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In this research, the combination of modal data is used to identify the damage of a FEM model using neural networks. The identification ability with different levels of noise and incomplete mode shapes are also investigated. It has been proved that the neural network using combination of modal parameters as input has a excellent identification ability with ideal error tolerance and robustness. Numberical...
Mechanical structures are widely used in engineering practice and usually they are bearing multiple independent random load applications and the performance analysis of them is difficult. In this paper, an intelligent method is introduced to analyze the performance of complex structure with multiple random load applications. First several load effect results are got through FEA, and then the artificial...
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...
In this paper the time-delay neural networks (TDNNs) have been implemented in detecting the damage in bridge structure using vibration signature analysis. A simulation study has been carried out for the incomplete measurement data. It has been observed that TDNNs have performed better than traditional neural networks in this application and the arithmetic of the TDNNs is simple.
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