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A deep CNN-based constitutive model for describing of statics characteristics of rock materials by Luyuan Wu; Dan Ma; Zifa Wang; Jianwei Zhang; Boyang Zhang; Jianhui Li; Jian Liao; Jingbo Tong is a Engineering article available to read on EtoBox.

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The inhomogeneity, discontinuity, and elastoplasticity of the rock mass affect the deformation and failure of rock, and it is difficult to describe the stress–strain relationship of the rock mass by traditional constitutive models with a certain mathematical models. In order to address the complex problems caused by multiple variables, firstly, 77 rock specimens were collected from overburden of the working face 1012001 in Yuanzigou coal mine, China. Triaxial compression tests were carried out on these samples, and 673,632 data samples were output. Secondly, based on deep convolutional neural networks (CNN), a CNN-based rock constitutive model (CNNCM) was proposed. The structure and hyperparameters of deep CNN include M , ρ , E d , υ d , σ z , and σ y , as the input features, ɛ z as the output features;Conv2D layers ×4; Max pooling2D layers×4; Dense layers ×4; learning rate\_0.001; Epoch\_ 200; Batch size\_1024; Total params: 160801. Comparing the test results of eleven rock samples with the predicted results of CNNCM, the scope of MAPE and R 2 from 0.52–1.94% and 0.999870–0.999988, which indicates the proposed CNNCM has good performance. The sensibility and correlation of physical

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Author

Luyuan Wu; Dan Ma; Zifa Wang; Jianwei Zhang; Boyang Zhang; Jianhui Li; Jian Liao; Jingbo Tong

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)