Abstract
Machine learning has been widely and successfully used in many Structural Health Monitoring (SHM) applications. However, many machine learning models can only make accurate predictions when the training and test data are measured from the same system; this is because most traditional machine learning methods assume that all the data are drawn from the same distribution. Therefore, to train a robust predictor, it is often required to recollect and label new training data every time when considering a new structure, which can be significantly expensive, and sometimes impossible in the SHM context. In such cases, the idea of transfer learning may be employed, which aims to transfer knowledge between task domains to improve learners. In this paper, a subfield of transfer learning i.e. domain adaptation, is considered, and its utility in SHM applications is briefly investigated.
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Acknowledgements
The authors would like to gratefully acknowledge the support of the UK Engineering and Physical Sciences Research Council via grants EP/J016942/1 and EP/K003836/2. KW would also like to thank Lawrence Bull of the University of Sheffield for discussions on the nature of active learning and for commenting on the manuscript.
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Liu, X., Worden, K. (2020). On the Application of Domain Adaptation in SHM. In: Dervilis, N. (eds) Special Topics in Structural Dynamics & Experimental Techniques, Volume 5. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-030-12243-0_17
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DOI: https://doi.org/10.1007/978-3-030-12243-0_17
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