Predicting the Shear Behavior of Cemented and Uncemented Carbonate Sands Using a Genetic Algorithm-Based Artificial Neural Network

Abstract

Carbonate sands that are known as problematic soils, have some unusual features like particle crushability and compressibility that discriminate their behavior from other types of soil. Because of their vast diversity, they have a wide range of mechanical behavior. In recent decades, there have been many attempts to model the mechanical behavior of carbonate sands but all these efforts have been focused on experimental and case studies of some especial sands and there is still no unique way which can appraises all types of carbonate sands behavior and describes their various aspects. In this paper, a new approach is presented based on the integration of Genetic Algorithm (GA) into an Artificial Neural Network (ANN) to predict the shear behavior of carbonate sands. In the proposed approach, the GA was utilized to optimize the connection weights of the ANN. The network was trained and tested using a comprehensive set of triaxial tests that were carried out on three different carbonate sands in both grouted and ungrouted (cemented and uncemented) condition. The network prediction was then compared to the experimental results and it was concluded that the GA-based ANN has a good potential in predicting the behavior and generalizing the training data to simulate new unseen data.