Explorations in parallel distributed processing: a handbook of models, programs, and exercises
Explorations in parallel distributed processing: a handbook of models, programs, and exercises
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Prediction of compressive and tensile strength of limestone via genetic programming
Expert Systems with Applications: An International Journal
Modeling of the angle of shearing resistance of soils using soft computing systems
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
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.