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基于随机森林回归分析的岩体结构面粗糙度研究

李文斌 冯文凯 胡云鹏 周永健 陈凯 刘云

李文斌,冯文凯,胡云鹏,等. 基于随机森林回归分析的岩体结构面粗糙度研究[J]. 水文地质工程地质,2023,50(1): 87-93 doi:  10.16030/j.cnki.issn.1000-3665.202110048
引用本文: 李文斌,冯文凯,胡云鹏,等. 基于随机森林回归分析的岩体结构面粗糙度研究[J]. 水文地质工程地质,2023,50(1): 87-93 doi:  10.16030/j.cnki.issn.1000-3665.202110048
LI Wenbin, FENG Wenkai, HU Yunpeng, et al. Roughness coefficient of rock discontinuities based on random forest regression analyses[J]. Hydrogeology & Engineering Geology, 2023, 50(1): 87-93 doi:  10.16030/j.cnki.issn.1000-3665.202110048
Citation: LI Wenbin, FENG Wenkai, HU Yunpeng, et al. Roughness coefficient of rock discontinuities based on random forest regression analyses[J]. Hydrogeology & Engineering Geology, 2023, 50(1): 87-93 doi:  10.16030/j.cnki.issn.1000-3665.202110048

基于随机森林回归分析的岩体结构面粗糙度研究

doi: 10.16030/j.cnki.issn.1000-3665.202110048
基金项目: 国家自然科学基金项目(41977252);地质灾害防治与地质环境保护国家重点实验室自主探索课题(SKLGP2020Z001);新华水力发电有限公司科研项目(XHWY-2020-DL-KY01)
详细信息
    作者简介:

    李文斌(1999-),男,硕士研究生,主要从事工程地质与地质灾害研究。E-mail:1147309562@qq.com

    通讯作者:

    冯文凯(1974-),男,博士,教授,博士生导师,主要从事区域与岩体稳定性评价与地质灾害防治研究。E-mail:fengwenkai@cdut.cn

  • 中图分类号: P642.2

Roughness coefficient of rock discontinuities based on random forest regression analyses

  • 摘要: 岩体结构面粗糙度系数是快速估算结构面峰值抗剪强度的重要参数。但是结构面轮廓曲线复杂,单一统计参数无法量化表征粗糙度。为解决这一问题,收集了112条结构面轮廓曲线起伏角、起伏度、迹线长度3方面的8项统计参数,利用随机森林回归模型交叉验证的方法评估统计参数的重要性。结果表明:最大起伏度、起伏高度标准偏差、平均起伏角、起伏角标准差、平均相对起伏度及粗糙度剖面指数等6项统计参数重要性占比达到93.2%,且回归拟合系数趋于平稳,基于重要性评估结果建立最优超参数决策树数目(ntree)为400、参与节点分割的数目(mtry)为2的随机森林回归模型,模型预测结果拟合优度高达98.1%。与基于坡度均方根、结构函数及粗糙度剖面指数等传统线性回归结果对比,随机森林回归模型结果精度更高,误差更小,拟合优度提高6%以上,表明随机森林回归模型更适用于结构面粗糙度反演。
  • 图  1  不同特征结构面轮廓曲线

    Figure  1.  Profile curves of different feature structure faces

    图  2  样本轮廓曲线JRC

    Figure  2.  JRC value of the sample profile curve

    图  3  归一化后统计参数分布图

    Figure  3.  Distribution of statistical parameters after normalization

    图  4  不同数量特征变量对拟合系数的影响

    Figure  4.  Effect of different quantitative feature variables on the fitting coefficients

    图  5  不同ntree时值时拟合系数变化

    Figure  5.  Changes in fitting coefficients with different ntree values

    图  6  随机森林模型预测结果

    Figure  6.  Random forest model predictions

    图  7  各模型预测结果

    Figure  7.  Predictions for each model

    表  1  结构面粗糙度统计参数重要性评分

    Table  1.   The importance score of the discontinuity roughness statistical parameters

    变量RmaxSDhiaveSDiRaveRpSFZ2
    重要性评分0.3230.2700.1590.0690.0660.0440.0410.027
    下载: 导出CSV

    表  2  各模型预测精度

    Table  2.   Predictive accuracy for each mode

    模型精度RFSFRPZ2
    R2/%98.192.191.791.3
    MSE0.2193.3636.36613.974
    RMSE0.5021.2271.6231.781
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-10-28
  • 录用日期:  2022-02-21
  • 修回日期:  2021-12-06
  • 网络出版日期:  2022-12-07
  • 刊出日期:  2023-01-13

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