Structural Engineering and Mechanics

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Enhancing prediction of the moment-rotation behavior in flush end plate connections using Multi-Gene Genetic Programming (MGGP)

  • Amirmohammad Rabbani (School of Civil Engineering, College of Engineering, University of Tehran) ;
  • Amir Reza Ghiami Azad (School of Civil Engineering, College of Engineering, University of Tehran) ;
  • Hossein Rahami (School of Engineering Science, College of Engineering, University of Tehran)
  • Received : 2023.08.13
  • Accepted : 2024.08.26
  • Published : 2024.09.25
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Abstract

The prediction of the moment rotation behavior of semi-rigid connections has been the subject of extensive research. However, to improve the accuracy of these predictions, there is a growing interest in employing machine learning algorithms. This paper investigates the effectiveness of using Multi-gene genetic programming (MGGP) to predict the moment-rotation behavior of flush-end plate connections compared to that of artificial neural networks (ANN) and previous studies. It aims to automate the process of determining the most suitable equations to accurately describe the behavior of these types of connections. Experimental data was used to train ANN and MGGP. The performance of the models was assessed by comparing the values of coefficient of determination (R2), maximum absolute error (MAE), and root-mean-square error (RMSE). The results showed that MGGP produced more accurate, reliable, and general predictions compared to ANN and previous studies with an R2 exceeding 0.99, an RMSE of 6.97, and an MAE of 38.68, highlighting its advantages over other models. The use of MGGP can lead to better modeling and more precise predictions in structural design. Additionally, an experimentally-based regression analysis was conducted to obtain the rotational capacity of FECs. A new equation was proposed and compared to previous ones, showing significant improvement in accuracy with an R2 score of 0.738, an RMSE of 0.014, and an MAE of 0.024.

Keywords

References

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