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.

scholarly journals A Data Fusion Method for Non-Destructive Testing by Means of Artificial Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2598
Author(s):  
Romain Cormerais ◽  
Aroune Duclos ◽  
Guillaume Wasselynck ◽  
Gérard Berthiau ◽  
Roberto Longo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Data Fusion ◽  
Complex Structure ◽  
Fusion Method ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Aluminum Block ◽  
Artificial Neural ◽  
Non Destructive

In the aeronautics sector, aircraft parts are inspected during manufacture, assembly and service, to detect defects eventually present. Defects can be of different types, sizes and orientations, appearing in materials presenting a complex structure. Among the different inspection techniques, Non Destructive Testing (NDT) presents several advantages as they are noninvasive and cost effective. Within the NDT methods, Ultrasonic (US) waves are widely used to detect and characterize defects. However, due the so-called blind zone, they cannot be easily employed for defects close to the surface being inspected. On the other hand, another NDT technique such Eddy Current (EC) can be used only for detecting flaws close to the surface, due to the presence of the EC skin effect. The work presented in this article aims to combine the use of these two NDT methods, exploiting their complementary advantages. To reach this goal, a data fusion method is developed, by using Machine Learning techniques such as Artificial Neural Networks (ANNs). A simulated training database involving simulations of US and EC signals propagating in an Aluminum block in the presence of Side Drill Holes (SDHs) has been implemented, to train the ANNs. Measurements have been then performed on an Aluminum block, presenting tree different SDHs at specific depths. The trained ANNs were used to characterize the different real SDHs, providing an experimental validation. Eventually, particular attention has been addressed to the estimation errors corresponding to each flaw. Experimental results will show that depths and radii estimations error were confined on average within a range of 4%, recording a peak of 11% for the second SDHs.

Dynamic Pattern Recognition in Sport by Means of Artificial Neural Networks

2011 ◽  
pp. 2233-2252
Author(s):  
Ju¨rgen Perl ◽  
Peter Dauscher
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Complex Structure ◽  
Optimization Methods ◽  
Motor Activities ◽  
Artificial Neural ◽  
Basic Ideas ◽  
Network Approaches

Behavioural processes like those in sports, motor activities or rehabilitation are often the object of optimization methods. Such processes are often characterized by a complex structure. Measurements considering them may produce a huge amount of data. It is an interesting challenge not only to store these data, but also to transform them into useful information. Artificial Neural Networks turn out to be an appropriate tool to transform abstract numbers into informative patterns that help to understand complex behavioural phenomena. The contribution presents some basic ideas of neural network approaches and several examples of application. The aim is to give an impression of how neural methods can be used, especially in the field of sport.

Dynamic Pattern Recognition in Sport by Means of Artificial Neural Networks

2011 ◽  
pp. 299-319 ◽  
Author(s):  
Jürgen Perl ◽  
Peter Dauscher
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Complex Structure ◽  
Optimization Methods ◽  
Motor Activities ◽  
Artificial Neural ◽  
Basic Ideas ◽  
Network Approaches

Behavioural processes like those in sports, motor activities or rehabilitation are often the object of optimization methods. Such processes are often characterized by a complex structure. Measurements considering them may produce a huge amount of data. It is an interesting challenge not only to store these data, but also to transform them into useful information. Artificial Neural Networks turn out to be an appropriate tool to transform abstract numbers into informative patterns that help to understand complex behavioural phenomena. The contribution presents some basic ideas of neural network approaches and several examples of application. The aim is to give an impression of how neural methods can be used, especially in the field of sport.

Evaluation of plates in similitude by experimental tests and artificial neural networks

2021 ◽  
pp. 095440622110025
Author(s):  
Alessandro Casaburo ◽  
Giuseppe Petrone ◽  
Viviana Meruane ◽  
Francesco Franco ◽  
Sergio De Rosa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Scaling Laws ◽  
Complex Structure ◽  
Experimental Tests ◽  
Response Characteristics ◽  
Artificial Neural ◽  
Training Examples ◽  
Robust To Noise ◽  
Similitude Theory

In the last century, the introduction of similitude theory allowed engineers to define the conditions to design a scaled-up or down version of the full-scale structure by means of a set of tools known as similitude methods: the scaled structure can be tested more easily, and then, by using the scaling laws, the prototype behavior can be recovered. However, such a response reconstruction may become hard for complex structure under incomplete or distorted similitude frameworks. Machine learning methods, with their automating characteristics, may help to circumvent these difficulties. This work is divided into two parts. First, five clamped-free-clamped-free plates in similitude are experimentally tested. In the case of complete similitude, these laws allow to accurately reconstruct the response. When the similitude is distorted, these laws are not always valid, failing to predict the dynamic behavior in some of the frequency ranges. Then, the experimental results are used to validate the prediction and identification capabilities of artificial neural networks. The artificial neural networks proved to be robust to noise and very helpful in predicting the response characteristics and identifying the model type, although an adequate number of training examples is needed. Further tests proved that the number of samples is drastically reduced by choosing accurately the features.

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
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