An optimized ANFIS model for predicting pile pullout resistance
Yuwei Zhao,Mesut Gor,Daria K. Voronkova,Hamed Gholizadeh Touchaei,Hossein Moayedi,Binh Nguyen Le
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).
Yuwei Zhao — 1)College of Civil Engineering, Xuzhou University of Technology, Xuzhou 221018, Jiangsu, China 2)Laboratory of Environmental Impact and Structural Safety in Civil Engineering, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China
Mesut Gor — Firat University, Engineering Faculty, Civil Engineering Department, Division of Geotechnical Engineering, 23119, Elazig, Turkey
Daria K. Voronkova — 1)Department of Mathematics and Natural Sciences, Gulf University for Science and Technology, Mishref Campus, Kuwait 2)Bauman Moscow State Technical University Moscow, Russia
Hamed Gholizadeh Touchaei — Department of Civil Engineering, Southern Illinois University Edwardsville, Edwardsville, IL 62026, USA
Hossein Moayedi and Binh Nguyen Le — 1)Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam 2)School of Engineering & Technology, Duy Tan University, Da Nang, Vietnam
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