Application of cuckoo search method in 3D slope stability analysis for limestone quarry mine

Abstract

The Cuckoo Search (CS) is a very fast and efficient global optimization method to locating the slip surface which carried out by iteration. However, the Grid Search (conventional method) method in 3D slope stability analysis takes longer than this method on the computation process. Slope stability analysis was performed using the 3D limit equilibrium method “Bishop” with Cuckoo Search of slip surface by maximizing iteration of the simulation and columns in X or Y. To ensure that the slip surface within the global minimum slip surface, the maximum iteration in CS was also specified from 40 to 1200. Based on maximum columns in X or Y, the safety factor value of the 3D CS results was then compared to the Grid Search results to determine the final 3D safety factor and the estimated volume of potential failure. The final 3D safety factor obtained from the average 3D safety factor (with maximum iteration 400, 800, 1000, and 1200) is about 2,01 with the average estimated volume of slope failure of 190.000 m3 that located at the north of the pit.

Probability Methods for Stability Design of Open Pit Rock Slopes: An Overview

“…This method has been used in welded beam and spring designs, data fusion in wireless network sensors, and recently in slope stability designs. [97,98] used this method in 2-D slope stability analysis [99]. The application of the cuckoo search method in 3-D slope stability analysis has also been reported (e.g., [99,100]).…”

“…[97,98] used this method in 2-D slope stability analysis [99]. The application of the cuckoo search method in 3-D slope stability analysis has also been reported (e.g., [99,100]). From [99,101], the following rules applies to the CS algorithm [96]: a.…”

“…Based on these rules, the host bird can either throw the egg away or abandon the nest and build a new nest [101]. By the last rule, the fraction p a can be used to determine the worst solutions of n nest that will be replaced with a new nest randomly [99,101]. To solve the problem, the illustration is that every egg in a nest represents one new solution.…”

“…The aim is to use the new and better solution to replace the current solution in the nest. In some cases, the nest may have two eggs (solution), but the problem can be simplified so one nest has only one solution [99,102]. From [97], the CS algorithm begins by initializing a fixed number of n valid solution vectors {P 0 , .…”

“…Several other authors have developed methods to refine the solution and readers are referred to the documents cited. The method has been compared with other algorithms, such as PSO and the genetic algorithm, and the results show that CS has a higher success rate [96,99].…”