Machine Learning Prediction of the Compressive Bearing Capacity of Concrete-Filled Steel Tubes Using Random Forest.
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| Title: | Machine Learning Prediction of the Compressive Bearing Capacity of Concrete-Filled Steel Tubes Using Random Forest. |
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| Authors: | Su, Weidi1,2 (AUTHOR), Cheng, Yaofei1,2,3 (AUTHOR), Wei, Li2,3 (AUTHOR) 2210391066@st.gxu.edu.cn, Zhong, Guangda1,2,4 (AUTHOR), Zhou, Linxiao1,3 (AUTHOR), Liu, Fei2,3,4 (AUTHOR), Xie, Kaizhong2,4 (AUTHOR) |
| Source: | Materials (1996-1944). Jun2026, Vol. 19 Issue 12, p2511. 27p. |
| Subjects: | Random forest algorithms, Compressive strength, Machine learning, Structural engineering, Concrete-filled tubes, Prediction models, Mechanical behavior of materials |
| Abstract: | Highlights: High-precision RF model for CFST capacity using 154 tests and 24 inputs. Steel tube inertia (Is) dominates prediction (35.56% contribution). Validated by nine CFST tests with prediction errors within 5%. Concrete-filled steel tube (CFST) members are widely used in long-span and high-rise structures due to their high load-bearing capacity and structural efficiency. Accurate prediction of their compressive bearing capacity is essential for reliable design. In this study, a data-driven prediction model based on the Random Forest (RF) algorithm was developed using a database of 154 axial compression tests. A total of 24 parameters, including geometric dimensions, material properties, and sectional characteristics, were considered as input variables, and the model was optimized through five-fold cross-validation and hyperparameter tuning. The results indicate that the proposed model achieves high accuracy and stability, with mean predicted-to-experimental ratios of 1.002 and 0.989 for the training and testing sets, respectively, and maximum deviations within 15%. Compared with existing design codes and alternative machine learning methods, the RF model improves prediction accuracy by approximately 9% and exhibits strong generalization capability. Furthermore, independent experimental validation using nine CFST column tests confirms its reliability, with prediction errors within 5%. These findings demonstrate that the proposed model provides an effective and practical tool for predicting the compressive bearing capacity of CFST members in engineering applications. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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