Structural damage detection using fuzzy cognitive maps and Hebbian learning
A new algorithmic approach for structural damage detection based on the fuzzy cognitive map (FCM) is developed in this paper. Structural damage is modeled using the continuum mechanics approach as a loss of stiffness at the damaged location. A finite element model of a cantilever beam is used to calculate the change in the first six beam frequencies because of structural damage. The measurement deviations due to damage are fuzzified and then mapped to a set of faults using FCM. The input concepts for the FCM are the frequency deviations and the output of the FCM is at five possible damage locations along the beam. The FCM works quite well for structural damage detection for ideal and noisy data. Further improvement in performance is obtained when an unsupervised neural network approach based on Hebbian learning is used to evolve the FCM. Numerical results clearly show that the use of FCM and Hebbian learning results in accurate damage detection and represents a powerful tool for structural health monitoring. (C) 2010 Elsevier B.V. All rights reserved.
damage detection fuzzy cognitive map neural network fuzzy logic pattern recognition