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Permanent link (DOI): https://doi.org/10.7939/R36T0H73S

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A Framework for Vibration based Damage Detection of Bridges under Varying Temperature Effects using Artificial Neural Networks and Time Series Analysis Open Access

Descriptions

Other title
Subject/Keyword
Damage Detection
Structural Health Monitoring
Time Series Analysis
Bridges
Neural Network
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Kostic, Branislav Z
Supervisor and department
Gül, Mustafa (Civil and Environmental Engineering)
Examining committee member and department
Cheng, J.J. Roger (Civil and Environmental Engineering)
Driver, Robert (Civil and Environmental Engineering)
Department
Department of Civil and Environmental Engineering
Specialization
Structural Engineering
Date accepted
2015-08-10T09:09:08Z
Graduation date
2015-11
Degree
Master of Science
Degree level
Master's
Abstract
Structural Health Monitoring (SHM) has become a very important area in civil engineering for evaluating the performance of critical civil infrastructure systems such as bridges. One of the most important issues with continuous SHM is the environmental effects (such as temperature, humidity, wind) on the measurement data, which can produce bigger effects in the response of the structures than the damage itself. Damage detection is considered as one of the most important components of SHM and without appropriately considering the environmental factors in the damage detection process, the efficiency and accuracy of this process may be questionable for practical applications. Temperature is considered as one of the most important and influential environmental effects on structures, especially in bridges. In this study, an artificial neural network based approach integrated with a sensor clustering based time series analysis is employed for damage detection under the temperature effects. Damage features from the time series analysis method (will be referred as DFARX) can indicate the damage, if there is no presence of detrimental temperature effect. However, if present, temperature effect can lead to false indication of the damage existence in the structure. Neural network damage features (DFANN) are used to compensate these effects. Final damage features (DF) are computed as absolute difference between these two damage features. The proposed methodology is applied to a footbridge finite element model and it is demonstrated that the method can successfully determine the existence, location and extent of the damage for different types of damage cases under environmental temperature variability, and with different levels of the noise. Finally, recommendations for the future work, as well as the limitations of the proposed methodology are addressed.
Language
English
DOI
doi:10.7939/R36T0H73S
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. 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.
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