A hybrid neural network strategy for the identification of structural damage using time domain responses

A multistage identification scheme for structural damage detection using time domain acceleration responses is proposed. Previous studies of damage assessment using neural networks mostly involved training a backpropagation neural network (BPN) to learn damage patterns with significant computational effort. A hybrid neural network method has been proposed that uses a counterpropagation neural network (CPN) in the first stage for sorting the training data into clusters, giving an approximate guess of the damage extent quickly. After an approximate estimate is obtained, a new set of training patterns of reduced size is generated using the CPN prediction. In the second stage, a BPN trained with the Levenberg–Marquardt algorithm is used to learn the new training data and predict a more accurate result. A superior convergence and a substantial decrease in central processing unit time have been observed for three numerical examples. These examples show the computational superiority of the hybrid method compared with the conventional single stage method. © 2009 Taylor & Francis Group, LLC.