Predicting the Lateral Load Carrying Capacity of Reinforced Concrete Rectangular Columns: Gene Expression Programming

Raheel Asghar; Muhammad Faisal Javed; Raid Alrowais; Alamgir Khalil; Abdeliazim Mustafa Mohamed; Abdullah Mohamed; Nikolai Ivanovich Vatin

doi:10.3390/ma15072673

Predicting the Lateral Load Carrying Capacity of Reinforced Concrete Rectangular Columns: Gene Expression Programming

Materials (Basel). 2022 Apr 5;15(7):2673. doi: 10.3390/ma15072673.

Authors

Raheel Asghar¹, Muhammad Faisal Javed¹, Raid Alrowais², Alamgir Khalil³, Abdeliazim Mustafa Mohamed^{4

5}, Abdullah Mohamed⁶, Nikolai Ivanovich Vatin⁷

Affiliations

¹ Department of Civil Engineering, Abbottabad Campus, COMSATS University Islamabad, Abbottabad 22060, Pakistan.
² Department of Civil Engineering, Jouf University, Sakaka, Al-Jouf 72388, Saudi Arabia.
³ Department of Civil Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan.
⁴ Department of Civil Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Alkharj 16273, Saudi Arabia.
⁵ Building and Construction Technology Department, Bayan College of Science and Technology, Khartoum 210, Sudan.
⁶ Research Centre, Future University in Egypt, New Cairo 11835, Egypt.
⁷ Peter the Great St. Petersburg Polytechnic University, 195291 St. Petersburg, Russia.

Abstract

This research presents a novel approach of artificial intelligence (AI) based gene expression programming (GEP) for predicting the lateral load carrying capacity of RC rectangular columns when subjected to earthquake loading. To achieve the desired research objective, an experimental database assembled by the Pacific Earthquake Engineering Research (PEER) center consisting of 250 cyclic tested samples of RC rectangular columns was employed. Seven input variables of these column samples were utilized to develop the coveted analytical models against the established capacity outputs. The selection of these input variables was based on the linear regression and cosine amplitude method. Based on the GEP modelling results, two analytical models were proposed for computing the flexural and shear capacity of RC rectangular columns. The performance of both these models was evaluated based on the four key fitness indicators, i.e., coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE), and root relative squared error (RRSE). From the performance evaluation results of these models, R², RMSE, MAE, and RRSE were found to be 0.96, 53.41, 38.12, and 0.20, respectively, for the flexural capacity model, and 0.95, 39.47, 28.77, and 0.22, respectively, for the shear capacity model. In addition to these fitness criteria, the performance of the proposed models was also assessed by making a comparison with the American design code of concrete structures ACI 318-19. The ACI model reported R², RMSE, MAE, and RRSE to be 0.88, 101.86, 51.74, and 0.39, respectively, for flexural capacity, and 0.87, 238.74, 183.66, and 1.35, respectively, for shear capacity outputs. The comparison depicted a better performance and higher accuracy of the proposed models as compared to that of ACI 318-19.

Keywords: bearing capacity; flexural capacity; gene expression programming; lateral load carrying capacity; reinforced concrete columns; shear capacity.