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Plastic damage prediction of concrete under compression based on deep learning

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Abstract

Diverse loads frequently damage concrete when it is in use. However, it might be challenging to immediately identify the stress and damage of concrete in actual engineering situations. In order to predict the stress and damage of concrete, a deep learning (DL) model based on the convolutional neural network (CNN) is proposed in this paper. To provide the training and validation data, a finite element (FE) model of uniaxial compression of concrete specimens based on the concrete damage-plasticity (CDP) model is constructed. The DL model is trained with the strain contours with a specified range provided by the FE model as the inputs and the stress and damage assessment of concrete as the outputs. The prediction of the stress and damage of concrete materials was effectively realized by the trained DL model, and it was verified in a larger range of working conditions distinct from the training and verification sets. The results show that the DL algorithm has good accuracy and reliability. By efficiently and correctly recreating the FE prediction results, the DL model offers a method for promptly evaluating the stress and damage of concrete structures under complicated stress circumstances in actual engineering.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 52175148), Shock and Vibration of Engineering Materials and Structures Key Laboratory of Sichuan Province (No. 22kfgk04), the National Key Laboratory Foundation 2022-JCJQ-LB-006 (No. 6142411232212), and the Regional Collaboration Project of Shanxi Province (No. 2022104041101122). The authors thank Mr. Xiangshen Song and Mr. Minghui Mao for their invaluable insights and significant contributions.

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Authors and Affiliations

  1. School of Civil Engineering, Liaoning Petrochemical University, Fushun, China

    Yunhan Niu & Wenwu Wang

  2. School of Mechanics, Civil Engineering and Architecture, Northwestern Polytechnical University, Xi’an, China

    Yutai Su & Xu Long

  3. Yangtze Delta Region Institute of Tsinghua University, Jiaxing, China

    Fengrui Jia

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  1. Yunhan Niu
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  2. Wenwu Wang
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  3. Yutai Su
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  5. Xu Long
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Correspondence to Yutai Su or Xu Long.

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Niu, Y., Wang, W., Su, Y. et al. Plastic damage prediction of concrete under compression based on deep learning. Acta Mech 235, 255–266 (2024). https://doi.org/10.1007/s00707-023-03743-8

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  • DOI: https://doi.org/10.1007/s00707-023-03743-8

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