Transfer Learning to Enhance the Damage Detection Performance in Bridges When Using Numerical Models
Publication: Journal of Bridge Engineering
Volume 28, Issue 1
Abstract
Classifiers based on machine learning algorithms trained through hybrid strategies have been proposed for structural health monitoring (SHM) of bridges. Hybrid strategies use numerical and monitoring data together to improve the learning process of the algorithms. The numerical models, such as finite-element (FE) models, are used for data augmentation based on the assumption of the existence of limited experimental data sets. However, a numerical model might fail in providing reliable data, as its parameters might not share the same underlying operating conditions observed in real situations. Meanwhile, the concept of transfer learning has evolved in SHM, in particular through domain adaptation techniques. The ability to adapt a classifier built on a well-known labeled data set to a new scenario with an unlabeled data set is an opportunity to transit bridge SHM from research to practice. Therefore, this paper proposes an unsupervised transfer learning approach for bridges with a domain adaptation technique, where classifiers are trained only with labeled data generated from FE models (source domain). Then, unlabeled monitoring data (target domain) are used to test the classification performance. As numerical and monitoring data are related to the same bridge, both domains are assumed to have similar statistical distributions, with slight differences caused by the uncertainties inherent to the FE models. The domain adaptation is performed using a transfer knowledge method called transfer component analysis, which transforms damage-sensitive features from the original space to a new one, called latent space, where the differences between feature distributions are reduced. This approach may increase the use of numerical modeling for long-term monitoring, as it overcomes some of the limitations imposed by the calibration process of FE models. The efficiency of this unsupervised approach is illustrated through the classification performance of classifiers built on source data with and without domain adaptation, and using the benchmark data sets from the Z-24 Bridge as the target data.
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Acknowledgments
The authors acknowledge the KU Leuven (Belgium) Structural Mechanics Section as the Z-24 Bridge data source. The authors are grateful for financial support provided by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES)—Finance Code 001 and to the Portuguese National Funding Agency for Science Research and Technology (FCT/Portugal) for promoting the collaboration between Brazil and Portugal; in addition, they thank the Brazilian National Counsel of Technological and Scientific Development (CNPq) (Grant Number 306526/2019-0); the São Paulo Research Foundation (FAPESP) (Grant Number 19/19684-3); and the Coordination for the Improvement of Higher Education Personnel (CAPES/Brazil) (Grant Numbers 88882.433643/2019-01 and 88887.647575/2021-).
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Published In
Journal of Bridge Engineering
Volume 28 • Issue 1 • January 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Jan 25, 2022
Accepted: Aug 30, 2022
Published online: Nov 10, 2022
Published in print: Jan 1, 2023
Discussion open until: Apr 10, 2023
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