Seismic fragility evaluation with incomplete structural appraisal data: an iterative statistical approach

Wang, Vincent Z., Mallett, Michael, and Priory, Andrew (2014) Seismic fragility evaluation with incomplete structural appraisal data: an iterative statistical approach. Journal of Structural Engineering, 140 (2). 04013048. pp. 1-13.

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This paper presents an iterative statistical approach to evaluating seismic structural safety using incomplete appraisal data. Despite the continuous improvement to traditional structural assessment procedures and the recent progress in structural health monitoring (SHM) methodologies, practically acquired structural appraisal data may often be incomplete. The occurrence of the appraisal data missingness could be ascribed to the malfunction of data acquisition systems, the abnormality during data transfer, and the inaccessibility of critical quantities, among other reasons. The study begins with a quantitative investigation into the sensitivity of the seismic fragility evaluation with respect to the structural appraisal data missingness through the defined additional information loss and probability of noninformativeness. Subsequently, a remedy for the missingness of the structural appraisal data, instead of a precaution against it, is formulated by employing the expectation-maximization (EM) algorithm. With synthetic or real seismic ground accelerations involved, the efficacy of the EM algorithm embedded remedy is demonstrated by examples of typical linear or nonlinear hysteretic systems in the framework of statistical hypothesis testing. Resorting to the bootstrap technique, the influence of the related correlations and missingness probability is also examined.

Item ID: 28482
Item Type: Article (Research - C1)
ISSN: 1943-541X
Keywords: seismic fragility; incomplete data; missing data; uncertainty quantification; EM algorithm; bootstrap
Date Deposited: 20 Aug 2013 05:15
FoR Codes: 09 ENGINEERING > 0905 Civil Engineering > 090506 Structural Engineering @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970109 Expanding Knowledge in Engineering @ 100%
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