基于WOA-XGBoost模型的胶结充填体强度预测

袁丛祥 刘志祥 杨小聪 郭金峰 万串串 熊帅 刘伟军

袁丛祥, 刘志祥, 杨小聪, 郭金峰, 万串串, 熊帅, 刘伟军. 基于WOA-XGBoost模型的胶结充填体强度预测[J]. 高压物理学报, 2023, 37(5): 054201. doi: 10.11858/gywlxb.20230668
引用本文: 袁丛祥, 刘志祥, 杨小聪, 郭金峰, 万串串, 熊帅, 刘伟军. 基于WOA-XGBoost模型的胶结充填体强度预测[J]. 高压物理学报, 2023, 37(5): 054201. doi: 10.11858/gywlxb.20230668
YUAN Congxiang, LIU Zhixiang, YANG Xiaocong, GUO Jinfeng, WAN Chuanchuan, XIONG Shuai, LIU Weijun. Strength Prediction of Cemented Paste Backfill Body Based on WOA-XGBoost Model[J]. Chinese Journal of High Pressure Physics, 2023, 37(5): 054201. doi: 10.11858/gywlxb.20230668
Citation: YUAN Congxiang, LIU Zhixiang, YANG Xiaocong, GUO Jinfeng, WAN Chuanchuan, XIONG Shuai, LIU Weijun. Strength Prediction of Cemented Paste Backfill Body Based on WOA-XGBoost Model[J]. Chinese Journal of High Pressure Physics, 2023, 37(5): 054201. doi: 10.11858/gywlxb.20230668

基于WOA-XGBoost模型的胶结充填体强度预测

doi: 10.11858/gywlxb.20230668
基金项目: 国家重点研发计划项目(2022YFC2904101);国家自然科学基金(52374107,51974359)
详细信息
    作者简介:

    袁丛祥(1999-),男,硕士研究生,主要从事充填体与岩石力学研究. E-mail:225511036@csu.edu.cn

    通讯作者:

    刘志祥(1967-),男,博士,教授,主要从事采矿工程与岩石力学研究. E-mail:liulzx@csu.edu.cn

  • 中图分类号: O346; TD853

Strength Prediction of Cemented Paste Backfill Body Based on WOA-XGBoost Model

  • 摘要: 单轴抗压强度作为胶结充填体重要的力学性能指标,通常使用传统的力学试验来确定。使用鲸鱼优化算法(whale optimization algorithm,WOA)对极限梯度提升模型(XGBoost)进行优化,建立了WOA-XGBoost混合模型。以某铅锌矿充填料浆配比试验得到的80组数据作为数据库,选取固体质量分数、水泥占比、尾砂占比及养护天数作为输入参数,充填体试块抗压强度作为输出参数。为了与WOA-XGBoost模型进行比较,还构建了XGBoost、RF和WOA-RF模型。结果表明:WOA-XGBoost模型的决定系数为0.965 0,均方根误差为0.207 4,平均绝对误差为0.170 3;XGBoost模型的决定系数、均方根误差、平均绝对误差分别为0.897 1、0.408 4和0.246 7。可见,鲸鱼优化算法能够显著提高XGBoost模型的预测能力。相比XGBoost、RF和WOA-RF模型,WOA-XGBoost混合模型具有更高的预测精度。研究结果对于胶结充填材料的设计和配比优化具有重要意义。

     

  • 图  尾砂粒级组成曲线

    Figure  1.  Particle size distribution curve of the tailings

    图  试块测试过程示意图

    Figure  2.  Schematic diagram of the test process

    图  WOA优化XGBoost模型流程

    Figure  3.  XGBoost optimization process with WOA

    图  WOA-XGBoost预测模型不同种群大小的适应度变化曲线

    Figure  4.  Fitness value versus iteration curves of the WOA-XGBoost model with different population sizes

    图  模型训练集和测试集的预测值与实测值的对比

    Figure  5.  Comparison of the predicted and actual values of the WOA-XGBoost model

    图  模型的性能评价参数的对比

    Figure  6.  Comparison of performance metrics with mentioned models

    图  模型的单轴抗压强度预测能力对比

    Figure  7.  Comparison of the compressive strength predictive ability of the developed methods

    图  各输入参数的重要性得分

    Figure  8.  Importance scores of the input parameters

    表  1  分级尾砂的物理性质和粒径分布

    Table  1.   Physical property and particle size distribution of classified tailings

    Physical property Particle size distribution/μm
    Loose bulk density/(g·cm−3) Loose porosity/% Density/(g·cm−3) Specific gravity D10 D50 D60 D90
    1.81 46.99 3.36 3.42 2.210 38.237 56.355 163.321
    下载: 导出CSV

    表  2  尾砂化学成分(阳离子)

    Table  2.   Chemical components of the tailings (cations) %

    MgAlKCaFeMnTiCuZnPbRest
    1.2302.6800.43016.70012.6000.7700.1200.0200.1500.010<0.003
    下载: 导出CSV

    表  3  尾砂胶结充填体强度测试结果

    Table  3.   Test results for unconfined compressive strength of cemented paste backfill

    GroupMass fraction of
    solid/%
    Cement
    proportion
    Tailings
    proportion
    σc/MPa
    7 d14 d28 d60 d90 d
    A1720.200.801.842.883.004.444.67
    A2720.110.890.851.352.062.152.23
    A3720.090.910.781.071.151.201.50
    A4720.080.920.550.911.011.081.13
    B1700.200.801.612.582.764.234.48
    B2700.110.890.711.151.281.341.52
    B3700.090.910.621.051.121.171.25
    B4700.080.920.430.700.750.780.94
    C1680.200.801.342.522.652.663.00
    C2680.110.890.560.900.991.061.20
    C3680.090.910.480.660.730.851.06
    C4680.080.920.330.550.700.740.92
    D1650.200.800.791.271.312.162.58
    D2650.110.890.370.600.830.860.91
    D3650.090.910.300.530.580.650.71
    D4650.080.920.270.440.490.620.68
    下载: 导出CSV

    表  4  WOA-XGBoost预测模型的性能参数

    Table  4.   Performance metrics of the proposed WOA-XGBoost model

    Swarm size Training set Test set
    R2 δRMSE δMAE R2 δRMSE δMAE
    25 0.999 7 0.016 8 0.012 4 0.931 0 0.283 4 0.192 6
    50 0.999 6 0.020 9 0.016 4 0.926 7 0.269 0 0.190 1
    75 0.999 5 0.021 1 0.016 4 0.917 9 0.344 0 0.355 4
    100 0.999 7 0.016 1 0.013 3 0.964 2 0.214 3 0.183 8
    125 0.999 8 0.014 3 0.010 9 0.965 0 0.207 4 0.170 3
    150 0.999 7 0.018 4 0.014 8 0.917 1 0.147 0 0.123 2
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-05-22
  • 修回日期:  2023-06-12
  • 录用日期:  2023-07-14
  • 网络出版日期:  2023-09-28
  • 刊出日期:  2023-11-07

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