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| Neural networks-based structural damage detection through modal parameter measurements |
| Revised:June 04, 2003 |
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| DOI：10.7511/jslx20052039 |
| KeyWord:structural damage detection,modal parameters,neural networks |
| ZHU Hong-ping~* QIAN Li |
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| Abstract: |
| The neural networks-based structural damage detection using measured modal data has been proposed. Two neural networks with different input modes (i.e., natural frequencies, the combination of natural frequencies and modal shapes) were built and analyzed in this paper, which show that BP neural network would be more intelligent and more quickly convergent with more input parameters. The combination of measured frequencies and modal shapes can improve the degree of accuracy of damage detection if the measured errors could be eliminated at most. A 3-storey steel frame model with 4 damage cases is used to verify the degree of accuracy and computation efficiency of the proposed approach. Numerical results show that the proposed approach can not only localize correctly the damage, but also identify the magnitude of damage with a relatively high degree of accuracy. |
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