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

scholarly journals BUCKLING LOAD ESTIMATION OF CRACKED COLUMNS USING ARTIFICIAL NEURAL NETWORK MODELING TECHNIQUE

2012 ◽  
Vol 18 (4) ◽  
pp. 568-579 ◽  
Author(s):  
Mahmut Bilgehan ◽  
Muhammet Arif Gürel ◽  
Recep Kadir Pekgökgöz ◽  
Murat Kısa
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Matrix Method ◽  
Buckling Load ◽  
Computational Time ◽  
Neural Network Modeling ◽  
Critical Buckling Load ◽  
The Matrix ◽  
Artificial Neural

In this paper, buckling analysis of slender prismatic columns with a single non-propagating open edge crack subjected to axial loads has been presented utilizing the transfer matrix method and the artificial neural networks. A multi-layer feedforward neural network learning by backpropagation algorithm has been employed in the study. The main focus of this work is the investigation of feasibility of using an artificial neural network to assess the critical buckling load of axially loaded compression rods. This is explored by comparing the performance of neural network models with the results of the matrix method for all considered support conditions. It can be seen from the results that the critical buckling load values obtained from the neural networks closely follow the values obtained from the matrix method for the whole data sets. The final results show that the proposed methodology may constitute an efficient tool for the estimation of elastic buckling loads of edge-cracked columns. Also, it can be seen from the results that the computational time reduces if the proposed method is used.

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
Sign in / Sign up

Export Citation Format

Share Document