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. 2023 Nov 27;13(1):20878.
doi: 10.1038/s41598-023-48044-1.

Application of machine learning models in the capacity prediction of RCFST columns

Khaled Megahed  1 , Nabil Said Mahmoud  1 , Saad Elden Mostafa Abd-Rabou  2
Affiliations

Application of machine learning models in the capacity prediction of RCFST columns

Khaled Megahed et al. Sci Rep. .

Abstract

Rectangular concrete-filled steel tubular (RCFST) columns are widely used in structural engineering due to their excellent load-carrying capacity and ductility. However, existing design equations often yield different design results for the same column properties, leading to uncertainty for engineering designers. Furthermore, basic regression analysis fails to precisely forecast the complicated relation between the column properties and its compressive strength. To overcome these challenges, this study suggests two machine learning (ML) models, including the Gaussian process (GPR) and the extreme gradient boosting model (XGBoost). These models employ a range of input variables, such as the geometric and material properties of RCFST columns, to estimate their strength. The models are trained and evaluated based on two datasets consisting of 958 axially loaded RCFST columns and 405 eccentrically loaded RCFST columns. In addition, a unitless output variable, termed the strength index, is introduced to enhance model performance. From evolution metrics, the GPR model emerged as the most accurate and reliable model, with nearly 99% of specimens with less than 20% error. In addition, the prediction results of ML models were compared with the predictions of two existing standard codes and different ML studies. The results indicated that the developed ML models achieved notable enhancement in prediction accuracy. In addition, the Shapley additive interpretation (SHAP) technique is employed for feature analysis. The feature analysis results reveal that the column length and load end-eccentricity parameters negatively impact compressive strength.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Flow charts of the introduced ML models.
Figure 2
Figure 2
RCFST column configurations under axial and eccentric loading conditions.
Figure 3
Figure 3
Distribution of the two databases.
Figure 3
Figure 3
Distribution of the two databases.
Figure 4
Figure 4
Frequency histogram of compressive strength and strength index for database 1.
Figure 5
Figure 5
Correlation matrix for the RCFST columns databases under axial and eccentric loading conditions.
Figure 6
Figure 6
Comparison between ML models for training and testing datasets.
Figure 7
Figure 7
Gaussian process regression on a semilog scale on the y-axis for axially loaded column database.
Figure 8
Figure 8
Summary plot and SHAP feature importance for the eccentrically loaded RCFST column database.
See this image and copyright information in PMC

References

    1. Xiong MX, Xiong DX, Liew JYR. Axial performance of short concrete filled steel tubes with high- and ultra-high- strength materials. Eng. Struct. 2017;136:494–510. doi: 10.1016/j.engstruct.2017.01.037. - DOI
    1. Han LH, Li W, Bjorhovde R. Developments and advanced applications of concrete-filled steel tubular (CFST) structures: Members. J. Constr. Steel Res. 2014;100:211–228. doi: 10.1016/j.jcsr.2014.04.016. - DOI
    1. Thai SJ, Thai HT, Ngo T, Uy B, Kang WH, Hicks S. Concrete-filled steel tubular (CFST) columns database with 3,208 tests. Mendeley Data. 2020 doi: 10.17632/j3f5cx9yjh.1. - DOI
    1. Goode CD. Composite columns-1819 tests on concrete-filled steel tube columns compared with Eurocode 4. Struct. Eng. 2008;86(16):33–38.
    1. Denavit, M. D. Characterization of behavior of steel-concrete composite members and frames with applications for design. (University of Illinois at Urbana-Champaign, 2012).