Optimization of neural network pattern recognition systems for guided waves damage identification in beams

Neural network pattern recognition is an advanced regression technique that can be applied to identify guided wave response signals for quantifying damages in structures. This paper describes a procedure to optimize the design of a multi-layer perceptron backpropagation neural network with signals preprocessed by the wavelet transform. The performance can be further improved using a weight-range selection technique in a series network since there is increased sensitivity of the neural network to experimental damage patterns if the training range is reduced. Damage identification in beams with longitudinal guided waves is used in this study.