Momentum BP neural networks in structural damage detection based on static displacements and natural frequencies

Abstract

Modeling error, measured noises and incomplete measured data are main difficulties for many structural damage processes being utilized. In this study, using static displacements and frequencies constitutes the input parameter vectors for neural networks. A damage numerical verification study on a five-bay truss was carried out by using an improved momentum BP neural network. Identification results indicate that the neural networks have excellent capability to identify structural damage location and extent under the conditions of limited noises and incomplete measured data. © Springer-Verlag Berlin Heidelberg 2007.