Probabilistic Slope Stability Evaluation Using Hybrid Metaheuristic Approach

Abstract

This paper develops an efficient evolutionary hybrid optimization technique based on the adaptive salp swarm algorithm (ASSA) and pattern search (PS) for the reliability evaluation of earth slopes considering spatial variability of soils under the framework of the limit equilibrium method. In the ASSA, to improve the salp swarm approach's exploration ability while also avoiding premature convergence, two new equations for the leaders' and followers' position updating procedure are introduced. The proposed hybrid algorithm (ASSPS) benefits from the effective global search ability of the adaptive salp swarm algorithm as well as the powerful local search capability of the pattern search method. The suggested ASSPS algorithm's efficiency is confirmed using mathematical test functions, and its findings are compared with the standard salp swarm algorithm as well as some efficient optimization techniques. Then, the ASSPS is applied for calculation of the lowest safety factor and reliability index of earth slopes. The safety factor is formulated using the Morgenstern and Price approach and the advanced first-order second-moment (AFOSM) method is implemented for the reliability calculation model. The ASSPS's efficacy for the evaluation of the minimum reliability index of slopes is investigated by considering two literature-based case studies. The numerical experiments demonstrate that the new algorithm could generate better optimal solutions and significantly outperform other methods in the literature.