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An active learning Bayesian network classifier for efficient structural reliability analysis
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DOI：10.1080/15732479.2026.2617914.png)
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Abstract
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Active learning methods have demonstrated their successful applications in structural reliability problems. Most active learning surrogate models are regression-based, while the classification-based surrogate model has been less discussed. This paper proposes an active learning Bayesian network classifier (ALBC) method to fill this gap. Unlike regression models that predict continuous responses, Bayesian network classifiers directly categorise inputs into discrete classes (safe/failed). This classification approach is particularly suitable for reliability analysis where the primary concern is determining whether a structure will fail under given conditions. A learning function based on misclassification probability is formulated to identify update points that refine the decision boundary with an error expectation function for the sample population work as a stopping criterion for the model iteration. Three mathematical examples and a box-beam buckling failure problem are used to extensively investigate the feasibility and performance of the proposed method. This approach is shown to effectively refine the Bayesian classifier to achieve efficiency and accuracy for structural reliability applications.
Keywords:
Active learning
Bayesian network classifier
box-girder structure
classification-based surrogate
error expectation function
reliability analysis
surrogate model
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