Damage Detection Approach for Bridges under Temperature Effects using Gaussian Process Regression Trained with Hybrid Data
Publication: Journal of Bridge Engineering
Volume 27, Issue 11
Abstract
The success of detecting damage robustly relies on the availability of long periods of past data covering multiple weather scenarios and on the information contained in the data used during the learning process. Thus, the innovation of this paper is to apply a hybrid data set to train a Gaussian process regression, assuming a practically plausible range of environmental conditions. The proposed model presents a satisfactory performance to detect damage when structural changes caused by damage are blurred with changes caused by temperature. Rather than relying exclusively on experimental data, this strategy use finite-element models to generate complementary data when the structure is undamaged under a broad spectrum of temperature variations that are not measured. Once the stochastic interpolation is defined, the damage detection model is tested using experimental data considering different damage levels and temperature conditions. Induced settlements of a bridge pier are used as realistic damage scenarios. The Z24 prestressed concrete highway bridge in Switzerland is used to demonstrate the applicability of the proposed strategy.
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Acknowledgements
The authors acknowledge the KU Leuven (Belgium) Structural Mechanics Section as the Z24 Bridge data source. The authors thank the financial support provided by Coordination for the Improvement of Higher Education Personnel (CAPES/Brazil)-Finance Code 001 and the Portuguese National Funding Agency for Science Research and Technology (FCT/Portugal) for promoting the collaboration between Brazil/Portugal. The first author is thankful for the Brazilian National Council of Technological and Scientific Development (CNPq) grant number 306526/2019-0 and the São Paulo Research Foundation (FAPESP) grant number 19/19684-3. The third author acknowledges the funding from the Portuguese National Funding Agency for Science Research and Technology (FCT/Portugal) through grant UIDB/04625/2020.
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Published In
Journal of Bridge Engineering
Volume 27 • Issue 11 • November 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Feb 4, 2022
Accepted: Jun 29, 2022
Published online: Sep 14, 2022
Published in print: Nov 1, 2022
Discussion open until: Feb 14, 2023
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