# Use of Evolutionary Computation to Improve Rock Slope Back Analysis
## 1 Introduction  
Many researchers have paid attention to assessing the stability of rock slope stability in the past few decades [1; 2; 3; 4], but it is still a major challenge for engineers. Traditionally, design input parameters are obtained from in situ rock mechanics testing and measurement. For example, the rock mass deformation modulus can be obtained from an in situ plate loading test and the shear strength parameters from in situ block shear tests. However, due to material heterogeneity, it is challenging to accurately determine the value of strength parameters through laboratory or field tests. Back analysis can provide a very effective and rapid means of determination of these parameters.  
Any failure process in rock and soil slopes can rationally be regarded as a large-scale in situ shear test performed by nature. With a proper understanding of the failure mechanism and gathering the necessary data of the failed masses, the geotechnical properties can be obtained by back analysis [5]. It can help to improve the knowledge in input parameters. In general, due to the nature of the variable, which often contains a jointed rock mass fracture, naturally occurring discontinuities and anisotropy make it a hard job for engineers to estimate the stability of a rock slope. As a result, back analysis is a very powerful method that can be implemented to determine the strength parameters of failed slopes. These back-calculated values could then be used for remedial work and the redesign of a failed slope, even new surrounding slopes with similar geotechnical conditions.  
Back analysis was introduced by [PERSON] [6] and [PERSON] et al. [7], for the sophistication of the observational method and establishes a crucial tool for estimating geotechnical design parameters. Back analysis has been applied in many areas in geotechnical engineering, such as: excavations [8; 9; 10], underground works [11; 12], as well as slope stability problems [13; 14; 15].  
Generally, in geotechnical engineering, two major types of algorithm are used for back analysis. Iterative optimization algorithms from the field of classical optimization theory such as the simplex, Newton-Raphson or gradient methods [11; 16] and optimization methods from the artificial intelligence fields like neural networks (_ANNs_), metaheuristics algorithms such as the genetic algorithm (\(GA\)) and particle swarm optimization (_PSO_) [17; 18; 19; 20]. In term of limitations, the classical optimization algorithms only determine a set of unique parameters and are often reduced to a limited number of parameters to be optimized. Moreover, due to the possible presence of several local minima, the algorithm may converge to the local minimum closer to the initial guess. Another problem that may arise is the difficulty of finding the derivatives of the objective function when using numerical models. Metaheuristic algorithms can conveniently bypass these limitations associated with the classical optimization algorithms. Thus, this study made use of two types of metaheuristic algorithm (\(GA\) and _PSO_) for rock slope back analysis considering material uncertainties.  
The numerical model simulates the geometry of the slope, using finite element upper bound limit analysis [21; 22; 23], while the optimization algorithm carries out an iterative process computing solution for the uncertainty geotechnical parameters.