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

2016 Joint IMEKO TC1-TC7-TC13 Symposium: Metrology Across the Sciences: Wishful Thinking?

Bolgov, I.^1 ; Kaverzneva, T.^1 ; Kolesova, S.^1 ; Lazovskaya, T.^1 ; Stolyarov, O.^1 ; Tarkhov, D.^1

Peter the Great St-Petersburg Polytechnic University, 29 Politechnicheskaya Str, Saint-Petersburg

195251, Russia^1

关键词: Building industry;    Dynamic loadings;    Elastic materials;    Neural network model;    Neural network parameters;    Rupture conditions;    Tensile behaviors;    Uni-axial loading;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/772/1/012032/pdf
DOI  :  10.1088/1742-6596/772/1/012032

来源: IOP

【 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.