Prediction of failure behavior of composite hat-stiffened panels under in-plane shear using artificial neural network

2021 ◽  
pp. 114238
Author(s):  
Zhenya Sun ◽  
Zhenkun Lei ◽  
Jianchao Zou ◽  
Ruixiang Bai ◽  
Hao Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Failure Behavior ◽  
Plane Shear ◽  
Stiffened Panels ◽  
Artificial Neural

Prediction of compression buckling load and buckling mode of hat-stiffened panels using artificial neural network

2021 ◽  
Vol 242 ◽  
pp. 112275
Author(s):  
Zhenya Sun ◽  
Zhenkun Lei ◽  
Ruixiang Bai ◽  
Hao Jiang ◽  
Jianchao Zou ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Buckling Load ◽  
Buckling Mode ◽  
Stiffened Panels ◽  
Artificial Neural

Multidisciplinary design optimization of stiffened panels using collaborative optimization and artificial neural network

2017 ◽  
Vol 232 (20) ◽  
pp. 3595-3611 ◽  
Author(s):  
Hamda Chagraoui ◽  
Mohamed Soula
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Problem ◽  
Optimization Method ◽  
Collaborative Optimization ◽  
System Level ◽  
Stiffened Panels ◽  
Multi Objective ◽  
Efficiency And Effectiveness ◽  
Artificial Neural

A new method for solving the multidisciplinary design optimization problems with a minimal computational effort is presented. The proposed methodology is based on the combination of artificial neural network model and Improved Multi-Objective Collaborative Optimization. In the artificial neural network–Improved Multi-Objective Collaborative Optimization scheme, the back-propagation algorithm is used for training the artificial neural network metamodel and the Non-dominated Sorting Genetic Algorithm-II is used to search a Pareto optimality set for the objective functions of stiffened panels. The artificial neural network–Improved Multi-Objective Collaborative Optimization algorithm aims firstly to decompose the global optimization problem hierarchically into optimization design problem at system level and several sub-problems at sub-system level and secondly to replace each optimization problem at the system and subsystem levels by artificial neural network model to limit the computational cost. To highlight the efficiency and effectiveness of the proposed artificial neural network–Improved Multi-Objective Collaborative Optimization method, mathematical and engineering examples are presented. Results obtained from the application of the artificial neural network–Improved Multi-Objective Collaborative Optimization approach to an optimization problem of a stiffened panel are compared with those obtained by traditional optimization without using prediction tools. The new method (artificial neural network–Improved Multi-Objective Collaborative Optimization) was proven to be superior to traditional optimization. These results have confirmed the efficiency and effectiveness of the artificial neural network–Improved Multi-Objective Collaborative Optimization method. In addition, it converges at faster rate than traditional optimization. The traditional optimization method converges within 7918 s, while artificial neural network–Improved Multi-Objective Collaborative Optimization requires only 42 s, clearly, the artificial neural network–Improved Multi-Objective Collaborative Optimization method is much more efficient.

SHM System Based on ANN for Aeronautical Applications

2011 ◽  
Vol 495 ◽  
pp. 129-133
Author(s):  
Christos Katsikeros ◽  
Claudio Sbarufatti ◽  
George Lampeas ◽  
Ioannis Diamantakos
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Structural Health Monitoring ◽  
Sensor Network ◽  
Health Monitoring ◽  
Stiffened Panels ◽  
Network Layout ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Ann Application

In the present work a Structural Health Monitoring (SHM) system based on the use of Artificial Neural Network (ANN) method is presented that is suitable for aeronautical applications. The proposed methodology can be applied for the case of stiffened panels that are typical in aeronautical structures. The effect of sensor network layout, as well as noise applied during the training and prediction phase of the ANN application, is examined.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural
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