Prediction of load–displacement curves of flow drill screw and RIVTAC joints between dissimilar materials using artificial neural networks

2020 ◽  
Vol 57 ◽  
pp. 400-408 ◽  
Author(s):  
Jaeho Kim ◽  
Hyunjoo Lee ◽  
Heungjae Choi ◽  
Bora Lee ◽  
Dongchoul Kim
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Dissimilar Materials ◽  
Artificial Neural ◽  
Load Displacement

Prediction of load-displacement curve in a complex structure using artificial neural networks: A study on a long bone

2020 ◽  
Vol 154 ◽  
pp. 103319 ◽  
Author(s):  
Hadi Rahmanpanah ◽  
Saeed Mouloodi ◽  
Colin Burvill ◽  
Soheil Gohari ◽  
Helen M.S. Davies
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Complex Structure ◽  
Long Bone ◽  
Displacement Curve ◽  
Artificial Neural ◽  
Load Displacement

Modeling the load-displacement curve for fully-grouted cable bolts using Artificial Neural Networks

2016 ◽  
Vol 86 ◽  
pp. 261-268
Author(s):  
Farhadian Ali ◽  
Shamsoddin Saeed Masoud ◽  
Ferdowsi Behnam ◽  
Chenaghlu Mohamadreza
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Displacement Curve ◽  
Cable Bolts ◽  
Artificial Neural ◽  
Load Displacement

scholarly journals An Intelligent Process to Estimate the Nonlinear Behaviors of an Elasto-Plastic Steel Coil Damper Using Artificial Neural Networks

Actuators ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 9
Author(s):  
Seongkyu Chang ◽  
Sung Gook Cho
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Parameters ◽  
Internal Diameter ◽  
Displacement Curve ◽  
Element Analysis ◽  
Steel Coil ◽  
Artificial Neural ◽  
Load Displacement ◽  
Plastic Steel

This study developed a nonlinear behavior prediction model for elasto-plastic steel coil dampers (SCDs) using artificial neural networks (ANN). To train the ANN, first, the input and output data of the behavior of the elasto-plastic SCD was prepared. This study utilized the design parameters and load–displacement curves of the SCD to train the ANN. The elasto-plastic load–displacement curve of the SCD was obtained from simulation results using an ANSYS workbench. The design parameters (wire diameter, internal diameter, number of active windings, yield strength) of the SCD were defined as the input patterns, while the yield deformation, first stiffness, and second stiffness were output patterns. During learning of the neural network model, 60 datasets of the SCD were used as the learning pattern, and the remaining 21 were used to verify the model. Although this study used a small number of learning patterns, the ANN predicted accurate results for yield displacement, first stiffness, and second stiffness. In this study, the ANN was found to perform very well, predicting the nonlinear response of the SCD, compared with the values obtained from a finite element analysis program.

Determination of S-N curves with the application of artificial neural networks

1999 ◽  
Vol 22 (8) ◽  
pp. 723-728 ◽  
Author(s):  
Artymiak ◽  
Bukowski ◽  
Feliks ◽  
Narberhaus ◽  
Zenner
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural

FUTURE OF ARTIFICIAL NEURAL NETWORKS IN SPACE EXPLORATION AND SPACE ENGINEERING

2018 ◽  
pp. 6-11
Author(s):  
Kobiljon Kh. Zoidov ◽  
◽  
Svetlana V. Ponomareva ◽  
Daniel I. Serebryansky ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Space Exploration ◽  
Artificial Neural
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