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基于遗传优化算法-反向传播神经网络的机制砂聚合物改性砂浆力学性能预测

田浩正 乔宏霞 张云升 冯琼 王鹏辉 谢晓扬

田浩正, 乔宏霞, 张云升, 等. 基于遗传优化算法-反向传播神经网络的机制砂聚合物改性砂浆力学性能预测[J]. 复合材料学报, 2024, 42(0): 1-14.
引用本文: 田浩正, 乔宏霞, 张云升, 等. 基于遗传优化算法-反向传播神经网络的机制砂聚合物改性砂浆力学性能预测[J]. 复合材料学报, 2024, 42(0): 1-14.
TIAN Haozheng, QIAO Hongxia, ZHANG Yunsheng, et al. Prediction of mechanical properties of manufactured sand polymer-modified mortar based on genetic optimization algorithm - backpropagation neural network[J]. Acta Materiae Compositae Sinica.
Citation: TIAN Haozheng, QIAO Hongxia, ZHANG Yunsheng, et al. Prediction of mechanical properties of manufactured sand polymer-modified mortar based on genetic optimization algorithm - backpropagation neural network[J]. Acta Materiae Compositae Sinica.

基于遗传优化算法-反向传播神经网络的机制砂聚合物改性砂浆力学性能预测

基金项目: 国家自然科学基金 (51868044;52008196;U21A20150;52178216);甘肃省科技计划资助(23JRRA799;23JRRA813)
详细信息
    通讯作者:

    张云升,博士,教授,博士生导师,研究方向为土木工程材料 E-mail: zhangyunsheng2011@163.com

  • 中图分类号: TU528.41

Prediction of mechanical properties of manufactured sand polymer-modified mortar based on genetic optimization algorithm - backpropagation neural network

Funds: National Natural Science Foundation of China (51868044; 52008196; U21A20150; 52178216); Supported by Gansu Provincial Science and Technology Plan (23JRRA799; 23JRRA813)
  • 摘要: 对聚合物改性砂浆(PMM)进行力学性能评价是保证安全使用的必要条件。为快速准确的获得具有优异力学性能的PMM,设计了拓扑结构为6-14-2的反向传播的神经网络(BPNN)预测模型,并使用遗传优化算法(GA)进行优化。GA-BPNN模型的输入层为水泥、纤维素醚、可再分散乳胶粉、消泡剂、凝灰岩石粉和粉煤灰的含量,输出层为抗压强度和粘结强度。数据集为520个,其中60%的数据用于建立模型,40%的数据用于验证模型。以实测抗折强度、抗压强度和粘结强度作为PMM的力学性能评价指标,通过相关性矩阵分析和主成分分析确定原材料与PMM力学性能之间的关系,同时对力学性能评价指标进行对比分析。结果表明:在7d和28d时,可再分散乳胶粉和消泡剂与PMM力学性能发展呈正相关;7 d时,石粉、粉煤灰与抗压、抗折强度呈负相关,纤维素醚与粘结强度呈正相关;28d时,水泥与抗压、粘结和抗折强度负相关,与石粉、粉煤灰呈正相关。GA优化算法可以显著提升BPNN模型的预测精度,GA-BPNN对抗压强度和粘结强度的预测性能评价指标分别为R2 = 0.918、RMAE = 17.507、RMAPE = 0.299、RRMSE = 7.849;R2 = 0.922、RMAE = 17.101、RMAPE = 0.282、RRMSE = 8.077。因此,GA-BPNN可以为PMM在力学性能方面提供精确的预测并对其配合比设计进行指导,对于工程实践具有重要意义。

     

  • 图  1  PMM的制备及测试过程

    Figure  1.  Preparation and testing process of PMM

    图  2  BPNN结构示意图

    Figure  2.  Schematic representation of the BPNN structure

    图  3  遗传优化算法的操作流程

    Figure  3.  Operation flow of GA

    图  4  PMM相关性矩阵图:(a)7 d;(b)28 d

    Figure  4.  PMM correlation matrix plots: (a) 7 d; (b) 28 d

    OPC–cement; CE–cellulose ether; DPP–dispersible polymer powder; AF–antifoam; SP–stone powder; FA–fly ash; CS–compressive strength; BS–bond strength; FS–flexural strength

    图  5  不同配合比主成分得分:(a)各组主成分得分;(b)综合主成分得分

    Figure  5.  Principal component scores for different mix ratios: (a) Principal component scores for each group; (b) Composite principal component score

    图  6  特征重要性排序

    Figure  6.  Ranking of principal components’ importance

    图  7  BPNN预测模型的建立:(a)模型训练;(b)模型验证;(c)模型测试;(d)总体建立阶段

    Figure  7.  BPNN prediction model building: (a) Model training; (b) Model validation; (c) Model testing; (d) Overall building phase

    图  8  GA-BPNN预测模型的建立:(a)模型训练;(b)模型验证;(c)模型测试;(d)总体建立阶段

    Figure  8.  GA-BPNN prediction model building: (a) Model training; (b) Model validation; (c) Model testing; (d) Overall building phase

    图  9  预测模型的评价指标:(a)训练阶段;(b)测试阶段

    Figure  9.  Evaluation indicators of the predictive model: (a) Training stage; (b) Testing phase

    图  10  预测模型预测值与实际值的误差范围:(a)抗压强度;(b)粘结强度

    Figure  10.  Error range between predicted and actual values of the prediction model: (a) Compressive strength; (b) Bond strength

    图  11  预测模型的验证指标:(a)抗压强度;(b)粘结强度

    Figure  11.  Validation metrics for predictive modeling: (a) Compressive strength; (b) Bond strength

    表  1  水泥、石粉和粉煤灰的化学组成和比表面积

    Table  1.   Chemical composition and specific surface area of stone powder and fly ash

    Materials SiO2/wt% Fe2O3/wt% Al2O3/wt% CaO/wt% MgO/wt% SO3/wt% K2O/wt% LOI/wt% Specific surface
    area /(m2·kg−1)
    Cement 26.36 4.49 10.62 49.27 3.24 2.01 1.27 3.71 853
    Fly ash 45.19 2.19 24.43 4.59 0.78 0.49 1.35 2.62 1220
    Stone powder 60.32 3.29 20.14 5.38 4.59 0.06 3.04 4.29 1440
    Note: LOI–Loss on ignition.
    下载: 导出CSV

    表  2  凝灰岩机制砂主要的性能指标

    Table  2.   Main indicators of tuff manufactured sand

    Fineness modulus Stone powder
    content /%
    Apparent
    density/(kg·m−3)
    Packing
    density /(kg·m−3)
    Voidage/% Crushing index/% Methylene blue
    value/(g·kg−1)
    2.4 9 2690 1710 39 9 2.1
    下载: 导出CSV

    表  3  聚合物改性砂浆配合比

    Table  3.   PMM mixing ratio (kg·m−3)

    Item NO. Cement Cellulose ether Dispersible polymer powder Antifoam Stone powder GradeⅡ fly ash
    O 460.00 - - - - -
    A1-CE 460.00 0.23 - - - -
    A2-CE 460.00 0.46 - - - -
    A3-CE 460.00 0.69 - - - -
    A4-CE 460.00 0.92 - - - -
    B1-DPP 460.00 0.46 4.6 - - -
    B2-DPP 460.00 0.46 9.2 - - -
    B3-DPP 460.00 0.46 13.8 - -
    B4-DPP 460.00 0.46 18.4 - - -
    C1-AF 460.00 0.46 13.8 0.092 - -
    C2-AF 460.00 0.46 13.8 0.184 - -
    C3-AF 460.00 0.46 13.8 0.368 - -
    C4-AF 460.00 0.46 13.8 0.736 - -
    D1-SP 437.00 0.46 13.8 0.184 23.0 -
    D2-SP 414.00 0.46 13.8 0.184 46.0 -
    D3-SP 391.00 0.46 13.8 0.184 69.0 -
    D4-SP 368.00 0.46 13.8 0.184 92.0 -
    D5-SP 345.00 0.46 13.8 0.184 115.0 -
    E1-FA 437.00 0.46 13.8 0.184 - 23.0
    E2-FA 414.00 0.46 13.8 0.184 - 46.0
    E3-FA 391.00 0.46 13.8 0.184 - 69.0
    E4-FA 368.00 0.46 13.8 0.184 - 92.0
    E5-FA 345.00 0.46 13.8 0.184 - 115.0
    F1-SPFA 368.00 0.46 13.8 0.184 36.8 55.2
    F2-SPFA 368.00 0.46 13.8 0.184 46.0 46.0
    F3-SPFA 368.00 0.46 13.8 0.184 55.2 36.8
    Notes: A-CE–Various amounts of cellulose ether; B-DPP–Various amounts of dispersible polymer powder; C-AF–Various amounts of antifoam; D-SP–Various amounts of stone powder; E-FA–Various amounts of fly ash; F-SPFA–Combined addition of stone powder and fly ash.
    下载: 导出CSV
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  • 收稿日期:  2024-04-22
  • 修回日期:  2024-05-22
  • 录用日期:  2024-06-05
  • 网络出版日期:  2024-06-29

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