Title

Damage Detection Using A Novel Time Series Methodology: Application To The Z24 Bridge Data

Abstract

In this study, a novel time series analysis methodology is used for detection, localization, and quantification of damage. The methodology is based on creating ARX models (Auto-Regressive models with eXogenous input) for different sensor clusters. The output of each sensor in a cluster is used as an input to the ARX model to predict the output of the reference channel of that sensor cluster. After the ARX models for the healthy structure at each DOF are created, the same models are used for predicting the data from the damaged structure. The difference between the fit ratios is used as damage indicating feature. The methodology is applied to the experimental data coming from the Z24 Bridge. It is shown that the approach is successfully used for identification, localization, and quantification of different damage cases. The potential and advantages of the methodology are discussed along with the analysis results. The limitations and shortcomings of the methodology are also addressed. © 2010 Taylor & Francis Group, London.

Publication Date

12-1-2010

Publication Title

Bridge Maintenance, Safety, Management and Life-Cycle Optimization - Proceedings of the 5th International Conference on Bridge Maintenance, Safety and Management

Number of Pages

754-759

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

84856715718 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84856715718

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