A Method for Predicting Ultimate Bearing Capacity of Bolts Based on PSO-RBF Neural Network

2016 ◽  
Author(s):  
Xiaoyun Sun ◽  
Wei Cao ◽  
Zhenghai Qing ◽  
En Cheng ◽  
Jianpeng Bian
Keyword(s):  
Neural Network ◽  
Bearing Capacity ◽  
Rbf Neural Network ◽  
Ultimate Bearing Capacity

Prediction of ultimate bearing capacity through various novel evolutionary and neural network models

2019 ◽  
Vol 36 (2) ◽  
pp. 671-687 ◽  
Author(s):  
Hossein Moayedi ◽  
Arash Moatamediyan ◽  
Hoang Nguyen ◽  
Xuan-Nam Bui ◽  
Dieu Tien Bui ◽  
...  
Keyword(s):  
Neural Network ◽  
Bearing Capacity ◽  
Network Models ◽  
Ultimate Bearing Capacity ◽  
Neural Network Models

scholarly journals REVIEW OF AVAILABLE APPROACHES FOR ULTIMATE BEARING CAPACITY OF TWO-LAYERED SOILS

2012 ◽  
Vol 18 (4) ◽  
pp. 469-482 ◽  
Author(s):  
M. Dalili Shoaei ◽  
A. Alkarni ◽  
J. Noorzaei ◽  
M. S. Jaafar ◽  
Bujang B. K. Huat
Keyword(s):  
Neural Network ◽  
Finite Element Method ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Bearing Capacity ◽  
State Of The Art ◽  
Classical Method ◽  
Ultimate Bearing Capacity ◽  
Layered Soils ◽  
The Finite Element Method

This paper presents the state of the art report on available approaches to predicting the ultimate bearing capacity of two-layered soils. The article discusses three most popular methods, including the classical method, application of the finite element method and artificial neural network. Various approaches based on these three powerful tools are studied and their methodologies are discussed.

Prediction of Ultimate Bearing Capacity of Prestressed Pipe Pile Based on BP Neural Network

2011 ◽  
Vol 101-102 ◽  
pp. 228-231
Author(s):  
Jian Ping Jiang
Keyword(s):  
Neural Network ◽  
Bearing Capacity ◽  
Bp Neural Network ◽  
Ultimate Bearing Capacity ◽  
Pipe Pile ◽  
Absolute Value ◽  
Independent Variables ◽  
Average Value ◽  
Standard Value ◽  
Bp Neural Network Model

Based on BP neural network, this paper had a prediction on ultimate bearing capacity of prestressed pipe pile. Taking pile diameter, effective pile length, ultimate average value of friction standard value, ultimate average value of end resistance standard value as influences factors, the prediction model of pile bearing capacity based on BP neural network was obtained. It was found that, the average value of absolute value for the relative error of fitting value of pile bearing capacity compared with the observed value for 70 groups of independent variables training BP neural network model was 3.1498%; And the average value of absolute value for the relative error of prediction value of pile bearing capacity compared with the observed value for 10 groups of independent variables validating BP neural network model was 3.50126% whose precision was better than ANFIS’5.32293%. The following conclusion can be drawn that, the prediction model of ultimate bearing capacity of prestressed pipe pile based on BP neural network is feasible.

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