Artificial Neural Networks for Structural Damage Detection and Classification
An analysis of artificial neural networks on damage assessment of an aluminum cantilever beam was conducted. The neural networks were trained and tested with deterministic data of resonant frequency information to test their ability in determining the magnitude, location and type of damage on the beam. Being a preliminary study, no experimental data has been included, since no information was found in the literature where neural networks were used in determining the type of damage on a structure. This paper includes a discussion on the theory of neural network and the process involved in developing the architecture for three layer backpropagation neural networks for damage assessment. The neural networks were tested for three types of damage using four damage magnitudes.