New hybrid models integrating the aquila optimizer with machine learning algorithms to predict the strength of geosynthetic-reinforced subgrade soil
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...
Jun-Jiat Tiang, Bayram Ateş, Mohammad Azım Eırgash et al.
· Frontiers in Built Environme... · 0 citations