{"id":2553,"date":"2026-04-06T14:35:11","date_gmt":"2026-04-06T06:35:11","guid":{"rendered":"https:\/\/iweb20wp-b205b.url.tku.edu.tw\/jase\/?post_type=tkuisotope&p=2553"},"modified":"2026-05-24T17:15:08","modified_gmt":"2026-05-24T09:15:08","slug":"design-hybrid-aro-svr-analysis-to-predict-the-pile-bearing-capacity","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=design-hybrid-aro-svr-analysis-to-predict-the-pile-bearing-capacity","title":{"rendered":"Design Hybrid ARO-SVR Analysis to Predict the Pile Bearing Capacity"},"content":{"rendered":"\n
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<\/i> 2025<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 28, Issue 7<\/a><\/p>\n<\/div>\n\n\n\n

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Yafan LIU1<\/sup><\/i><\/a> and Wenjun MA2,3<\/sup><\/p>\n\n\n\n

1<\/sup>Department of Architectural Engineering, Shijiazhuang College of Applied Technology, Shijiazhuang, Hebei 050000, China<\/p>\n\n\n\n

2<\/sup>Shijiazhuang Tiedao University, Shijiazhuang, Hebei 050000, China<\/p>\n\n\n\n

3<\/sup>Hebei Provincial Academy of Emergency Management Scieces, Shijiazhuang, Shijiazhuang Hebei 050000, China<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0December 12, 2023
Accepted:\u00a0August 12, 2024
Publication Date:\u00a0April 6, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Correlation coefficient values.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202507_28(7).0002<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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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<\/sup><\/em> in all of training R2<\/sup><\/em> = 0.9876 , validating R2<\/sup><\/em> = 0.9778 , and testing sections R2<\/sup><\/em> = 0.9874 , 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.<\/p>\n\n\n\n

Keywords:\u00a0Concrete piles; Bearing capacity; Estimation; Regression analysis; Artificial Rabbit optimization<\/em><\/p>\n\n\n\n

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