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강섬유의 형상비와 혼입률에 따른 강섬유 보강 콘크리트 보의 역학적 특성 추정 모형 개발
곽계환,황해성,성배경,장화섭,Kwak, Kae-Hwan,Hwang, Hae-Sung,Sung, Bai-Kyung,Jang, Hwa-Sup 한국농공학회 2006 한국농공학회논문집 Vol.48 No.3
Practially useful method of steel fiber for construction work is presented in this study. The most important purpose of this study is to develop a model which can predict mechanical behavior of the structure according to aspect ratio and volume fraction of steel fiber. Experiments on compressive strength, elastic modulus, and splitting strength were performed with self-made cylindrical specimens of variable aspect ratios and volume fractions. The experiment showed that compressive strength was not in direct proportion to volume fraction which doesn't seem to have great influence over compressive strength. However, splitting strength showed almost direct proportion to aspect ratio and volume fraction. Improvement of optimal efficiency was confirmed when the aspect ratio was 70. Experiments on flexural strength, fracture energy, and characteristic length were carried out with self-manufactured beams with notch. As a result, increases of flexural strength, fracture energy, and characteristic length according to increase of volume fraction tend to be prominent when aspect ratio is 70. The steel fiber improves concrete to be more ductile and tough. Moreover, regression analysis was the performed and predictable model was developed after determining variables. With comparison and analysis of suggested estimated values and measured data, reliance of the model was verified.
Hybrid FRP Rod의 변형률을 이용한 축방향 변위추정 모형 개발
곽계환(Kwak Kae-Hwan),성배경(Sung Bai-Kyung),장화섭(Jang Hwa-Sup) 대한토목학회 2006 대한토목학회논문집 A Vol.26 No.4A
FRP(Fiber Reinforced Polymer)는 부식의 저항성, 고강도, 피로저항 능력 및 성형성 동에서 우수한 건설 신소재이다. 광섬유 브래그 격자(Fiber Bragg Grating; FBG) 센서는 전자기 저항, 작은 소재의 크기, 그리고 높은 내구성 등의 이점으로 smart sensor로서 현재 많이 사용되고 있다. 하지만 FBG 센서의 변위 측정 기술 능력의 부족으로 현재까지는 변형률, 온도 등의 물리량 측정센서로서 활용되고 있는 실정이다. 본 연구에서는 FRP와 FBG센서의 기능 복합화(Hybrid)를 통하여 smart FRP Rod를 개발 한 후 인장시험을 실시하였다. 또한, FBG센서에 의해 측정된 변형률 데이터를 신경망(Neural Network) 기법을 이용하여 변위 추정 모형을 개발함으로서 FBG 센서 단점인 변형률 계측만을 위한 센싱 역할을 극복하고자 한다. 인공신경망 모형은 MLP(Multi-layer Perceptron)로, 오차범위 0.001에 수렴 될 수 있도록 학습(training)을 실시하였다. 학습에는 비선형 목적함수와 역전파 학습(Back-propagation) 알고리즘을 적용하였으며 모형의 검증은 UTM에서 측정된 변위 값과 수치해석에 의한 결과 값을 비교함으로서 실시하였다. FRP (Fiber Reinforced Polymer) is an excellent new constructional material in resistibility to corrosion, high intensity, resistibility to fatigue, and plasticity. FBG (Fiber Bragg Grating) sensor is widely used at present as a smart sensor due to lots of advantages such as electric resistance, small-sized material, and high durability. However, with insufficiency of measuring displacement, FBG sensor is used only as a sensor measuring physical properties like strain or temperature. In this study, FRP and FBG sensors are to be hybridized, which could lead to the development of a smart FRP rod. Moreover, developing the estimated model for deflection with neural network method, with the data measured through FBG sensor, could make conquest of a disadvantage of FBG sensor - uniquely used for sensing strain. Artificial neural network is MLP (Multi-layer perceptron), trained within error rate of 0.001. Nonlinear object function and back-propagation algorithm is applied to training and this model is verified with the measured axial displacement through UTM and the estimated numerical values.
김호선(Kim, Ho-Sun),곽계환(Kwak, Kae-Hwan),성배경(Sung, Bai-Kyung) 한국구조물진단유지관리학회 2010 한국구조물진단학회 학술발표회논문집 Vol.2010 No.2
The function of FRP members has proven so that the study is actively ongoing now. However, due to weakening FRP manufacturers in Korea, there are many limits to design FRP members. This study is conducted to decide the optimum section for applying FRP members to flexural members. It aims to suggest the section that can be produced in Korea, satisfy limited conditions, and minimize the section, the maximum working stress and the maximum deformation.