A prediction model for plastic hinge length of rectangular RC columns using gene expression programming

Creative Commons License

Alacalı S., Arslan G.

Neural Computing and Applications, vol.36, no.16, pp.9481-9501, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 36 Issue: 16
  • Publication Date: 2024
  • Doi Number: 10.1007/s00521-024-09578-1
  • Journal Name: Neural Computing and Applications
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Page Numbers: pp.9481-9501
  • Keywords: Gene expression programming, Plastic hinge length, Reinforced concrete column, sensitivity analysis
  • Yıldız Technical University Affiliated: Yes


In the reinforced concrete (RC) columns which are exposed extreme loads such as earthquake effects, the plastic hinge length can be defined as the length of the region where flexural moments exceed the yielding capacity, and the plastic deformations are concentrated. More accurate estimation of plastic hinge length increases the reliability of the seismic design. However, a sensitivity prediction of plastic hinge length is difficult due to a large number of model parameters. Therefore, this study aims to predict the plastic hinge length using the gene expression programming (GEP). An experimental database of 133 RC columns gathered from the literature was utilized for prediction with GEP. The results of GEP model are statistically compared with those of 13 models existing in the literature proposed by various researchers. The comparison results reveal that the proposed GEP-based formulation has the best efficiency among all models. Furthermore, a sensitivity analysis and parametric study are conducted to identify the most influential parameters affecting the GEP formulation.