Gaussian Process-Based Particle-Filtering Approach for Real-Time Damage Prediction with Application
Publication: Journal of Aerospace Engineering
Volume 30, Issue 1
Abstract
A prognostic model capable of predicting temporal damage evolution is essential to prevent catastrophic failure of structures. Data driven techniques, such as neural networks and support vector machines, are widely used for prediction of damage in a variety of aerospace and civil applications. Most of the available techniques cannot be applied for real-time prediction because they assume the measured value to be the true value, which is often not true. They also require training data from a similar set of experiments based on which predictions are made, which may not always be available. In this paper, the authors propose a novel integrated approach that intelligently combines particle filter updating with a fully probabilistic Gaussian process model to predict complex physical phenomena (e.g., temporal local pier scour) considering both measurement and prediction uncertainties. In this example, the measurement model is obtained using radio frequency identification (RFID) sensors and the state space model is the Gaussian process-based prognosis model. The performance of the algorithm was tested using corrupt training data. Different scenarios are presented with application to predicting local scour near bridge piers, which is a highly stochastic phenomenon with training data mostly unavailable. The algorithm is used to make predictions using corrupt training data. The results indicate that the proposed approach predicts the scour depth accurately under varying field conditions.
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Acknowledgments
This project is supported by Grant No. RITARS-12-H-ASU from the Office of the Assistant Secretary for Research and Transportation (OST-R), Program Manager Caesar Singh. The authors also acknowledge the support of Dr. Anne Ellis from the Arizona Department of Transportation (ADOT). The authors would also like to thank Dr. Itty P. Itty, head of the hydraulics section at Arizona Department of Transportation; Dr. Bing Zhao, engineering application division river mechanics branch manager; and Mr. Amir Motamedi, hydrology/hydraulics branch manager at Flood Control District of Maricopa County for their valuable suggestions. The authors would also like to thank Dr. Jian-Hao Hong, Research Fellow at Nanyang Technological University Singapore, for providing the data set from laboratory experiments.
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Published In
Journal of Aerospace Engineering
Volume 30 • Issue 1 • January 2017
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Aug 7, 2015
Accepted: Jun 17, 2016
Published online: Aug 5, 2016
Published in print: Jan 1, 2017
Discussion open until: Jan 5, 2017
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