Estimation of the Ultimate Bearing Capacity of the Rocks via Utilization of the AI-Based Frameworks

Precise anticipation of the ultimate bearing capacity (Qu)for rock-socketed piles is an important task in civil engineering, construction, and the design of foundations. The approach adopted here is new and solves the problem using KNN combined with two modern nature-inspired optimization frameworks, namely the Honey Badger Algorithm (HBA) and Equilibrium Slime Mould Algorithm (ESMA). The hybrid model in this paper combines the K-nearest neighbor with HBA and ESMA. The main objective was to improve the prediction performance of Qu for rock-socketed piles. This hybridization technique utilized the KNN model's strengths and two new optimizers to overcome the inherent uncertainty related to all variables that affect bearing capacity. These HBA and ESMA frameworks were proven capable of performing decent tuning of the KNN model, while their outcomes showed significantly improved predictive power. The hybrid model realized high accuracy for Qu estimation by considering various influencing parameters like soil properties, pile dimensions, and load conditions. The output of this study adds to the development in the area of geotechnical engineering by providing a sound methodology for Qu estimation in rock-socketed piles. The hybridization technique is a promising avenue for future exploration and practical applications, especially the KNHB frameworks that have obtained reliable outcomes by their very accurate R2 of 0.9945 and RMSE of 771.0886Outcomes support engineers and designers in formulating educated judgments concerning the foundation in soft soil environments. Ultimately, this exploration promotes safer and more efficient construction practices by minimizing risks associated with foundation failures.