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原状黄土天然孔隙比定量评价方法

高智慧 左璐

高智慧, 左璐. 原状黄土天然孔隙比定量评价方法[J]. 地质科技通报, 2023, 42(6): 53-62. doi: 10.19509/j.cnki.dzkq.tb20220172
引用本文: 高智慧, 左璐. 原状黄土天然孔隙比定量评价方法[J]. 地质科技通报, 2023, 42(6): 53-62. doi: 10.19509/j.cnki.dzkq.tb20220172
Gao Zhihui, Zuo Lu. A quantitative evaluation method regarding the natural void ratio of undisturbed loess[J]. Bulletin of Geological Science and Technology, 2023, 42(6): 53-62. doi: 10.19509/j.cnki.dzkq.tb20220172
Citation: Gao Zhihui, Zuo Lu. A quantitative evaluation method regarding the natural void ratio of undisturbed loess[J]. Bulletin of Geological Science and Technology, 2023, 42(6): 53-62. doi: 10.19509/j.cnki.dzkq.tb20220172

原状黄土天然孔隙比定量评价方法

doi: 10.19509/j.cnki.dzkq.tb20220172
基金项目: 

国家自然科学基金项目 41790441

国家自然科学基金项目 41772316

详细信息
    作者简介:

    高智慧(1998—), 男, 现正攻读土木工程专业硕士学位, 主要从事黄土力学方面的研究工作。E-mail: 3120122018@stu.xjtu.edu.cn

    通讯作者:

    左璐(1989—), 男, 副教授, 主要从事岩土力学方面的教学与科研工作。E-mail: zuol07@xjtu.edu.cn

  • 中图分类号: TU444

A quantitative evaluation method regarding the natural void ratio of undisturbed loess

  • 摘要:

    天然孔隙比是初始结构的基本表征参数, 故从岩土角度对黄土天然孔隙比分布规律进行分析和预测, 对于掌握原位黄土灾变力学行为并进行灾害预警工作具有重要意义。通过选取典型场地不同层位原状黄土开展了颗粒分析试验、X射线衍射(XRD)试验、天然孔隙比试验和一维固结试验, 分析得到了天然孔隙比与颗粒组分、应力历史的相关规律。结果表明, 天然孔隙比受应力历史和颗粒级配影响, 上覆压力越大, 级配越均匀, 天然孔隙比越小, 同时含水状态也可能是天然孔隙比变化的原因之一。在此基础上, 以层位埋深、颗粒级配不均匀系数和曲率系数、天然含水量作为影响因素, 基于BP神经网络对天然孔隙比进行了定量评价。引入麻雀算法(SSA)与粒子群优化算法(PSO), 建立了BP、SSA-BP与PSO-BP神经网络的天然孔隙比预测模型。随机选取51组实测数据进行了模型训练, 将训练后的模型对16组验证与测试数据进行了预测, 并将预测结果与实测天然孔隙比进行了对比。结果表明基于PSO-BP的神经网络模型预测效果显著优于SSA-BP、BP神经网络模型, 可以有效预测天然孔隙比。

     

  • 图 1  场地位置与探坑布置图

    Figure 1.  Layout of site location and exploratory pit

    图 2  钻孔柱状图

    Figure 2.  Drill hole histogram

    图 3  粒径级配曲线

    a.T0探井粒径微分质量分数曲线; b.T0探井累计质量分数曲线; c.T5探井粒径微分质量分数曲线; d. T5探井累计质量分数曲线; e.T8探井粒径微分质量分数曲线; f.T8探井累计质量分数曲线

    Figure 3.  Particle size distribution curves

    图 4  Q3黄土(a)、古土壤(b)以及Q2黄土(c)的XRD曲线

    Figure 4.  XRD curves of Q3 loess(a), paleosols (b) and Q2 loess (c)

    图 5  天然孔隙比与Q3黄土、古土壤和Q2黄土的固结曲线

    Figure 5.  Natural void ratio and consolidation curves of Q3 loess, paleosols and Q2 loess

    图 6  天然孔隙比随天然含水量变化散点图

    Figure 6.  Scatter plot of the natural void ratio versus water content

    图 7  SSA-BP神经网络流程

    Figure 7.  SSA-BP neural network flow

    图 8  PSO-BP神经网络流程

    Figure 8.  PSO-BP neural network flow

    图 9  不同训练集组数下验证集部分评价指标图

    Figure 9.  Partial evaluation index chart of the validation set with different numbers of trainings

    图 10  天然孔隙比预测值与真实值对比图

    Figure 10.  Compared values of predicted and measured natural void ratio

    表  1  一维固结试验方案

    Table  1.   One-dimensional consolidation test scheme

    土层类型 固结压力/kPa
    Q3黄土 14.35, 64.40, 120.45, 222
    古土壤 259, 333
    Q2黄土 370, 480
    下载: 导出CSV

    表  2  天然孔隙比部分实测数据

    Table  2.   Selected measured data for soil with natural void ratio

    类型 孔隙比 深度/m 曲率系数 不均匀系数 天然含水量/%
    验证集 0.975 16 0.880 4 7.511 5 15.28
    0.773 18 0.890 4 9.123 3 15.46
    1.030 8 0.950 8 12.406 2 11.46
    1.029 10 0.728 1 13.472 8 13.48
    1.149 4 1.355 0 8.730 0 12.19
    1.133 2 1.396 0 8.796 0 11.66
    0.903 26 1.026 9 7.860 8 15.35
    0.741 20 1.372 7 9.001 8 16.93
    0.937 12 0.835 9 8.548 1 14.02
    0.835 24 1.446 6 9.107 3 17.27
    1.003 6 1.278 6 8.668 2 7.33
    0.913 24 1.571 4 7.516 5 19.51
    1.094 8 1.356 8 8.966 8 15.03
    测试集 1.194 3.3 2.240 0 6.170 0 10.10
    0.802 19.3 1.570 0 11.480 0 15.70
    0.762 17.3 1.420 0 7.800 0 8.60
    下载: 导出CSV

    表  3  隐含层激活函数对BP神经网络验证集预测结果影响

    Table  3.   Influence of the hidden layer activation function on the BP neural network validation set predicted results

    隐含层函数 相关系数R2 均方误差MSE/% 平均绝对误差MAE/% 平均绝对百分比误差MAPE/% 纯均方误差MSPE/%
    Logsig 0.682 0.47 6.01 6.62 14.86
    Tansig 0.729 0.40 5.64 6.16 13.57
    下载: 导出CSV

    表  4  不同训练集组数下BP神经网络预测结果

    Table  4.   Predicted results of the BP neural network under different numbers of trainings

    训练集组数 相关系数R2 均方误差MSE/% 平均绝对误差MAE/% 平均绝对百分比误差MAPE/% 纯均方误差MSPE/%
    51 0.729 0.40 5.64 6.16 1.357
    52 0.733 0.46 5.92 6.61 1.483
    53 0.681 0.42 5.78 6.36 1.418
    54 0.739 0.33 5.70 6.26 1.275
    55 0.737 0.32 6.14 6.77 1.267
    56 0.665 0.38 6.43 7.33 1.361
    下载: 导出CSV

    表  5  BP神经网络、SSA-BP神经网络、PSO-BP神经网络预测结果

    Table  5.   Predicted results of the BP neural network, SSA-BP neural network, PSO-BP neural network

    类型 预测方法 相关系数R2 均方误差MSE/% 平均绝对误差MAE/% 平均绝对百分比误差MAPE/% 纯均方误差MSPE/%
    验证集 BP神经网络 0.729 0.40 5.64 6.16 13.57
    SSA-BP神经网络 0.769 0.35 5.45 6.05 12.97
    PSO-BP神经网络 0.772 0.33 5.12 5.41 11.39
    测试集 BP神经网络 0.756 0.21 9.39 10.77 10.19
    SSA-BP神经网络 0.860 0.22 7.08 8.86 11.53
    PSO-BP神经网络 0.946 0.09 6.33 6.71 6.00
    下载: 导出CSV
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  • 收稿日期:  2022-04-20
  • 录用日期:  2022-05-23
  • 修回日期:  2022-05-16

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