Neural network model of rupture conditions for elastic material sample based on measurements at static loading under different strain rates

被引：5
|
作者：
Bolgov, I. [1 ]
Kaverzneva, T. [1 ]
Kolesova, S. [1 ]
Lazovskaya, T. [1 ]
Stolyarov, O. [1 ]
Tarkhov, D. [1 ]
机构：
[1] Peter Great St Petersburg Polytech Univ, 29 Politech Skaya Str, St Petersburg 195251, Russia
来源：
2016 JOINT IMEKO TC1-TC7-TC13 SYMPOSIUM: METROLOGY ACROSS THE SCIENCES: WISHFUL THINKING? | 2016年 / 772卷
基金：
俄罗斯基础研究基金会;
关键词：
D O I：
10.1088/1742-6596/772/1/012032
中图分类号：
TH7 [仪器、仪表];
学科分类号：
0804 ; 080401 ; 081102 ;
摘要：
The article deals with the problem of predicting of the temporal elongation law of the sample under dynamic loading. The determination of tensile behavior of samples under uniaxial loading is performed by a standard tensile method. The neural network approach is applied to construct an approximate elongation-force dependence using measurement data and posterior model of the dependence of rupture conditions on the neural network parameters. The considered approach can be used in the building industry.
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页数：6
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