An ultrasonic-AI hybrid approach for predicting void defects in concrete-filled steel tubes via enhanced XGBoost with Bayesian optimization

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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Bibliographic Details
Main Authors: Shuai Wan, Shipan Li, Zheng Chen, Yunchao Tang
Format: Article
Language:English
Published: Elsevier 2025-07-01
Series:Case Studies in Construction Materials
Subjects:
CFST
Void detection
XGBoost
Bayes optimization
Online Access:http://www.sciencedirect.com/science/article/pii/S2214509525001573
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