Enhancing Ultimate Bearing Capacity Assessment of Rock Foundations using a Hybrid Decision Tree Approach

Abstract

Accurately estimating the ultimate bearing capacity of piles embedded in rock is of paramount importance in the domains of civil engineering, construction, and foundation design. This research introduces an innovative solution to tackle this issue, leveraging a fusion of the Decision Tree method with two state-of-the-art optimization algorithms: the Zebra Optimization Algorithm and the Coronavirus Herd Immunity Optimizer. The research approach encompassed the creation of a hybridized model, unifying the DT with the Zebra Optimization Algorithm and Coronavirus Herd Immunity Optimizer. The primary objective was to augment the precision of the ultimate bearing capacity of prediction for piles embedded in rock. This hybridization strategy harnessed the capabilities of DT along with the two pioneering optimizers to address the inherent uncertainty stemming from diverse factors impacting bearing capacity. The Zebra Optimization Algorithm and Coronavirus Herd Immunity Optimizer showcased their efficacy in refining the base model, leading to substantial enhancements in predictive performance. This study's discoveries make a significant stride in the realm of geotechnical engineering by furnishing a sturdy approach to forecasting ultimate bearing capacity in rock-socketed piles. The hybridization method is a hopeful path for future research endeavors and practical implementations. Specifically, the DT + Zebra Optimization Algorithm model yielded dependable outcomes, as evidenced by their impressive R-squared value of 0.9981 and a low Root mean squared error value of 629.78. The attained outcomes empower engineers and designers to make well-informed choices concerning structural foundations in soft soil settings. Ultimately, this research advocates for safer and more efficient construction methodologies, mitigating the hazards linked to foundation failures.