A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Modeling Drained Triaxial Compression Behavior of Sand Using ANN
The stress-strain and volume change behavior of sand under triaxial compression test was modeled using feed-back artificial neural networks. The present approach used a three layer neural net with a back-propagation learning algorithm having an adaptable learning rate and momentum factor. The experimental data obtained from published literature was used as the database in the training, testing, and prediction phases of two neural network based sand models. For a constant confining pressure, Model-1 represents the effects of mineralogy, particle shape and size distribution, and initial void ratio on the isotropically consolidated drained compression (CIDC) behavior of sand. Model-2 represents the effects of relative density, and confining pressure on the deviator stress and volumetric strain variation for Sacramento river sand. Issues related to the number of processing units in the hidden layer, magnitude of strain increment during feed-back, and over-training error are discussed. An application describing the use of these well trained sand models for predicting the three dimensional failure behavior is presented.
Modeling Drained Triaxial Compression Behavior of Sand Using ANN
The stress-strain and volume change behavior of sand under triaxial compression test was modeled using feed-back artificial neural networks. The present approach used a three layer neural net with a back-propagation learning algorithm having an adaptable learning rate and momentum factor. The experimental data obtained from published literature was used as the database in the training, testing, and prediction phases of two neural network based sand models. For a constant confining pressure, Model-1 represents the effects of mineralogy, particle shape and size distribution, and initial void ratio on the isotropically consolidated drained compression (CIDC) behavior of sand. Model-2 represents the effects of relative density, and confining pressure on the deviator stress and volumetric strain variation for Sacramento river sand. Issues related to the number of processing units in the hidden layer, magnitude of strain increment during feed-back, and over-training error are discussed. An application describing the use of these well trained sand models for predicting the three dimensional failure behavior is presented.
Modeling Drained Triaxial Compression Behavior of Sand Using ANN
Penumadu, Dayakar (author) / Zhao, Rongda (author)
Geo-Denver 2000 ; 2000 ; Denver, Colorado, United States
2000-07-24
Conference paper
Electronic Resource
English
Modeling Drained Triaxial Compression Behavior of Sand Using Artificial Neural Networks
| British Library Conference Proceedings | 2000
Drained behaviour of cemented sand in high pressure triaxial compression tests
| Online Contents | 2012
Drained behaviour of cemented sand in high pressure triaxial compression tests
| Taylor & Francis Verlag | 2012
Drained behaviour of Changi sand in triaxial and plane-strain compression
| Online Contents | 2007