Applying evolutionary optimization algorithms for improving fuzzy C-mean clustering performance to predict the deformation modulus of rock mass

Abstract

This paper focuses on the capability of the evolutionary computation methods namely, genetic algorithm (GA) and particle swarm optimization (PSO) in design and optimizing the fuzzy c-means clustering (FCM) structure and their applications to predict the deformation modulus of rock masses. Accordingly, evolutionary algorithms are used to tune the pre-determined FCM clustering-based model to make better the accuracy of modulus estimation. A new empirical equation with the aid of multiple regression analysis is also suggested and on the basis of it a prediction chart is presented for determination of the rock mass deformation modulus. Finally, a comprehensive credibility assessment of the prediction performances of some existing empirical equations is done and the results are compared with that obtained by the evolutionary algorithms based FCM clustering models. It is concluded that the new proposed approaches provide more accurate results compared with the existing empirical equations.