Explicit formulation of bearing capacity of shallow foundations on rock masses using artificial neural networks: application and supplementary studies

2014 ◽  
Vol 73 (7) ◽  
pp. 3417-3431 ◽  
Author(s):  
Seyed Ali Ziaee ◽  
Ehsan Sadrossadat ◽  
Amir H. Alavi ◽  
Danial Mohammadzadeh Shadmehri
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Bearing Capacity ◽  
Shallow Foundations ◽  
Rock Masses ◽  
Explicit Formulation ◽  
Artificial Neural

scholarly journals Application of Artificial Neural Networks for Predicting the Bearing Capacity of Shallow Foundations on Rock Masses

2021 ◽  
Author(s):  
M. A. Millán ◽  
R. Galindo ◽  
A. Alencar
Keyword(s):  
Bearing Capacity ◽  
Analytical Solutions ◽  
Shear Failure ◽  
Numerical Models ◽  
Computational Effort ◽  
Shallow Foundations ◽  
Geological Strength Index ◽  
Rock Masses ◽  
Ann Model ◽  
Artificial Neural

AbstractCalculation of the bearing capacity of shallow foundations on rock masses is usually addressed either using empirical equations, analytical solutions, or numerical models. While the empirical laws are limited to the particular conditions and local geology of the data and the application of analytical solutions is complex and limited by its simplified assumptions, numerical models offer a reliable solution for the task but require more computational effort. This research presents an artificial neural network (ANN) solution to predict the bearing capacity due to general shear failure more simply and straightforwardly, obtained from FLAC numerical calculations based on the Hoek and Brown criterion, reproducing more realistic configurations than those offered by empirical or analytical solutions. The inputs included in the proposed ANN are rock type, uniaxial compressive strength, geological strength index, foundation width, dilatancy, bidimensional or axisymmetric problem, the roughness of the foundation-rock contact, and consideration or not of the self-weight of the rock mass. The predictions from the ANN model are in very good agreement with the numerical results, proving that it can be successfully employed to provide a very accurate assessment of the bearing capacity in a simpler and more accessible way than the existing methods.

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.

Prediction of ultimate bearing capacity of Tubular T-joint under fire using artificial neural networks

2012 ◽  
Vol 50 (7) ◽  
pp. 1495-1501 ◽  
Author(s):  
Jixiang Xu ◽  
Jincheng Zhao ◽  
Zhenseng Song ◽  
Minglu Liu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Bearing Capacity ◽  
Ultimate Bearing Capacity ◽  
Artificial Neural
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