• Journal of the Korean Geotechnical Society
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• v.39 no.12
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• pp.37-46
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• 2023

Dynamic soil properties are essential factors for predicting the detailed behavior of the ground. However, there are limitations to gathering soil samples and performing additional experiments. In this study, we used an artificial neural network (ANN) to predict dynamic soil properties based on static soil properties. The selected static soil properties were soil cohesion, internal friction angle, porosity, specific gravity, and uniaxial compressive strength, whereas the compressional and shear wave velocities were determined for the dynamic soil properties. The Levenberg-Marquardt and Bayesian regularization methods were used to enhance the reliability of the ANN results, and the reliability associated with each optimization method was compared. The accuracy of the ANN model was represented by the coefficient of determination, which was greater than 0.9 in the training and testing phases, indicating that the proposed ANN model exhibits high reliability. Further, the reliability of the output values was verified with new input data, and the results showed high accuracy.

• Journal of the Korean Geotechnical Society
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• v.40 no.1
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• pp.5-14
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• 2024

PHC piles demonstrate superior resistance to compression and bending moments, and their factory-based production enhances quality assurance and management processes. Despite these advantages that have resulted in widespread use in civil engineering and construction projects, the design process frequently relies on empirical formulas or N-values to estimate the soil-pile friction, which is crucial for bearing capacity, and this reliance underscores a significant lack of experimental validation. In addition, environmental factors, e.g., the pH levels in groundwater and the effects of seawater, are commonly not considered. Thus, this study investigates the influence of vibrating machine foundations on PHC pile models in consideration of the effects of varying pH conditions. Concrete model piles were subjected to a one-month conditioning period in different pH environments (acidic, neutral, and alkaline) and under the influence of seawater. Subsequent repeated direct shear tests were performed on the pile-soil interface, and the disturbed state concept was employed to derive parameters that effectively quantify the dynamic behavior of this interface. The results revealed a descending order of shear stress in neutral, acidic, and alkaline conditions, with the pH-influenced samples exhibiting a more pronounced reduction in shear stress than those affected by seawater.

• Journal of Food Hygiene and Safety
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• v.39 no.2
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• pp.152-162
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• 2024

In this study, we investigated the levels of the natural preservatives, benzoic acid, sorbic acid, and propionic acid, in raw unprocessed vegetables. Quantitative analysis of benzoic acid and sorbic acid was performed using high-performance liquid chromatography with a diode array detector (HPLC-DAD) and confirmed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Propionic acid was analyzed using a gas chromatography-flame ionization detector (GC-FID) and confirmed using gas chromatography-mass spectrometry (GC-MS). From a total of 497 samples, benzoic acid, sorbic acid, and propionic acid were found in 50 (10%), 8 (0.2%), and 61 samples (12.3%), respectively. The highest quantity of benzoic acid, sorbic acid, and propionic acid was found in peony root (1,057 mg/kg), nut-bearing torreya seeds (27.3 mg/kg), and myrrha (175 mg/kg), respectively. The background concentration range of naturally occurring preservatives in raw vegetables determined in this study could be used as standard inspection criteria to address consumer complaints and trade disputes.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.1
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• pp.82-89
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• 2024

Black ice is very difficult to recognize and reduces the friction of the road surface, causing automobile accidents. Since black ice is difficult to detect, there is a need for a system that identifies black ice in real time and warns the driver. Various studies have been conducted to prevent black ice on road surfaces, but there is a lack of research on systems that identify black ice in real time and warn drivers. In this paper, an real-time image-based analysis system was developed to identify the condition of asphalt road surface, which is widely used in Korea. For this purpose, a dataset was built for each asphalt road surface image, and then the road surface condition was identified as dry, wet, black ice, and snow using deep learning. In addition, temperature and humidity data measured on the actual road surface were used to finalize the road surface condition. When the road surface was determined to be black ice, the salt spray equipment installed on the road was automatically activated. The surface condition recognition system for the asphalt concrete pavement and black ice automatic prevention system developed in this study are expected to ensure safe driving and reduce the incidence of traffic accidents.

• 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.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.7-11
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• 2024

The bedsore prevention cushion serves to prevent the skin on the buttocks of a wheelchair user from being damaged through friction with the wheelchair seat. This is because it absorbs some of the weight applied to the wheelchair seat and allows the weight to be applied evenly to the entire contact surface. For the impact test, which is part of the performance testing of bedsore prevention cushions, a hip model that represents the sitting posture of a wheelchair user is required. In this study, a hip model was manufactured for impact testing of bedsore prevention cushions for wheelchairs. Performance tests for pressure bedsore prevention cushions for wheelchairs include KSP 0236, a Korean standard, and KS P ISO 16840-2, an ISO international standard. The hip model proposed in KS P ISO 16840-2 was more suitable for impact testing of bedsore prevention cushions for wheelchairs. However, the guidelines for making hip models proposed by international standards need to be modified to reflect the advancement of model making technology and use easier methods. We propose a new hip model production method that produces a hip model out of plastic all at once and additionally attaches SS-41 steel plates processed into the shape of the hip to make the mass of the model similar to the human body.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.3
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• pp.169-189
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• 2024

In the current work, a series of three-dimensional finite element analyses have been carried out to understand the behaviour of pre-existing single piles and pile groups to adjacent Shield TBM tunnelling by considering various reinforcement conditions. The numerical modelling has analysed the effect of the pile cutting, ground reinforcement and pile cap reinforcement. The analyses concentrate on the ground settlements, the pile head settlements, the axial pile forces and the shear stress transfer mechanism at the pile-soil interface. In all cases of the pile tips supported by weathered rock, the distributions of shear stresses presented a similar trend. Also, when the pile tips were cut, tensile forces or compressive forces were induced on the piles depending on the relative positions of the piles. Furthermore, when the pile tips are supported by weathered rock, approximately 70% of the load is supported by surface friction, and only the remaining 30% is supported by the pile tip. Furthermore the final settlement of the piles without reinforcement showed approximately 70% more settlement than the piles for which ground reinforcement is considered. It has been found that the ground settlements and the pile settlements are heavily affected by the pile cutting and reinforcement conditions. The behaviour of the single pile and group piles, depending on the pile cutting, conditions of ground and pile cap reinforcement, has been extensively examined and analysed by considering the key features in great details.

• Land and Housing Review
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• v.15 no.3
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• pp.165-175
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• 2024

As living standards improve, the demand for quiet and comfortable environments is increases. However, noise sources are rising due to population growth, urbanization, and industrialization. One solution to this problem is low-noise asphalt pavement, which has fewer installation regulations and can reduce noise at the source. Despite its advantages, there are few evaluation cases and no domestic guidelines for low-noise asphalt pavement. This study aims to evaluate the overall performance of low-noise asphalt pavement on roads in Seoul, focusing on average noise levels, skid resistance, and permeability. Noise measurements were conducted using a digital noise analyzer according to the Ministry of Environment's standards. Results indicated that noise reduction was maintained for 1 to 2 years but decreased over time. Skid resistance, assessed with the British Pendulum Tester, showed excellent performance with values above the standard of 57 in the initial stages after paving. Permeability, evaluated using the KS F 2394 method, was initially effective across all sections but declined over time.

• Journal of Korean Society of Forest Science
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• v.113 no.3
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• pp.382-392
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• 2024

This study aimed to identify differences in soil properties and geological characteristics between slow-moving landslide prone areas and areas without landslide (control areas) in the Republic of Korea. During this 3-year field surveys (2019-2021), 11 soil and geological parameters were measured at 300 sites, i.e., 107 slow-moving landslide prone areas and 193 control areas. T-tests were conducted to identify differences in field-observed parameters between the two areas, followed by χ²-tests and density plots considering distribution patterns of the sample groups. We identified statistically significant differences between the areas in terms of soil internal friction angle, weathered rock thickness, bedrock thickness, and number of major geological anomaly zones. In conclusion, slow-moving landslide prone areas exhibited significant differences in geological characteristics, such as stratum thickness and presence of anomaly zones, compared with the control areas. To better understand the relationship between the geological characteristics identified in this study and the occurrence of slow-moving landslides, further field data collection and systematic analyses are warranted.

• Korean Chemical Engineering Research
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• v.62 no.4
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• pp.355-363
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• 2024

Although copolymer aramid is a fiber with excellent flexural performance, there is no test method to evaluate flexural fatigue performance. Various studies are currently being conducted in korea to develop copolymer aramid, and in order to develop the reliability of aramid fibers to a global level, it is necessary to develop a test method to evaluate the flexural fatigue performance of aramid fibers. In this study, we developed an test equipment and test method that can evaluate the flexural fatigue performance of copolymer aramid and analyzed the flexural fatigue performance of aramid fiber. Flexing rollers are made of ceramic materials and rotating shapes to minimize friction. The diameter of the roller was set to 10 mm by calculating the minimum allowable curvature. The B₁₀ life was calculated through a flexural fatigue test, and the para-aramid was 125,770 cycles, the copolymer aramid was 598,150 cycles, and the aramid nano fiber(ANF) coated copolymer aramid was 589,073 cycles. Through the S-N diagram, the fatigue life relationship according to the load change was confirmed. copolymer aramid fibers exhibit better flexural fatigue performance than para-aramid fibers at high loads. The ANF coated copolymer aramid also exhibits excellent flexural fatigue performance.