Application of artificial neural network to predict buckling load of thin cylindrical shells under axial compression

2021 ◽  
Vol 248 ◽  
pp. 113221
Author(s):  
Zia ul Rehman Tahir ◽  
Partha Mandal ◽  
Muhammad Taimoor Adil ◽  
Farah Naz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Axial Compression ◽  
Cylindrical Shells ◽  
Buckling Load ◽  
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

Development of a Burst Capacity Model for Corroded Pipelines Under Internal Pressure and Axial Compression Using Artificial Neural Network

2020 ◽  
Author(s):  
Shulong Zhang ◽  
Wenxing Zhou ◽  
Shenwei Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Internal Pressure ◽  
Axial Compression ◽  
Full Scale ◽  
Assessment Model ◽  
Ground Movement ◽  
Ann Model ◽  
Capacity Model ◽  
Artificial Neural

Abstract In-service pipelines are often subjected to longitudinal forces and bending moments resulting from, for example, ground movement or formation of free spans in addition to internal pressures. In practice, there are some site-specific cases where corrosion anomalies interact with the external loads. A refined assessment model is required to understand the load carrying capacity of pipe. In this study, a burst capacity model for corroded pipelines under combined internal pressure and axial compression is developed based on extensive parametric three-dimensional (3D) elasto-plastic finite element analyses (FEA) and artificial neural network (ANN) technique. The parametric FEA employs the ultimate tensile strength (UTS)-based burst criterion and idealizes corrosion defects as semi-ellipsoidal shaped flaws. The FEA model is validated by full-scale burst tests of pipe specimens containing semi-ellipsoidal shaped flaws reported in the literature. Extensive parametric FEA are carried out to evaluate the burst capacity of corroded pipelines under combined internal pressure and axial compression by varying the pipe geometric and material properties, defect depth, length and width, and magnitude of axial compressive stress. Based on the FEA results, an ANN model is developed utilizing the open-source platform PYTHON to predict the burst capacity of corroded pipelines under combined internal pressure and axial compression. The well-trained ANN model is further validated by full-scale burst tests of corroded pipelines under combined internal pressure and axial compression carried out by Det Norske Veritas (DNV).

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.

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
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