Ant colony optimization for slope stability analysis applied to an embankment failure in eastern India

Abstract

Slope stability assessment is regularly carried out by engineers to analyse the stability of natural or man-made slopes and understand their failure mechanisms. In slope stability assessment, it is essential to determine the critical slip surface, i.e. the one characterised by the lowest safety factor, and, in professional practice, the analysis is routinely carried out using Limit Equilibrium Methods (LEM). Unstable conditions can be induced by several causes such as excessive surcharge load, earthquakes, heavy rainfall and dynamic loads. They can lead to slope failures causing considerable damage to structures and infrastructure facilities arising on them. The task is to find the minimum factor of safety against all these destabilising factors. The most widely used methods in slope stability assessment are the traditional limit equilibrium methods [1-4]. In addition, finite difference technique [5, 6], distinct element codes [7] and finite element strength reduction technique (FEM) [8-13] are also used extensively. Use of optimisation methods such as dynamic programming [14], downhill simplex algorithm [15] and Monte-Carlo (MC) technique [16] are also reported. Recently, other optimisation techniques inspired by nature like ant colony optimisation (ACO) [17-19], particle swarm optimisation (PSO) [20, 21], harmony

Recent Tendencies in the Use of Optimization Techniques in Geotechnics: A Review

“…The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole. In 2020, Mishra et al [47] used MOA for capturing the critical slip surface in slope stability analysis. It was applied for slip surface generation and factor of safety calculation.…”

Teaching–learning-based optimization algorithm for solving structural damage detection problem in frames via changes in vibration responses

“…Firstly, most of them require the proper assignment of suitable values for algorithm-specific parameters which would be unique to those algorithms for a certain detection study [14]. For example, GA requires the provision of optimum values of crossover probability and mutation probability [15], ant-colony optimiser requiring its pheromone evaporation rate related parameters [16,17], while PSO requires to define proper constriction coefficients and inertia factor in order to obtain the best results [18]. This parameter-tuning needs additional scrutiny which increases the actual implementation time.…”