Buy article PDF
The purchased file will be sent to you
via email after the payment is completed.
US$ 35
|
Steel and Composite Structures Volume 51, Number 4, May 25 2024 , pages 417-440 DOI: https://doi.org/10.12989/scs.2024.51.4.417 |
|
|
|
Pile bearing capacity prediction in cold regions using a combination of ANN with metaheuristic algorithms |
||
Zhou Jingting, Hossein Moayedi, Marieh Fatahizadeh and Narges Varamini
|
||
| Abstract | ||
| Artificial neural networks (ANN) have been the focus of several studies when it comes to evaluating the pile's bearing capacity. Nonetheless, the principal drawbacks of employing this method are the sluggish rate of convergence and the constraints of ANN in locating global minima. The current work aimed to build four ANN-based prediction models enhanced with methods from the black hole algorithm (BHA), league championship algorithm (LCA), shuffled complex evolution (SCE), and symbiotic organisms search (SOS) to estimate the carrying capacity of piles in cold climates. To provide the crucial dataset required to build the model, fifty-eight concrete pile experiments were conducted. The pile geometrical properties, internal friction angle Φ shaft, internal friction angle Φ tip, pile length, pile area, and vertical effective stress were established as the network inputs, and the BHA, LCA, SCE, and SOS-based ANN models were set up to provide the pile bearing capacity as the output. Following a sensitivity analysis to determine the optimal BHA, LCA, SCE, and SOS parameters and a train and test procedure to determine the optimal network architecture or the number of hidden nodes, the best prediction approach was selected. The outcomes show a good agreement between the measured bearing capabilities and the pile bearing capacities forecasted by SCE-MLP. The testing dataset's respective mean square error and coefficient of determination, which are 0.91846 and 391.1539, indicate that using the SCE-MLP approach as a practical, efficient, and highly reliable technique to forecast the pile's bearing capacity is advantageous. | ||
| Key Words | ||
| artificial neural network; bearing capacity; metaheuristic algorithms; pile | ||
| Address | ||
| Zhou Jingting:School of Civil Engineering, Southwest Jiatong University, Chengdu, China Hossein Moayedi:1)Institute of Research and Development, Duy Tan University, Da Nang, Vietnam 2)School of Engineering & Technology, Duy Tan University, Da Nang, Vietnam Marieh Fatahizadeh:ICUBE, UMR 7357, CNRS, INSA de Strasbourg, Strasbourg, France Narges Varamini:Department of Civil and Environmental Engineering, Shiraz University, Iran | ||