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¡¶½¨Öþ¿ÆѧÓ빤³Ìѧ±¨¡·[ISSN:1673-2049/CN:61-1442/TU]

Issue:

2026Äê01ÆÚ

Page:

75-84

Research Field:

ÖÇÄܼì²âÓ뽨Ôì¼¼ÊõרÀ¸

Publishing date:

Info

Title:

Study on axial compressive bearing capacity of recycled aggregate concrete-filled circular steel tubular stub columns based on ensemble learning

Author(s):

ZHANG Yuzhuo¹;  XI Meng¹;  LIU Jinlong²;  ZHANG Xinlong³; 4;  LIU Faqi⁴

1. School of Management, Shenyang Jianzhu University, Shenyang 110168, Liaoning, China; 2. School of Civil Engineering, Southeast University, Nanjing 211189, Jiangsu, China; 3. China Northeast Architectural Design and Research Institute Co., Ltd., Shenyang 110000, Liaoning, China; 4. Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin 150090, Heilongjiang, China

Keywords:

machine learning;  ensemble learning;  recycled aggregate concrete-filled steel tubular;  axial compressive bearing capacity;  SHAP

PACS:

TU392.3

DOI:

10.19815/j.jace.2024.11076

Abstract:

In order to quickly and accurately calculate the axial compressive bearing capacity of recycled aggregate concreteª²filled circular steel tubular stub columns and to investigate the influence mechanism of design variables on the axial compressive bearing capacity, an intelligent prediction model based on ensemble learning algorithms was proposed. A database comprising 120 experimental samples was established by collecting existing literature, and Spearman correlation analysis was applied to assess the relationship between design variables and axial compressive bearing capacity. Prediction models for axial compressive bearing capacity were developed using both single and ensemble learning algorithms, such as Bagging and Boosting. On the basis, the model performance was comprehensively evaluated using the coefficient of determination, root mean square error, and mean absolute error. The SHAP method was employed to quantitatively analyze how each input variable affects the axial compressive bearing capacity. The results show that the Spearman rank correlation coefficients between the axial compressive bearing capacity and the section diameter, the axial compressive capacity of concrete and the axial compressive capacity of steel tubular are all greater than 0.9. Notably, the axial compressive capacity of concrete and the axial compressive capacity of steel tubular emerge as critical factors influencing the capacity of recycled aggregate concreteª²filled circular steel tubular stub columns. Among the models, the random forest (RF) model demonstrates superior performance, and the coefficient of determination, root mean square error, and mean absolute error are 0.973, 20.001 and 12.758, respectively. Compared to the three calculation methods in the current specifications and procedures, the RF model improves the prediction accuracy based on the average relative error by 12.79%, 19.87%, and 9.30%, respectively. The ensemble learning model significantly outperforms single model predictions, offering substantial improvements in prediction accuracy. The research findings can provide a reference for the design of recycled aggregate concreteª²filled circular steel tubular stub columns.

References:

£Û 1 £Ý YANG D D, LIU F Q, WANG Y Y. Axial compression behaviour of rectangular recycled aggregate concrete - filled steel tubular stub columns £Û J £Ý . Journal of Constructional Steel Research, 2023, 201: 107687.

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Last Update: 2026-01-20