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Machine learning with data augmentation for UHPC blast response
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AI
DOI：10.1016/j.cie.2026.112093.png)
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Abstract
En 中文
• A systematic data augmentation and machine learning framework was proposed to predict the blast response of UHPC beams under limited experimental data. • Kriging surrogate model-based augmentation combined with Gradient Boosting Regressor achieved the best prediction performance among all configurations. • SHAP-based interpretability analysis with independent validation confirmed physically consistent feature importance rankings across multiple model architectures.
Keywords:
UHPC
blast response
data augmentation
machine learning
Kriging surrogate model
Journal
C
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6.5
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489
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0