Prediction of Concrete Meso-Model Stress-Strain Curves Based on GoogLeNet

ZHOU Jie; ZHAO Tingting; CHEN Qingqing; WANG Zhiyong; WANG Zhihua

doi:10.21656/1000-0887.420136

Volume 43 Issue 3

Mar. 2022

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Article Navigation > Applied Mathematics and Mechanics > 2022 > 43(3): 290-299

ZHOU Jie, ZHAO Tingting, CHEN Qingqing, WANG Zhiyong, WANG Zhihua. Prediction of Concrete Meso-Model Stress-Strain Curves Based on GoogLeNet[J]. Applied Mathematics and Mechanics, 2022, 43(3): 290-299. doi: 10.21656/1000-0887.420136

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Prediction of Concrete Meso-Model Stress-Strain Curves Based on GoogLeNet

doi: 10.21656/1000-0887.420136

ZHOU Jie^{1, 2
,},
ZHAO Tingting^{1, 2},
CHEN Qingqing^{1, 2},
WANG Zhiyong^{1, 2
,
,},
WANG Zhihua^{1, 2}

1.
Institute of Applied Mechanics, College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, P.R.China
2.
Shanxi Key Laboratory of Material Strength and Structural Impact, Taiyuan 030024, P.R.China

Received Date: 2021-05-17
Rev Recd Date: 2021-06-28

Available Online: 2022-02-16

Publish Date: 2022-03-08

Abstract

Abstract

Generally, the macro-scopic mechanical properties of heterogeneous composites depend on meso-components’ distribution and mechanical properties, but it is extremely difficult to establish a clear macro-meso relationship expression. To cope with this challenge, for concrete, a strategy based on deep learning was proposed to obtain the stress-strain curves through meso-model image information. First, the GoogLeNet model based on convolutional neural networks was used for image information recognition and extraction. According to the complexity of the stress-strain curve, data preprocessing operations were performed and the corresponding multi-task loss function was designed. The meso-model images in the data set were generated with the random aggregate model based on the Monte Carlo method, and numerical simulation experiments were conducted to obtain the uniaxial compressive stress-strain curve of the corresponding meso-model. Finally, the feasibility of the proposed method was evaluated through training and testing. The training efficiency and prediction accuracy of the GoogLeNet model are better than the AlexNet and ResNet models, and have good generalization ability and robustness.
- concrete,
- meso-model,
- GoogLeNet,
- convolutional neural network,
- stress-strain curve

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References(28)

References

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