Probabilistic Structural Health Assessment with Identified Physical Parameters from Incomplete Measurements

AbstractThis paper studies the performance of identified physical parameters in structural health-monitoring applications. A probabilistic method is discussed to assess the location and severity of structural damage. This method attempts to account for the variability in both the baseline (healthy) and unknown (damaged or healthy) states of the monitored system through empirical distributions modeling the ratios of stiffness estimates from different tests. The presence and severity of damage at any location are detected by comparing the distribution in the unknown state with the baseline distribution; damage severity is expressed through damage probability versus severity curves corresponding to different confidence levels of the baseline state. Experimental data from a 3-story sliding base frame, modeled as a free–free system, and different damaged versions of the frame, are considered as applications. The structural identification is performed in a situation of highly-incomplete measured and a priori as...

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