A Numerical Simulation Study of Structural Damage Based on RBF Neural Network

2006 ◽  
pp. 322-330
Author(s):  
Xu-dong Yuan ◽  
Hou-bin Fan ◽  
Cao Gao ◽  
Shao-xia Gao
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Simulation Study ◽  
Structural Damage ◽  
Rbf Neural Network

Numerical Simulation Study on Healthy Monitoring of the Framework Based on Aseismatic Behavior

2012 ◽  
Vol 479-481 ◽  
pp. 1253-1257
Author(s):  
Xiao Ming Yang ◽  
Jun Yi Wang
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Theoretical Analysis ◽  
Natural Frequency ◽  
Simulation Study ◽  
Structural Damage ◽  
Frame Structure ◽  
Earthquake Loading ◽  
The Neural Network ◽  
Frequency Changes

Considering back and forth and the complexity of earthquake loading, according to theoretical analysis, this paper is verified theoretically that the natural frequency changes of structure contain the information such as location and degree of the damage in earthquake, based on this theory, applying the large common software – ANSYS, and an experiment to simulate the damage of a six-storey-high frame structure is used, extracting the natural frequency changes and forming the input vectors of the neural network, the structural damage is detected by the neural network trained, the result showed the effectiveness of this method in the engineering.

Study on Structural Damage Detection Using RBF Network

2014 ◽  
Vol 578-579 ◽  
pp. 1125-1128
Author(s):  
Jin Sheng Fan ◽  
Ying Yuan ◽  
Xiu Ling Cao
Keyword(s):  
Neural Network ◽  
Mode Shape ◽  
Structural Damage ◽  
Rbf Neural Network ◽  
Frame Structure ◽  
Structural Damage Detection ◽  
Element Analysis ◽  
Rbf Network ◽  
Damage Location ◽  
Trained Neural Network

Based on mode shape, a new parameter was put forward—mode shape curvature ratio, for detecting structure damages. And it was also the input vector of the RBF neural network. Then through finite element analysis and calculating, the training and forecasting samples were got for the network. The trained neural network can identify the damage location and degree of the frame structure. It proved that this method is simple and valid.

scholarly journals Study on the viscoelastic–viscoplastic model of layered siltstone using creep test and RBF neural network

2021 ◽  
Vol 13 (1) ◽  
pp. 72-84
Author(s):  
Yiran Yang ◽  
Xingping Lai ◽  
Tao Luo ◽  
Kekuo Yuan ◽  
Feng Cui
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Relative Error ◽  
Test Data ◽  
Creep Test ◽  
Rbf Neural Network ◽  
Creep Property ◽  
Creep Tests ◽  
Viscoplastic Model ◽  
Mean Relative Error

Abstract Creep is a fundamental time-dependent property of rock. As one of the main surrounding rocks of underground engineering, layered siltstone is governed by creep to a great extent because of special structure. Based on the structural characteristics of layered siltstone, a viscoelastic–viscoplastic model was proposed to simulate and present its creep property. To verify the accuracy of the model, governing equation of the viscoelastic–viscoplastic model was introduced into finite element difference program to simulate a series of creep tests of layered siltstone. Meanwhile, creep tests on layered siltstone were conducted. Numerical simulation results of the viscoelastic–viscoplastic model were compared with creep test data. Mean relative error of creep test data and numerical simulation result was 0.41%. Combined with Lyapunov function, the radial basis function (RBF) neural network trained with creep test data was adopted. Mean relative error of creep test data and RBF neural network data was 0.57%. The results further showed high accuracy and stability of RBF neural network and the viscoelastic–viscoplastic model.

NUMERICAL SIMULATION STUDY ON BIONIC MUCUS DRAG REDUCTION OF UNDERWATER VEHICLE

2020 ◽  
Vol 47 (4) ◽  
pp. 371-385
Author(s):  
Kaisheng Zhang ◽  
Chaofan Ma ◽  
Baocheng Zhang ◽  
Bo Zhao ◽  
Qiang Wang
Keyword(s):  
Numerical Simulation ◽  
Drag Reduction ◽  
Simulation Study ◽  
Underwater Vehicle
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