An ultrasonic-AI hybrid approach for predicting void defects in concrete-filled steel tubes via enhanced XGBoost with Bayesian optimization
This study proposes an Ultrasonic-AI Hybrid Approach for predicting void defects in concrete-filled steel tubes (CFST). Based on 3600 ultrasonic measurement samples, an Extreme Gradient Boosting (XGBoost) model was enhanced through oversampling and hyperparameter optimization via Bayesian optimizati...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
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| Series: | Case Studies in Construction Materials |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509525001573 |
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