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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
  • [1] 张炜, 张苏民. 非饱和黄土的结构强度特性[J]. 水文地质工程地质, 1990, 17(4): 22-25, 49.

    Zhang W, Zhang S M. Structural strength characteristics of unsaturated loess[J]. Hydrogeology & Engineering Geology, 1990, 17(4): 22-25, 49(in Chinese with English abstract).
    [2] 姜高磊, 刘林敬, 毕志伟, 等. 河北丰宁黄土粒度特征及其环境意义[J]. 地质科技情报, 2018, 37(4): 83-89. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ201804011.htm

    Jiang G L, Liu L J, Bi Z W, et al. Grain-size characteristics and its environmental significance of loess in Fengning, Hebei Province[J]. Geological Science and Technology Information, 2018, 37(4): 83-89(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ201804011.htm
    [3] 党进谦, 李靖. 非饱和黄土的结构强度与抗剪强度[J]. 水利学报, 2001, 32(7): 79-83, 90.

    Dang J Q, Li J. Structural strength and shear strength of unsaturated loess[J]. Journal of Hydraulic Engineering, 2001, 32(7): 79-83, 90(in Chinese with English abstract).
    [4] 张伯平, 王力, 袁海智. 含水量对黄土结构强度影响的定量分析[J]. 西北农业大学学报, 1994, 22(1): 54-60.

    Zhang B P, Wang L, Yuan H Z. Quantitative analysis of influence of water content on structural strength of loess[J]. Journal of Northwest Agricultural University, 1994, 22(1): 54-60(in Chinese with English abstract).
    [5] 冯立, 张茂省, 胡炜, 等. 黄土垂直节理细微观特征及发育机制探讨[J]. 岩土力学, 2019, 40(1): 235-244.

    Feng L, Zhang M S, Hu W, et al. Discussion on microscopic, microcosmic characteristics and developmental mechanism of loess vertical joints[J]. Rock and Soil Mechanics, 2019, 40(1): 235-244(in Chinese with English abstract).
    [6] 罗浩, 伍法权, 常金源, 等. 马兰黄土孔隙结构特征: 以赵家岸地区黄土为例[J]. 工程地质学报, 2021, 29(5): 1366-1372.

    Luo H, Wu F Q, Chang J Y, et al. Pore characteristics of Malan loess: A case study at Zhaojia'an landslide[J]. Journal of Engineering Geology, 2021, 29(5): 1366-1372(in Chinese with English abstract).
    [7] 田堪良, 王沛, 张慧莉. 考虑结构性的原状黄土应力-应变关系的探讨[J]. 岩土力学, 2013, 34(7): 1893-1898.

    Tian K L, Wang P, Zhang H L. Discussion on stress-strain relation of intact loess considering soil structure[J]. Rock and Soil Mechanics, 2013, 34(7): 1893-1898(in Chinese with English abstract).
    [8] Jiang M, Zhang F, Hu H, et al. Structural characterization of natural loess and remolded loess under triaxial tests[J]. Engineering Geology, 2014, 181: 249-260. doi: 10.1016/j.enggeo.2014.07.021
    [9] 郑佳, 庄建琦, 孔嘉旭, 等. 基于CT扫描的黄土孔隙结构特征研究[J]. 地质科技通报, 2022, 41(6): 211-222. doi: 10.19509/j.cnki.dzkq.2022.0210

    Zheng J, Zhuang J Q, Kong J X, et al. Study on pore structure characteristics of loess based on CT scanning[J]. Bulletin of Geological Science and Technology, 2022, 41(6): 211-222(in Chinese with English abstract). doi: 10.19509/j.cnki.dzkq.2022.0210
    [10] 李泽坤, 马鹏辉, 彭建兵, 等. 黑方台地区马兰黄土渗透特性及结构损伤试验研究[J]. 地质科技通报, 2022, 41(6): 200-210. doi: 10.19509/j.cnki.dzkq.2022.0251

    Li Z K, Ma P H, Peng J B, et al. Experimental study on permeability characteristics and structural damage of Malan loess in Heifangtai area[J]. Bulletin of Geological Science and Technology, 2022, 41(6): 200-210(in Chinese with English abstract). doi: 10.19509/j.cnki.dzkq.2022.0251
    [11] 井旭, 谢婉丽, 单帅. 原状及重塑黄土双轴试验微观力学特征离散元模拟[J]. 地质科技通报, 2021, 40(3): 184-193. doi: 10.19509/j.cnki.dzkq.2021.0311

    Jing X, Xie W L, Shan S. Discrete element simulation study on micromechanical characteristics of undisturbed and remolded loess in biaxial test[J]. Bulletin of Geological Science and Technology, 2021, 40(3): 184-193(in Chinese with English abstract). doi: 10.19509/j.cnki.dzkq.2021.0311
    [12] 井彦林. 基于数据挖掘技术的黄土湿陷性研究[D]. 西安: 西安理工大学, 2006.

    Jing Y L. Study on loess collapsibility based on data mining[D]. Xi 'an: Xi 'an University of Technology, 2006(in Chinese with English abstract).
    [13] 马闫, 王家鼎, 彭淑君, 等. 黄土湿陷性与土性指标的关系及其预测模型[J]. 水土保持通报, 2016, 36(1): 120-128.

    Ma Y, Wang J D, Peng S J, et al. Relationship between physical-mechanical parameters and collapsibility of loess soil and its prediction model[J]. Bulletin of Soil and Water Conservation, 2016, 36(1): 120-128(in Chinese with English abstract).
    [14] 叶为民, 崔玉军, 黄雨, 等. 黄土的湿陷性及其评价准则[J]. 岩石力学与工程学报, 2006, 35(3): 550-556. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX200603021.htm

    Ye W M, Cui Y J, Huang Y, et al. Collapsibility of loess and its discrimination criteria[J]. Chinese Journal of Rock Mechanics and Engineering, 2006, 35(3): 550-556(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX200603021.htm
    [15] 徐志军, 郑俊杰, 张军, 等. 聚类分析和因子分析在黄土湿陷性评价中的应用[J]. 岩土力学, 2010, 31(增刊2): 407-411. https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX2010S2073.htm

    Xu Z J, Zheng J J, Zhang J, et al. Application of cluster analysis and factor analysis to evaluation of loess collapsibility[J]. Rock and Soil Mechanics, 2010, 31(S2): 407-411(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX2010S2073.htm
    [16] 高凌霞, 栾茂田, 杨庆. 基于微结构参数主成分的黄土湿陷性评价[J]. 岩土力学, 2012, 33(7): 1921-1926. https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX201207002.htm

    Gao L X, Luan M T, Yang Q. Evaluation of loess collapsibility based on principal components of microstructural parameters[J]. Rock and Soil Mechanics, 2012, 33(7): 1921-1926(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX201207002.htm
    [17] Xu L, Coop M R. The mechanics of a saturated silty loess with a transitional mode[J]. Géotechnique, 2017, 67(7): 581-596.
    [18] Xu L, Gao C, Lan T, et al. Influence of grading on the compressibility of saturated loess soils[J]. Géotechnique Letters, 2020, 10(2): 198-204.
    [19] Zuo L, Xu L, Baudet B A, et al. The structure degradation of a silty loess induced by long-term water seepage[J]. Engineering Geology, 2020, 272: 105634.
    [20] Paz-Ferreiro J, Vázquez E V, Miranda J G V. Assessing soil particle-size distribution on experimental plots with similar texture under different management systems using multifractal parameters[J]. Geoderma, 2010, 160(1): 47-56.
    [21] 谢远云, 李长安, 何葵, 等. 青海省民和黄土的粒度组成及气候含义[J]. 地质科技情报, 2002, 21(2): 41-44. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ200202008.htm

    Xie Y Y, Li C A, He K, et al. Climatic implication and grains size composition from Minhe loess in Qinghai Province[J]. Geological Science and Technology Information, 2002, 21(2): 41-44(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ200202008.htm
    [22] Sun J. Provenance of loess material and formation of loess deposits on the Chinese Loess Plateau[J]. Earth and Planetary Science Letters, 2002, 203(3/4): 845-859.
    [23] Li Y, Shi W, Aydin A, et al. Loess genesis and worldwide distribution[J]. Earth-Science Reviews, 2020, 201: 102947.
    [24] Liu Z, Liu F, Ma F, et al. Collapsibility, composition, and microstructure of loess in China[J]. Canadian Geotechnical Journal, 2016, 53(4): 673-686.
    [25] Derbyshire E. Geological hazards in loess terrain, with particular reference to the loess regions of China[J]. Earth-Science Reviews, 2001, 54(1/3): 231-260.
    [26] 曹宇清, 吴永, 安向勇, 等. 考虑应力历史和应力水平影响的土体压缩模量计算方法[J]. 工程地质学报, 2019, 27(4): 760-765. https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ201904009.htm

    Cao Y Q, Wu Y, An X Y, et al. Calculation method of soil compression modulus considering the influence of stress history and stress level[J]. Journal of Engineering Geology, 2019, 27(4): 760-765(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ201904009.htm
    [27] Estabragh A R, Javadi A A, Boot J C. Effect of compaction pressure on consolidation behaviour of unsaturated silty soil[J]. Canadian Geotechnical Journal, 2004, 41(3): 540-550.
    [28] Chang C S, Deng Y, Yang Z. Modeling of minimum void ratio for granular soil with effect of particle size distribution[J]. Journal of Engineering Mechanics, 2017, 143(9): 04017060.
    [29] Xu Z, Xu N, Wang H. Effects of particle shapes and sizes on the minimum void ratios of sand[J]. Advances in Civil Engineering, 2019, 2019: 5732656.
    [30] 简涛, 李喜安, 王力, 等. 颗粒组构对黄土压缩特性及其粒间状态的影响[J]. 科学技术与工程, 2018, 18(30): 212-219.

    Jian T, Li X A, Wang L, et al. Effect of grain fabric on compressive properties and intergranular state of loess[J]. Science Technology and Engineering, 2018, 18(30): 212-219(in Chinese with English abstract).
    [31] 杨坪, 吴民晖, 许德鲜. 含水率对重塑黄土的变形特性影响的实验研究[J]. 工程地质学报, 2015, 23(6): 1066-1071. https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ201506006.htm

    Yang P, Wu M H, Xu D X. Experimental study on the effect of water content on deformation characteristics of remolded loess[J]. Journal of Engineering Geology, 2015, 23(6): 1066-1071(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ201506006.htm
    [32] 张晓周, 卢玉东, 李鑫, 等. 增湿条件下泾阳南塬马兰黄土孔隙率变化研究[J]. 干旱区资源与环境, 2019, 33(6): 99-104.

    Zhang X Z, Lu Y D, Li X, et al. The change of Malan loess porosity in south Jingyang Plateau under humidification condition[J]. Journal of Arid Land Resources and Environment, 2019, 33(6): 99-104(in Chinese with English abstract).
    [33] Russell A R. How water retention in fractal soils depends on particle and pore sizes, shapes, volumes and surface areas[J]. Géotechnique, 2014, 64(5): 379-390.
    [34] 沈花玉, 王兆霞, 高成耀, 等. BP神经网络隐含层单元数的确定[J]. 天津理工大学学报, 2008, 91(5): 13-15. https://www.cnki.com.cn/Article/CJFDTOTAL-TEAR200805006.htm

    Shen H Y, Wang Z X, Gao C Y, et al. Determination the number of BP neural network hidden layer units[J]. Journal of Tianjin University of Technology, 2008, 91(5): 13-15(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-TEAR200805006.htm
    [35] 薛建凯. 一种新型的群智能优化技术的研究与应用[D]. 上海: 东华大学, 2020.

    Xue J K. Research and application of a novel swarm intelligence optimization technique: Sparrow search algorithm[D]. Shanghai: Donghua University, 2020(in Chinese with English abstract).
    [36] Zhang W, Gu X, Tang L, et al. Application of machine learning, deep learning and optimization algorithms in Geoengineering and Geoscience: Comprehensive review and future challenge[J]. Gondwana Research, 2022, 109: 1-17.
    [37] 涂娟娟. PSO优化神经网络算法的研究及其应用[D]. 江苏镇江: 江苏大学, 2020.

    Tu J J. Research onlearning algorithm of neural network optimized with PSO and its application[D]. Zhenjiang Jiangsu: Jiangsu University, 2020(in Chinese with English abstract).
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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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