基于模糊贝叶斯网络的隧道围岩富水破碎风险分析方法

朱庆; 郑威鹏; 吴浩宇; 丁雨淋; 郭永欣; 王强; 刘利; 张骏骁

doi:10.3969/j.issn.0258-2724.20230397

基于模糊贝叶斯网络的隧道围岩富水破碎风险分析方法

doi: 10.3969/j.issn.0258-2724.20230397

朱庆^1,,
郑威鹏¹,
吴浩宇¹,
丁雨淋^1, ,,
郭永欣¹,
王强²,
刘利^{1, 3},
张骏骁^{1, 4}

1.
西南交通大学地球科学与环境工程学院，四川成都 611756
2.
兰州交通大学测绘与地理信息学院，甘肃兰州 730070
3.
中铁十八局集团有限公司，天津 300222
4.
齐鲁空天信息研究院，山东济南 250100

基金项目: 国家重点研发计划（2022YFB3904103）；国家自然科学基金项目（42001336）；国家铁路集团重大课题（K2021G027）；成都市重点研发支撑计划（2022YF0800001GX）

详细信息

作者简介:
朱庆（1966—），男，教授，博士，博士生导师，研究方向为地理信息系统，E-mail：zhuq66@263.net

通讯作者:
丁雨淋，教授，博士，博士生导师，研究方向包括灾害模拟评估，大数据分析等，E-mail：rainforests@126.com

中图分类号: P631.84
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- 文章访问数: 66
- HTML全文浏览量: 31
- PDF下载量: 11
- 被引次数: 0
出版历程
- 收稿日期: 2023-08-15
- 修回日期: 2023-11-06
- 网络出版日期: 2025-04-21

Analysis Method for Water-Rich and Fractured Risks in Tunnel Surrounding Rock Based on Fuzzy Bayesian Network

ZHU Qing^1
,,
ZHENG Weipeng¹,
WU Haoyu¹,
DING Yulin^{1
, ,},
GUO Yongxin¹,
WANG Qiang²,
LIU Li^{1, 3},
ZHANG Junxiao^{1, 4}

1.
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
2.
Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
3.
China Railway 18th Bureau Group Corporation Limited, Tianjin 300222, China
4.
Qilu Aerospace Information Research Institute, Jinan 250100, China

摘要

摘要:
富水破碎不良地质区在隧道施工中容易诱发涌水灾害，为准确分析隧道围岩的富水破碎风险，且满足自动化、定量化风险分析需求，基于开挖数据构建模糊贝叶斯网络风险评估模型，通过隶属函数量化地质参数的不确定性，并结合贝叶斯概率推理，融合隧道地震预报法与瞬变电磁法的探测数据，得到围岩富水破碎风险概率；进一步利用三维体素模型将风险概率映射至三维坐标，可视化表达风险的空间分布特征. 选取典型长大深埋隧道进行实验分析，结果表明：评估模型对地下水情况与岩体完整性分类准确率分别为80.91%和82.81%，且不受数据完备性限制，能够在单一或多源数据条件下完成定量分析；所建三维体素模型为风险防控提供有效参考，其中，相较于单一数据，多源数据融合分析结果与现场揭露的富水区、破碎带位置吻合度更高.
- 隧道 /
- 风险分析 /
- 三维地质建模 /
- 模糊贝叶斯网络 /
- 数据融合
Abstract:
Unfavorable water-rich and fractured geological zones easily bring about water inrush disasters during tunnel construction. To accurately analyze water-rich and fractured risks in tunnel surrounding rock and address the need for automated and quantitative risk analysis, a fuzzy Bayesian network model for risk assessment was constructed by using tunnel excavation data. Geological parameter uncertainty was quantified via membership functions, and Bayesian probabilistic inference was employed to integrate data from tunnel seismic prediction and transient electromagnetic methods, yielding the probability of water-rich and fractured risks. A three-dimensional voxel model was used to map the risk probability to spatial coordinates, visualizing the spatial distribution of risks. A typical deep-buried long tunnel was selected for analysis. The results demonstrate that the assessment model achieves classification accuracies of 80.91% for groundwater conditions and 82.81% for rock mass integrity. Not affected by incomplete data, the model can conduct quantitative analysis under both single-source and multi-source data conditions. The constructed three-dimensional voxel model provides an effective reference for risk prevention and control. Analysis results of multi-source data fusion show higher spatial consistency with field-exposed water-rich and fractured zones than those of single-source data.
- tunnel /
- risk analysis /
- three-dimensional geological modeling /
- fuzzy Bayesian network /
- data fusion

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图 1 隧道围岩富水破碎风险分析方法流程

Figure 1. Analysis method flowchart for water-rich and fractured risks in tunnel surrounding rock

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图 2 围岩富水破碎风险评估模糊贝叶斯网络结构

Figure 2. Fuzzy Bayesian network topology for assessment of water-rich and fractured risks in surrounding rock

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图 3 各地球物理参数体素模型

Figure 3. Voxel models of geophysical parameters

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图 4 围岩富水破碎风险评估模型

Figure 4. Assessment model for water-rich and fractured risks in surrounding rock

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图 5 各变量模糊集

Figure 5. Fuzzy sets of each variable

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图 6 地下水情况与岩体完整性分类精确率、召回率和F值

Figure 6. Precision, recall, and F-measure of groundwater condition and rock mass integrity classification

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图 7 实际隧道围岩富水破碎情况与三维建模结果

Figure 7. Actual water-rich and fractured conditions in surrounding rock and results of three-dimensional modeling

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表 1 各指标变量富水破碎围岩响应特征

Table 1. Water-rich and fractured response features in surrounding rock of each indicator variable

数据源	指标变量	富水围岩响应特征	破碎围岩响应特征
TSP	纵波速度	岩体含水影响纵波传递，含水量越高，波速越低^[16]	岩体完整性显著影响纵波传递，岩体破碎导致传播路径不规律，表现出较低的波速^[17-18]
	横波速度	水体对横波传递抑制作用明显，横波从干燥岩体进入富水岩体，速度将显著降低^{[5, 16]}	岩体完整性影响横波传递，岩体破碎导致传播路径不规律，表现出较低的波速^[16]
	泊松比	水分影响岩体的弹性性质，通常情况下，含水量较高的岩体表现出较高的泊松比^[5]	岩体完整性影响岩体的弹性性质，通常情况下，高孔隙率的破碎岩体具有较高的泊松比^[17]
TEM	视电阻率	岩体中的水分作为导电体，对视电阻率有显著影响，含水量越高，视电阻率越低^{[5, 6, 18]}	岩体完整性影响视电阻率，破碎岩体更多的孔隙和裂隙，通常允许电流更容易通过，从而表现出较低的视电阻率^[6]

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表 2 各评价隶属函数类型

Table 2. Membership function types of each evaluation

变量类型	评价	隶属函数类型
指标变量	高	S形函数
	中	双高斯函数
	低	Z形函数
目标变量	富水/破碎	阶梯形函数

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表 3 岩体完整性与地下水情况变量敏感性分析结果

Table 3. Sensitivity analysis results of rock mass integrity and groundwater condition variables

影响程度排序	地下水情况		岩体完整性
影响程度排序	评估指标	MI	评估指标	MI
1	视电阻率	0.40	纵波速度	0.67
2	横波速度	0.21	横波速度	0.50
3	泊松比	0.15	泊松比	0.27
4	纵波速度	0.12	视电阻率	0.17

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