Steel and Composite Structures

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An optimized ANFIS model for predicting pile pullout resistance

  • Yuwei Zhao (College of Civil Engineering, Xuzhou University of Technology) ;
  • Mesut Gor (Firat University, Engineering Faculty, Civil Engineering Department, Division of Geotechnical Engineering) ;
  • Daria K. Voronkova (Department of Mathematics and Natural Sciences, Gulf University for Science and Technology, Mishref Campus) ;
  • Hamed Gholizadeh Touchaei (Department of Civil Engineering, Southern Illinois University Edwardsville) ;
  • Hossein Moayedi (Institute of Research and Development, Duy Tan University) ;
  • Binh Nguyen Le (Institute of Research and Development, Duy Tan University)
  • Received : 2021.09.14
  • Accepted : 2023.07.06
  • Published : 2023.07.25
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Abstract

Many recent attempts have sought accurate prediction of pile pullout resistance (Pul) using classical machine learning models. This study offers an improved methodology for this objective. Adaptive neuro-fuzzy inference system (ANFIS), as a popular predictor, is trained by a capable metaheuristic strategy, namely equilibrium optimizer (EO) to predict the Pul. The used data is collected from laboratory investigations in previous literature. First, two optimal configurations of EO-ANFIS are selected after sensitivity analysis. They are next evaluated and compared with classical ANFIS and two neural-based models using well-accepted accuracy indicators. The results of all five models were in good agreement with laboratory Puls (all correlations > 0.99). However, it was shown that both EO-ANFISs not only outperform neural benchmarks but also enjoy a higher accuracy compared to the classical version. Therefore, utilizing the EO is recommended for optimizing this predictive tool. Furthermore, a comparison between the selected EO-ANFISs, where one employs a larger population, revealed that the model with the population size of 75 is more efficient than 300. In this relation, root mean square error and the optimization time for the EO-ANFIS (75) were 19.6272 and 1715.8 seconds, respectively, while these values were 23.4038 and 9298.7 seconds for EO-ANFIS (300).

Keywords

Acknowledgement

This work was supported by Industry-University-Research Project of Jiangsu Province. Project ID: BY2022-1293

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