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基于“AM-GoogLeNet + BP”联合数据驱动的混凝土细观模型压缩应力−应变曲线预测

刘溢凡 张杰 张新宇 王志勇 王志华

刘溢凡, 张杰, 张新宇, 王志勇, 王志华. 基于“AM-GoogLeNet + BP”联合数据驱动的混凝土细观模型压缩应力−应变曲线预测. 力学学报, 2023, 55(4): 925-938 doi: 10.6052/0459-1879-22-506
引用本文: 刘溢凡, 张杰, 张新宇, 王志勇, 王志华. 基于“AM-GoogLeNet + BP”联合数据驱动的混凝土细观模型压缩应力−应变曲线预测. 力学学报, 2023, 55(4): 925-938 doi: 10.6052/0459-1879-22-506
Liu Yifan, Zhang Jie, Zhang Xinyu, Wang Zhiyong, Wang Zhihua. Prediction of concrete meso-model compression stress-strain curve based on “AM-GoogLeNet + BP” combined data-driven methods. Chinese Journal of Theoretical and Applied Mechanics, 2023, 55(4): 925-938 doi: 10.6052/0459-1879-22-506
Citation: Liu Yifan, Zhang Jie, Zhang Xinyu, Wang Zhiyong, Wang Zhihua. Prediction of concrete meso-model compression stress-strain curve based on “AM-GoogLeNet + BP” combined data-driven methods. Chinese Journal of Theoretical and Applied Mechanics, 2023, 55(4): 925-938 doi: 10.6052/0459-1879-22-506

基于“AM-GoogLeNet + BP”联合数据驱动的混凝土细观模型压缩应力−应变曲线预测

doi: 10.6052/0459-1879-22-506
基金项目: 国家自然科学基金(12272257, 12102292)和山西省基础研究计划(20210302124083)资助项目
详细信息
    通讯作者:

    王志勇, 副教授, 主要研究方向为断裂力学. E-mail: wangzhiyong@tyut.edu.cn

  • 中图分类号: TU3T, TP39, O34

PREDICTION OF CONCRETE MESO-MODEL COMPRESSION STRESS-STRAIN CURVE BASED ON “AM-GOOGLENET + BP” COMBINED DATA-DRIVEN METHODS

  • 摘要: 本文结合GoogLeNet卷积神经网络和BP神经网络分别在图像数据挖掘和数据分析方面的良好性能, 采用“AM-GoogLeNet + BP”联合数据驱动方法, 对混凝土细观模型(含砂浆、骨料及孔隙)的单轴压缩应力−应变曲线进行了有效预测. 通过引入力学参量对图像数据驱动的训练结果进行优化, 从而提升了神经网络的物理可解释性. 基于Python语言实现混凝土细观模型在Abaqus中的自动建模及细观图像生成过程, 并将生成的细观图像数据库与相应的压缩应力−应变曲线作为训练数据集. 在GoogLeNet中分别引入SENet, ECANet和CBAM三种代表性注意力机制并对三种注意力机制的性能进行对比和分析, 以自适应方式提升神经网络对混凝土各相组分的分析能力, 并以此得到混凝土细观模型的初步应力−应变预测曲线; 将骨料体积分数、孔隙率及初步峰值应力等物理参量作为输入引入BP神经网络以改善峰值应力的预测精度, 并与将物理参量直接引入卷积神经网络输入层的方法进行了对比, 最后定量给出了骨料体积分数和孔隙率对峰值应力的影响权重. 结果表明, 对于不同骨料体积分数及孔隙率的混凝土细观模型, 该方法均展现了较高的预测精度. 本文采用的“AM-GoogLeNet + BP”联合数据驱动预测模型从统计角度解决了传统方法对细观尺度参量分析的复杂性, 为复合材料的跨尺度力学行为研究提供了新思路.

     

  • 图  1  SENet模块

    Figure  1.  SENet module

    图  2  ECANet模块

    Figure  2.  ECANet module

    图  3  CBAM模块

    Figure  3.  CBAM module

    图  4  AM-GoogLeNet架构

    Figure  4.  The architecture of AM-GoogLeNet

    图  5  混凝土(a)细观有限元模型和(b) RGB格式图像

    Figure  5.  (a) The mesoscopic finite element model of concrete and (b) the image in RGB format

    图  6  不同骨料体积分数和孔隙率下的部分数据集

    Figure  6.  The partial image dataset with different aggregate volume fraction and porosity

    图  7  不同 GoogLeNet学习率曲线

    Figure  7.  The curves of the learning rate of different GoogLeNets

    图  8  不同 GoogLeNet的训练结果对比

    Figure  8.  The comparison of the training results of different GoogLeNets

    图  9  不同骨料体积分数和孔隙率下的CBAM-GoogLeNet预测结果

    Figure  9.  The prediction results of CBAM-GoogLeNet with different aggregate volume fraction and porosity

    9  不同骨料体积分数和孔隙率下的CBAM-GoogLeNet预测结果 (续)

    9.  The prediction results of CBAM-GoogLeNet with different aggregate volume fraction and porosity (continued)

    图  10  CBAM-GoogLeNet测试集峰值应力预测结果

    Figure  10.  The prediction results of the peak stress of the CBAM-GoogLeNet

    图  11  引入物理参量的CBAM-GoogLeNet训练过程

    Figure  11.  The training process of the physics CBAM-GoogLeNet

    图  12  BP神经网络架构、训练过程和结果评估

    Figure  12.  The architecture, training process and results evaluation of the BP neural network

    表  1  两种细观组分的力学参量

    Table  1.   Mechanical parameters of the two meso-components

    Elasticity modulus
    E/GPa
    Poisson’s ratio
    υ
    Compressive strength
    Fc/MPa
    Density
    ρ/(t·m−3)
    Dilatancy angle
    Ψ/(º)
    Eccentricity
    η/%
    Stress ratio
    σb0/σc0
    aggregate430.232.67
    mortar250.2352.40380.11.16
    下载: 导出CSV

    表  2  试验环境的硬件和软件参数

    Table  2.   Hardware and software parameters of the experimental environment

    NameParameters
    central processing unitInter Core i7-11800 H CPU @ 2.3 GHz
    memoryDDR4 memory 8 GB
    graphics cardNIVIDA GeForce RTX3060
    systemWindows 10
    environmentPython 3.6 TensorFolw 2.8.0 Keras 2.8.0 NUMPY 1.22.2
    compute unified device architectureCUDA 11.2
    下载: 导出CSV

    表  3  不同GoogLeNet的训练过程参数

    Table  3.   The training process parameters of different GoogLeNet

    CNNParameters of AMTotal parametersModel size/MBEpochTime/sValidation loss
    GoogLeNet601557722.955062541.2454
    SE-GoogLeNet8352654114524.955064661.2070
    ECA-GoogLeNet5601558222.955063211.3250
    CBAM-GoogLeNet4194627781923.955062801.2053
    下载: 导出CSV

    表  4  BP神经网络的权重和偏置值

    Table  4.   The weights and biases of BP neural network

    NeuronsWinputBiasWoutputBias
    Peak stressVolume fractionPorosity
    1−0.00172−0.123540.59741−2.15972−2.690271.21105
    2−0.173801.08733−0.535641.610952.00213
    3−1.360820.338790.019261.449151.63320
    40.33198−1.493040.672361.674491.40409
    50.03562−0.19979−1.140931.756581.35286
    61.12495−0.56972−1.042701.195521.38380
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-10-19
  • 录用日期:  2023-01-17
  • 网络出版日期:  2023-01-19
  • 刊出日期:  2023-04-18

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