Use of Neural Networks in Damage Detection of Strutures

Abstract

An application of TNN on the damage detection of steel bridge structures is presented. The issues relating to the design of network and learning algorithm are addressed and network architectures have been developed with reference to trussed bridge structures. The training patterns are generated for multiple damaged zones in a structure. The results of simulation show that the algorithm is suitable for structural identification of bridges where the measured data are expected to be imprecise and often incomplete. The engineering importance of the method is demonstrated from the fact that measured input at only a few locations in the structure is needed in the identification process using the TNN.

Frequency response function based damage identification using principal component analysis and pattern recognition technique

“…One of the soft computing techniques, neural networks have been utilized increasingly for damage identification due to their excellent pattern recognition, auto-association, self-organization, self-learning and non-linear modeling capability [13][14][15][16][17]. ANNbased methods can operate on a finite element (FE) model of the structure or on real measurement data and a neural network approach can be used to identify faults in the tested structure [18].…”

Structural damage detection method using frequency response functions

“…4 ANNs have their excellent pattern recognition, auto-association, self-organization, selflearning, and nonlinear modeling capability. [5][6][7][8][9][10] ANN is capable of extracting and obtaining precise and reliable information from imprecise, unreliable, inconsistent, uncertain, and noise-polluted data. As a result, ANN is fault tolerant which makes the fault diagnosis procedure automatic, once the network is trained.…”