Intelligent Parameter Inversion of Fractional-Order Model Based on BP Neural Network | Zendy

Renbo Gao | Zendy; Fei Wu | Zendy; Cunbao Li | Zendy; Jie Chen | Zendy; Chenxin Ji | Zendy

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Intelligent Parameter Inversion of Fractional-Order Model Based on BP Neural Network

Author(s) -

Renbo Gao,

Fei Wu,

Cunbao Li,

Jie Chen,

Chenxin Ji

Publication year - 2021

Publication title -

lithosphere

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.737

H-Index - 43

eISSN - 1941-8264

pISSN - 1947-4253

DOI - 10.2113/2021/9477507

Subject(s) - creep , artificial neural network , inversion (geology) , constitutive equation , experimental data , geology , test data , salt (chemistry) , geotechnical engineering , structural engineering , computer science , engineering , mathematics , materials science , finite element method , seismology , artificial intelligence , statistics , composite material , chemistry , tectonics , programming language

To explore creep parameters and creep characteristics of salt rock, an Ansys numerical model of salt rock sample was established by using fractional creep constitutive model of salt rock, and an orthogonal test scheme was designed based on uniaxial compression test of salt rock samples. A large number of training data were obtained by combining the numerical model with the experimental scheme, and the model parameters were inverted by using the BP neural network. The model parameters are used for forwarding calculation, and the results are in good agreement with the measured strain data. This shows that the model parameter inversion method proposed in this paper can obtain reasonable parameter values and then accurately predict the creep behaviour of salt rock, which provides a good technical basis for related engineering practice and scientific research in the future.

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