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Statistical Approach to Structural Damage Diagnosis under Uncertainty

Statistical Approach to Structural Damage Diagnosis under Uncertainty

Shankar Sankararaman, Sankaran Mahadevan
Copyright: © 2015 |Pages: 18
ISBN13: 9781466684904|ISBN10: 1466684909|EISBN13: 9781466684911
DOI: 10.4018/978-1-4666-8490-4.ch008
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MLA

Sankararaman, Shankar, and Sankaran Mahadevan. "Statistical Approach to Structural Damage Diagnosis under Uncertainty." Emerging Design Solutions in Structural Health Monitoring Systems, edited by Diego Alexander Tibaduiza Burgos, et al., IGI Global, 2015, pp. 153-170. https://doi.org/10.4018/978-1-4666-8490-4.ch008

APA

Sankararaman, S. & Mahadevan, S. (2015). Statistical Approach to Structural Damage Diagnosis under Uncertainty. In D. Burgos, L. Mujica, & J. Rodellar (Eds.), Emerging Design Solutions in Structural Health Monitoring Systems (pp. 153-170). IGI Global. https://doi.org/10.4018/978-1-4666-8490-4.ch008

Chicago

Sankararaman, Shankar, and Sankaran Mahadevan. "Statistical Approach to Structural Damage Diagnosis under Uncertainty." In Emerging Design Solutions in Structural Health Monitoring Systems, edited by Diego Alexander Tibaduiza Burgos, Luis Eduardo Mujica, and Jose Rodellar, 153-170. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-8490-4.ch008

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Abstract

This chapter presents a statistical methodology for structural damage diagnosis (detection, localization and estimation), in the context of continuous online monitoring. There are several sources of uncertainty such as physical variability, measurement uncertainty and model errors that affect structural damage diagnosis, and therefore, it may not be possible to precisely detect, localize, and estimate damage. Hence, a statistical approach can help to identify these sources of uncertainty, quantify their combined effect on diagnosis, and thereby, provide an estimate of the confidence in the results of diagnosis. 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 the example of a structural frame.

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