본 연구는 쇄빙연구선 아라온호의 국부 빙하중을 정확하게 추정하기 위해, 국제선급협회(IACS) Polar Class에 따른 실제 설계 하중(Design Ice Load)과 빙하중 면적(Load Patch)을 활용하여 영향계수행렬을 산정하였다. 기존 연구에서는 단위하중 또는 집중하중 형태 로 구조해석을 수행하여 영향계수행렬을 구했으나, 본 연구에서는 아라온호의 설계하중을 기반으로 실제 빙 충돌 상황과 유사한 면적 에 하중을 부여함으로써 보다 현실적인 해석 결과를 얻고자 하였다. 해석에는 선급 규칙으로부터 도출한 직사각형 형태의 빙하중 패치 와 평균 압력을 적용하였으며, 아라온호 선수부에 총 21개의 스트레인 게이지 위치에 대해 각각 해석을 진행하였다. 이후, 각 하중 조건 에서의 변형량을 토대로 21×21 행렬 형태의 영향계수행렬을 도출하였다. 본 연구의 결과로 도출된 영향계수행렬을 사용하면 실제 계측 된 스트레인 값을 기반으로 빙하중을 보다 정밀하게 추정할 수 있을 것으로 기대된다. 이는 기존 방식 대비 얼음 충돌 시 발생하는 면 적 효과를 고려했다는 점에서 의미가 있으며, 향후 쇄빙선 설계 및 운항 과정에서의 빙하중 평가 정확도를 높이는 데 기여할 것으로 판단된다.

Springtails (class Collembola) play a crucial role in soil ecosystems. They are commonly used as standard species in soil toxicity assessments. According to the ISO 11267 guidelines established by the International Organization for Standardization (ISO), Allonychiurus kimi uses adult survival and juvenile production as toxicity assessment endpoint. Conventional toxicity assessment methods require manually counting adults and larvae under a microscope after experiments, which is time-consuming and laborintensive. To overcome these limitations, this study developed a model using YOLOv8 to detect and count both adults and juveniles of A. kimi. An AI model was trained using a training dataset and evaluated using a validation dataset. Both training and validation datasets used for AI model were created by picturing plate images that included adults and larvae. Statistical comparison of validation dataset showed no significant difference between manual and automatic counts. Additionally, the model achieved high accuracies (Precision=1.0, Recall=0.95 for adults; Precision=0.95, Recall=0.83 for juveniles). This indicates that the model can successfully detect objects. Additionally, the system can automatically measure body areas of individuals, enabling more detailed assessments related to growth and development. Therefore, this study establishes that AI-based counting methods in toxicity assessments with offer high levels of accuracy and efficiency can effectively replace traditional manual counting methods. This method significantly enhances the efficiency of large-scale toxicity evaluations while reducing researcher workload.

도로 주행 시 차량의 제동거리 확보와 곡선부 주행 안전성은 도로 설계에서 중요한 요소이며, 강우로 인해 형성되는 수 막은 타이어와 노면 간 마찰력을 저하시켜 제동거리를 증가시키고 주행 안전성을 저해하는 요인으로 작용한다. 기존 도 로 설계에서는 습윤 상태에서의 마찰계수를 기준으로 하지만, 측정 방식마다 수막두께 기준이 상이하여 적용에 한계가 있다. 본 연구에서는 RRL 및 Gallaway 수막두께 예측 모델을 활용하여 다양한 도로 및 기상 조건에서의 수막두께를 예 측하고, 실측 실험을 통해 신뢰도가 높은 모델을 선정한 후, Gallaway의 마찰계수 예측식을 적용하여 강우강도, 배수 거 리, 포장 경사, 노면 조직 깊이, 타이어 트레드 깊이에 따른 마찰계수 변화를 분석하였다. 연구 결과, 강우강도가 증가하 고 배수 거리가 길어질수록 수막두께가 증가하면서 마찰계수가 감소하는 경향을 보였으며, 반대로 포장 경사와 노면 조 직 깊이가 증가할 경우 배수 성능이 향상되어 수막두께가 얇아지고 마찰계수가 증가하는 것으로 나타났다. 이러한 마찰 계수 변화는 도로 안전성에 직접적인 영향을 미쳐, 강우가 심한 조건에서는 제동거리가 길어지고 곡선부에서는 더 큰 반 경이 필요해지는 것으로 분석되었다. 특히 설계속도가 높은 구간이나 수막두께가 깊이 형성되는 구간, 타이어 트레드 깊 이가 얕은 경우 현행 도로 설계 기준이 요구하는 정지 시거를 충족하지 못하는 사례가 발생하였으며, 곡선부에서도 동일 한 문제점이 확인되었다. 결과적으로 강우 조건을 반영한 도로 설계 기준의 보완이 필요하며, 배수 성능을 강화하고 마 찰력 저하를 방지할 수 있는 포장 기법을 적용하는 것이 요구된다. 또한, 강우 시 주행 안전성을 확보하기 위해 동적 속 도 제한 시스템 도입 및 유지관리 체계를 강화하고, 강우 조건을 고려한 정지 시거 및 곡선 반경 설계 기준을 마련함으 로써 도로 안전성을 향상시킬 필요가 있다.

In this study, when manufacturing a rectangular plate with a pinhole, the following conclusions were obtained as a result of analyzing the shape characteristics of the unit cell that fills the interior of the plate and the effect of changes in the size of the pinhole on the stress concentration coefficient. 1. The stress value generated in the case of the completely empty model was 13.9 MPa, which is an 18.8% increase in stress compared to the stress value 11.7 MPa generated in the fully filled model. However, since the weight of the empty model is 43.6% lower than that of the filled model, it is believed to be very advantageous in terms of cost. 2. The stress concentration coefficient value is lowest for the solid model and highest for the hollow model at the same d/H value. In other words, you can see that the values are changing within the boxes of the solid model and hollow model. However, the grid matching model is closest to the solid model and the filling rate is as low as 33.4-9.1%, showing the most stable strength when filling empty space.

In this study, the pre-stress characteristics of magnetic rheological rubber, an intelligent material widely applied to mechanical systems, are measured. Intelligent materials are substances that change their properties in response to external inputs and are extensively used in mechanical systems. Magnetic rheological rubber is a representative intelligent material that can exhibit variable characteristics depending on the conditions. When measuring the physical properties of magnetic rheological rubber, it is placed in a magnetic field application device, where a magnetic field is applied, and the material is subjected to pre-stress. Similarly, when manufacturing intelligent mechanical systems using magnetic rheological rubber, pre-stress is induced by components used to apply the magnetic field. Generally, when a material is subjected to pre-stress, its properties change. Consequently, the performance of magnetic rheological rubber under pre-stress also varies. If the characteristics of the material under pre-stress change, the expected performance during design may deviate, leading to differences in the mechanical system's performance from the intended design. This variability makes it challenging to design mechanical systems based on intelligent materials, highlighting the importance of experimentally investigating their characteristics. Therefore, this study measures and identifies the pre-stress characteristics of magnetic rheological rubber under pre-stress. These findings can be applied to improve the measurement methods and design approaches for magnetic rheological rubber in pre-stressed conditions.

This study sought to improve the accuracy of estimating national emissions of volatile organic compounds (VOCs) from consumer solvent products (CSPs) by updating emission factors and category-specific activity data. The classification of the CSPs, which was originally proposed by the U.S. Environmental Protection Agency, was reorganized to reflect domestic consumption patterns in Korea. VOC contents, product sales, and atmospheric evaporation rates of the CSPs were analyzed for subcategories including personal care products, household products, and automotive aftermarket products to update their emission factors. Additionally, the category-specific activity data, previously based on only population statistics, were newly applied to count the characteristics of each classification, such as the number of households and the number of registered automobiles. The updated emission factors were calculated to be 1.90 kg/capita·yr for personal care products, 4.37 kg/household·yr for household products, and 2.36 kg/car·yr for automotive products. An evaluation of uncertainties revealed the limitation in the product classification, the shortage of sales data, and the lack of information on VOC contents depending on the product forms (liquid, solid, and aerosol). This study highlighted the necessity of developing detailed classification systems and standardized VOC content measurement methods, ultimately contributing to more accurate and practical assessments of VOC emissions from the CSPs.

This study aims to calculate the passenger car equivalent (PCE) of heavy vehicles on one-lane exit sections of underground roads. Traffic-flow simulations were performed using the VISSIM software program. The scenarios were designed under varying conditions, including design speeds of 40 and 50 km/h, slope gradients ranging from 3% to 9%, and heavy vehicle proportions between 5% and 40%. The mixed-traffic flow capacity was calculated for each scenario at the threshold levels, and the PCE was estimated based on the two capacity conditions. The results revealed that the PCE decreased at lower design speeds. For slopes less than 6% at a design speed of 40 km/h, and for slopes less than 4% at 50 km/h, the PCE remained consistent across all conditions. Single-lane exit sections are often employed on underground roads because of congestion and structural constraints. However, when heavy vehicles are present in these sections, the overall traffic flow is dictated by the speeds of the heavy vehicles. To address this issue, the implementation of variable or temporary two-lane exit sections has been proposed to enhance traffic flow and capacity.

The purpose of this study was to optimize the design of asphalt concrete pavements for Jeju Island by considering the regional characteristics of the island. This study employed an MEPDG program to determine the allowable traffic loads for class 4 vehicles by considering the axle loads, climate, and material properties. Samples of basalt asphalt concrete from Jeju were used to measure the dynamic modulus for material property estimation. The climate input was based on 30-year climate data from Jeju. The thicknesses and moduli of the subgrade, subbase, and asphalt layers were incorporated into the design. The regression-analysis program SPSS was used to develop a regression equation for the overlay design, factoring in the modulus and thickness ratios between the existing and overlay asphalt layers. A pavement-thickness design formula tailored to Jeju's characteristics was derived. An equivalent single-axle load factor (ESALF) formula was developed to facilitate traffic-load estimation for different roads, enabling the easy incorporation of varying traffic volumes into the design. The ESALF formula demonstrated a high correlation with the pavement thickness, subgrade conditions, and axle loads, whereas the pavementthickness design formula exhibited strong correlations with the pavement thickness, subgrade state, thickness ratios, and modulus ratios. The use of basalt aggregates in asphalt concrete pavements provides an economically viable and technically sound solution for Jeju. The proposed design methodology not only reduces costs but also enhances pavement performance and road safety. The developed formulas offer flexibility in adjusting designs based on specific traffic conditions, providing optimal pavement solutions for different road categories.

This study investigates the thermal expansion characteristics of hydroxyl-terminated polybutadiene(HTPB) based solid propellants, focusing on batch-to-batch variability and accelerated aging effects. Coefficient of thermal expansion(CTE) measurements were conducted using thermomechanical analyzer(TMA) on samples from different manufacturing batches and specimens aged at various temperatures for different durations. Results indicate variations in CTE values between batches, highlighting the significance of manufacturing process control. Accelerated aging experiments reveal minimal systematic changes in CTE, suggesting stability of thermal expansion properties under short-term thermal stress. The overall distribution of CTE values shows concentration within a specific range, indicating consistency in thermal expansion characteristics. These findings provide insights into the thermal behavior of HTPB-based solid propellants, contributing to improved missile design and lifecycle prediction models.

본 연구에서는 호흡동조화기법의 대안으로 딥러닝 자유호흡기법에서 b-value 별 겉보기확산계수 값을 평가하고 확 산강조영상과 겉보기확산계수 지도의 해부학적 일치성을 분석하여 적절한 여기횟수 값을 알아보고자 하였다. 연구 방법은 2023년 7월부터 2024년 1월까지 간 자기공명영상 검사가 의뢰된 성인 남녀 35명을 대상으로 하였고 사용 장비는 Magnetom Skyra 3.0T(Siemens, Germany)를 이용하였다. 자유호흡기법의 비교를 위해 b-value 50, 400, 800(s/mm2)의 여기횟수를 각각 딥러닝 호흡동조화기법에서 2,3,4으로 딥러닝을 이용하지 않은 일반 자유호 흡기법에서 4,6,8으로 검사하였다. 딥러닝을 추가한 일반 자유호흡기법에서는 1,2,3 여기횟수, 2,3,4 여기횟수, 3,5,6 여기횟수, 4,6,8 여기횟수로 변화하였다. 연구 결과 딥러닝 자유호흡기법에서 간의 좌엽과 우엽, 담낭의 평균 겉보기확산계수 값은 딥러닝 호흡동조화기법과 비교하여 모두 통계적 유의성을 확인하였다. 한편 정성적 평가의 해 부학적 일치성을 분석한 결과 딥러닝 자유호흡기법의 3,5,6 여기횟수와 4,6,8 여기횟수에서 가장 높은 점수를 얻었 으며 검사 시간에서는 딥러닝 호흡동조화기법과 비교하여 약 51%, 40% 감소하였다. 따라서 간 진단에 있어 딥러닝 자유호흡기법에서 b-value 별 적절한 여기횟수 값을 이용한다면 겉보기확산계수 지도의 정확도 유지와 함께 검사 시간을 감소시킬 수 있어 임상적으로 유용한 검사가 될 것으로 사료된다.

PURPOSES : The skid resistance between tires and the pavement surface is an important factor that directly affects driving safety and must be considered when evaluating the road performance. In especially wet conditions, the skid resistance of the pavement surface decreases considerably, increasing the risk of accidents. Moreover, poor drainage can lead to hydroplaning. This study aimed to develop a prediction equation for the roughness coefficient—that is, an index of frictional resistance at the interface of the water flow and surface material—to estimate the thickness of the water film in advance to prevent human and material damage. METHODS : The roughness coefficient can be changed depending on the surface material and can be calculated using Manning's theory. Here, the water level (h), which is included in the cross-sectional area and wetted perimeter calculations, can be used to calculate the roughness coefficient by using the water film thickness measurements generated after simulating specific rainfall conditions. In this study, the pavement slope, drainage path length, and mean texture depth for each concrete surface type (non-tined, and tined surfaces with 25-mm and 16-mm spacings) were used as variables. A water film thickness scale was manufactured and used to measure the water film thickness by placing it vertically on top of the pavement surface along the length of the scale protrusion. Based on the measured water film thickness, the roughness coefficient could be back-calculated by applying Manning's formula. A regression analysis was then performed to develop a prediction equation for the roughness coefficient based on the water film thickness data using the water film thickness, mean texture depth, pavement slope, and drainage path length as independent variables. RESULTS : To calculate the roughness coefficient, the results of the water film thickness measurements using rainfall simulations demonstrated that the water film thickness increased as the rainfall intensity increased under N/T, T25, and T16 conditions. Moreover, the water film thickness decreased owing to the linear increase in drainage capacity as the mean texture depth and pavement slope increased, and the shorter the drainage path length, the faster the drainage, resulting in a low water film thickness. Based on the measured water film thickness data, the roughness coefficient was calculated, and it was evident that the roughness coefficient decreased as the rainfall intensity increased. Moreover, the higher the pavement slope and the shorter the drainage path length, the faster the drainage reduced the water film thickness and increased the roughness coefficient (which is an indicator of the friction resistance). It was also evident that as the mean texture depth increased, the drainage capacity increased, which also reduced the roughness coefficient. CONCLUSIONS : As the roughness coefficient of the concrete road surface changes based on the environmental factors, road geometry, and pavement surface characteristics, we developed a prediction equation for the concrete pavement roughness coefficient that considered these factors. To validate the proposed prediction equation, a sensitivity analysis was conducted using the water film thickness prediction equation from previous studies. Existing models have limitations on the impact of the pavement type and rainfall intensity and can be biased toward underestimation; in contrast, the proposed model demonstrated a high correlation between the calculated and measured values. The water film thickness was calculated based on the road design standards in Korea—in the order of normal, caution, and danger scenarios—by using the proposed concrete pavement roughness coefficient prediction model under rainy weather conditions. Specifically, because the normal and caution stages occur before the manifestation of hydroplaning, it should be possible to prevent damage before it leads to the danger stage if it is predicted and managed in advance.

본 논문에서는 충격파 형태의 폭발 하중을 받는 부재의 소성 범위를 고려한 SDOF 해석의 수정계수를 개발하였다. SDOF 해석의 수 정계수는 MDOF 해석 결과 값을 비교하여 도출하였다. SDOF 해석에 영향을 미치는 매개변수로 부재의 경계조건, 폭발 하중 지속시 간과 고유주기 비를 선정하였다. 수정계수는 탄성 하중-질량 변환 계수를 기준으로 산정하였다. 수정계수 곡선은 상한, 하한 매개변수 경계 사이에 있도록 타원 방정식을 이용하여 도출하였다. 서로 다른 단면과 경계조건을 가지는 예제에 수정계수를 적용한 결과 SDOF 해석의 오차율이 15%에서 3%로 감소하였다. 본 연구의 결과는 수정계수를 적용하여 SDOF 해석의 정확도를 높임에 따라 폭발 해석 에 널리 활용될 수 있다.

PURPOSES : The study aims to establish a comprehensive life cycle assessment model for bridges in South Korea considering domestic carbon emission factors. The main aims are to evaluate the carbon emission of bridge construction, focusing on the Seong-ri Bridge as a case study, and to improve national environmental policies and management strategies. METHODS : We utilized the life cycle assessment (LCA) methodology, adhering to standards set by ISO, to categorize each phase of the bridge's life cycle. The process involved selecting the bridge type based on the compilation of a detailed analysis range. The analysis covered various stages from raw material supply (A1-A3) to construction (A4-A5) and maintenance (B2-B5), excluding certain stages due to data unavailability. Carbon emission factors were then applied to quantify emissions at each stage. RESULTS : The findings indicate that the raw material production phase (A1-A3) contributes to approximately 96% of the total carbon emissions, highlighting its significant impact. We report detailed calculations of emissions using domestically developed emission factors for materials such as steel and concrete and establish a carbon emission per unit length measure for comparative analysis with other infrastructure. CONCLUSIONS : We leveraged LCA ISO standards to analyze each stage of the Seong-ri bridge, calculating its carbon emissions based on domestic factors for CO2, CH4, and N2O. By tailoring the study to Korea-specific emission factors, we develop a greenhouse gas model closely aligned with the nation’s environmental conditions. The results contribute to improving environmental impact assessments and strategically aiding national policy and management decisions.

In this study, the characteristics of wind pressure distribution on circular retractable dome roofs with a low rise-to-span ratio were analyzed under various approaching flow conditions by obtaining and analyzing wind pressures under three different turbulent boundary layers. Compared to the results of previous studies with a rise-to-span ratio of 0.1, it was confirmed that a lower rise-to-span ratio increases the reattachment length of the separated approaching flow, thereby increasing the influence of negative pressure. Additionally, it was found that wind pressures varied significantly according to the characteristics of the turbulence intensity. Based on these experimental results, a model for peak net pressure coefficients for cladding design was proposed, considering variations in turbulence intensity and height.

여름철은 타 계절에 비해 장마와 불안정한 대기 등으로 인하여 빗길 교통사고의 위험성이 크게 증대될 수 있으며, 최근 5년 (2018~2022)간 여름철 빗길 교통사고는 전체 빗길 교통사고의 39%를 차지할 정도로 높은 수준이다. 이러한 빗길 운전은 노면의 배수 불량 및 미끄럼 저항 감소 등으로 인하여 수막현상을 발생시키게 된다. 이에 본 연구에서는 우천 시 도로의 안전성 강화 및 사고 위 험을 최소화하기 위한 요소인 수막두께를 산정하기 위하여 Manning의 평균 유속식을 기반으로 콘크리트 노면의 조도계수 예측 모델을 개발하는 것을 목표로 하였다. 조도계수의 영향인자를 고려하기 위하여 실외 강우 모의 장비를 이용하여 콘크리트를 타설한 뒤 실험 인자로 포장 경사, 배수거리, 강우강도, 노면 조직 특성을 고려하였으며, 이 중 노면 조직 특성은 타이닝 처리를 하지 않은 구간만 고 려한 타 연구의 기존 예측 모델 단점을 보완하기 위하여 16, 25mm 간격의 타이닝 표면 처리한 구간을 추가로 고려하였다. 수막두께 측정은 측정 범위 0.3~5mm의 수막두께 측정 게이지를 제작하여 강우가 모사된 조건에서 배수 거리 1~5m 이내 지점의 노면 조직 상 단과 수면이 접하는 수직 높이를 총 3회 측정하여 평균값을 사용하였다. 실측된 수막두께 데이터베이스를 기반으로 Manning 공식을 이용하여 조도계수를 역산한 결과, 강우강도가 증가함에 따라 조도계수는 감소하였으며, 이는 강우의 증가로 인해 물의 흐름과 콘크리 트 노면 사이의 마찰 저항 감소에 기인한 것으로 판단되었다. 또한 포장 경사가 높고 배수 거리가 짧을수록 배수성이 증가하여 마찰 저항의 지표인 조도계수가 증가하는 것으로 확인되었다. 평균 조직 깊이에 따른 조도계수 영향의 경우, 평균 조직 깊이가 증가할수록 콘크리트 표면에 노출되는 표면적이 증가하여 수막두께가 얕게 생성되고, 얕은 수심으로 인해 물의 흐름 저항이 감소하여 조도계수는 감소하는 것으로 산정되었다. 이후 135개의 데이터를 종합하여 조도계수를 종속변수로 하고 강우강도, 포장경사, 배수거리, 평균 조직 깊이, 수막두께를 독립변수로 하는 회귀분석을 수행하여 조도계수 산정식을 개발하였다.

PURPOSES : In this study, the applicability of the water content, suction, and suction stress in a resilient modulus prediction model for a subbase was reviewed. METHODS : To compare the applicability of water content, suction, and suction stress models for resilient modulus prediction, the suction stress was determined based on the soil water characteristic curve. The model parameters for each approach were derived from the measured resilient moduli. Finally, the relationships between the degree of saturation and resilient modulus were analyzed using the calculated model parameters. RESULTS : Prediction models of the resilient modulus based on water content and suction demonstrated high correlation with measured values, but overestimated the resilient modulus at saturation levels beyond the laboratory testing range. In contrast, the model accounting for suction stress effectively reduced this overestimation, likely owing to a decrease in suction stress as the suction increased. CONCLUSIONS : Based on the above results, the resilient modulus of subbase materials could be estimated through the change in the degree of saturation and the stress-dependent resilient modulus model using the suction stress proposed in this study.

PURPOSES : In this study, an empirical approach was established to estimate the parameters of the resilient modulus based on various geotechnical properties of subgrade soils. METHODS : Multiple regression analyses were performed to analyze the relationship between resilient modulus (k1) and deformation. The most important factors are the #200 sieve passing ratio, moisture content, and dry unit weight of the soil. The applicability of this approach was verified using selected field data and the literature. RESULTS : The correlation between the results predicted using the prediction equation of the model constant (k1) and the actual k1-value was high. The applicability of the prediction equation was considered high owing to its high suitability with the existing data. The range of values obtained using the constant prediction equation of the proposed model was also judged to be reasonable. In the comparison of the CBR value of the subgrade material of the actual design section and the predicted elastic modulus (k1), almost no relationship was observed between the CBR and the model coefficient (k1). Thus, the estimation of the elastic modulus through CBR is likely to contain errors. CONCLUSIONS : Based on these results, the parameters of the universal model can be predicted using the stress-dependent modulus model proposed in this study.