Machine learning approaches for prediction of the bearing capacity of ring foundations on rock masses

Abstract

Determining the bearing capacity of ring foundations on rock masses holds utmost importance within the framework of foundation design methodology. To examine the failure mechanism of ring foundations situated on Hock-Brown rock masses, the crucial bearing capacity factor ${(N}_{\sigma })$ is analyzed. This analysis considered three dimensionless input parameters: the geological strength index (GSI), the yield parameter (m_i), and the ratio of the internal and external radii (r_i/r_o). This study focuses on the development of a precise hybrid extreme learning machine (ELM) and least-square support vector machine (LSSVM) based on two swarm-based intelligence optimization algorithms, utilizing Harris hawks optimization (HHO) and particle swarm optimization (PSO). The primary objective of this study is to provide accurate predictions of the bearing capacity factor (${N}_{\sigma }$) for a ring foundation. Furthermore, the accuracy of the developed hybrid ELM-PSO, ELM-HHO, LSSVM-PSO, and LSSVM-HHO models was assessed through a comparison between the actual and predicted values of ${N}_{\sigma }$ using various performance metrics, uncertainty analysis, and rank analysis. The LSSVM-HHO and ELM-HHO outperformed the LSSVM-PSO and ELM-PSO models in predicting the ${N}_{\sigma }$ value. The proposed models can be used as soft computing tools to predict the ${N}_{\sigma }$ values in practical applications.