• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4A
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• pp.639-645
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• 2006

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.