particularly, we investigated three conduit arrangements: horizontal, vertical, and diagonal. Automatic gain control was applied to amplify the signals, enabling the analysis of variations in shadow regions and signal characteristics for each arrangement. In the horizontal arrangement, the proximity of the two conduits resulted in the emergence of a new hyperbolic pattern between the existing conduits. In the vertical arrangement, the lower conduit could be detected using hyperbolic signals on either side, but the detection was challenging when the upper conduit diameter exceeded that of the lower conduit. In the diagonal arrangement, signal characteristics varied based on the position of shadow regions relative to the detection range of the equipment. Asymmetrical signal patterns were observed when the shadow regions fell within the detection range, whereas the signals of the two conduits were minimally impacted when the shadow regions were outside the detection range. This study provides vital insights into accurately detecting and characterizing subsurface multichannel conduits using GPR-a significant contribution to the field of subsurface exploration and management.

• Journal of Cadastre & Land InformatiX
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• v.53 no.2
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• pp.5-18
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• 2023

As the development of drones and sensors accelerates, new services and values are created by fusing data acquired from various sensors mounted on drone. However, the construction of spatial information through data fusion is mainly constructed depending on the image, and the quality of data is determined according to the specification and performance of the hardware. In addition, it is difficult to utilize it in the actual field because expensive equipment is required to construct spatial information of high-quality. In this study, super-resolution was performed by applying deep learning to low-resolution images acquired through RGB and THM cameras mounted on a drone, and quantitative evaluation and feature point extraction were performed on the generated high-resolution images. As a result of the experiment, the high-resolution image generated by super-resolution was maintained the characteristics of the original image, and as the resolution was improved, more features could be extracted compared to the original image. Therefore, when generating a high-resolution image by applying a low-resolution image to an super-resolution deep learning model, it is judged to be a new method to construct spatial information of high-quality without being restricted by hardware.