Uncertainty Assessment in Structural Damage Diagnosis

  • Shankar Sankararaman
  • Sankaran Mahadevan
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)


This paper develops methods for the quantification of uncertainty in each of the three steps of damage diagnosis (detection, localization and quantification), in the context of continuous online monitoring. A model-based approach is used for diagnosis. Sources of uncertainty include physical variability, measurement uncertainty and model errors. Damage detection is based on residuals between nominal and damaged system-level responses, using statistical hypothesis testing whose uncertainty can be captured easily. Localization is based on the comparison of damage signatures derived from the system model. Both classical statistics-based methods and Bayesian statistics-based methods are investigated to quantify the uncertainty in all the three steps of diagnosis, i.e. detection, localization, and quantification. While classical statistics-based methods use the concept of least squares-based optimization, Bayesian methods make use of likelihood function and Bayes theorem. The uncertainties in damage detection, isolation and quantification are combined to quantify the overall uncertainty in diagnosis. The proposed methods are illustrated using two types of example problems, a structural frame and a hydraulic actuation system.


Probability Density Function Damage Detection Structural Health Monitoring Damage Parameter Uncertainty Quantification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Shankar Sankararaman
    • 1
  • Sankaran Mahadevan
    • 1
  1. 1.Department of Civil and Environmental EngineeringVanderbilt UniversityNashvilleUSA

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