A METHOD FOR STRUCTURAL DAMAGE DETECTION USING NEURAL NETWORKS

A method for damage detection in structures using backpropagation neural networks is proposed in this paper. The dynamic residual vector of a structure is taken as inputs of the neural network for parameter identification. The Generalized-Spaced-Lattice(GSL) transformation is used to transform original input and/or output data points of all training pattern onto uniformly spaced lattice points over a multi-dimensional space. Thus, the neural network can learn the training patterns efficiently as well as accurately. Two examples are given to show the effectiveness of the proposed method.