Displacement Model for Concrete Dam Safety Monitoring via Gaussian Process Regression Considering Extreme Air Temperature
Publication: Journal of Structural Engineering
Volume 146, Issue 1
Abstract
Structural health monitoring models provide important information for safety control of large dams. The main challenge in developing an accurate dam behavior prediction model lies in the modeling of extreme temperature effect. This paper presents a Gaussian process regression-based displacement model for health monitoring of concrete gravity dams, which can model the temperature effect by using long-term air temperature data. Important attractions of Gaussian processes include accurate simulation results, convenient training, and so forth. Different covariance functions and temperature variable sets are tested on the horizontal displacement prediction problem of concrete dams. Results show that segmented air temperature based Gaussian process regression models can reflect the extreme air temperature effect on displacements of concrete gravity dams, considering the prediction accuracy is much better than that of a mathematical model based on periodic functions.
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Acknowledgments
The research is supported by the National Key R & D Program of China (2016YFC0401600 and 2017YFC0404900), the National Natural Science Foundation of China (51769033 and 51779035), the Fundamental Research Funds for the Central Universities (DUT17ZD205 and DUT19LK14), and the Open Research Fund of the State Key Laboratory of Structural Analysis for Industrial Equipment (GZ15207). The open-source toolbox GPML is employed to perform the analyses of GPR.
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Published In
Journal of Structural Engineering
Volume 146 • Issue 1 • January 2020
Copyright
©2019 American Society of Civil Engineers.
History
Received: Jul 12, 2018
Accepted: May 10, 2019
Published online: Oct 23, 2019
Published in print: Jan 1, 2020
Discussion open until: Mar 23, 2020
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