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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| Summary: | 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 optimization (BO-XGBoost). The BO-XGBoost model demonstrated superior performance compared to baseline models (Random Forest, AdaBoost, and Gradient Boosting Decision Tree), achieving an overall prediction accuracy of 0.92, precision and recall of 0.90, and an AUC of 0.98. SHAP (SHapley Additive exPlanations) analysis revealed that sound velocity, sound time, acoustic amplitude, concrete strength, and fly ash content were the most influential features for model predictions. This hybrid approach offers high efficiency and accuracy for void defect detection in CFST, providing a novel solution that leverages the strengths of both traditional ultrasonic methods and artificial intelligence algorithms. The method not only detects the presence of void defects but also quantifies their extent, advancing CFST inspection from qualitative analysis to quantitative assessment. |
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| ISSN: | 2214-5095 |