Structural Health Monitoring Accounting for Thermal Variability and Damage using Approximate Bayesian Computation

By: Contributor(s): Material type: TextTextLanguage: en. Publication details: Bangalore IISc 2023Description: ix, 126p. col. ill. ; 29.1 cm * 20.5 cm e-Thesis 4.611MbSubject(s): DDC classification:
  • 624 AKA
Online resources: Dissertation note: MTech (Res); 2023; Civil engineering Summary: In structural engineering, damage is characterized as a change in material property, boundary condition, or geometry. The changes in these properties/parameters lead to a change in the measured response. The difference in measurements can be due to actual damage in the member (due to crack formation, corrosion of rebars, or crushing of concrete), or it might be due to temperature variations while making measurements. Temperature variability significantly affects the accuracy of structural health monitoring strategies in quantifying structural damage. Performing damage detection without isolating/incorporating these variations can lead to false damage detection, i.e., the undamaged structure can be detected as damaged. Hence, a method is required to isolate the effect of these variabilities while detecting damage. Researchers have developed methods to analyze and separate the effects of environmental variability from damageinduced changes in the measures. The main two approaches are (a) data-based, which uses statistics-based tools for analyzing patterns in the data or compute parameters, and (b) model-based, where the method considers both environmental and damagebased changes of stiffness value.
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MTech (Res); 2023; Civil engineering

In structural engineering, damage is characterized as a change in material property, boundary condition, or geometry. The changes in these properties/parameters lead to a change in the measured response. The difference in measurements can be due to actual damage in the member (due to crack formation, corrosion of rebars, or crushing of concrete), or it might be due to temperature variations while making measurements. Temperature variability significantly affects the accuracy of structural health monitoring strategies in quantifying structural damage. Performing damage detection without isolating/incorporating these variations can lead to false damage detection, i.e., the undamaged structure can be detected as damaged. Hence, a method is required to isolate the effect of these variabilities while detecting damage. Researchers have developed methods to analyze and separate the effects of environmental variability from damageinduced changes in the measures. The main two approaches are (a) data-based, which uses statistics-based tools for analyzing patterns in the data or compute parameters, and (b) model-based, where the method considers both environmental and damagebased changes of stiffness value.

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