Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses

The determination of deformation modulus of rock masses is one of the most difficult tasks in the field of rock mechanics. Due to the high cost and measurement difficulties of in situ tests in modulus determination, the predictive models using regression based statistical methods, back propagation neural networks (BPNN) and fuzzy systems are recently employed for the indirect estimation of the modulus. Among these methods, the BPNN has been reported to be very useful in modeling the rock material behavior, such as deformation modulus, by many researchers. Despite its extensive applications, design and structural optimization of BPNN are still done via a time-consuming reiterative trial-and-error approach. This research focuses on the efficiency of the genetic algorithm (GA) in design and optimizing the BPNN structure and its application to predict the deformation modulus of rock masses. GA is utilized to find the optimal number of neurons in hidden layer, learning rates and momentum coefficients of hidden and output layers of network. Then the result is compared with that of trial-and-error procedure. For the purpose, a database including 120 data sets was employed from four dam sites and power house locations in Iran. Taking advantages of performance criteria such as MSE, MAE, r, proved that the GA-ANN model gives superior predictions over the trial-and-error model.