Liquefaction of saturated loose and cemented granular soils

2008 ◽  
Vol 184 (2) ◽  
pp. 254-265 ◽  
Author(s):  
M. Zeghal ◽  
U. El Shamy
Keyword(s):  
Granular Soils

scholarly journals Quantifying and modelling fabric anisotropy of granular soils

Géotechnique ◽  
2008 ◽  
Vol 58 (4) ◽  
pp. 237-248 ◽  
Author(s):  
Z. X. Yang ◽  
X. S. Li ◽  
J. Yang
Keyword(s):  
Granular Soils ◽  
Fabric Anisotropy

Filtration behaviour of granular soils under cyclic load

Géotechnique ◽  
2008 ◽  
Vol 58 (6) ◽  
pp. 517-522 ◽  
Author(s):  
A. H. M. Kamruzzaman ◽  
A. Haque ◽  
A. Bouazza
Keyword(s):  
Cyclic Load ◽  
Granular Soils

Bearing Capacity Characteristic of Unsaturated Granular Soils

2011 ◽  
Vol 261-263 ◽  
pp. 989-993 ◽  
Author(s):  
Anuchit Uchaipichat ◽  
Ekachai Man Koksung
Keyword(s):  
Bearing Capacity ◽  
Matric Suction ◽  
Ultimate Bearing Capacity ◽  
Experimental Program ◽  
Granular Soils ◽  
Test Results ◽  
High Level ◽  
Unsaturated Granular Soils ◽  
Capacity Characteristic

An experimental program of laboratory bearing tests was performed to characterize the bearing capacity of foundation on unsaturated granular soils. All tests were performed by pushing a circular rod on the surface of compacted sand specimens with different values of matric suction until failure. The test results show an increase in ultimate bearing capacity with increasing matric suction at low suction value but a decrease in that at high level of suction. The comparisons between the test results and simulations using the expressions proposed in this paper are presented and discussed. Good agreements are achieved for all testing values of suction.

Neural network based stochastic design charts for settlement prediction

2005 ◽  
Vol 42 (1) ◽  
pp. 110-120 ◽  
Author(s):  
M A Shahin ◽  
M B Jaksa ◽  
H R Maier
Keyword(s):  
Neural Networks ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Shallow Foundations ◽  
Granular Soils ◽  
Traditional Methods ◽  
Settlement Prediction ◽  
Design Charts ◽  
Artificial Neural ◽  
Level Of Risk

Traditional methods of settlement prediction of shallow foundations on granular soils are far from accurate and consistent. This can be attributed to the fact that the problem of estimating the settlement of shallow foundations on granular soils is very complex and not yet entirely understood. Recently, artificial neural networks (ANNs) have been shown to outperform the most commonly used traditional methods for predicting the settlement of shallow foundations on granular soils. However, despite the relative advantage of the ANN based approach, it does not take into account the uncertainty that may affect the magnitude of the predicted settlement. Artificial neural networks, like more traditional methods of settlement prediction, are based on deterministic approaches that ignore this uncertainty and thus provide single values of settlement with no indication of the level of risk associated with these values. An alternative stochastic approach is essential to provide more rational estimation of settlement. In this paper, the likely distribution of predicted settlements, given the uncertainties associated with settlement prediction, is obtained by combining Monte Carlo simulation with a deterministic ANN model. A set of stochastic design charts, which incorporate the uncertainty associated with the ANN method, is developed. The charts are considered to be useful in the sense that they enable the designer to make informed decisions regarding the level of risk associated with predicted settlements and consequently provide a more realistic indication of what the actual settlement might be.Key words: settlement prediction, shallow foundations, neural networks, Monte Carlo, stochastic simulation.

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