The automotive industry continuously strives to enhance safety for both drivers and passengers through technological advancements. Car side impacts have the potential to significant risks to passengers, So the automotive industry has proposed various technological solutions. As part of these efforts, the development of side impact beams, which are affixed to the inner frame of vehicle side doors to absorb and dissipate collision energy, has been a safety enhancement. Conventional side impact beams are manufactured using hot-rolled steel sheets and have a pipe-like configuration. However, these impact beams are fixed to the vehicle's chassis, which directly transfers the energy generated during a collision to the chassis frame. This paper aims to address this issue by proposing the development and optimization of vehicle door impact beams using a dual-beam structure and fastening method, utilizing shear bolts. Moreover, the focus is on optimizing the cross-sectional shape of the dual-beam impact structure. The evaluation criterion for optimization is based on the second moment of area of the cross-section. To validate these improvements, Static experiments were conducted, comparing the proposed dual-beam structure with the traditional impact beam. This research is expected to serve as a guideline for enhancing vehicle safety through design directions and validation methods.

목적 : 인공지능의 기계학습 또는 심층학습을 이용한 연구가 다양한 분야에서 시도되고 있다. 본 연구는 공공 시력데이터를 자동화 수집하고, 수집한 데이터를 기계학습에 적용 및 예측하였다. 다양한 학습모델간 성능을 비교 함으로써, 시과학분야에서 적용 가능한 기계학습 최적화모델을 제시함에 있다. 방법 : 국민건강보험(NHISS) 및 통계포털(KOSIS)에 발표된 국민 시력분포 현황관련 자료를 특정 색인을 포함하 는 자료검색기법인 크롤링(crawling)을 사용하여 검색 및 수집을 자동화하였다. 2011년부터 2018년까지 보고된 모든 자료를 수집하였으며, 데이터 학습을 위해 Linear Regression, LASSO, Ridge, Elastic Net, Huber Regression, LASSO/LARS, Passive Aggressive Regressor 그리고 Pansacregressor 총 8개 모델을 사용하여 각각 데이터 학습 하였다. 결과 : 수집한 데이터를 기반으로 기계학습 모델을 통해 2018년을 예측하였다. 각 모델간 2018년도 실제-예측데 이터 차이를 MAE(Mean Absolute Error)와 RMSE(Root Mean Square Error) 점수로 각각 나타냈다. 학습모델 별 차이 중 MAE 평가결과 모델간 우/좌 Linear Regression(0.22/0.22), LASSO(0.83/0.81), RIDGE(0.31/0.31), Elastic Net(0.86/0.84), Huber Regression(0.14/0.07), LASSO/LARS(0.15/0.14), Passive Aggressive Regressor (0.29/0.18) 그리고 RANSA Regressor(0.22/0.22)를 보였다. RMSE에서 Linear Regression(0.40/0.40), LASSO (1.08/1.06), Ridge(0.54/0.54), Elastic Net(1.19/1.17), Huber Regression(0.20/0.20), LASSO/LARS(0.24/0.23), Passive Aggressive Regressor(0.21/0.58) 그리고 RANSA Regressor(0.40/0.40) 각각 나타냈다. 결론 : 본 연구는 자동화 자료검색 및 수집을 위한 크롤링 기법을 이용하여 데이터를 수집하였다. 이를 기반으 로 고전 선형모델을 기계학습에 적용할 수 있도록 하고, 데이터 학습을 위한 8개 학습모델들 간 성능을 비교하였다.

본 연구의 목적은 In-DEBS (In-situ Demonstration of Engineered Barrier System) 시험장치에 대한 설계안을 도출하고, 현장실증용 공학적방벽재의 생산을 위한 최적 제작조건을 도출하는 것이다. 이와 관련하여 그간 한국원자력연구원에서 수행한 실증실험 수행경험과 문헌분석 그리고 선진핵주기 고준위폐기물처분시스템(AKRS)을 근거로 시험장치를 설계하였다. 또한 처분용기와 벤토나이트 완충재는 시험제작을 통해 최적의 제작조건을 도출하였고, 예비 성능평가를 통해 제작된 공학적방벽재의 성능을 검증하였다. In-DEBS 현장시험을 위해서 AKRS의 1/2.3 규모로 설계하였으며, 고른 온도분포의 핵연료 모사를 위하여 설계 전력량 4.2 kW의 알루미늄 재질 몰드히터를 사용하였다. 한편 In-DEBS에 사용될 공학규모 이상의 균질 완충재 블록을 제작하기 위해 플롯팅 다이(floating die) 방식의 프레스 재하 및 냉간등방압프레스(CIP; Cold Isostatic Press) 기법을 국내 최초로 완충재 제작에 적용하였다. 연구결과 AKRS 완충재 블록 제한요건(건조밀도 › 1.6 kg·cm-3)을 충족하기 위해서는 1차로 40 MPa 이상의 플롯팅 다이 프레스 압력을 가하고, 2차로 50 MPa의 CIP 압력이 소요됨을 확인하였다. 또한 완충재 블록 내 센서설치를 위하여 CNC (Computer Numerical Control) 기법을 이용하여 센서위치를 정교하게 성형하였다

Recently in Construction field, It has been the big issues to produce an Eco-friendly Construction material and to solve problems about the First grade–Aggregates’ supply&demand. While the Eco-friendly Construction materials which are refurbished and reproduced from construction wastes and industrial by-products have a great deal of effectiveness such as cost or CO2 emission reduction, there is an additional logistical cost due to go through with some processes for recycle such as Intermediary treatments or management and collection of materials. Furthermore, Demand of the First grade-Aggregates is rising and spreading all over the nation for the improvement of Road driving performance, But there is also an additional logistical cost for supply&demand due to the cost of transport growth by sites of construction. In this study, the process and methodology of the new material supply and demand route routings using the Arc Gis Program and the calculation of the available distance through economic analysis are presented. After examining the cost status of construction materials and logistics costs by examining the literature review and related industry, economic feasibility was obtained by comparing the price of general construction materials with the total cost of comparable materials and logistics costs. After an economic analysis, ArcGis3.0 was used to visualize the materials’ supply&demand route and As a result, We can observe the economically secured route from the construction materials’ production plant to where the domestic transportable route and nodes mapped. Throughout the study, the pre-groundwork for an efficient use of the construction materials is able to be prepared and It will be helpful to invigorate supply&demand. In addition to the economic analysis in the future, If the real-time traffic information (traffic volume, speed, environment, etc.) and the performance (structure, functionality, etc.) of each construction materials are reflected, It will be possible to build a decision system for selecting construction materials which meet consumers’ various needs.

PURPOSES:Emulsified asphalt is critical for road construction. The objective of applying asphalt emulsion as an adhesive is to prevent the phenomenon of debonding between the upper and lower layers. The quantity and veriety of bituminous material can be varied according to the type of pavement and site conditions. The objective of this study is to reveal the optimum application rates of the emulsified asphalt materials by types of tack-coats using Interface Shear Strength(ISS).METHODS:In the research, emulsified asphalt was paved on the surface of the divided mixture. The specimens of paving asphalt emulsion were utilized to evaluate the bond strength of tack-coat materials. In the evaluation process, NCHRP Report 712 was utilized to investigate the Interface Shear Strength, which reflects the bond capacity of asphalt emulsion. Then, the optimum residual application rates by tack-coat types were determined using regression analysis.RESULTS:As a consequence of squared R values investigated from 0.7 to 1 as part of the regression analysis, the tendency of predicted ISS values was compared with the results. The optimum residual application rates of AP-3, RS(C)-4, QRS-4, and BD-Coat were determined to be 0.78ℓ/m2, 0.51ℓ/m2, 0.53ℓ/m2, and 0.73ℓ/m2, respectively, utilizing 4th regression analysis.CONCLUSIONS:Based on the result of this study, it was not feasible to conclude whether higher residual application of tack-coat material leads to improved bond capacity. Rather, the shearing strength varies depending on the type of pavement.

This study deals with optimized structural analysis of stainless rectangular water reservoirs with 5,000ton capacity for various combined load cases. The objective of this study is to propose most efficient structural models through the comparison of various model cases. In order to perform an optimized analysis, three dimensional finite element analyses are carried out for large sized models. The numerical results obtained provides the detailed size and thickness for optimal design of water reservoir. In particular, results reported in this paper show the influence of various types of loading and dimensions of the wall and stiffened column on the structural behavior of the large sized water tanks.

PURPOSES : In this study blast furnace slag, an industrial byproduct, was used with an activating chemicals, Ca(OH)2 and Na2SiO3 for carbon capture and sequestration as well as strength development. METHODS: This paper presents the optimized mixing design of Carbon-Capturing and Sequestering Activated Blast-Furnace Slag Mortar. Design of experiments in order to the optimized mixing design was applied and commercial program (MINITAB) was used. Statistical analysis was used to Box-Behnken (B-B) method in response surface analysis. RESULTS : The influencing factors of experimental are water ratio, Chemical admixture ratio and Curing temperature. In the results of response surface analysis, to obtain goal performance, the optimized mixing design for Carbon-Capturing and Sequestering Activated Blast- Furnace Slag Mortar were water ratio 40%, Chemical admixture ratio 58.78% and Curing temperature of 60℃. CONCLUSIONS: Compared with previous studies of this experiment is to some extent the optimal combination is expected to be reliable.

인공신경망 이론을 이용하여 강한 비선형성의 경향을 보이고 있는 강우-유출간의 관계를 모형화하기 위한 연구들은 예측뿐만이 아니라 대상자료들의 양상을 분류하여 그 특성을 분석하는 데에도 이용되고 있다. 이와 같은 패턴분류를 위한 SOM(Self-Organizing Map: SOM)의 연구 결과를 검토해보면 SOM 훈련을 위한 지도크기 및 배열의 결정은 SOM 성능에 큰 영향을 미치는 것으로 보고되고 있으나 지도크기 결정시 지도의 종방향 크기와 횡방향 크기를