Multiclass Damage Identification in a Full-Scale Bridge Using Optimally Tuned One-Dimensional Convolutional Neural Network
Publication: Journal of Computing in Civil Engineering
Volume 36, Issue 2
Abstract
In this paper, a novel method is proposed based on a windowed one-dimensional convolutional neural network (1D CNN) for multiclass damage identification using vibration responses of a full-scale bridge. The measured data are first augmented by extracting samples of windows of raw acceleration time series to alleviate the problem of a limited training data set. 1D CNN is developed to classify the windowed time series into multiple damage classes. The damage is quantified using the predicted class probabilities, and the damage is localized if the predicted class is equal to the assigned damage class, exceeding a threshold associated with majority voting. The proposed network is optimally tuned with respect to various hyperparameters such as window size and random initialization of weights to achieve the best classification performance using a global 1D CNN model. The proposed method is validated using a benchmark bridge data for multiclass classification for two different damage scenarios, namely, pier settlement and rupture of tendons, under the various extents of damage. The damage identification is carried out on various bridge components to collectively identify the structural component with a damaged signature. The results show that the proposed windowed 1D CNN method achieves an accuracy of 97%, and performs well with different types of damage.
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Data Availability Statement
All of the data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. The data for the bridge were made publicly available by the researchers at the Katholieke Universiteit Leuven (https://bwk.kuleuven.be/bwm/z24).
Acknowledgments
The authors would like to acknowledge funding provided to the first author through the Government of Ontario, Canada, as an Ontario Graduate Scholarship (OGS), and Queen Elizabeth II Graduate Scholarship in Science and Technology (QEGS-II) for Ph.D. studies. The authors also acknowledge Natural Sciences and Engineering Research Council (NSERC) for providing the financial support to conduct this research through the third and fourth authors’ NSERC Discovery Grant.
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Published In
Journal of Computing in Civil Engineering
Volume 36 • Issue 2 • March 2022
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Nov 20, 2020
Accepted: Sep 25, 2021
Published online: Nov 30, 2021
Published in print: Mar 1, 2022
Discussion open until: Apr 30, 2022
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