An evolutionary approach for predicting the axial load-bearing capacity of concrete-encased steel (CES) columns

Armin Memarzadeh, Hassan Sabetifar, Mahdi Nematzadeh, Aliakbar Gholampour

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)253-265
Number of pages13
JournalComputers and Concrete
Issue number3
Publication statusPublished - Mar 2023


  • axial load bearing capacity
  • codes
  • concrete encased steel (CES)
  • gene expression programming
  • sensitivity analysis
  • strength prediction


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