In this study, we evaluate artificial neural network (ANN) models that estimate the positions of gamma-ray sources from plastic scintillating fiber (PSF)-based radiation detection systems using different filtering ratios. The PSF-based radiation detection system consists of a single-stranded PSF, two photomultiplier tubes (PMTs) that transform the scintillation signals into electric signals, amplifiers, and a data acquisition system (DAQ). The source used to evaluate the system is Cs-137, with a photopeak of 662 keV and a dose rate of about 5 μSv/h. We construct ANN models with the same structure but different training data. For the training data, we selected a measurement time of 1 minute to secure a sufficient number of data points. Conversely, we chose a measurement time of 10 seconds for extracting time-difference data from the primary data, followed by filtering. During the filtering process, we identified the peak heights of the gaussian-fitted curves obtained from the histogram of the time-difference data, and extracted the data located above the height which is equal to the peak height multiplied by a predetermined percentage. We used percentage values of 0, 20, 40, and 60 for the filtering. The results indicate that the filtering has an effect on the position estimation error, which we define as the absolute value of the difference between the estimated source position and the actual source position. The estimation of the ANN model trained with raw data for the training data shows a total average error of 1.391 m, while the ANN model trained with 20%-filtered data for the training data shows a total average error of 0.263 m. Similarly, the 40%-filtered data result shows a total average error of 0.119 m, and the 60%-filtered data result shows a total average error of 0.0452 m. From the perspective of the total average error, it is clear that the more data are filtered, the more accurate the result is. Further study will be conducted to optimize the filtering ratio for the system and measuring time by evaluating stabilization time for position estimation of the source.

국내 주요 사회기반시설의 70% 이상이 철근콘크리트 구조물로 구성되어 있다. 최근 다양한 사회적ㆍ환경적 변화로 인한 내하력 저하 및 노후화 진행이 발생됨에 따라 섬유강화 복합소재(FRP)를 활용한 유지보수 수요 및 비용이 급격히 증가되 고 있다. 이에 따라 보다 경제적이고 효율적으로 FRP 보강재를 활용함에 있어서 성능을 예측할 수 있는 방법이 요구된다. 본 연구에서는 CFRPㆍBFRP 복합재료를 실험 대상으로 선정하고 성능을 결정하는 주요 인자인 섬유/수지 함침률을 54.3%, 43.9%, 39% 3가지로 분류하여 성능을 평가하고 이를 활용하여 FRP의 성능을 예측할 수 있는 모델식을 개발하고자 하였다. 매개변수에 따른 성능평가 결과, 두 섬유 모두 함침률이 낮아질수록 재료성능 또한 감소되는 것이 확인되었으며, 특히 BFRP의 경우 39%의 함침률에서 감소폭이 CFRP 대비 더 큰 것으로 나타났다. 실험 결과와 기존의 예측 모델식과의 성능 비교를 통해 약 15%의 오 차가 나타나는 것을 확인하였으며, 이에 따른 보정계수를 산정하여 예측 모델식을 재정립하였다.

Due to environmental pollution, regulations on existing petroleum-based fuels are increasing day by day. LNG is in the spotlight as an eco-friendly fuel that does not emit NOx or SOx, but its boiling point is -163°C, so it needs to be handled with care. Materials that can be used at the above temperature are defined by IMO through the IGC Code. Among them, 9% nickel steel has great advantages in yield strength and tensile strength under cryogenic conditions, but it is difficult to use in arc welding such as FCAW for various reasons. This study is a study to apply fiber laser welding to solve this problem. As a previous study, this study conducted a study to find a welding heat source. After performing bead on plate welding, the optimal heat source was derived by analyzing the shape of the bead and adjusting the parameters of the heat source model. In this case, by applying the multi-island genetic algorithm, which is a global optimization algorithm, not the intuition of the researcher, accurate results could be derived in a wide range.

The multi-layered heat source model is a model that can cover most of existing studies and can be defined with a simple formula. Based on the methodology performed in previous studies, the welding heat source was found through experiments and FEM under the welding power conditions of three cases and the parameters of the welding heat source were analyzed according to the welding power. In this study, parameters of fiber laser welding heat source according to welding power were searched through optimization algorithm and finite element analysis, and the correlation was analyzed. It was confirmed that the concentration of the welding heat source in the 1st layer was high regardless of the welding power, and it was confirmed that the concentration of the welding heat source in the 5th layer (last layer) increased as the welding power increased. This reflects the shape of the weld bead that appears during actual fiber laser welding, and it was confirmed that this study represents the actual phenomenon.

In this study, a welding heat source model was presented and verified during fiber laser welding. The multi-layered heat source model is a model that can cover most of existing studies and can be defined with a simple formula. It consists of a total of 12 parameters, and an optimization algorithm was used to find them. As optimization algorithms, adaptive simulated annealing, multi island genetic algorithm, and Hooke-Jeeves technique were applied for comparative analysis. The parameters were found by comparing the temperature distribution when the STS304L was bead on plate welding and the temperature distribution derived through finite element analysis, and all three models were able to derive a model with similar trends. However, there was a deviation between parameters, which was attributed to the many variables. It is expected that a more clear welding heat source model can be derived in subsequent studies by giving a guide to the relational expression and range between variables and increasing the temperature measurement point, which is the target value.

Welding is the most widely used technology for manufacturing in the automobile, and shipbuilding industries. Fiber laser welding is rapidly introduced into the field to minimize welding distortion and fast welding speed. Although it is advantageous to use finite element analysis to predict welding distortion and find optimized welding conditions, there are various heat source model for fiber laser welding. In this study, a welding heat source was proposed using a multi-layered heat source model that encompasses most of the existing various welding heat source models: conical shape, curved model, exponential model, conical-cylindrical model, and conical-conical model. A case study was performed through finite element analysis using the radius of each layer and the ratio of heat energy of the layer as variables, and the variables were found by comparing them with the actual experimental results. For case study, by applying Adaptive simulated annealing, one of the global optimization algorithms, we were able to find the heat source model more efficiently.

본 연구에서는 섬유보강콘크리트(SFRC) 구조물의 수치해석을 위한 K&C모델의 보정기법을 소개하였다. SFRC 1축 및 3축 압축강도 실험결과를 기반으로 보정을 수행하였으며, 단일요소 해석결과를 실험결과와 비교함으로써 보정 기법의 검증을 수행하였다. 또 한, 변형률 속도의 영향을 반형하기 위해 동적증가계수(DIF)를 고려하여 SFRC 구조물의 발사체 관통해석을 수행함으로써 보정기법의 적용 가능성을 확인하였다.

본 연구는 지진에 저항하는 부재인 비보강 조적벽체로 구성된 건물의 내진성능평가에 활용되는 비선형 정적해석을 위한 비보강 조적벽체의 해석모델을 수립하고자 하였다. 본 연구의 해석모델은 비보강 조적벽체의 휨거동을 모사하기 위한 파이버 요소와 비보강 조적벽체의 전단에 대한 응답을 예측하기 위한 전단스프링 요소로 구성된다. 본 논문은 먼저 제안하고 있는 모델의 형상에 대해서 설명하고, 기존에 행해진 조적조 프리즘의 실험결과로부터 얻은 응력-변형률 곡선을 근거로 파이버와 전단스프링 요소의 물성치에 대한 결정 방법을 설명한다. 제시하고 있는 모델은 비선형 정적 해석결과와 다른 연구자들에 의해 수행된 실험결과를 비교하여 타당성을 검증한다. 해당 모델은 최대강도, 초기강성, 그리고 이들로부터 얻어지는 비보강 조적벽체의 하중-변위 곡선을 적절하게 모사하고 있다. 또한, 해석모델이 비보강 조적벽체의 파괴모드를 예측할 수 있는 것으로 나타난다.

The contemporary high-tech structures have become enlarged and their functions more diversified. Steel concrete structure and composite material structures are not exceptions. Therefore, there have been on-going studies on fiber reinforcement materials to improve the characteristics of brittleness, bending and tension stress and others, the short-comings of existing concrete. In this study, the purpose is to develop the estimated model with dynamic characteristics following the steel fiber mixture rate and formation ration by using the nerve network in mixed steel fiber reinforced concrete (SFRC). This study took a look at the tendency of studies by collecting and analyzing the data of the advanced studies on SFRC, and facilitated it on the learning data required in the model development. In addition, by applying the diverse nerve network model and various algorithms to develop the optimal nerve network model appropriate to the dynamic characteristics. The accuracy of the developed nerve network model was compared with the experiment data value of other researchers not utilized as the learning data, the experiment data value undertaken in this study, and comparison made with the formulas proposed by the researchers. And, by analyzing the influence of learning data of nerve network model on the estimation result, the sensitivity of the forecasting system on the learning data of the nerve network is analyzed.

An orthotropic plastic constitutive model for fiber-reinforced composite material, is developed, which is simple and efficient to be implemented into computer program for a predictive analysis procedure of composite laminates. An orthotropic initial yield criterion, as well as work-hardening model and subsequent yield surface are established that includes the effects of different yield strengths in each material direction, and between tension and compression. The current model is implemented into a computer code, which is Predictive Analysis for Composite Structures (PACS). The accuracy and efficiency of the anisotropic plastic constitutive model and the computer program PACS are verified by solving a number of various fiber-reinforced composite laminates. The comparisons of the numerical results to the experimental and other numerical results available in the literature indicate the validity and efficiency of the developed model.

본 연구에서는 사각형 모듈의 인발성형된 복합재료 바닥판의 휨 거동에 대한 해석 모델을 개발하였다. FRP 바닥판의 해석 모델은 FSDT 평판 이론을 기반으로 임의 적층각을 지닌 FRP 바닥판의 처짐을 예측할 수 있었다. 수치적 예제에서는 네 변이 단순 지지된 등분포 하중을 받는 사각형 모듈의 FRP 바닥판을 2차원 평판 유한 요소해석을 적용하여 수행하였고, 해석 결과에 대해서는 바닥판 길이-높이의 비와 화이버 각도의 변화에 따른 효과에 대해 역점을 두고 다루었다. 연구 결과, 본 연구에서 제안한 해석 모델이 FRP 바닥판의 휨 거동을 해석하고 예측하는데 효과적이고 정확하다는 것이 입증되었다. 또한, FRP 바닥판의 높이가 높아질수록 plate 해석 이론에 있어서 일차전단변형이론(First order Shear Deformable laminated plate Theory : FSDT)이 아닌 고차전단변형(Higher order Shear Deformable plate Theory : HSDT)의 필요성에 대해 언급하였다.

Short-fiber reinforcement is commonly added to concrete to improve various aspects of their durability and performance. Effective designs of fiber reinforced concrete depend not only on material composition, but also on the methods of processing. In particular, the distribution of fibers within a structural component can significantly affect its resistance to cracking and, therefore, its durability when exposed to severe environments. Probability-based analyses can be used to accommodate such factors in life cycle performance evaluation, in which the relevant performance measures are described by probability density functions and their evolution over time. This paper concerns the simulation of fiber reinforced concrete using lattice models, in which the individual fibers are explicitly modeled within the material domain. This approach facilitates the study of non-uniform fiber dispersions and their potential effects on structural performance.

본 연구의 목적은 CFRP판을 다양한 방법으로 보강한 RC보의 휨거동을 실험적으로 비교․분석하고, 프리스트레싱을 도입하여 보강된 콘크리트 구조물의 성능개선 효과와 구조거동을 예측할 수 있는 해석모델을 개발하는 것이다. 이를 위하여 프리스트레싱이 도입된 CFRP판의 부착 및 휨거동 특성을 분석하고, CFRP 및 Epoxy 수지의 거동특성을 규명하였다. 또한 CFRP판의 보강방법과 프리스트레싱 수준 등을 실험변수로 설정하여 콘크리트 보의 휨실험을 수행하고, 개발된 해석모델의 결과와 비교․검증하였다. 연구결과 본 해석기법은 충분한 신뢰도를 가지고 있으므로 CFRP를 사용한 보강설계에 효과적으로 적용이 가능하다고 판단된다.

친환경적이면서 신속한 비파괴 분석방법인 FT-NIR를 이용하여 백미의 총식이섬유(TDF)함량 예측모델을 개발하였다. 백미는 국내산으로 전남지방에서 재배된 47개 품종과, 시중 유통 중인 13개 브랜드 미에 대해서 AOAC 방법에 준한 효소법에 의해 TDF 함량을 분석하였다. 습식 분석된 TDF함량의 범위는 이었다. FT-NIR로 측정된 스펙트럼의 검량식은 빛의 산란 효과를 최소화하기 위해 수학적 처리를 하였고, 몇 개의 특정 파장이 아닌 전 파