• 全国中文核心期刊
  • 中国科技核心期刊
  • 美国工程索引(EI)收录期刊
  • Scopus数据库收录期刊
WANG Shuhong, DONG Furui. Stability analysis of surrounding rock of mountain tunnels based on deformation prediction and parameter inversion[J]. Chinese Journal of Geotechnical Engineering, 2023, 45(5): 1024-1035. DOI: 10.11779/CJGE20220288
Citation: WANG Shuhong, DONG Furui. Stability analysis of surrounding rock of mountain tunnels based on deformation prediction and parameter inversion[J]. Chinese Journal of Geotechnical Engineering, 2023, 45(5): 1024-1035. DOI: 10.11779/CJGE20220288

Stability analysis of surrounding rock of mountain tunnels based on deformation prediction and parameter inversion

More Information
  • Received Date: March 16, 2022
  • Available Online: May 18, 2023
  • There is a complex nonlinear relationship between the deformation of the surrounding rock of tunnels and the mechanical parameters of rock mass, which is the most intuitive expression for change of state of the surrounding rock, and is also an important index for the comprehensive discrimination of its stability. A stability analysis method for the surrounding rock of tunnels based on the deformation prediction and the mechanical parameter inversion is proposed. Firstly, by introducing the tent chaotic disturbance and the adaptive vigilance adjustment mechanism, the deformation time series prediction model and the mechanical parameter inversion model based on the adaptive chaos sparrow algorithm optimized extreme learning machine (ACSSA-ELM) are established. Further, the cubic spline interpolation and the variational modal decomposition (VMD) are used to preprocess the measured deformation values of the surrounding rock of the excavated section, and the deformation time series prediction model is used to predict the final deformation values of the surrounding rock of the excavated section using the dynamic window rolling single-step prediction, which is used to obtain the real mechanical parameters of the surrounding rock of the excavation section in the mechanical parameter inversion model. Based on the forward calculation results of the numerical model and the measured deformation values of the excavation section, the deformation and deformation rate of the surrounding rock in the excavation section are predicted, and then its stability is analyzed. Taking the Huayang tunnel of Chongqing as an example, the proposed method is verified and applied, and the stability of the surrounding rock of ZK40+820 section of the tunnel is reliably predicted and analyzed. Finally, the application conditions of the method and the accuracy of the inversion parameters are discussed.
  • [1]
    孙振宇, 张顶立, 侯艳娟, 等. 基于现场实测数据统计的隧道围岩全过程变形规律及稳定性判据确定[J]. 岩土工程学报, 2021, 43(7): 1261-1270, 1376. http://manu31.magtech.com.cn/Jwk_ytgcxb/CN/abstract/abstract18670.shtml

    SUN Zhenyu, ZHANG Dingli, HOU Yanjuan, et al. Whole-process deformation laws and determination of stability criterion of surrounding rock of tunnels based on statistics of field measured data[J]. Chinese Journal of Geotechnical Engineering, 2021, 43(7): 1261-1270, 1376. (in Chinese) http://manu31.magtech.com.cn/Jwk_ytgcxb/CN/abstract/abstract18670.shtml
    [2]
    王述红, 董福瑞, 朱宝强, 等. 山岭隧道围岩参数智能反演及稳定性分析[J]. 应用基础与工程科学学报, 2021, 29(5): 1171-1185.

    WANG Shuhong, DONG Furu, ZHU Baoqiang, et al. Intelligent inversion and stability analysis of surrounding rock of mountain tunnel[J]. Journal of Basic Science and Engineering, 2021, 29(5): 1171-1185. (in Chinese)
    [3]
    田明杰, 牟智恒, 仇文革. 基于BP神经网络的隧道稳定性分析研究[J]. 土木工程学报, 2017, 50(增刊2): 260-266. doi: 10.15951/j.tmgcxb.2017.s2.041

    TIAN Mingjie, MOU Zhiheng, QIU Wenge. Research of the model comprehensive judgement for tunnel stability based on BP neural network[J]. China Civil Engineering Journal, 2017, 50(S2): 260-266. (in Chinese) doi: 10.15951/j.tmgcxb.2017.s2.041
    [4]
    吴秋军, 王明年, 刘大刚. 基于现场位移监测数据统计分析的隧道围岩稳定性研究[J]. 岩土力学, 2012, 33(增刊2): 359-364. https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX2012S2057.htm

    WU Qiujun, WANG Mingnian, LIU Dagang. Research on stability of tunnel surrounding rocks based on statistical analysis of on-site displacement monitoring data[J]. Rock and Soil Mechanics, 2012, 33(S2): 359-364. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX2012S2057.htm
    [5]
    孙潇昊, 缪林昌, 林海山. 不同埋深盾构隧道开挖面稳定问题数值模拟[J]. 东南大学学报(自然科学版), 2017, 47(1): 164-169. https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX201701028.htm

    SUN Xiaohao, MIAO Linchang, LIN Haishan. Numerical simulation research on excavation face stability of different depths of shield tunnel[J]. Journal of Southeast University (Natural Science Edition), 2017, 47(1): 164-169. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX201701028.htm
    [6]
    阮永芬, 高春钦, 刘克文, 等. 基于粒子群算法优化小波支持向量机的岩土力学参数反演[J]. 岩土力学, 2019, 40(9): 3662-3669. https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX201909042.htm

    RUAN Yongfen, GAO Chunqin, LIU Kewen, et al. Inversion of rock and soil mechanics parameters based on particle swarm optimization wavelet support vector machine[J]. Rock and Soil Mechanics, 2019, 40(9): 3662-3669. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX201909042.htm
    [7]
    阮永芬, 余东晓, 吴龙, 等. DE-GWO算法优化SVM反演软土力学参数[J]. 岩土工程学报, 2021, 43(增刊1): 166-170. http://manu31.magtech.com.cn/Jwk_ytgcxb/CN/abstract/abstract18739.shtml

    RUAN Yongfen, YU Dongxiao, WU Long, et al. DE-GWO algorithm to optimize SVM inversion mechanical parameters of soft soil[J]. Chinese Journal of Geotechnical Engineering, 2021, 43(S1): 166-170. (in Chinese) http://manu31.magtech.com.cn/Jwk_ytgcxb/CN/abstract/abstract18739.shtml
    [8]
    GAO W, CHEN D L, DAI S, et al. Back analysis for mechanical parameters of surrounding rock for underground roadways based on new neural network[J]. Engineering With Computers, 2018, 34(1): 25-36.
    [9]
    王开禾, 罗先启, 沈辉, 等. 围岩力学参数反演的GSA-BP神经网络模型及应用[J]. 岩土力学, 2016, 37(增刊1): 631-638. https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX2016S1083.htm

    WANG Kaihe, LUO Xianqi, SHEN Hui, et al. GSA-BP neural network model for back analysis of surrounding rock mechanical parameters and its application[J]. Rock and Soil Mechanics, 2016, 37(S1): 631-638. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX2016S1083.htm
    [10]
    BERTUZZI R. Back-analysing rock mass modulus from monitoring data of two tunnels in Sydney, Australia[J]. Journal of Rock Mechanics and Geotechnical Engineering, 2017, 9(5): 877-891.
    [11]
    JIANG A N, LI P. Back-analysis of mechanics parameters of tunnel based on particle swarm optimization and numerical simulation[J]. Key Engineering Materials, 2011(474/475/476): 1373-1376.
    [12]
    刘开云, 方昱, 刘保国, 等. 隧道围岩变形预测的进化高斯过程回归模型[J]. 铁道学报, 2011, 33(12): 101-106.

    LIU Kaiyun, FANG Yu, LIU Baoguo, et al. Intelligent deformation prediction model of tunnel surrounding rock based on genetic-Gaussian process regression coupling algorithm[J]. Journal of the China Railway Society, 2011, 33(12): 101-106. (in Chinese)
    [13]
    李茂达, 樊磊, 李磊, 等. 隧道与地下工程围岩变形的灰色优化与预测[J]. 土木建筑与环境工程, 2013, 35(增刊2): 143-145. https://www.cnki.com.cn/Article/CJFDTOTAL-JIAN2013S2037.htm

    LI Maoda, FAN Lei, LI Lei, et al. Tunnels and underground engineering optimization and gray rock deformation prediction[J]. Journal of Civil, Architectural & Environmental Engineering, 2013, 35(S2): 143-145. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JIAN2013S2037.htm
    [14]
    靖洪文, 吴俊浩, 马波, 等. 基于模糊灰色系统的深部巷道围岩变形预测模型及应用[J]. 煤炭学报, 2012, 37(7): 1099-1104.

    JING Hongwen, WU Junhao, MA Bo, et al. Prediction model and its application of deep mine tunnel surrounding rock deformation based on fuzzy-gray system[J]. Journal of China Coal Society, 2012, 37(7): 1099-1104. (in Chinese)
    [15]
    ZHANG L, SHI B, ZHU H H, et al. PSO-SVM-based deep displacement prediction of Majiagou landslide considering the deformation hysteresis effect[J]. Landslides, 2021, 18(1): 179-193.
    [16]
    朱宝强, 王述红, 张泽, 等. 基于时间序列与DEGWO-SVR模型的隧道变形预测方法[J]. 浙江大学学报(工学版), 2021, 55(12): 2275-2285. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC202112007.htm

    ZHU Baoqiang, WANG Shuhong, ZHANG Ze, et al. Prediction method of tunnel deformation based on time series and DEGWO-SVR model[J]. Journal of Zhejiang University (Engineering Science), 2021, 55(12): 2275-2285. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC202112007.htm
    [17]
    YANG B B, YIN K L, LACASSE S, et al. Time series analysis and long short-term memory neural network to predict landslide displacement[J]. Landslides, 2019, 16(4): 677-694.
    [18]
    XU S L, NIU R Q. Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short-term memory neural network in Three Gorges area, China[J]. Computers and Geosciences, 2018, 111(1): 87-96. doi: 10.1016/j.cageo.2017.10.013
    [19]
    XUE J K, SHEN B. A novel swarm intelligence optimization approach: sparrow search algorithm[J]. Systems Science & Control Engineering, 2020, 8(1): 22-34.
    [20]
    滕志军, 吕金玲, 郭力文, 等. 一种基于Tent映射的混合灰狼优化的改进算法[J]. 哈尔滨工业大学学报, 2018, 50(11): 40-49. https://www.cnki.com.cn/Article/CJFDTOTAL-HEBX201811007.htm

    TENG Zhijun, LV Ji-ling, GUO Liwen, et al. An improved hybrid grey wolf optimization algorithm based on Tent mapping[J]. Journal of Harbin Institute of Technology, 2018, 50(11): 40-49. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HEBX201811007.htm
    [21]
    王培珍, 刘曼, 王高, 等. 基于改进极限学习机的焦煤惰质组分类方法[J]. 煤炭学报, 2020, 45(9): 3262-3268.

    WANG Peizhen, LIU Man, WANG Gao, et al. Classification approach for inertinite of coking coal based on an improved extreme learning machine[J]. Journal of China Coal Society, 2020, 45(9): 3262-3268. (in Chinese)
    [22]
    罗亦泳, 黄城, 张静影. 基于变分模态分解的变形监测数据去噪方法[J]. 武汉大学学报(信息科学版), 2020, 45(5): 784-790.

    LUO Yiyong, HUANG Cheng, ZHANG Jingying. Denoising method of deformation monitoring data based on variational mode decomposition[J]. Geomatics and Information Science of Wuhan University, 2020, 45(5): 784-790. (in Chinese)
    [23]
    张霄. 基于改进极限学习机的隧道围岩位移反分析[D]. 成都: 西南交通大学, 2017.

    ZHANG Xiao. Displacement Back Analysis of Tunnel Surrounding Rock Based on Improved Limit Learning Machine[D]. Chengdu: Southwest Jiaotong University, 2017. (in Chinese)
    [24]
    公路隧道施工技术规范: JTG/T 3660—2020[S]. 北京: 人民交通出版社, 2020.

    Technical Specifications for Construction of Highway Tunnel: JTG/T 3660—2020[S]. Beijing: China Communications Press, 2020. (in Chinese)
    [25]
    王述红, 朱宝强. 山岭隧道洞口段地表沉降时序预测研究[J]. 岩土工程学报, 2021, 43(5): 813-821. http://manu31.magtech.com.cn/Jwk_ytgcxb/CN/abstract/abstract18612.shtml

    WANG Shuhong, ZHU Baoqiang. Time series prediction for ground settlement in portal section of mountain tunnels[J]. Chinese Journal of Geotechnical Engineering, 2021, 43(5): 813-821. (in Chinese) http://manu31.magtech.com.cn/Jwk_ytgcxb/CN/abstract/abstract18612.shtml

Catalog

    Article views (336) PDF downloads (101) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return