Chenyue Ma, Qiang ShaoThis email address is being protected from spambots. You need JavaScript enabled to view it., and Mengtian Yin
Hebei University of Water Resources and Electric Engineering foundation department, Cangzhou 061001, China
Received: February 4, 2024 Accepted: June 3, 2024 Publication Date: July 15, 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.
The California bearing ratio (CBR) as a widespread index is employed in Soil mechanics and geotechnical structures such as bridge abutments, earth dams, highway embankments, and so on. Generally, this index can be determined through laboratory or field tests. However, CBR determining procedure is time and cost-consuming. Therefore, the present study represents different hybrid models established on Least Square Support Vector Regression (LSSVR) combined with three meta-heuristic algorithms (namely, Arithmetic optimization algorithm, Bald eagle search optimization, and Seagull Optimization Algorithm) to estimate CBR values in a way that is cheap and quick to perform, and more accurate in solving real-world problems. 70% of developed hybrid models were allocated to the train stage and remained 30% were defined as testing models. The impression of five inputs (lime sludge percentage, lime percentage, maximum dry density, curing period, and optimum moisture content) on the predicted CBR values were considered. Finally, to assess the accuracy of the two-category created models, a comparison study between predicted and observed results is made by using five statistical indexes. The observations have shown that LSBE (LSSVR combined with Bald Eagle search optimization) model has strong potential for predicting the CBR.
Keywords: California Bearing Ratio; Least Square Support Vector Regression; Arithmetic optimization algorithm; Bald eagle search optimization; Seagull Optimization Algorithm
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