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基于GoogLeNet的混凝土细观模型应力-应变曲线预测

周杰 赵婷婷 陈青青 王志勇 王志华

周杰,赵婷婷,陈青青,王志勇,王志华. 基于GoogLeNet的混凝土细观模型应力-应变曲线预测 [J]. 应用数学和力学,2022,43(3):290-299 doi: 10.21656/1000-0887.420136
引用本文: 周杰,赵婷婷,陈青青,王志勇,王志华. 基于GoogLeNet的混凝土细观模型应力-应变曲线预测 [J]. 应用数学和力学,2022,43(3):290-299 doi: 10.21656/1000-0887.420136
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
Citation: 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

基于GoogLeNet的混凝土细观模型应力-应变曲线预测

doi: 10.21656/1000-0887.420136
基金项目: 国家自然科学基金(12072217;11702186)
详细信息
    作者简介:

    周杰(1997—),男,硕士生(E-mail:zhoujie_23@126.com

    王志勇(1982—),男,博士,硕士生导师(通讯作者. E-mail:wangzhiyong@tyut.edu.cn

  • 中图分类号: TU37; TP39; O34

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

  • 摘要:

    非均质复合材料的宏观力学性能往往取决于细观组分的分布方式和力学性能,但是建立明确的关系表达式极其困难。为了应对这一挑战,以混凝土为研究对象,提出了一种基于深度学习的策略,能够高效、准确地通过细观模型图像信息获取应力-应变曲线。首先,使用基于卷积神经网络(convolutional neural network,CNN)的GoogLeNet模型进行图像信息识别和提取,并针对应力-应变曲线的复杂性特点,进行了数据预处理操作,并且设计了相应的多任务损失函数。数据集中的细观模型图像采用基于Monte-Carlo的随机骨料模型生成,并且使用数值模拟试验获取对应细观模型的单轴压缩应力-应变曲线。最后,通过对神经网络的训练和测试评估了所提出方法的可行性。结果表明,GoogLeNet模型训练效率和预测精度均优于AlexNet和ResNet模型,具有良好的泛化能力和鲁棒性。

  • 图  1  卷积神经网络基本组成

    Figure  1.  The basic composition of a CNN

    图  2  Inception 模块

    Figure  2.  The inception module

    图  3  激活函数:(a) sigmoid;(b) tanh;(c) Leaky ReLU;(d) ReLU

    Figure  3.  Activation functions: (a) sigmoid; (b) tanh; (c) Leaky ReLU; (d) ReLU

    图  4  混凝土细观模型

    注 为了解释图中的颜色,读者可以参考本文的电子网页版本,后同。

    Figure  4.  Concrete’s meso-model

    图  5  单轴压缩实验加载示意图

    Figure  5.  Diagram of uniaxial compression test loading

    图  6  混凝土细观模型图像

    Figure  6.  The image of concrete meso-structure

    图  7  应力-应变曲线预处理

    Figure  7.  Preprocessing of the stress-strain curve

    图  8  训练集和验证集损失曲线:(a)训练集损失曲线;(b)验证集损失曲线

    Figure  8.  Loss curves of the training set and the validation set: (a) the loss curve of the training set; (b) the loss curve of the validation set

    图  9  部分测试集的预测结果和数值计算结果对比:(a) 骨料体积分数39%,孔隙率1%;(b) 骨料体积分数39%,孔隙率6%;(c) 骨料体积分数39%,孔隙率9%;(d) 骨料体积分数40%,孔隙率1%;(e) 骨料体积分数40%,孔隙率3%;(f) 骨料体积分数40%,孔隙率8%

    Figure  9.  Comparison of prediction results with numerical test data: (a) aggregate volume fraction at 39%, porosity at 1%; (b) aggregate volume fraction at 39%, porosity at 6%; (c) aggregate volume fraction at 39%, porosity at 9%; (d) aggregate volume fraction at 40%, porosity at 1%; (e) aggregate volume fraction at 40%, porosity at 3%; (f) aggregate volume fraction at 40%, porosity at 8%

    图  10  不同CNN预测结果对比

    Figure  10.  Comparison of different CNN prediction results

    11  预测结果:(a) 峰值应力预测值;(b) 峰值应力预测值误差;(c) 弹性模量预测值;(d) 弹性模量预测值误差

    11.  Predictions: (a) peak stress prediction; (b) errors of peak stress prediction; (c) elastic modulus prediction; (d) errors of elastic modulus of prediction

    表  1  细观组分的力学参数

    Table  1.   Mechanical parameters of meso-compositions

    itemPoisson’s ratio υYoung’s modulus E / GPacompressive strength fc / MPatensile strength ft / MPa
    aggregate0.2343
    mortar0.225353.5
    下载: 导出CSV

    表  2  各卷积神经网络模型的对比

    Table  2.   Comparison of CNN models

    CNNepochnumber of layersmodel sizevalidation
    loss
    time
    GoogLeNet1002223.1 MB1.0162308.0 s
    AlexNet1008223 MB6.1393826.2 s
    ResNet501005090.6 MB1.7736263.8 s
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-05-17
  • 修回日期:  2021-06-28
  • 网络出版日期:  2022-02-16
  • 刊出日期:  2022-03-08

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