Use of Neural Networks in Damage Detection of Structures

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.