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Probabilistic Structural Health Assessment with Identified Physical Parameters from Incomplete Measurements
This 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 assumed information. Specifically, for free–free systems, it is shown that only a single colocated actuator–sensor pair, and only knowledge of the total mass of the system, assures global identifiability of the system. This minimal permissible instrumentation set-up is used to identify the floor masses and story stiffnesses of the experimental frame. Even under the constraints of very limited instrumentation and available a priori information, the identification approach is able to obtain reasonably-accurate estimates of the mass and stiffness parameters. Using these identified parameters, it is shown that the damage-detection method is able to classify both damaged and undamaged locations, as well as provide a probabilistic estimate of the damage severity, with sufficient accuracy.
Probabilistic Structural Health Assessment with Identified Physical Parameters from Incomplete Measurements
This 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 assumed information. Specifically, for free–free systems, it is shown that only a single colocated actuator–sensor pair, and only knowledge of the total mass of the system, assures global identifiability of the system. This minimal permissible instrumentation set-up is used to identify the floor masses and story stiffnesses of the experimental frame. Even under the constraints of very limited instrumentation and available a priori information, the identification approach is able to obtain reasonably-accurate estimates of the mass and stiffness parameters. Using these identified parameters, it is shown that the damage-detection method is able to classify both damaged and undamaged locations, as well as provide a probabilistic estimate of the damage severity, with sufficient accuracy.
Probabilistic Structural Health Assessment with Identified Physical Parameters from Incomplete Measurements
Mukhopadhyay, Suparno (Autor:in) / Luş, Hilmi (Autor:in) / Betti, Raimondo (Autor:in)
07.09.2015
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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