Predicting ultimate bearing capacity of shallow foundations on reinforced cohesionless soils using artificial neural networks

2005 ◽  
Vol 12 (6) ◽  
pp. 321-332 ◽  
Author(s):  
A. Soleimanbeigi ◽  
N. Hataf
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Bearing Capacity ◽  
Ultimate Bearing Capacity ◽  
Shallow Foundations ◽  
Cohesionless Soils ◽  
Artificial Neural

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

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.

scholarly journals Prediction of Ultimate Bearing Capacity of Shallow Foundations on Cohesionless Soils: A Gaussian Process Regression Approach

2021 ◽  
Vol 11 (21) ◽  
pp. 10317
Author(s):  
Mahmood Ahmad ◽  
Feezan Ahmad ◽  
Piotr Wróblewski ◽  
Ramez A. Al-Mansob ◽  
Piotr Olczak ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Bearing Capacity ◽  
Gaussian Process Regression ◽  
Ultimate Bearing Capacity ◽  
Shallow Foundations ◽  
Cohesionless Soil ◽  
Unit Weight ◽  
Cohesionless Soils ◽  
Computing Technique ◽  
Theoretical Approaches

This study examines the potential of the soft computing technique—namely, Gaussian process regression (GPR), to predict the ultimate bearing capacity (UBC) of cohesionless soils beneath shallow foundations. The inputs of the model are width of footing (B), depth of footing (D), footing geometry (L/B), unit weight of sand (γ), and internal friction angle (ϕ). The results of the present model were compared with those obtained by two theoretical approaches reported in the literature. The statistical evaluation of results shows that the presently applied paradigm is better than the theoretical approaches and is competing well for the prediction of UBC (qu). This study shows that the developed GPR is a robust model for the qu prediction of shallow foundations on cohesionless soil. Sensitivity analysis was also carried out to determine the effect of each input parameter.

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