基于BP神经网络反演分析的隧道塌方机理研究

陈航; 张贝贝; 旷华江; 肖丽娜

doi:10.16030/j.cnki.issn.1000-3665.202208066

基于BP神经网络反演分析的隧道塌方机理研究

doi: 10.16030/j.cnki.issn.1000-3665.202208066

1.
贵阳学院城乡规划与建筑工程学院，贵州贵阳　550005
2.
贵州路桥集团有限公司，贵州贵阳　550001
3.
中国地质大学（武汉）工程学院，湖北武汉　430074

基金项目: 国家自然科学基金项目( 51608141）；贵州省教育厅青年科技人才成长项目（黔教合KY字[2022]008）；贵阳市科技局贵阳学院专项资助项目（GYU-KY-[2021]）；贵州省教育厅青年科技人才成长项目（黔教合KY字[2020]079）

详细信息

作者简介:
陈航（1990-），男，硕士，主要从事岩土工程、地下工程等领域的教学与科研工作。E-mail：330596225@qq.com

通讯作者:
张贝贝（1986-），男，博士，副教授，主要从事岩土、隧道等相关领域的工作。E-mail：369226241@qq.com

中图分类号: U455
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出版历程
- 收稿日期: 2022-08-19
- 录用日期: 2022-10-24
- 修回日期: 2022-10-02
- 网络出版日期: 2023-03-23
- 刊出日期: 2023-05-15

A study of the tunnel collapse mechanism based on the BP neural network inversion analysis

1.
School of Urban and Rural Planning and Architectural Engineering, Guiyang University, Guiyang, Guizhou　550005, China
2.
Guizhou Road and Bridge Group Co. Ltd., Guiyang, Guizhou　550001, China
3.
School of Engineering, China University of Geosciences (Wuhan), Wuhan, Hubei　430074, China

摘要

摘要: 岩溶区隧道在施工过程中易发生崩塌，针对力学方面的隧道塌方机理分析较多，而针对岩溶软弱破碎带等地层方面的隧道塌方机理研究分析较少。为保证隧道施工的安全性、经济性和可行性，掌握隧道施工中的塌方机理非常有必要。依托贵州某岩溶破碎地层隧道在开挖过程中发生的坍塌现象，结合隧道的监测数据，运用BP神经网络的构建原理，对隧道的地层参数进行反演。将反演土体力学参数输入到FLAC3D有限元软件构建的不同施工方法模型中，对典型断面的崩塌破坏机制和风险进行判断和分析。结果表明：施工方法对隧道开挖的稳定性影响较大，针对围岩等级为Ⅴ级的隧道，采用三台阶七步法和单侧壁导坑法施工较安全，隧道塌方与隧道双向同时开挖没有关系；反演所得的隧道拱顶位移预测值为2.3 cm，地表位移预测值为1.2 cm，与监测数据偏差13%左右，反演结果具有一定的可信度。研究结果对岩溶区软弱破碎地层断面隧道公路建设具有重要指导意义。
- 塌方机理 /
- BP神经网络 /
- FLAC3D有限元 /
- 参数反演 /
- 崩塌破坏机制
Abstract: Tunnels in karst areas are prone to collapse during construction. There are many analyses on the mechanism of tunnel collapse in mechanical aspects, but the mechanism of tunnel collapse in karst weak fracture zones and other strata were seldom examined. In order to ensure the safety, economy and feasibility of tunnel construction, it is necessary to master the mechanism of collapse in the tunnel construction. Relying on a tunnel project in a karst broken stratum in Guizhou, where the collapse phenomenon occurred during the excavation process, the monitoring data of the tunnel are examined, and the construction principle of the BP neural network is used to invert the stratum parameters of the tunnel. The inversion soil mechanical parameters are input into different construction models constructed by using the FLAC3D finite element software, and the collapse failure mechanism and risk of typical sections are judged and analyzed. The results show that the construction method has a great influence on the stability of the tunnel excavation, and for the tunnel with the surrounding rock grade V , the three-step seven-step method and the single-side wall pilot pit method are safer for construction, and the tunnel collapse has no relationship with the simultaneous excavation of the tunnel in both directions. The predicted value of the tunnel vault displacement obtained by the inversion is 2.3 cm, and the predicted value of the surface displacement is 1.2 cm. The deviation from the monitoring data is about 13%, and the inversion result has certain reliability. The research results are of important guiding significance for the construction of tunnels and highways in weak and broken strata in karst areas.
- collapse mechanism /
- BP neural network /
- FLAC3D finite element /
- parameter inversion /
- collapse failure mechanism

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图 1 ZK98+738坍塌现场

Figure 1. ZK98+738 collapse site

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图 2 模型计算网格

Figure 2. Computation grids of the Model

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图 3 隧道边界条件示意图

Figure 3. Boundary conditions of the tunnel

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图 4 监测点位置

Figure 4. Location of the monitoring points

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图 5 大断面隧道施工模型

Figure 5. Large section tunnel construction model

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图 6 两台阶施工法

Figure 6. Two-step construction method

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图 7 两台阶施工法和位移监测点

Figure 7. Two-step construction method and displacement monitoring points

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图 8 隧道断面位移图

Figure 8. Displacement diagram of the tunnel section

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图 9 隧道断面塑性区图

Figure 9. Plastic zone of the tunnel section

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图 10 不同施工法和位移监测点

Figure 10. Different construction methods and displacement monitoring points

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图 11 模拟不同施工法沉降位移图

Figure 11. Settlement displacement diagram of different construction methods

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图 12 两台阶单双线开挖沉降位移图

Figure 12. Settlement displacement map of two-step single and double line excavation

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表 1 隧道不同级别围岩长度及占比

Table 1. Surrounding rock grade and length ratio of the tunnel

隧道名称	围岩级别	长度/m	占比/%
青岗山隧道	Ⅲ	395	61.7
	Ⅳ	190	29.7
	Ⅴ	55	8.6

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表 2 正交设计试验表

Table 2. Orthogonal test tables

编号	粉质黏土			围岩			位移
编号	弹性模量/MPa	黏聚力/kPa	摩擦角/（°）	弹性模量/GPa	黏聚力/kPa	摩擦角/（°）	拱顶位移/mm	地表位移/mm
1	5	8.5	20	10	5	10	21	0.42
2	16.25	11.875	20	15	32.5	18.7	44	0.88
3	27.5	15.25	20	20	60	27.5	62	1.24
4	38.75	18.62	20	25	87.5	36.2	78.5	1.57
5	50	22	20	30	115	45	96	1.92
6	38.75	22	26.25	10	32.5	27.5	54.5	1.09
7	50	8.5	26.25	15	60	36.2	60.5	1.21
8	5	11.875	26.25	20	87.5	45	32.5	0.65
9	16.25	15.25	26.25	25	115	10	57.5	1.15
10	27.5	18.62	26.25	30	5	18.7	38	0.76
11	16.25	18.62	32.5	10	60	45	49.5	0.99
12	27.5	22	32.5	15	87.5	10	64.5	1.29
13	38.75	8.5	32.5	20	115	18.7	69	1.38
14	50	11.875	32.5	25	5	27.5	48.5	0.97
15	5	15.25	32.5	30	32.5	36.2	38.5	0.77
16	50	15.25	38.75	10	87.5	18.7	62	1.24
17	5	18.62	38.75	15	115	27.5	48	0.96
18	16.25	22	38.75	20	5	36.2	59	1.18
19	27.5	8.5	38.75	25	32.5	45	55.5	1.11
20	38.75	11.875	38.75	30	60	10	69	1.38
21	27.5	11.875	45	10	115	36.2	56	1.12
22	38.75	15.25	45	15	5	45	64	1.28
23	50	18.62	45	20	32.5	10	53	1.06
24	5	22	45	25	60	18.7	53.5	1.07
25	16.25	8.5	45	30	87.5	27.5	42	0.84

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表 3 地层参数反演数值

Table 3. Inversion of formation parameters

地层	弹性模量/MPa	黏聚力/kPa	摩擦角/（°）
粉质黏土	5.17	13.52	13.24
围岩	142.51	428.12	25.54

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