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Permanent link (DOI): https://doi.org/10.7939/R34M91H77

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Investigation of Time Series Analysis Based Damage Detection Methodologies for Structural Health Monitoring Open Access

Descriptions

Other title
Subject/Keyword
Structural Health Monitoring
Damage Detection
Time Series Analysis
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Mei, Qipei
Supervisor and department
Gül, Mustafa (Department of Civil and Environmental Engineering)
Examining committee member and department
Bouferguene, Ahmed (Campus Saint-Jean)
Cheng, J.J. Roger (Department of Civil and Environmental Engineering)
Gül, Mustafa (Department of Civil and Environmental Engineering)
Department
Department of Civil and Environmental Engineering
Specialization
Structural Engineering
Date accepted
2014-09-25T13:30:33Z
Graduation date
2014-11
Degree
Master of Science
Degree level
Master's
Abstract
Structural Health Monitoring (SHM) is widely accepted as a valuable tool for monitoring, inspecting and maintaining infrastructure systems. Damage detection is one of the most critical components of SHM to identify the existence, location and severity of damage so that effective preventive actions can be taken to improve the condition of the structure. In this study, time series analysis based damage detection methods using output-only vibration data are investigated for global condition assessment of structures. The main body of the thesis falls into two key parts. In the first part, a novel damage detection method based on Auto-Regressive models with eXogenous (ARX models) and sensor clustering is proposed. Two different Damage Features (DFs) based on the difference of fit ratios and ARX model coefficients are considered in this part. Applying this method to experimental data from a steel grid type structure and a 4-span bridge type structure, it is demonstrated that the existence, location and severity could be successfully assessed by both two DFs for most of the cases. In the second part, an improved version of the previous method is developed to separate the changes in stiffness and mass of the structure using output only data. In order to verify this approach, it is first applied to a 4-DOF mass spring system and then to the shear type IASC-ASCE numerical benchmark problem. It is demonstrated that the approach can not only accurately determine the location and severity of the damage, but also distinguish between changes in stiffness and mass. This study constitutes the first approach of its kind which can distinguish between change of stiffness and mass by using output only vibration data. At the end of the thesis, the limitations of current methods, recommendations, and future work are also addressed.
Language
English
DOI
doi:10.7939/R34M91H77
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
Mei, Q., & Gul, M. (2013). An Improved Methodology for Anomaly Detection Based on Time Series Modeling. In Topics in Dynamics of Civil Structures, Volume 4 (pp. 277-281). Springer New York.Mei, Q and Gul, M (2013), “Detection, localization and quantification of changes in mass, stiffness and damping using output-only data.” 9th International Workshop on Structural Health Monitoring (IWHSM 2013), Stanford University, Stanford, CA, September 10-12.Mei, Q and Gul, M (2013), “Anomaly detection using a novel Time series approach: application to the ASCE Benchmark problem.” 6th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-6), Hong Kong, December 9-11.Mei, Q and Gul, M (2014), “Application of a novel time series based method for damage detection, localization and quantification using output only acceleration data.” 7th International Conference on Bridge Maintenance, Safety and Management (IABMAS 2014), Shanghai, China, July 7-11.Mei, Q and Gul, M (2014), “Experimental study for a time series based method for damage detection using output data only.” 9th International Conference on Short and Medium Span Bridges (SMSB 2014), Calgary, Alberta, Canada, July 15-18.Mei, Q and Gul, M (2014), “Damage assessment of a 4-span bridge type structure using time series analysis.” Istanbul Bridge Conference, Istanbul, Turkey, August 11-13.Mei, Q., and Gül, M. (2014). “A novel sensor clustering-based approach for simultaneous detection of stiffness and mass changes using output-only data.” Journal of Structural Engineering, ASCE, under review.

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