A Comparative Study for Locating Critical Failure Surface in Slope Stability Analysis via Meta-Heuristic Approach

A Comparative Study for Locating Critical Failure Surface in Slope Stability Analysis via Meta-Heuristic Approach

Jayraj Singh (Indian Institute of Technology Dhanbad, India), A. K. Verma (Indian Institute of Technology Dhanbad, India) and Haider Banka (Indian Institute of Technology Dhanbad, India)
DOI: 10.4018/978-1-5225-4766-2.ch001
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Locating critical failure surface in a rock or soil slope is performed in stability analysis to access the optimal safe design of the slope. Finding the critical failure surface associated with a minimum factor of safety value in slope stability analysis is very cumbersome and becomes a global optimization problem in the field of geotechnical and mining engineering. The presence of many local minimal points in the search space and discontinuous function made this factor of safety margin big and proves to be a chief constraint global optimization problem. In this chapter, some meta-heuristic techniques such as genetic algorithm, particle swarm optimization algorithm are adopted for analyzing the critical failure surface. A comparative study has been done to analyze safety factor for the slope stability analysis. The outcome result acquires acceptable performance over existing methods and confirms the higher slope stability analysis. The validation and simulation design of the proposed methodology will be investigated by using “slide-tool” from rock science engineering.
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1. Introduction

Slope stability analysis of soil/rock is a classical problem in the field of geotechnical engineering, hydraulics, mining and soil mechanics. The slope stability evaluation provides the safety of slope failures under the influence of gravity load. The failure may arise due to floods, landslides, earthquake and many more hazards. These Geo-hazards can often be catastrophic and involving many social economic and environmental loss. An important aspect to achieve the stability of slopes or mechanical behavior of material is to resolve all the factors that cause failures. In this regard, more detailed study about various restored forces that stabilizing the overall sliding mass, forces tending to make the slope failure, and the computation of available margin of safety are to be needed. Various approaches have been developed for these analyses such as; limit equilibrium method, finite element method, rigid element method, probabilistic analysis methods, distinct element method and many more. The most widely analytical technique used for stability evaluation is limit equilibrium analysis (Krishna Prasad Aryal), where some methods of slice, such as Fellenius method (Fellenius, 1936), Bishop method (Bishop, 1955), Janbu method (Janbu, 1975), Morgenstem and (Morgenstern & Price, 1965), Spencer method (Spencer, 1967) and others derives factor of safety based on some assumption criteria (Abramson, 2002). The essence of these methods is to divide the whole sliding slope mass into a finite number of vertical slices and locate the critical failure surface which is associated minimum factor of safety ranging from 1 to 1.5. In this way of analysis, Ching and Fredlund (1983) discussed some encountered problems associated with the limit equilibrium methods of slices. Baker and Garber (1978) also demonstrated the problem of limiting equilibrium in terms of the variation calculus and then it is proven that the minimal factor of safety must occur on slip surfaces with a special geometrical property. The presence of several local minima points in the search space made this safety factor margin big and proves to be a chief optimization problem. Thus, classical optimization algorithms fail to optimize to a valid solution. Numerous metaheuristic methods have been developed for such constrained global optimization problems, which helps to understand instability due to geological, hydrological, seismology, and geo-technical exploration. In recent year's engineers and researcher attracting the attention in this growing area. But due to existing lot of imprecision and uncertainties, many complex decision-making problems encountered in the actual scenario and are still challenging issues. Many stochastic as well as probabilistic and fuzzy based techniques has been rapidly increased to solve such optimization problems (Vageesha, Mathada, Venkatachalam, & Srividya, 2007; Dodagoudar & Venkatachalam, 2000; Rubio, Hall, & Anderson, 2004). In recent days meta-heuristic approaches receiving a lot of attention, because of its elegance and efficiency. Various researcher have successfully implemented meta-heuristic optimization techniques for stability analysis problems. Such as: Chen and Morgenstern (Chen & Morgenstern, 1983), Das (Kumar Das, 2005), Sengupta and Upadhyay (2009), Zolfaghari et al. (2005), McCombie and Wilkinson (2002), Jianping et al. (2008) applied different search techniques like grid method and genetic algorithm. Greco (Greco, 1996) and Malkawi et al. (2007), used Monte Carlo technique; Rashedi et al. (2009) have implemented a new metaheuristic approach, named as GSA algorithm to optimize the safety factor associated with the critical failure surface. Yamagami and Ueta (1986), Kahatadeniya et al. (2009) and Kashani et al. (2016) used various stochastic approach such as BFGS & DFP method, ant colony optimization, and ICA-based search to investigate the factor of safety. In the present study, a comparative study has been done using meta-heuristic approach like; GA and PSO algorithms to locate the critical failure surface where, the fellenius method of slice is used as a fitness function to obtain safety factor. Based on this, it is possible to make an optimal technical solution with respect to an acceptable risk level towards slope failures. The validation and effectiveness of the proposed methodology is examined by solving a benchmark case study from the literature, where simulation design for slope model is accomplished by `slide’ a rock-science-tool. From the statistical analysis of experimental result, it is clearly visible that the proposed meta-heuristic approach for locating critical failure surface for homogeneous soil slope acquires acceptable performance over existing methods and confirm the optimum factor of safety.

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