2023 Vol. 50, No. 5
Article Contents

RUAN Yongfen, QIU Long, QIAO Wenjian, YAN Ming, GUO Yuhang. Prediction of the maximum ground settlement caused by shield tunneling based on the improved limit learning machine model[J]. Hydrogeology & Engineering Geology, 2023, 50(5): 124-133. doi: 10.16030/j.cnki.issn.1000-3665.202210007
Citation: RUAN Yongfen, QIU Long, QIAO Wenjian, YAN Ming, GUO Yuhang. Prediction of the maximum ground settlement caused by shield tunneling based on the improved limit learning machine model[J]. Hydrogeology & Engineering Geology, 2023, 50(5): 124-133. doi: 10.16030/j.cnki.issn.1000-3665.202210007

Prediction of the maximum ground settlement caused by shield tunneling based on the improved limit learning machine model

More Information
  • Excessive ground deformation caused by shield tunneling of urban metro will seriously affect the normal use of surrounding structures, and even cause engineering accidents. In view of the problems that the data dimension in traditional prediction methods is too large, which easily leads to lower accuracy and complex calculation, this study proposes an extreme learning machine (ELM) prediction model based on the principal component analysis (PCA) algorithm and Harris Hawk optimization algorithm (HHO). Ten influence factors are preliminarily selected from the geological, geometric and shield parameters. PCA is used to separate and extract five principal component variables from the 10 dimensional arrays as the input of the model. HHO is used to optimize the input layer weights and hidden layer threshold parameters of the ELM model, and the optimal solution of the prediction model is obtained. The monitoring data of the Yiguang section of Kunming Rail Transit Line 5 are used for simulation verification, and the model is compared with the BP neural network, RBF and non-optimized ELM model. The results show that the root mean square error of the PCA-HHO-ELM prediction model is 0.1435, the average absolute error is 0.0262, and the determination coefficient R2 is 0.9596. Compared with other models, this model has better prediction performance. Compared with the non-optimized ELM, HHO can improve the prediction accuracy and generalization ability of ELM. The PCA-HHO-ELM model can reliably predict the maximum ground settlement induced by shield, and can provide a more feasible new idea for similar deformation prediction.

  • 加载中
  • [1] 胡群芳,李敏,王飞. 我国城市地铁隧道盾构施工刀具磨损性分区研究[J]. 现代隧道技术,2016,53(2):26 − 34. [HU Qunfang,LI Min,WANG Fei. Research on regionalization of cutting-tool wear conditions in shield tunnels of urban subways in China[J]. Modern Tunnelling Technology,2016,53(2):26 − 34. (in Chinese with English abstract)

    Google Scholar

    HU Qunfang, LI Min, WANG Fei. Research on regionalization of cutting-tool wear conditions in shield tunnels of urban subways in China[J]. Modern Tunnelling Technology, 2016, 532): 2634. (in Chinese with English abstract)

    Google Scholar

    [2] 路德春,马一丁,王国盛. 近接隧道列车运行时地表振动响应数值模拟[J]. 吉林大学学报(地球科学版),2021,51(5):1452 − 1462. [LU Dechun,MA Yiding,WANG Guosheng. Numerical study on ground surface vibration response under train load in multi adjacent tunnels[J]. Journal of Jilin University (Earth Science Edition),2021,51(5):1452 − 1462. (in Chinese with English abstract)

    Google Scholar

    LU Dechun, MA Yiding, WANG Guosheng. Numerical study on ground surface vibration response under train load in multi adjacent tunnels[J]. Journal of Jilin University (Earth Science Edition), 2021, 515): 14521462. (in Chinese with English abstract)

    Google Scholar

    [3] XU Qianwei,ZHU Hehua,MA Xianfeng,et al. A case history of shield tunnel crossing through group pile foundation of a road bridge with pile underpinning technologies in Shanghai[J]. Tunnelling and Underground Space Technology,2015,45:20 − 33. doi: 10.1016/j.tust.2014.09.002

    CrossRef Google Scholar

    [4] 蒋彪,皮圣,阳军生,等. 长沙地铁典型地层盾构施工地表沉降分析与预测[J]. 地下空间与工程学报,2016,12(1):181 − 187. [JIANG Biao,PI Sheng,YANG Junsheng,et al. Analysis and prediction of ground surface settlements due to EPB shield tunneling of Changsha metro[J]. Chinese Journal of Underground Space and Engineering,2016,12(1):181 − 187. (in Chinese with English abstract)

    Google Scholar

    JIANG Biao, PI Sheng, YANG Junsheng, et al. Analysis and prediction of ground surface settlements due to EPB shield tunneling of Changsha metro[J]. Chinese Journal of Underground Space and Engineering, 2016, 121): 181187. (in Chinese with English abstract)

    Google Scholar

    [5] PARK K H. Analytical solution for tunnelling-induced ground movement in clays[J]. Tunnelling and Underground Space Technology,2005,20(3):249 − 261. doi: 10.1016/j.tust.2004.08.009

    CrossRef Google Scholar

    [6] 赖文杰,齐昌广,郑金辉,等. 含分数阶的灰色模型及其在地基沉降预测中的应用[J]. 水文地质工程地质,2019,46(3):124 − 128. [LAI Wenjie,QI Changguang,ZHENG Jinhui,et al. Gray model with fractional order and its application to settlement prediction[J]. Hydrogeology & Engineering Geology,2019,46(3):124 − 128. (in Chinese with English abstract)

    Google Scholar

    LAI Wenjie, QI Changguang, ZHENG Jinhui, et al. Gray model with fractional order and its application to settlement prediction[J]. Hydrogeology & Engineering Geology, 2019, 463): 124128. (in Chinese with English abstract)

    Google Scholar

    [7] 郭社军,郝晓龙,舒国明. Peck公式在小半径曲线隧道沉降分析的应用[J]. 公路,2020,65(8):405 − 408. [GUO Shejun,HAO Xiaolong,SHU Guoming. Application of Peck formula in settlement analysis of small radius curved tunnel[J]. Highway,2020,65(8):405 − 408. (in Chinese).

    Google Scholar

    GUO Shejun, HAO Xiaolong, SHU Guoming. Application of Peck formula in settlement analysis of small radius curved tunnel[J]. Highway, 2020, 658): 405408. (in Chinese).

    Google Scholar

    [8] PECK R B. Deep excavations and tunneling in soft ground[C]//Mexico:Proceeding of 7th ICSMFE,1969:225 − 290.

    Google Scholar

    [9] 范珊珊,郭海朋,朱菊艳,等. 线性回归模型在北京平原地面沉降预测中的应用[J]. 中国地质灾害与防治学报,2013,24(1):70 − 74. [FAN Shanshan,GUO Haipeng,ZHU Juyan,et al. Application of linear regression model for land subsidence prediction in Beijing plain[J]. The Chinese Journal of Geological Hazard and Control,2013,24(1):70 − 74. (in Chinese with English abstract)

    Google Scholar

    FAN Shanshan, GUO Haipeng, ZHU Juyan, et al. Application of linear regression model for land subsidence prediction in Beijing plain[J]. The Chinese Journal of Geological Hazard and Control, 2013, 24(1): 70 − 74. (in Chinese with English abstract)

    Google Scholar

    [10] KIM C Y,BAE G J,HONG S W,et al. Neural network based prediction of ground surface settlements due to tunnelling[J]. Computers and Geotechnics,2001,28(6/7):517 − 547.

    Google Scholar

    [11] DIAS D,KASTNER R. Movements caused by the excavation of tunnels using face pressurized shields:Analysis of monitoring and numerical modeling results[J]. Engineering Geology,2013,152(1):17 − 25. doi: 10.1016/j.enggeo.2012.10.002

    CrossRef Google Scholar

    [12] 于德海,舒娇娇,秦凯凯. 盾构地铁隧道穿越既有铁路桥的沉降分析[J]. 水文地质工程地质,2020,47(2):148 − 152. [YU Dehai,SHU Jiaojiao,QIN Kaikai. An analysis of the settlement of a shield tunnel passing under the operating railway bridge[J]. Hydrogeology & Engineering Geology,2020,47(2):148 − 152. (in Chinese with English abstract)

    Google Scholar

    YU Dehai, SHU Jiaojiao, QIN Kaikai. An analysis of the settlement of a shield tunnel passing under the operating railway bridge[J]. Hydrogeology & Engineering Geology, 2020, 472): 148152. (in Chinese with English abstract)

    Google Scholar

    [13] 潘涛. 软土地区双线区间盾构隧道施工对周边地表以及建筑物沉降的影响[J]. 水文地质工程地质,2022,49(1):101 − 108. [PAN Tao. Influences of double-track shield tunnel construction on settlements of adjacent ground and buildings in a soft soil area[J]. Hydrogeology & Engineering Geology,2022,49(1):101 − 108. (in Chinese with English abstract)

    Google Scholar

    PAN Tao. Influences of double-track shield tunnel construction on settlements of adjacent ground and buildings in a soft soil area[J]. Hydrogeology & Engineering Geology, 2022, 491): 101108. (in Chinese with English abstract)

    Google Scholar

    [14] 徐明祥,黄强兵,王庆兵,等. 西安地裂缝地段浅埋暗挖地铁隧道施工沉降规律[J]. 水文地质工程地质,2020,47(1):161 − 170. [XU Mingxiang,HUANG Qiangbing,WANG Qingbing,et al. Settlement rules of shallow-buried metro tunnel construction in the Xi’an ground fissure section[J]. Hydrogeology & Engineering Geology,2020,47(1):161 − 170. (in Chinese with English abstract)

    Google Scholar

    XU Mingxiang, HUANG Qiangbing, WANG Qingbing, et al. Settlement rules of shallow-buried metro tunnel construction in the Xi’an ground fissure section[J]. Hydrogeology & Engineering Geology, 2020, 471): 161170. (in Chinese with English abstract)

    Google Scholar

    [15] ZHANG Pin,CHEN Renpeng,WU Huaina. Real-time analysis and regulation of EPB shield steering using Random Forest[J]. Automation in Construction,2019,106:102860. doi: 10.1016/j.autcon.2019.102860

    CrossRef Google Scholar

    [16] 赵楠,李洁. 基于LSTM-SVM的隧道围岩位移预测[J]. 公路,2021,66(6):404 − 407. [ZHAO Nan,LI Jie. Displacement prediction of tunnel surrounding rock based on LSTM-SVM[J]. Highway,2021,66(6):404 − 407. (in Chinese)

    Google Scholar

    ZHAO Nan, LI Jie. Displacement prediction of tunnel surrounding rock based on LSTM-SVM[J]. Highway, 2021, 66(6): 404 − 407. (in Chinese)

    Google Scholar

    [17] 李建生,阳军生,杨铠,等. 改进遗传算法在浅埋隧道施工倾斜地表沉降预测中的应用[J]. 公路工程,2008,33(6):46 − 49. [LI Jiansheng,YANG Junsheng,YANG Kai,et al. Application of improved genetic algorithm in prediction of inclined ground settlement in shallow tunnel construction[J]. Highway Engineering,2008,33(6):46 − 49. (in Chinese)

    Google Scholar

    LI Jiansheng, YANG Junsheng, YANG Kai, et al. Application of improved genetic algorithm in prediction of inclined ground settlement in shallow tunnel construction[J]. Highway Engineering, 2008, 336): 4649. (in Chinese)

    Google Scholar

    [18] CHEN Renpeng,ZHANG Pin,KANG Xin,et al. Prediction of maximum surface settlement caused by earth pressure balance (EPB) shield tunneling with ANN methods[J]. Soils and Foundations,2019,59(2):284 − 295. doi: 10.1016/j.sandf.2018.11.005

    CrossRef Google Scholar

    [19] 乔金丽,范永利,刘波,等. 基于改进BP网络的盾构隧道开挖地表沉降预测[J]. 地下空间与工程学报,2012,8(2):352 − 357. [QIAO Jinli,FAN Yongli,LIU Bo,et al. Predicting the surface settlement by shield tunneling based on modified BP network[J]. Chinese Journal of Underground Space and Engineering,2012,8(2):352 − 357. (in Chinese with English abstract)

    Google Scholar

    QIAO Jinli, FAN Yongli, LIU Bo, et al. Predicting the surface settlement by shield tunneling based on modified BP network[J]. Chinese Journal of Underground Space and Engineering, 2012, 82): 352357. (in Chinese with English abstract)

    Google Scholar

    [20] WANG Fan,GOU Biancai,QIN Yawei. Modeling tunneling-induced ground surface settlement development using a wavelet smooth relevance vector machine[J]. Computers and Geotechnics,2013,54:125 − 132. doi: 10.1016/j.compgeo.2013.07.004

    CrossRef Google Scholar

    [21] 陈仁朋,戴田,张品,等. 基于机器学习算法的盾构掘进地表沉降预测方法[J]. 湖南大学学报(自然科学版),2021,48(7):111 − 118. [CHEN Renpeng,DAI Tian,ZHANG Pin,et al. Prediction method of tunneling-induced ground settlement using machine learning algorithms[J]. Journal of Hunan University (Natural Sciences),2021,48(7):111 − 118. (in Chinese with English abstract)

    Google Scholar

    CHEN Renpeng, DAI Tian, ZHANG Pin, et al. Prediction method of tunneling-induced ground settlement using machine learning algorithms[J]. Journal of Hunan University (Natural Sciences), 2021, 487): 111118. (in Chinese with English abstract)

    Google Scholar

    [22] 宫思艺,孔宪光,刘丹,等. 融入复杂地层动态识别的盾构施工地表沉降预测方法研究[J]. 仪器仪表学报,2019,40(6):228 − 236. [GONG Siyi,KONG Xianguang,LIU Dan,et al. An approach for predicting shield construction ground surface settlement of complex stratum using dynamical strata identification[J]. Chinese Journal of Scientific Instrument,2019,40(6):228 − 236. (in Chinese with English abstract)

    Google Scholar

    GONG Siyi, KONG Xianguang, LIU Dan, et al. An approach for predicting shield construction ground surface settlement of complex stratum using dynamical strata identification[J]. Chinese Journal of Scientific Instrument, 2019, 406): 228236. (in Chinese with English abstract)

    Google Scholar

    [23] 林荣安,孙钰丰,戴振华,等. 基于RS-SVR的上软下硬地层盾构施工地表沉降预测[J]. 中国公路学报,2018,31(11):130 − 137. [LIN Rongan,SUN Yufeng,DAI Zhenhua,et al. Predicting for ground surface settlement induced by shield tunneling in upper-soft and lower-hard ground based on RS-SVR[J]. China Journal of Highway and Transport,2018,31(11):130 − 137. (in Chinese with English abstract)

    Google Scholar

    LIN Rongan, SUN Yufeng, DAI Zhenhua, et al. Predicting for ground surface settlement induced by shield tunneling in upper-soft and lower-hard ground based on RS-SVR[J]. China Journal of Highway and Transport, 2018, 3111): 130137. (in Chinese with English abstract)

    Google Scholar

    [24] 李洛宾,龚晓南,甘晓露,等. 基于循环神经网络的盾构隧道引发地面最大沉降预测[J]. 土木工程学报,2020,53(增刊1):13 − 19. [LI Luobin,GONG Xiaonan,GAN Xiaolu,et al. Prediction of maximum ground settlement induced by shield tunneling based on recurrent neural network[J]. China Civil Engineering Journal,2020,53(Sup 1):13 − 19. (in Chinese with English abstract)

    Google Scholar

    LI Luobin, GONG Xiaonan, GAN Xiaolu, et al. Prediction of maximum ground settlement induced by shield tunneling based on recurrent neural network[J]. China Civil Engineering Journal, 2020, 53(Sup 1): 13 − 19. (in Chinese with English abstract)

    Google Scholar

    [25] LIU Peng,LI Zhengtian,ZHUO Yixin,et al. Design of wind turbine dynamic trip-off risk alarming mechanism for large-scale wind farms[J]. IEEE Transactions on Sustainable Energy,2017,8(4):1668 − 1678. doi: 10.1109/TSTE.2017.2701348

    CrossRef Google Scholar

    [26] TANG Jiexiong,DENG Chenwei,HUANG Guangbin. Extreme learning machine for multilayer perceptron[J]. IEEE Transactions on Neural Networks and Learning Systems,2016,27(4):809 − 821. doi: 10.1109/TNNLS.2015.2424995

    CrossRef Google Scholar

    [27] HUANG Guangbin,ZHU Qinyu,SIEW C K. Extreme learning machine:A new learning scheme of feedforward neural networks[C]//Budapest,Hungary:2004 IEEE International Joint Conference on Neural Networks,2005:985 − 990.

    Google Scholar

    [28] HEIDARI A A,MIRJALILI S,FARIS H,et al. Harris Hawks optimization:Algorithm and applications[J]. Future Generation Computer Systems,2019,97:849 − 872. doi: 10.1016/j.future.2019.02.028

    CrossRef Google Scholar

    [29] BOUAYAD D,EMERIAULT F. Modeling the relationship between ground surface settlements induced by shield tunneling and the operational and geological parameters based on the hybrid PCA/ANFIS method[J]. Tunnelling and Underground Space Technology,2017,68:142 − 152. doi: 10.1016/j.tust.2017.03.011

    CrossRef Google Scholar

    [30] BOUVEYRON C,LATOUCHE P,MATTEI P A. Exact dimensionality selection for Bayesian PCA[J]. Scandinavian Journal of Statistics,2020,47(1):196 − 211. doi: 10.1111/sjos.12424

    CrossRef Google Scholar

    [31] 苗凤娟,孙同日,陶佰睿,等. PSO与PCA融合优化核极限学习机说话人识别算法仿真[J]. 科学技术与工程,2019,19(21):195 − 199. [MIAO Fengjuan,SUN Tongri,TAO Bairui,et al. Algorithmic research on kernel extreme learning machine for speaker recognition based on PSO and PCA optimization[J]. Science Technology and Engineering,2019,19(21):195 − 199. (in Chinese with English abstract) doi: 10.3969/j.issn.1671-1815.2019.21.029

    CrossRef Google Scholar

    MIAO Fengjuan, SUN Tongri, TAO Bairui, et al. Algorithmic research on kernel extreme learning machine for speaker recognition based on PSO and PCA optimization[J]. Science Technology and Engineering, 2019, 1921): 195199. (in Chinese with English abstract) doi: 10.3969/j.issn.1671-1815.2019.21.029

    CrossRef Google Scholar

  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Figures(5)

Tables(4)

Article Metrics

Article views(1416) PDF downloads(118) Cited by(0)

Access History

Other Articles By Authors

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint