Efficient behavior factor estimation in moment-resisting reinforced concrete frames through gene expression programming

Azhdari, Niloufar and Hashemi, Seyed Shaker and Javidi, Saeid and Fazeli, Abdorreza (2025) Efficient behavior factor estimation in moment-resisting reinforced concrete frames through gene expression programming. Journal of Soft Computing in Civil Engineering. ISSN 2588-2872

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Abstract

This study presents a novel approach for estimating the behavior factor of moment-resisting reinforced concrete (RC) frames using a gene expression programming (GEP) method, which involves designing and analyzing over three hundred RC frames. A comprehensive database detailing the specifications of moment-resistant RC frames has been established. This database has several influential parameters as the input parameters. The performance of the developed models was evaluated using statistical indicators, and the best model was determined. The chosen model demonstrated values of 0.0061, 0.049, and 0.0037 for root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE), respectively. Additionally, the R2 values for the training and test data were 0.93 and 0.82, respectively. Finally, a highly accurate mathematical equation was obtained to predict the behavior factor of the RC frames using GeneXpro Tools software. After sensitivity analysis of the behavior factor predicted to the investigated parameters, the results indicated that seismic conditions have minimal impact on the behavior factor of moment-resisting RC frames. The number of stories has an inverse relationship with the behavior factor, while the impact of changing the span length ratio to story height on the behavior factor is not uniform. The study's findings indicated that the GEP method effectively predicted the behavior coefficient of RC frames.This study presents a novel approach for estimating the behavior factor of moment-resisting reinforced concrete (RC) frames using a gene expression programming (GEP) method, which involves designing and analyzing over three hundred RC frames. A comprehensive database detailing the specifications of moment-resistant RC frames has been established. This database has several influential parameters as the input parameters. The performance of the developed models was evaluated using statistical indicators, and the best model was determined. The chosen model demonstrated values of 0.0061, 0.049, and 0.0037 for root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE), respectively. Additionally, the R2 values for the training and test data were 0.93 and 0.82, respectively. Finally, a highly accurate mathematical equation was obtained to predict the behavior factor of the RC frames using GeneXpro Tools software. After sensitivity analysis of the behavior factor predicted to the investigated parameters, the results indicated that seismic conditions have minimal impact on the behavior factor of moment-resisting RC frames. The number of stories has an inverse relationship with the behavior factor, while the impact of changing the span length ratio to story height on the behavior factor is not uniform. The study's findings indicated that the GEP method effectively predicted the behavior coefficient of RC frames.

Item Type:	Article
Identification Number:	10.22115/scce.2024.444559.1808
Dates:	Date Event 11 August 2024 Accepted 1 July 2025 Published Online
Uncontrolled Keywords:	Gene expression programming (GEP), Artificial intelligence, Reinforced concrete frames, Behavior factor, Seismic behavior
Subjects:	CAH10 - engineering and technology > CAH10-01 - engineering > CAH10-01-07 - civil engineering
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Engineering
Depositing User:	Gemma Tonks
Date Deposited:	02 Jul 2025 10:04
Last Modified:	02 Jul 2025 10:04
URI:	https://www.open-access.bcu.ac.uk/id/eprint/16458

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