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Published in:
2023 | OriginalPaper | Chapter
Integrating Physical Knowledge into Gaussian Process Regression Models for Probabilistic Fatigue Assessment
Authors : Samuel J. Gibson, Timothy J. Rogers, Elizabeth J. Cross
Published in: European Workshop on Structural Health Monitoring
Publisher: Springer International Publishing
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Abstract
Fatigue is a common cause of the failure of structures: previous work by the authors has shown the usefulness of using Gaussian process regression to develop a probabilistic assessment of fatigue damage accumulation. By propagating uncertainty from a predictive model for structural response (strain) under unknown loading, a more robust assessment of the damage state of a structure is enabled. Although these black-box models have previously shown good results for quasi-static problems, dynamic behaviour is difficult to predict in this way. Explored here is a promising and novel means of accounting for this, by integrating physical knowledge specifically through the GP kernel. The impact of this on accuracy of fatigue damage prediction is shown to be significant and the damage variance from a probabilistic perspective is reduced substantially.