This study aimed to measure the in-situ target strength (TS) of the moon jellyfish (Aurelia aurita) using a 200 kHz scientific echo sounder in a natural coastal environment. The acoustic survey was conducted in the coastal waters of Soho Port, Yeosu, Korea, from June 4 to 5, 2024. TS measurements were performed by installing the transducer in both horizontal and vertical orientations, and the TS distribution characteristics were analyzed. The measured TS values ranged from -89.8 to -64.8 dB in the horizontal direction and from -90.0 to -59.1 dB in the vertical direction, showing no significant difference between detection orientations (p<0.05). Additionally, a bell diameter-wet weight relationship for Aurelia coerulea was derived based on the specimens collected at the survey site. The empirical TS model proposed by Mutlu (1996) was applied to estimate TS values using the measured morphological data, and the results exhibited a similar trend to the field-measured TS distribution. These findings provide fundamental data for acoustic monitoring and stock assessment of jellyfish populations in natural environments.

이 연구는 산업 부산물을 활용한 무시멘트 숏크리트의 압축강도를 분석하고, 기존 숏크리트와 비교하여 최적 배합설계를 도출하였다. KS F 2403 및 KS F 2405에 따라 시험을 진행한 결과, 일반 숏크리트 및 무시멘트 숏크리트 모두 숏크리트 품질기준에서 제시하고 있 는 압축강도 기준을 만족하였다. 무시멘트 숏크리트 W/B 0.45 변수의 경우 초기강도가 가장 높게 나타났다. 28일 강도는 W/B 0.45 및 W/B 0.50 변수가 일반 숏크리트 대비 동등하거나 우수한 성능을 나타냈다.이러한 친환경 무시멘트 숏크리트 현장 적용을 위해서는 급 결제를 혼입한 배합에 대한 추가 검토가 필요할 것으로 판단되고, 또한 내구성능에 대한 추가적인 실험적 연구가 필요할 것으로 판단 된다.

이 연구는 라텍스 콘크리트(LMC)의 대체재로 해조류에서 추출한 알긴산을 활용한 콘크리트를 개발하고, 이에 따른 압축 강도 및 미 세구조 변화를 분석하였다. LMC는 공극 구조 개선으로 내구성과 강도가 향상되지만, 높은 원자재 비용이 단점이다. 이를 보완하기 위 해 라텍스 대신 알긴산을 혼합한 콘크리트를 제작하고, 주사전자현미경(SEM) 분석 및 압축 강도 실험을 수행하였다. 재령 4시간 및 1일 후의 압축 강도와 10000배율 SEM 분석을 진행하였다. 알긴산 0.05% 혼입 시 가장 높은 강도를 보였으며, 0.2% 혼 입 시 강도가 감소하였다. SEM 분석에서는 LMC가 균일한 표면과 미세한 입자 분포를 보인 반면, 알긴산 혼합 콘크리트는 섬유 및 막대 구조가 형성되었으며, 알긴산 함량이 증가할수록 기공이 증가하여 강도발현이 저하되었다. 재령이 증가하면서 C-S-H 구조가 조 밀해지고 결합력이 향상되어 강도가 증가하는 양상이 관찰되었다. 보다 정확한 결과를 얻기 위해서는 에너지 분산형 X선 분광법 (Energy Dispersive Spectroscopy, EDS)등 추가적인 실험이 필요할 것으로 사료된다.

본 연구는 2023년 중소기업 기술통계조사 데이터를 바탕으로, 혁신형 중소기업의 R&D 투자 강도, R&D 인력 비중, R&D 외부 협력 범위가 재무적 및 비재무적 혁신성과에 미치는 영향에 대한 업력의 조절 효과를 실증적으로 분석하였다. 재무적 성과는 R&D에 의한 매출 비율로 측정되었으며, 토빗 회귀분석(tobit regression analysis)을 통해 분석하였다. 비 재무적 성과는 지식재산권 보유 및 등록 현황으로 측정되어 음이항 회귀모형(negative binomial regression model)을 활용하여 분석하였다. R&D 투자 강도와 R&D 인력 비중은 재무적 혁신 성과에 유의미한 긍정적 영향을 미치며, 업력과의 상호작용 효과 또한 통계적으로 유의미하게 나타났다. 반면, R&D 외부 협력 범위는 재무적 혁신 성과에 직접적인 영향을 미치지 않았으나, 업력과의 상호작용에서는 유의미한 긍정적 효과가 확인되었다. 비재무적 혁신 성과의 경우, R&D 투자 강도는 유의미한 영향을 미치지 못했으나, 업력의 조절 효과는 젊은 기업에서 더 큰 영향을 미치는 것으로 나타났다. R&D 인력 비중은 비재무적 성과에 부정적 영향을 미쳤으나, 젊은 기업에서는 학습 속도와 흡수 능력을 통해 이러한 부정적 효과가 완화되었다. R&D 외부 협력 범위는 비재무적 성과에 긍정적 영향을 주었으며, 협력 범위와 비재무적 성과 간 역 U자형 관계가 존재함을 확인하였다.

The choice of lashing material is considered to be one of the important decision-making matters in securing the stability of cargo and ships in a situation where in-ship cargo must be tied by cargo and unit and fixed to the ship. This study focused on examining the breaking strength of woven lashing in use at the lashing work site and confirming safety through a comparison of related technical standards. As a result of the experiment, the minimum system fracture strength required according to the manufacturer's specifications was 8,500 daN/(18,700 lbf), and the experimental results averaged 9,012 daN(20,259 lbf), indicating that it met the criteria. In addition, the average actual elongation at maximum fixed loads (MSL: 4,620 daN(10,164 lbf)) was 5.9%, which met the criteria specified in CTU Code Appendix 7(MSC.1/Circ.1497). Applying woven lashing to coil lashing work in ships, lashing material costs were reduced by an average of 25% for the same workload during coil lashing work, the lashing time was shortened, resulting in an average reduction of 10% in ship mooring time.

본 연구는 SCGPA(Spent Coffee Grounds Pellet Ash)를 활용하여 다양한 혼합비율로 시멘트 복합체를 제작함으로써 SCGPA 의 신규 건설 재료로서의 적용 가능성을 확인하는 것을 목적으로 한다. 강도 시험 결과, 28일 압축강도 36.31 MPa 및 휨강도 12 MPa 를 나타낸 혼합비가 최적 혼합비로 도출되었으며, SCGPA의 시멘트 치환율이 증가함에 따라 복합체의 전반적인 강도 특성이 감소하는 경향성을 발견하였다. SEM 및 XRD 분석 결과, SCGPA는 최대 10%의 적절한 치환율을 적용할 경우 큰 강도의 저하를 일으키지 않고 시멘트 대체재로 기능할 수 있음이 확인되었다. 본 연구 결과를 바탕으로, 고품질의 굵은 골재를 함께 사용하여 콘크리트를 생산할 경 우, 30 MPa 이상의 압축강도를 가진 구조용 콘크리트도 제조할 수 있을 것으로 기대된다.

이 연구는 상변화 물질(PCM)을 함침시킨 경량 골재(LWA)를 활용한 고강도 콘크리트의 개발에 중점을 두고 있다. 상변화 온도가 5.5°C인 Tetradecane을 PCM으로 사용하였으며, LWA(Expended shale, ES)가 PCM 운반체 역할을 수행하였다. ES의 공극 은 진공 함침 기법을 통해 PCM으로 충전하였고, PCM-ES 복합체의 누출을 방지하기 위해 이중 코팅 처리가 추가로 이루어졌다. PCM-ES에 대한 시차주사열량계 시험 결과, 발열 및 흡열 엔탈피가 각각 96 J/g와 97 J/g로 나타났다. 콘크리트 혼합물은 밀도 최적 화를 위해 마이크로 실리카(MS), 실리카 분말(S), 실리카 모래를 사용하여 설계되었으며, PCM-ES는 실리카 모래의 25% 및 50%를 체 적 기준으로 대체하였다. 기계적 강도 테스트 결과, PCM-ES 콘크리트는 25% 및 50% 대체 시 각각 56.39 MPa와 45.94 MPa의 압 축 강도를 기록하였다. 열 성능 테스트는 다양한 주변 온도 조건에서 PCM-ES 콘크리트의 거동을 확인하기 위해 수행되었다. PCM-ES 콘크리트는 15°C에서 −5°C까지의 세 가지 열 사이클 시험을 진행하였으며, 이 과정에서 내부 온도가 지속적으로 모니터링 되었다. 결과적으로 주변 온도가 −5°C로 떨어지더라도 콘크리트 내부 온도는 0°C 이상을 유지하는 것으로 나타났다.

The purpose of this study was to develop a more accurate model for predicting the in-situ compressive strength of concrete pavements using Internet-of-Things (IoT)-based sensors and deep-learning techniques. This study aimed to overcome the limitations of traditional methods by accounting for various environmental conditions. Comprehensive environmental and hydration data were collected using IoT sensors to capture variables such as temperature, humidity, wind speed, and curing time. Data preprocessing included the removal of outliers and selection of relevant variables. Various modeling techniques, including regression analysis, classification and regression tree (CART), and artificial neural network (ANN), were applied to predict the heat of hydration and early compressive strength of concrete. The models were evaluated using metrics such as mean absolute error (MAE) to determine their effectiveness. The ANN model demonstrated superior performance, achieving a high prediction accuracy for early-age concrete strength, with an MAE of 0.297 and a predictive accuracy of 99.8%. For heat-of-hydration temperature prediction, the ANN model also outperformed the regression and CART models, exhibiting a lower MAE of 1.395. The analysis highlighted the significant impacts of temperature and curing time on the hydration process and strength development. This study confirmed that AI-based models, particularly ANNs, are highly effective in predicting early-age concrete strength and hydration temperature under varying environmental conditions. The ability of an ANN model to handle non-linear relationships and complex interactions among variables makes it a promising tool for real-time quality control in construction. Future research should explore the integration of additional factors and long-term strength predictions to further enhance the model accuracy.

The purpose of this study was to derive an optimal mix design for bridge deck pavements that can compensate for the limitations of latexmodified concrete (LMC). To address the limitations of LMC, this paper proposes the incorporation of silica fume into LMC. Concrete mixtures with varying ratios of latex and silica fume were prepared, and tests for compressive strength, flexural strength, and chloride-ion penetration resistance were conducted to compare and analyze the fundamental performance of each mix. Latex contributed to the improvement of the initial pore structure and significantly affected the chloride-ion penetration resistance in the early stages of curing. However, its influence gradually diminished over time. In contrast, silica fume induced additional C-S-H formation and further improved the pore structure through pozzolanic reactions as time progressed, thus exerting a greater impact in the later stages of curing. The L7-SF8 variable demonstrated the best performance in terms of compressive strength and chloride-ion penetration resistance. Given the characteristics of bridge-deck pavements, this variable is considered the most suitable for ensuring long-term durability. Therefore, this paper proposes a mixture of 7% latex and 8% silica fume as the optimal mix design.

In this study, the strength properties of recycled plastic materials using polypropylene, polyethylene, and high-density polyethylene were evaluated by measuring their compressive and flexural strengths, which are typically measured in cement-concrete pavements, to assess the feasibility of using recycled plastic materials as construction materials for modular pavements that can easily integrate advanced sensors, such as those for future autonomous driving. Two types of recycled plastic (composite resin and high-density polyethylene (HDPE)) and two types of inorganic materials (fly ash and limestone filler) were selected to evaluate the strengths of recycled plastic materials. Specimens for the compressive and flexural strength tests were prepared with four different recycled plastic contents (100%, 80%, 60%, and 40%). The compressive and flexural strengths of the recycled plastic specimens were measured according to the KSL ISO 679 and KSL 5105 methods, and the strength properties were analyzed based on the type and content of the recycled plastic and type of inorganic material used. Distortion and shrinkage problems were observed during specimen preparation using the 100% recycled plastic material. This indicated that inorganic materials must be incorporated to improve the flexural strength and facilitate specimen preparation. The compressive strength of plastic materials was comparable to the 28-day compressive strength of conventional cement-concrete pavements. The compressive strength of the composite resin was approximately twice that of HDPE. The flexural strengths of both the composite and HDPE were in the range of 15–25 MPa, suggesting their suitability as materials for the construction of modular pavement structures. Based on the limited strength test results, we can conclude that the strength properties of recycled plastic materials are similar to those of conventional cement-concrete paving materials. From the strength perspective, we confirmed that recycled plastic materials can be utilized as construction materials for modular pavements. However, further research should be conducted on factors such as molding methods for modular pavement structures based on different types of recycled plastic materials.

This project aimed to understand the near-infrared (NIR), intensity, and reflectivity characteristics of LiDAR for measuring retroreflectivity and to understand the correlation between the characteristics of LiDAR and retroreflectivity. A 600 m-testbed was investigated using a survey vehicle equipped with LiDAR, and the testbed retroreflectivity and LiDAR data measurement values were compared. The reflectivity and intensity at night were not affected by sunlight compared with daytime, enabling stable data collection. However, NIR reacted very sensitively to sunlight, and the difference between daytime and nighttime NIR values was very large. In addition, by comparing the absolute error between the retroreflectivity and LiDAR data, we observed that the reflectivity was consistent with the data difference between day and night, and it was not significantly affected by sunlight. However, the intensity showed that the daytime measurement data were more scattered than the nighttime measurement data, resulting in low-precision collection stability caused by sunlight. An analysis of the correlation between retroreflectivity and LiDAR data using 40 data points revealed that the reflectivity and intensity data at night were highly correlated with retroreflectivity, with a P-value of less than 0.05. Reflectivity and intensity values at night correlate with retroreflectivity. The NIR light is sensitive to sunlight. Thus, it can be used as a solar correction index for future retroreflectivity analyses using intensity.

In this research, the concrete breakout strength in tension of cast-in-place anchors (CIP) is experimentally investigated to be used as fundamental data for the seismic fragility analysis of equipment in nuclear power plants. Experimental variables are chosen, such as the embedment depth of the anchor, single/group anchor configurations, diameter of the head plate, and crack width. Monotonic and cyclic loading are applied to all types of specimens. As measured from the experiments, concrete breakout strength in tension is 1.5 to 2 times higher than the expected strengths from concrete capacity design (CCD) method-based model equations. In alignment with the model’s predictions, concrete breakout strength increases with deeper embedment depth, and the strength of group anchors also increases based on the expansion of the projected concrete failure area. This study also explores the effects of head plate diameter and crack width, which are not considered in the model equation. Experimental results show that the diameter of the head plate is not directly correlated to the concrete breakout strength, whereas the crack width is. The presence of cracks, with widths of 0.3 mm and 0.5 mm, leads to reductions of approximately 7% and 17%, respectively, compared to single anchors in non-cracked concrete.

기후 변화에 의해 해수면 온도 상승, 태풍의 최고 강도 북상, 태풍 강도 증가가 나타나고 있으며, 미래의 태풍 강도 변화가 더 심화될 것으로 예상하고 있다. 본 논문에서는 기후 변화 시나리오에 의해서 발생할 수 있는 한반도 부근의 태풍 강도를 예측하기 위하여 딥러닝 기반 태풍 강도 예측 모델을 개발하였다. 기후 예측정보를 이용하여 미래 기후 변화 환경장 변화에 따른 태풍의 강도를 예측할 수 있도록 과거 환경장을 학습 자료로 사용하였다. 학습자료는 1980년에서 2022년까지의 태풍 발생 빈도가 높은 6~10월의 기상 및 해양 재분 석 월평균 자료와 Best Track 태풍 241개를 입력자료로 사용하였다. 환경장 변화에 따른 태풍 강도 예측을 위해 자료의 공간적인 특징과 시간적인 특징을 함께 고려하는 딥러닝 모델인 ConvLSTM 기반으로 모델을 개발하였다. 태풍 트랙 시퀀스의 각 이동 경로에 대한 월평균 환경장 자료를 모델에 학습하여 태풍의 중심 기압을 예측하였다. 태풍의 공간적 특성을 반영할 수 있도록 범위를 설정하여 입력자료로 학습하였으며, 5°⨉ 5°의 범위일 때 가장 좋은 결과를 보였다. 몬테카를로 방법을 이용한 민감도 실험을 통해 모델 예측에 가장 큰 영향을 미치는 변수는 SST로 확인되었다.

The mechanical performance of SiC/SiC composites is significantly influenced by the architecture of fiber reinforcement. Among the various fabrication methods, the nano-powder infiltration transition/eutectic (NITE) process is a promising technique that is capable of achieving a dense and stoichiometric SiC matrix. The reinforcement architecture, such as cross-ply (CP) or woven prepreg (WP), is determined during the preform stage of the NITE process, which is crucial in determining the mechanical properties of SiC/SiC composites. In this study, the tensile test and double notch shear (DNS) test were conducted using NITE-SiC/SiC composites to investigate the effect of the fiber reinforcing architecture on the fracture mechanism of SiC/SiC composites. The tensile strength and maximum shear strength of both CP and WP specimens were nearly identical. However, other mechanical properties, particularly those of CP specimens, exhibited significant variability. A comparison of fracture surfaces and load-displacement curve analyses from the DNS tests revealed that the cross points of the longitudinal or transverse fibers act as obstacles to both deformation and crack propagation. These obstacles were found to be more densely distributed in WP specimens than in CP specimens. The variability observed in the mechanical properties of CP specimens is likely due to size effects caused by the sparser distribution of these obstacles compared to the WP specimens.