Classification of impulse radar waveforms using neural networks

In this paper, it is demonstrated that multilayer neural networks, trained with the backpropagation algorithm and radial basis functions, can classify impulse radar waveforms from three different asphalt-covered bridge decks, each with its own structure. It might be thought that the thickness of asphalt and the depth of concrete over the reinforcing bars would be nearly constant for any one bridge deck; however in practice this is not the case. There are often significant changes in the thickness of the asphalt and the cover over reinforcement. Furthermore, a certain amount of damage to the concrete caused by severe winter climate often produces a random variation in the reflected waveforms obtained from different locations. These factors lead to a significant number of combinations of waveforms that can be obtained from any given structural type of deck. The classification accuracies achieved ranged between 89.9% and 100%. The accuracies achieved after using principal components analysis to reduce the dimensionality of the input data ranged between 95.6% and 100%.