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Computers and Concrete Volume 32, Number 2, August 2023 , pages 217-232 DOI: https://doi.org/10.12989/cac.2023.32.2.217 |
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Computational intelligence models for predicting the frictional resistance of driven pile foundations in cold regions |
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Shiguan Chen, Huimei Zhang, Kseniya I. Zykova, Hamed Gholizadeh Touchaei, Chao Yuan, Hossein Moayedi and Binh Nguyen Le
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| Abstract | ||
| Numerous studies have been performed on the behavior of pile foundations in cold regions. This study first attempted to employ artificial neural networks (ANN) to predict pile-bearing capacity focusing on pile data recorded primarily on cold regions. As the ANN technique has disadvantages such as finding global minima or slower convergence rates, this study in the second phase deals with the development of an ANN-based predictive model improved with an Elephant herding optimizer (EHO), Dragonfly Algorithm (DA), Genetic Algorithm (GA), and Evolution Strategy (ES) methods for predicting the piles' bearing capacity. The network inputs included the pile geometrical features, pile area (m2), pile length (m), internal friction angle along the pile body and pile tip (0), and effective vertical stress. The MLP model pile's output was the ultimate bearing capacity. A sensitivity analysis was performed to determine the optimum parameters to select the best predictive model. A trialand-error technique was also used to find the optimum network architecture and the number of hidden nodes. According to the results, there is a good consistency between the pile-bearing DA-MLP-predicted capacities and the measured bearing capacities. Based on the R2 and determination coefficient as 0.90364 and 0.8643 for testing and training datasets, respectively, it is suggested that the DA-MLP model can be effectively implemented with higher reliability, efficiency, and practicability to predict the bearing capacity of piles. | ||
| Key Words | ||
| driven piles; hybrid; nature-inspired; shaft friction capacity | ||
| Address | ||
| Shiguan Chen: School of Architecture and Civil Engineering, Xi'an University of Science and Technology, Xi'an 710054, Shaanxi, China Huimei Zhang and Chao Yuan: College of Sciences, Xi'an University of Science and Technology, Xi'an 710054, Shaanxi, China Kseniya I. Zykova: 1) Department of Mathematics and Natural Sciences, Gulf University for Science and Technology, Mishref Campus, Kuwait, 2) Department of Safety in cyberworld, Bauman Moscow State Technical University Moscow, Russia Hamed Gholizadeh Touchaei: Department of Civil Engineering, Southern Illinois University Edwardsville, Edwardsville, IL 62026, United States Hossein Moayedi and Binh Nguyen Le: 1) Institute of Research and Development, Duy Tan University, Da Nang, Vietnam, 2) School of Engineering & Technology, Duy Tan University, Da Nang, Vietnam | ||