A hybrid learning approach for simulating settlement of shallow foundation

Settlement modeling is essential because of the cohesive soil texture’s intricacy. This study seeks to identify settlement (Sm) of shallow foundations using newly discovered ML approaches named the hybridized random forests analysis (RF) with grasshoppers optimization algorithm (GOA), the bat-inspired approach (BAT), beluga whale optimization (BWH). RF serves as the primary predictive model due to its robust ensemble learning approach. Optimization algorithms enhance its predictive accuracy. Hybrid models (GOARF, BATRF, BWHRF) leverage the strengths of both RF and optimization algorithms to provide superior settlement predictions. By combining RF with these optimization techniques, the study aims to achieve highly accurate predictions of settlement for shallow foundations, demonstrating the effectiveness of these advanced machine-learning approaches. All the BATRF, GOARF, and BWHRF models accurately emulated the Sm, with R² values of at least 0.985 for the training and 0.978 for the testing collection, respectively. Comparing the BWHRF to other models and literature, it is believed to be the appropriate system with the highest categorization. R², RMSE, and MAE values during learning are 0.9913, 2.341, and 1.239 mm, respectively, which are superior by 0.9025, 8.09, and 4.92 mm over ANFIS-PSO. The values of the A_(15-index) index depict that the BWHRF can be outperformed by other models. Finally, after examining the validity and considering the assumptions, it is clear that the RF paired with BWH can perform more effectively than the BAT and GOA, and even ANFIS-PSO (literature), in the Sm simulation.