Modelling the monotonic and cyclic behaviour of sands using Artificial Neural Networks

In this study artificial neural networks (ANN) are used to simulate the monotonic and cyclic behaviour of sands observed in laboratory tests on Karlsruhe sand under drained and undrained conditions. A genetic algorithm (GA) is used to obtain an optimal framework for the ANN. The results show that the proposed genetic adaptive neural network (GANN) can effectively simulate drained and undrained monotonic triaxial behaviour of saturated sand under isotropic or anisotropic consolidation. The GANN is also able to predict satisfactorily the cyclic behaviour of sands under undrained triaxial test with strain and stress cycles. In addition, GANN is able to distinguish between monotonic drained and undrained conditions by delivering a good prediction when trained with the combined database.