A Hybrid Data Interpretation Framework For Automated Performance Monitoring Of Infrastructure
Abstract
The objective of this study is to establish and propose a hybrid data interpretation framework for automated performance monitoring of infrastructures. The framework is established by integrating non-parametric and model-based approaches. In fact, these approaches are merged in such a way that they can cover the associated drawbacks. The proposed framework consists of three main steps including analytical simulation of critical damage scenarios for a given structure, advanced statistical analysis and finally damage classification. In order to verify the efficiency of the proposed framework a 4-Span Bridge, is selected and exposed to the common damage scenarios. The framework has shown promising results in terms of continuous monitoring (detection the damage and classifying the critical abnormal behaviors).
Publication Date
1-1-2015
Publication Title
Structures Congress 2015 - Proceedings of the 2015 Structures Congress
Number of Pages
426-434
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1061/9780784479117.037
Copyright Status
Unknown
Socpus ID
84929255540 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84929255540
STARS Citation
Malekzadeh, M.; Atia, G.; and Catbas, F. N., "A Hybrid Data Interpretation Framework For Automated Performance Monitoring Of Infrastructure" (2015). Scopus Export 2015-2019. 1980.
https://stars.library.ucf.edu/scopus2015/1980