Design Hybrid ARO-SVR Analysis to Predict the Pile Bearing Capacity

Yafan  LIU; Wenjun  MA

doi:10.6180/jase.202507_28(7).0002

Design Hybrid ARO-SVR Analysis to Predict the Pile Bearing Capacity

Yafan LIU¹This email address is being protected from spambots. You need JavaScript enabled to view it. and Wenjun MA^2,3

¹Department of Architectural Engineering, Shijiazhuang College of Applied Technology, Shijiazhuang, Hebei 050000, China

²Shijiazhuang Tiedao University, Shijiazhuang, Hebei 050000, China

³Hebei Provincial Academy of Emergency Management Scieces, Shijiazhuang, Shijiazhuang Hebei 050000, China

Received: December 12, 2023
Accepted: August 12, 2024
Publication Date: September 25, 2024

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202507_28(7).0002

The assessment of the load-bearing capacity of piles holds significant importance in the design of pile foundations. This paper presents hybridized support vector regression (SVR) models by utilizing the Artificial Rabbit Optimization (ARO) and the Black Widow Optimization Algorithm (BWOA) to predict the bearing capacity of concrete piles. A repository comprising 472 reports on static load tests conducted on driven piles was employed for the study. The dataset was allocated into three parts: the training set (70%), the validation set ( 15% ), and the testing set ( 15% ). Multiple criteria for assessing quality were utilized to evaluate the effectiveness of the models. The first rank belonged to the SVR model integrated with the ARO algorithm, where it could gain the higher value of R² in all of training (R²=0.9876), validating (R²=0.9778), and testing sections $\left(R^2=0.9874\right)$, and the lowest value of RMSE in all the training (RMSE=39.393), validating (RMSE=53.727) and testing sections (RMSE=38.082). The findings indicate that the suggested model is highly appropriate for predicting the capacity of concrete piles.

Keywords: Concrete piles; Bearing capacity; Estimation; Regression analysis; Artificial Rabbit optimization

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