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Steel and Composite Structures Volume 46, Number 2, January25 2023 , pages 153-173 DOI: https://doi.org/10.12989/scs.2023.46.2.153 |
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A robust approach in prediction of RCFST columns using machine learning algorithm |
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Van-Thanh Pham and Seung-Eock Kim
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| Abstract | ||
| Rectangular concrete-filled steel tubular (RCFST) column, a type of concrete-filled steel tubular (CFST), is widely used in compression members of structures because of its advantages. This paper proposes a robust machine learning-based framework for predicting the ultimate compressive strength of RCFST columns under both concentric and eccentric loading. The gradient boosting neural network (GBNN), an efficient and up-to-date ML algorithm, is utilized for developing a predictive model in the proposed framework. A total of 890 experimental data of RCFST columns, which is categorized into two datasets of concentric and eccentric compression, is carefully collected to serve as training and testing purposes. The accuracy of the proposed model is demonstrated by comparing its performance with seven state-of-the-art machine learning methods including decision tree (DT), random forest (RF), support vector machines (SVM), deep learning (DL), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and categorical gradient boosting (CatBoost). Four available design codes, including the European (EC4), American concrete institute (ACI), American institute of steel construction (AISC), and Australian/New Zealand (AS/NZS) are refereed in another comparison. The results demonstrate that the proposed GBNN method is a robust and powerful approach to obtain the ultimate strength of RCFST columns. | ||
| Key Words | ||
| concrete-filled steel tube; composite structure; gradient boosting neural networks; machine learning; predictive model; ultimate compression strength | ||
| Address | ||
| Van-Thanh Pham and Seung-Eock Kim:Department of Civil and Environmental Engineering, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul 05006, South Korea | ||