Determination of theoretical stress concentration factor for circular/elliptical holes with reinforcement using analytical, finite element method and artificial neural network techniques

2021 ◽  
Author(s):  
Murat Tolga Ozkan ◽  
Fulya Erdemir
Keyword(s):  
Neural Network ◽  
Finite Element Method ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Stress Concentration ◽  
Stress Concentration Factor ◽  
Elliptical Holes ◽  
Theoretical Stress ◽  
Theoretical Stress Concentration Factor

scholarly journals Artificial Neural Network-Based Method for Seismic Analysis of Concrete-Filled Steel Tube Arch Bridges

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhen Liu ◽  
Shibo Zhang
Keyword(s):  
Neural Network ◽  
Finite Element Method ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Seismic Analysis ◽  
Steel Tube ◽  
Arch Bridge ◽  
Concrete Filled Steel Tube ◽  
Cfst Arch Bridge ◽  
Artificial Neural

Seismic analysis of concrete-filled steel tube (CFST) arch bridge based on finite element method is a time-consuming work. Especially when uncertainty of material and structural parameters are involved, the computational requirements may exceed the computational power of high performance computers. In this paper, a seismic analysis method of CFST arch bridge based on artificial neural network is presented. The ANN is trained by these seismic damage and corresponding sample parameters based on finite element analysis. In order to obtain more efficient training samples, a uniform design method is used to select sample parameters. By comparing the damage probabilities under different seismic intensities, it is found that the damage probabilities of the neural network method and the finite element method are basically the same. The method based on ANN can save a lot of computing time.

Multi-physics Analysis of Multi-chip LED Light Source Based on Finite Element Method and Artificial Neural Network

2019 ◽  
Vol 40 (6) ◽  
pp. 795-802 ◽  
Author(s):  
刘宏伟 LIU Hong-wei ◽  
牛萍娟 YU Dan-dan ◽  
郭 凯 NIU Ping-juan ◽  
张建新 ZHANG Zan-yun ◽  
王 闯 GUO Kai ◽  
...  
Keyword(s):  
Neural Network ◽  
Finite Element Method ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Light Source ◽  
Led Light ◽  
Artificial Neural ◽  
Element Method

Computation of Theoretical Stress Concentration Factor of V Shaped Notch Tip

2012 ◽  
Vol 152-154 ◽  
pp. 765-769
Author(s):  
Li Jun Zhang ◽  
Jie Qiong Xue ◽  
Yong Rui Zhao ◽  
Heng Fu Xiang
Keyword(s):  
Stress Intensity Factor ◽  
Stress Intensity ◽  
Stress Concentration ◽  
Stress Concentration Factor ◽  
Concentration Factor ◽  
Groove Depth ◽  
Engineering Practice ◽  
Notch Tip ◽  
Theoretical Stress ◽  
Theoretical Stress Concentration Factor

To obtain the crack life and value of the external load applied on the bar in the course of the bar precision cropping, the mathematic expression of the theoretical stress concentration factor in the tip of the V shaped notch containing its geometric parameters is built theoretically by analyzing the stress field distribution of V shaped notch tip and the stress intensity factor. The influences of the flare angle Φ, the radius at the groove bottom, and the groove depth d on the theoretical stress concentration factor are analyzed in detail and the obtained rational value is d/D=0.1 , s/D=0.015, Φ=90 in engineering practice. The analytical results show that the data obtained by the mathematic expression of theoretical stress concentration factor in the tip of the V shaped notch presented in the paper are coincident with the results of the corresponding parameters obtained by stress concentration manual in some extension.

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