A hybrid neural network strategy for identification of structural parameters

作者：Pillai Prashanth; krishnapillai Shankar^*

来源：Structure and Infrastructure Engineering, 2010, 6(3): 379-391.

DOI：10.1080/15732470701718197

• backpropagation network
• counterpropagation network
• Levenberg-Marquardt method

This paper presents a multistage identification scheme for structural damage detection using modal data. Previous studies of damage assessment using neural networks mostly involved training a backpropagation neural network (BPN) to learn damage patterns that were obtained either experimentally or by simulation for different damage cases. Damage identification for large structures, especially those involving multiple member damage, could result in large training data sets that require a large BPN and consequently greater computational effort. The proposed scheme involves using a counterpropagation neural network (CPN) in the first stage for sorting the training data into clusters and giving an approximate guess of the damage extent within a very short time. After an approximate estimate of the damage 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 (CPU) time are observed for three numerical examples.

• 出版日期2010