Prediction of shear development in clean sands by use of particle shape information and artificial neural networks

Particle shape is one of the most important factors affecting the shear strength of granular soils. Regarding to the knowledge that the grain size distribution is more effective on strength characteristics of soils in comparison with the particle shape information, clean sands of similar grain size distributions and diverse particle shapes are disposed. Afterwards, shear box tests are employed on these sands to obtain the stress–strain relationships, and resulting internal friction angles. For the simulation of results, artificial neural networks (ANN) of eight architectures using three different learning algorithms are constituted. The results revealed that the network with two hidden layers utilizing Levenberg–Marquardt learning algorithm is the most successful alternative. Nevertheless, on account of the possible improvements on the database and the learning duration, scaled conjugate algorithm should be preferred, which yields mathematically congruent curves, in comparison with the experimental values. Finally, it can be underlined that, use of ANN for simulation of shear development in granular soils is promising, if the inputs and output parameters are correctly determined.