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Abstract Accurately predicting the California Bearing Ratio (CBR) of geosynthetic-reinforced subgrades is critical for pavement design but remains challenging due to the complex, nonlinear soil-reinforcement interaction. This study develops a novel hybrid modeling framework that integrates the Aquila Optimization Algorithm (AOA) with machine learning models such as Artificial Neural Networks (ANN), Ridge Regression, and XGBoost to enhance the prediction of soaked CBR values.
Mohammad A. Bahry1, Charlene Hanlon1, 2, Clara J. Ziezold1, Sierra Schaus1 and Gregoy Y. Bedecarrats1* 1Department of Animal Biosciences, University of Guelph, Canada 2Department of Poultry Science, College of Agriculture, Auburn University, United States The final, formatted version of the article will be published soon.
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