Damage Detection of Structures using Unsupervised Fuzzy Neural Network

C.M. Wen, S.L. Hung, C.S. Huang, and J.C. Jan (Taiwan)


neural network; unsupervised fuzzy learning model; damage detection; structural engineering


This work presents an artificial neural network (ANN) approach for detecting structural damage. An unsupervised neural network which incorporates the fuzzy concept (named the Unsupervised Fuzzy Neural Network, UFN) is adopted to detect localized damage. The structural damage is assumed to take the form of reduced elemental stiffness. The damage site is demonstrated to correlate with the changes in the modal parameters of the structure. Therefore, a feature representing the damage location termed the Damage Localization Feature (DLF) is presented. When the structure experiences damage or change in the structural member, the measured DLF is obtained by analyzing the recorded dynamic responses of the structure. The location of the structural damage then can be identified using the UFN according to the measured DLF information. This study verifies the proposed model using an example involving a five-storey frame building. Both single and multiple damaged sites are considered. The effects of measured noise and the use of incomplete modal data are introduced to inspect the capability of the proposed detection approach.

Important Links:

Go Back