다층 인식자 신경망 모형을 이용한 FRP 판의 부착강도 예측 모형 개발

Development of Bond Strength Model for FRP-Plates Using Multi-layer Perceptron

Synthetic materials with excellent thermodynamic characteristics and the merit of anti-corrosion are frequently used in buildings and constructions for enforcement of bent in stead of steel plates. Among them, many practical studies have been conducted on bond strength because of increased bond strength of FRP plates. Previous investigators identified the bond strength of FRP plates through experiments with settlement of various variables to identify the bond strength. However, the experiments to identify the bond force are difficult to be conducted because they requires large expenses and long time for equipment arrangement, thus, are conducted with limitation. In this study, for bond experiment, optimum neural network model was developed with use of Back-propagation and Conjugate gradient technique of previous investigators. Learning was performed with use of the variables of previous investigators in developed neural network model so as to identify the bond strength of FRP plates. for verification of developed model, credibility and excellence was proven by comparing with the models of previous investigators.