Classifying impulse radar waveforms using principle components analysis and neural networks

Abstract

A multilayer neural network, trained with the back-propagation algorithm, is used to classify impulse radar waveforms from asphalt-covered bridge decks. A strategy for determining the structure of a bridge deck by using principal components analysis to reduce the dimensionality of the input data is demonstrated, showing classification accuracies ranging between 95.6% and 100%. The results show that neural networks can be used to extract information about a bridge deck's structure when waveforms from the deck are presented to it. Once the network has been trained to recognize several different structures, it should be possible to obtain very accurate estimates about the specific deck structure. The neural network will eliminate the need for taking core samples from the bridge deck, and a truly nondestructive bridge-deck evaluation system will be realized