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Computers and Concrete Volume 31, Number 3, March 2023 , pages 253-265 DOI: https://doi.org/10.12989/cac.2023.31.3.253 |
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An evolutionary approach for predicting the axial load-bearing capacity of concrete-encased steel (CES) columns |
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Armin Memarzadeh, Hassan Sabetifar, Mahdi Nematzadeh and Aliakbar Gholampour
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| Abstract | ||
| In this research, the gene expression programming (GEP) technique was employed to provide a new model for predicting the maximum loading capacity of concrete-encased steel (CES) columns. This model was developed based on 96 CES column specimens available in the literature. The six main parameters used in the model were the compressive strength of concrete (fc), yield stress of structural steel (fys), yield stress of steel rebar (fyr), and cross-sectional areas of concrete, structural steel, and steel rebar (Ac, As and Ar respectively). The performance of the prediction model for the ultimate load-carrying capacity was investigated using different statistical indicators such as root mean square error (RMSE), correlation coefficient (R), mean absolute error (MAE), and relative square error (RSE), the corresponding values of which for the proposed model were 620.28, 0.99, 411.8, and 0.01, respectively. Here, the predictions of the model and those of available codes including ACI ITG, AS 3600, CSA-A23, EN 1994, JGJ 138, and NZS 3101 were compared for further model assessment. The obtained results showed that the proposed model had the highest correlation with the experimental data and the lowest error. In addition, to see if the developed model matched engineering realities and corresponded to the previously developed models, a parametric study and sensitivity analysis were carried out. The sensitivity analysis results indicated that the concrete cross-sectional area (Ac) has the greatest effect on the model, while parameter (fyr) has a negligible effect. | ||
| Key Words | ||
| axial load bearing capacity; codes; concrete encased steel (CES); gene expression programming; sensitivity analysis; strength prediction | ||
| Address | ||
| Armin Memarzadeh, Hassan Sabetifar and Mahdi Nematzadeh: Department of Civil Engineering, Faculty of Engineering and Technology, University of Mazandaran, Babolsar, Iran Aliakbar Gholampour: College of Science and Engineering, Flinders University, SA, Australia | ||