Development of a new structural damage detection method using chaos attractor re-composition technique

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For the first purpose we propose a new attractor-based structural damage detection technique using chaotic excitation. We reconstruct attractors from responses and obtain quantitative features from the response attractors by using Recurrence Analysis. We use this feature as damage indicator. By comparing indicators between intact and damage structures, we detect and localize structural damage. The robustness against noise and sensitivity to minor damage of the proposed method are shown through numerical examples, in which we assume minor damage to a structure and high noises on input excitation and observed data.For the second purpose we apply the Monte Carlo Filter technique to identify the level of structural damage. We develop the Relaxation MCF, by which we improve the deficit of the classical MCF. The efficiency of the proposed method is confirmed by identifying the dynamic parameters of a ten-story model building be using noise contaminated simulated structural response data.