隧道掘进机大数据研究进展：数据挖掘助推隧道挖掘

doi:10.3901/JME.2021.22.344

摘要/Abstract

摘要： 随着传感与智能化技术不断发展，隧道掘进机的运行监测日趋完善，所记录的海量实测数据不仅包含了装备作业过程的重要信息，也蕴含了装备内部及其与外部环境的相互作用机理，通过一定方法对这些数据进行深度挖掘与分析对于提升装备设计、分析、运行与维护水平具有十分重要的意义。为总结和分析隧道掘进机实测数据研究方法与应用状况，首先概述隧道掘进机实测数据的来源、构成与特点，从数据驱动的装备状态识别与性能预测、地质识别与地表改变预测、隧道健康监测与预警三个方面综述国内外相关文献，总结和归纳当前研究的难点、优点与不足，最后从隧道掘进机实测数据预处理方法、多源异构数据建模方法、模型泛化能力提升方法、数据计算平台等方面对未来研究方向进行初步分析与展望，为后续隧道掘进机大数据研究提供参考与借鉴。

关键词: 隧道掘进机, 大数据, 数据挖掘, 数据驱动技术

Abstract: With the application of micro-nano technology, the new generation of detonators based on MEMS technology has become one of the research hot spots in the field of energetic devices. MEMS detonator can be realized by mass and highly intensive integrated fabrication, and the output performance is consistent, with the characteristics of miniaturization, integration and high-energy. Based on different structures, the MEMS detonator can be divided into three categories, which are explosive foil, semiconductor bridge(SCB) and porous silicon. The explosive foil MEMS detonator has simple structure and is highly safe. SCB MEMS detonator has a broader application field, and can be initiated by low voltage. By adding energetic materials, the output energy of SCB can be improved drastically. Compared to traditional C-H-N-O energetic material, porous silicon is more active and environmental friendly. Through analyzing the composed structure, way of detonation, fabrication process and output performance, is points out that the miniature structure, high output energy and controllable detonation are the main characters of MEMS detonator, and proposes the MEMS detonation system, which can balance the high output energy and high safety, is an important development direction of MEMS detonator.

Key words: tunnel boring machine, big data, data mining, data-driven techniques

中图分类号:

TH12

石茂林, 孙伟, 宋学官. 隧道掘进机大数据研究进展：数据挖掘助推隧道挖掘[J]. 机械工程学报, 2021, 57(22): 344-358.

SHI Maolin, SUN Wei, SONG Xueguan. Research Progress on Big Data of Tunnel Boring Machine: How Data Mining Can Help Tunnel Boring[J]. Journal of Mechanical Engineering, 2021, 57(22): 344-358.

参考文献

[1] 《中国公路学报》编辑部. 中国隧道工程学术研究综述·2015[J]. 中国公路学报, 2015, 28(5):1-65. Editorial Department of China Journal of Highway and Transport. Review on China's tunnel engineering research·2015[J]. China Journal of Highway and Transport, 2015, 28(5):1-65.
[2] KALIAMPAKOS D, BENARDOS A, MAVRIKOS A. Areview on the economics of underground space utilization[J]. Tunnelling and Underground Space Technology, 2016, 55:236-244.
[3] LI S, LIU R, ZHANG Q, et al. Protection against water or mud inrush in tunnels by grouting:A review[J]. Journal of Rock Mechanics and Geotechnical Engineering, 2016, 8(5):753-766.
[4] GUAN B. Deformation of tunnels with soft surrounding rocks and its control[J]. Tunnel Construction, 2011, 1:1-17.
[5] HUANG X. Influence factors of water bursting and mud bursting of karst tunnels and its countermeasures[J]. Journal of Railway Engineering Society, 2013(1):45-48.
[6] 张镜剑, 傅冰骏. 隧道掘进机在我国应用的进展[J]. 岩石力学与工程学报, 2007(2):226-238. ZHANG Jingjian, FU Bingjun. Advances in tunnel boring machine application in China[J]. Chinese Journal of Rock Mechanics and Engineering, 2007(2):226-238.
[7] 王梦恕. 中国盾构和掘进机隧道技术现状、存在的问题及发展思路[J]. 隧道建设, 2014, 34(3):179-187. WANG Mengshu. Tunneling by TBM/shield in China:State-of-art, problems and proposals[J]. Tunnel Construction, 2014, 34(3):179-187.
[8] 杨华勇, 周星海, 龚国芳. 全断面隧道掘进装备智能化的一些思考[J]. 隧道建设, 2018, 38(12):1919-1926. YANG Huayong, ZHOU Xinghai, GONG Guofang. Perspectives in intelligentization of tunnel boring machine(TBM)[J]. Tunnel Construction, 2018, 38(12):1919-1926.
[9] 孙志洪, 李东利, 张家年. 复合盾构滚刀磨损的无线实时监测系统[J]. 隧道建设, 2016, 36(4):485-489. SUN Zhihong, LI Dongli, ZHANG Jianian. Wireless real time disc cutter wear monitoring system for composite shield machine[J]. Tunnel Construction, 2016, 36(4):485-489.
[10] LAN H, XIA Y, JI Z, et al. Online monitoring device of disc cutter wear-Design and field test[J]. Tunnelling and Underground Space Technology, 2019, 89:284-294.
[11] ZHAO Y, JIANG H, ZHAO X, et al. Tunnel seismic tomography method for geological prediction and its application[J]. Applied Geophysics, 2006, 3(2):69-74.
[12] GALLARDO L A, MEJU M A. Joint two-dimensional DC resistivity and seismic travel time inversion with cross-gradients constraints[J]. Journal of Geophysical Research, 2004, 109(B3):1-11.
[13] WU J, LIU S, LI Y, et al. Study of cross-hole radar tomography using full-waveform inversion[J]. Chinese Journal of Geophysics, 2014, 57(5):1623-1635.
[14] LI S, LIU B, XU X, et al. An overview of ahead geological prospecting in tunneling[J]. Tunnelling and Underground Space Technology, 2017, 63:69-94.
[15] 张康, 黄亦翔, 赵帅, 等. 基于t-SNE数据驱动模型的盾构装备刀盘健康评估[J]. 机械工程学报, 2019, 55(7):19-26. ZHANG Kang, HUANG Yixiang, ZHAO Shuai, et al. Health assessment of shield equipment cutterhead based on t-SNE data-driven model[J]. Journal of Mechanical Engineering, 2019, 55(7):19-26.
[16] 李亚, 黄亦翔, 赵路杰, 等. 基于t分布邻域嵌入与XGBoost的刀具多工况磨损评估[J]. 机械工程学报, 2020, 56(1):132-140. LI Ya, HUANG Yixiang, ZHAO Lujie, et al. Multi-condition wear evaluation of tool based on t-SNEand XGBoost[J]. Journal of Mechanical Engineering, 2020, 56(1):132-140.
[17] HAN H, GAO X. Fault prediction of shield machine based on rough set and BP neural network[C]//2017 4th International Conference on Information Science and Control Engineering (ICISCE). IEEE, 2017:994-998.
[18] 宫玮丽, 梁波, 王晓兰. 基于小波包和Hilbert包络分析的隧道掘进机主轴承故障诊断方法研究[J]. 工业仪表与自动化装置, 2018(2):15-18. GONG Weili, LIANG Bo, WANG Xiaolan. Research on fault diagnosis method of main bearing of tunnel boring machine based on wavelet packet and Hilbert envelope analysis[J]. Industrial Instrumentation&Automation, 2018(2):15-18.
[19] ZOU L, LIANG L. Fault diagnosis of shield machine based on SOM-BP neural network fusion[C]//2018International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC). IEEE, 2018:232-237.
[20] 刘金刚, 周晓群, 王凯. 基于PCA和SVM的盾构液压系统故障诊断[J]. 计算机仿真, 2017, 34(12):426-430. LIU Jingang, ZHOU Xiaoqun, WANG Kai. Fault diagnosis of hydraulic system for shield machine based on PCA and SVM[J]. Computer Simulation, 2017, 34(12):426-430.
[21] 彭余. 盾构机刀盘驱动液压马达故障诊断研究[D]. 广州:广东工业大学, 2017. PENG Yu. Research on fault diagnosis of hydraulic motor for driving the cutterhead of shield machine[D]. Guangzhou:Guangdong University of Technology, 2017.
[22] 陈发达, 吴贤国, 王彦玉, 等. 基于贝叶斯网络的土压盾构刀盘失效故障诊断[J]. 土木工程与管理学报, 2017, 34(6):57-62, 72. CHEN Fada, WU Xianguo, WANG Yanyu, et al. Fault diagnosis of cutterhead failure of earth pressure shield based on bayesian network[J]. Journal of Civil Engineering and Management, 2017, 34(6):57-62, 72.
[23] GAO X, SHI M, SONG X, et al. Recurrent neural networks for real-time prediction of TBM operating parameters[J]. Automation in Construction, 2019, 98:225-235.
[24] 周奇才, 王益飞, 赵炯, 等. 基于LSTM循环神经网络的盾构机故障预测系统设计[J]. 现代机械, 2018(5):35-40. ZHOU Qicai, WANG Yifei, ZHAO Jiong, et al. Design of fault prediction system for shield machine based on LSTM recurrent neural network[J]. Modern Machinery, 2018(5):35-40.
[25] 徐进, 刘丽莎, 章龙管, 等. 基于PCA-LSTM的盾构故障多标签预测模型研究[J]. 山东农业大学学报, 2019(6):1-5. XU Jin, LIU Lisha, ZHANG Longguan, et al. Research on multi-label prediction model for shield faults based on PCA-LSTM[J]. Journal of Shandong Agricultural University, 2019(6):1-5.
[26] LÜF, WEN C, BAO Z, et al. Fault diagnosis based on deep learning[C]//2016 American Control Conference(ACC). IEEE, 2016:6851-6856.
[27] ZHANG X, POVEVY D, KHUDANPUR S, et al. Adiversity-penalizing ensemble training method for deep learning[C]//Conference of the International Speech Communication Association, 2015:3590-3594.
[28] GUO H, LI S, LI B, et al. A new learning automata based pruning method to train deep neural networks[J]. IEEEInternet of Things Journal, 2017, 5(5):3263-3269.
[29] GAL Y, GHAHRAMANI Z. Dropout as a Bayesian approximation:Representing model uncertainty in deep learning[C]//International Conference on Machine Learning. PMLR, 2016:1050-1059.
[30] LI S, WU C, LI H, et al. FPGA acceleration of recurrent neural network based language model[C]//2015 IEEE23rd Annual International Symposium on Field-Programmable Custom Computing Machines. IEEE, 2015:111-118.
[31] GUAN Y, YUAN Z, SUN G, et al. FPGA-based accelerator for long short-term memory recurrent neural networks[C]//2017 22nd Asia and South Pacific Design Automation Conference (ASP-DAC). IEEE, 2017:629-634.
[32] SALIMI A, ROSTAMI J, MOORMANN C, et al. Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs[J]. Tunnelling and Underground Space Technology, 2016, 58:236-246.
[33] FATTAHI H, BABANOURI N. Applying optimized support vector regression models for prediction of tunnel boring machine performance[J]. Geotechnical and Geological Engineering, 2017, 35(5):2205-2217.
[34] STYPULKOWSKI J B, BERNARDEAU F G, JAKUBOWSKI J. Descriptive statistical analysis of TBMperformance at Abu hamour tunnel phase I[J]. Arabian Journal of Geosciences, 2018, 11(9):1-11.
[35] FATTAHI H, BAZDAR H. Applying improved artificial neural network models to evaluate drilling rate index[J]. Tunnelling and Underground Space Technology, 2017, 70:114-124.
[36] AFRADI A, EBRAHIMABADI A, HALLAJIAN T. Prediction of the penetration rate and number of consumed disc cutters of tunnel boring machines (TBMs)using artificial neural network (ANN) and support vector machine (SVM)-Case study:Beheshtabad water conveyance tunnel in iran[J]. Asian Journal of Water, Environment and Pollution, 2019, 16(1):49-57.
[37] GAO B, WANG R R, LIN C, et al. TBM penetration rate prediction based on the long short-term memory neural network[J]. Underground Space, 2021, 6(6):718-731.
[38] QIN C, SHI G, TAO J, et al. Precise cutterhead torque prediction for shield tunneling machines using a novel hybrid deep neural network[J]. Mechanical Systems and Signal Processing, 2021, 151:10738.
[39] KOOPIALIPOOR M, TOOTOONCHI H, ARMAGHANID J, et al. Application of deep neural networks in predicting the penetration rate of tunnel boring machines[J]. Bulletin of Engineering Geology and the Environment, 2019, 78(8):6347-6360.
[40] SALIMI A, FARADONBEH R S, MONJEZI M, et al. TBM performance estimation using a classification and regression tree (CART) technique[J]. Bulletin of Engineering Geology and the Environment, 2018, 77(1):429-440.
[41] MAHDEVARI S, SHAHRIAR K, YAGIZ S, et al. Asupport vector regression model for predicting tunnel boring machine penetration rates[J]. International Journal of Rock Mechanics and Mining Sciences, 2014, 72:214-229.
[42] XU H, ZHOU J, G ASTERIS P, et al. Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate[J]. Applied Sciences, 2019, 9(18):3715.
[43] KOOPIALIPOOR M, NIKOUEI S S, MARTO A, et al. Predicting tunnel boring machine performance through a new model based on the group method of data handling[J]. Bulletin of Engineering Geology and the Environment, 2019, 78(5):3799-3813.
[44] ARMAGHANI D J, KOOPIALIPOOR M, MARTO A, et al. Application of several optimization techniques for estimating TBM advance rate in granitic rocks[J]. Journal of Rock Mechanics and Geotechnical Engineering, 2019, 11(4):779-789.
[45] ZHOU J, BEJARBANEH B Y, ARMAGHANI D J, et al. Forecasting of TBM advance rate in hard rock condition based on artificial neural network and genetic programming techniques[J]. Bulletin of Engineering Geology and the Environment, 2019, 79:2069-2084.
[46] 陶冶, 周诚, 秦艳. 基于掘进参数分析的地铁盾构施工效率研究[J]. 施工技术, 2016, 45(增刊1):416-421. TAO Ye, ZHOU Cheng, QIN Yan. Research on shield tunneling parameter analysis for shield's construction efficiency[J]. Construction Technology, 2016, 45(Suppl. 1):416-421.
[47] FENG S, CHEN Z, LUO H, et al. Tunnel boring machines (TBM) performance prediction:A case study using big data and deep learning[J]. Tunnelling and Underground Space Technology, 2021, 110:103636.
[48] SUN W, SHI M, ZHANG C, et al. Dynamic load prediction of tunnel boring machine (TBM) based on heterogeneous in-situ data[J]. Automation in Construction, 2018, 92:23-34.
[49] ZHANG Q, HU W, LIU Z, et al. TBM performance prediction with Bayesian optimization and automated machine learning[J]. Tunnelling and Underground Space Technology, 2020, 103:103493.
[50] ARMAGHANI D J, MOHAMAD E T, NARAYANASAMY M S, et al. Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition[J]. Tunnelling and Underground Space Technology, 2017, 63:29-43.
[51] FATTAHI H. Adaptive neuro fuzzy inference system based on fuzzy c-means clustering algorithm, a technique for estimation of TBM penetration rate[J]. Iran University of Science&Technology, 2016, 6(2):159-171.
[52] MINH V T, KATUSHIN D, ANTONOV M, et al. Regression models and fuzzy logic prediction of TBMpenetration rate[J]. Open Engineering, 2017, 7(1):60-68.
[53] YAGIZ S, KARAHAN H. Application of various optimization techniques and comparison of their performances for predicting TBM penetration rate in rock mass[J]. International Journal of Rock Mechanics and Mining Sciences, 2015, 80:308-315.
[54] BENATO A, ORESTE P. Prediction of penetration per revolution in TBM tunneling as a function of intact rock and rock mass characteristics[J]. International Journal of Rock Mechanics and Mining Sciences, 2015, 74:119-127.
[55] SAMAEI M, RANJBARNIA M, NOURANI V, et al. Performance prediction of tunnel boring machine through developing high accuracy equations:A case study in adverse geological condition[J]. Measurement, 2020, 152:107244.
[56] MIKAEIL R, NAGHADEHI M Z, GHADERNEJAD S. An extended multifactorial fuzzy prediction of hard rock TBM penetrability[J]. Geotechnical and Geological Engineering, 2018, 36(3):1779-1804.
[57] LEE H L, SONG K I, KIM K Y. Development and implementation of statistical prediction procedure for field penetration index using ridge regression with best subset selection[J]. Journal of Korean Tunnelling and Underground Space Association, 2017, 19(6):857-870.
[58] ADOKO A C, GOKCEOGLU C, YAGIZ S. Bayesian prediction of TBM penetration rate in rock mass[J]. Engineering Geology, 2017, 226:245-256.
[59] GHASEMI E, YAGIZ S, ATAEI M. Predicting penetration rate of hard rock tunnel boring machine using fuzzy logic[J]. Bulletin of Engineering Geology and the Environment, 2014, 73(1):23-35.
[60] EFTEKHARI M, BAGHBANAN A, BAYATI M. Predicting penetration rate of a tunnel boring machine using artificial neural network[C]//ISRM International Symposium-6th Asian Rock Mechanics Symposium. International Society for Rock Mechanics and Rock Engineering, 2010.
[61] ORAEE K, KHORAMI M T, HOSSEINI N. Prediction of the penetration rate of TBM using adaptive neuro fuzzy inference system (ANFIS)[C]//Proceeding of SME Annual Meeting and Exhibit, from the Mine to the Market, Now it's Global, Seattle, WA, USA. 2012:297-302.
[62] TORABI S R, SHIRAZI H, HAJALI H, et al. Study of the influence of geotechnical parameters on the TBMperformance in Tehran-Shomal highway project using ANN and SPSS[J]. Arabian Journal of Geosciences, 2013, 6(4):1215-1227.
[63] JAVAD G, NARGES T. Application of artificial neural networks to the prediction of tunnel boring machine penetration rate[J]. Mining Science and Technology(China), 2010, 20(5):727-733.
[64] SHAO C, LI X, SU H. Performance prediction of hard rock TBM based on extreme learning machine[C]//International Conference on Intelligent Robotics and Applications. Springer, Berlin, Heidelberg, 2013:409-416.
[65] MARTINS F F, MIRANDA T F S. Prediction of hard rock TBM penetration rate based on Data Mining techniques[C]//18th International Conference on Soil Mechanics and Geotechnical Engineering. Presses des Ponts, 2013:1751-1754.
[66] PHAM H V, YUJI F, KAMEI K. Hybrid artificial neural networks for TBM performance prediction in complex underground conditions[C]//2011 IEEE/SICE International Symposium on System Integration (SII). IEEE, 2011:1149-1154.
[67] TÓTHÁ, GONG Q, ZHAO J. Case studies of TBMtunneling performance in rock-soil interface mixed ground[J]. Tunnelling and Underground Space Technology, 2013, 38:140-150.
[68] YAGIZ S, GOKCEOGLU C, SEZER E, et al. Application of two non-linear prediction tools to the estimation of tunnel boring machine performance[J]. Engineering Applications of Artificial Intelligence, 2009, 22(4-5):808-814.
[69] CARDU M, ORESTE P, CICALA T. Analysis of the tunnel boring machine advancement on the Bologna-Florence railway link[J]. American Journal of Engineering and Applied Sciences, 2009, 2(2):416-420.
[70] 侯永茂, 杨国祥, 葛修润, 等. 超大直径土压平衡盾构土舱压力和开挖面水土压力分布特性研究[J]. 岩土力学, 2012, 33(9):2713-2718. HOU Yongmao, YANG Guoxiang, GE Xiurun, et al. Study of distribution properties of water and earth pressure at excavation face and in chamber of earth pressure balance shield with super-large diameter[J]. Rock and Soil Mechanics, 2012, 33(9):2713-2718.
[71] 杨旸, 谭忠盛, 彭斌, 等. 富水圆砾地层土压平衡盾构掘进参数优化研究[J]. 土木工程学报, 2017, 50(增刊1):94-98. YANG Yang, TAN Zhongsheng, PENG Bin, et al. Study on optimization boring parameters of earth pressure balance shield in water-soaked round gravel strata[J]. China Civil Engineering Journal, 2017, 50(Suppl. 1):94-98.
[72] 刘宣宇, 张凯举, 邵诚. 基于数据驱动的盾构机密封舱土压预测[J]. 煤炭学报, 2019, 44(9):2898-2904. LIU Xuanyu, ZHANG Kaiju, SHAO Cheng. Earth pressure prediction in soil chamber of shield machine based on data-driven[J]. Journal of China Coal Society, 2019, 44(9):2898-2904.
[73] LI X, GONG G. Predictive control of slurry pressure balance in shield tunneling using diagonal recurrent neural network and evolved particle swarm optimization[J]. Automation in Construction, 2019, 107:102928.
[74] ZHOU C, XU H, DING L, et al. Dynamic prediction for attitude and position in shield tunneling:A deep learning method[J]. Automation in Construction, 2019, 105:102840.
[75] WANG P, KONG X, GUO Z, et al. Prediction of axis attitude deviation and deviation correction method based on data driven during shield tunneling[J]. IEEE Access, 2019, 7:163487-163501.
[76] CACHIM P, BEZUIJEN A. Modelling the torque with artificial neural networks on a tunnel boring machine[J]. KSCE Journal of Civil Engineering, 2019, 23(10):4529-4537.
[77] 王超, 龚国芳, 杨华勇, 等. NSVR硬岩隧道掘进机刀盘转矩预测分析[J]. 浙江大学学报, 2018, 52(3):479-486. WANG Chao, GONG Guofang, YANG Huayong, et al. NSVR based predictive analysis of cutterhead torque for hard rock TBM[J]. Journal of Zhejiang University, 2018, 52(3):479-486.
[78] 吴俊, 袁大军. 大连极硬岩地层复合盾构刀具磨损的分析与预测[J]. 土木工程学报, 2015, 48(增刊1):250-255. WU Jun, YUAN Dajun. Analysis and prediction on composite shield cutters wear in extremely hard rock in Dalian metro[J]. China Civil Engineering Journal, 2015, 48(Suppl. 1):250-255.
[79] LAN H, XIA Y, MIAO B, et al. Prediction model of wear rate of inner disc cutter of engineering in Yinsong, Jilin[J]. Tunnelling and Underground Space Technology, 2020, 99:103338.
[80] WANG L, LI H, ZHAO X, et al. Development of a prediction model for the wear evolution of disc cutters on rock TBM cutterhead[J]. Tunnelling and Underground Space Technology, 2017, 67:147-157.
[81] SHAO C, LIAO J, LI X, et al. An adaptive robust control for hard rock tunnel boring machine cutterhead driving system[C]//ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers Digital Collection, 2015.
[82] ZHANG Q, LIU Z, TAN J. Prediction of geological conditions for a tunnel boring machine using big operational data[J]. Automation in Construction, 2019, 100:73-83.
[83] ZHOU C, KONG T, ZHOU Y, et al. Unsupervised spectral clustering for shield tunneling machine monitoring data with complex network theory[J]. Automation in Construction, 2019, 107:102924.
[84] JUNG J H, CHUNG H, KWON Y S, et al. An ANN to predict ground condition ahead of tunnel face using TBMoperational data[J]. KSCE Journal of Civil Engineering, 2019, 23(7):3200-3206.
[85] SALIMI A, ROSTAMI J, MOORMANN C, et al. Examining feasibility of developing a rock mass classification for hard rock TBM application using non-linear regression, regression tree and generic programming[J]. Geotechnical and Geological Engineering, 2018, 36(2):1145-1159.
[86] ERHARTER G H, MARCHER T, REINHOLD C. Comparison of artificial neural networks for TBM data classification[C]//Rock mechanics for natural resources and Infrastructure Development:Full Papers:Proceedings of the 14th International Congress on Rock Mechanics and Rock Engineering (ISRM 2019). CRC Press, 2019.
[87] KIM T H, KWAK N S, KIM T K, et al. A TBMdata-based ground prediction using deep neural network[J]. Journal of Korean Tunnelling and Underground Space Association, 2021, 23(1):13-24.
[88] ERHARTER G H, MARCHER T. MSAC:Towards data driven system behavior classification for TBMtunneling[J]. Tunnelling and Underground Space Technology, 2020, 103:103466.
[89] ZHAO J, SHI M, HU G, et al. A data-driven framework for tunnel geological-type prediction based on TBMoperating data[J]. IEEE Access, 2019, 7:66703-66713.
[90] KIM T H, KO T Y, PARK Y S, et al. Prediction of uniaxial compressive strength of rock using shield TBMmachine data and machine learning technique[J]. Tunnel and Underground Space, 2020, 30(3):214-225.
[91] SHI M, SUN W, ZHANG T, et al. Geology prediction based on operation data of TBM:Comparison between deep neural network and soft computing methods[C]//2019 1st International Conference on Industrial Artificial Intelligence (IAI). IEEE, 2019:1-5.
[92] LIU Q, WANG X, HUANG X, et al. Prediction model of rock mass class using classification and regression tree integrated Ada Boost algorithm based on TBM driving data[J]. Tunnelling and Underground Space Technology, 2020, 106:103595.
[93] SEBBEH-NEWTON S, AYAWAH P E A, AZURE J WA, et al. Towards TBM automation:On-the-fly characterization and classification of ground conditions ahead of a TBM using data-driven approach[J]. Applied Sciences, 2021, 11(3):1060.
[94] GONG Q, ZHOU X, LIU Y, et al. Development of a real-time muck analysis system for assistant intelligence TBM tunnelling[J]. Tunnelling and Underground Space Technology, 2021, 107:103655.
[95] 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.
[96] KOHESTANI V R, BAZARGANLARI M R. Prediction of maximum surface settlement caused by earth pressure balance shield tunneling using random forest[J]. Journal of AI and Data Mining, 2017, 5(1):127-135.
[97] ZHANG P, CHEN R P, WU H N. Real-time analysis and regulation of EPB shield steering using random forest[J]. Automation in Construction, 2019, 106:102860.
[98] CHEN R, ZHANG P, WU H, et al. Prediction of shield tunneling-induced ground settlement using machine learning techniques[J]. Frontiers of Structural and Civil Engineering, 2019, 13(6):1363-1378.
[99] ZHANG W G, LI H R, WU C Z, et al. Soft computing approach for prediction of surface settlement induced by earth pressure balance shield tunneling[J]. Underground Space, 2021, 6(4):353-363.
[100] 田管凤, 马宏伟, 吴起星, 等. 盾构施工地面沉降预测的大数据技术应用研究[J]. 防灾减灾工程学报, 2016, 36(1):146-152. TIAN Guanfeng, MA Hongwei, WU Qixing, et al. Application of big data technology in ground settlement forecast in shield construction[J]. Journal of Disaster Prevention and Mitigation Engineering, 2016, 36(1):146-152.
[101] 李涛, 仇文革, 刘毅. 基于数据驱动的隧道地质信息精细化管理系统研发与应用[J]. 隧道建设, 2019, 39(1):68-74. LI Tao, QIU Wenge, LIU Yi. Development and application of refined management system of tunnel geological information based on data drive[J]. Tunnel Construction, 2019, 39(1):68-74.
[102] 汤扬屹, 吴贤国, 陈虹宇, 等. 基于云模型与D-S证据理论的盾构施工隧道管片上浮风险评价[J]. 隧道建设, 2019, 39(12):2011-2019. TANG Yangyi, WU Xianguo, CHEN Hongyu, et al. Evaluation of floating risk of shield tunnel segments based on cloud model and D-S evidence theory[J]. Tunnel Construction, 2019, 39(12):2011-2019.
[103] 黎晨, 赵小军, 范中林, 等. 盾构隧道结构健康监测系统研究[J]. 武汉理工大学学报, 2014, 38(2):346-350. LI Chen, ZHAO Xiaojun, FAN Zhonglin, et al. Research on structural health monitoring system for shield tunnel[J]. Journal of Wuhan University of Technology, 2014, 38(2):346-350.
[104] 孙可, 张巍, 朱守兵, 等. 盾构隧道健康监测数据的模糊层次分析综合评价方法[J]. 防灾减灾工程学报, 2015, 35(6):769-776. SUN Ke, ZHANG Wei, ZHU Shoubing, et al. Fuzzy analytical hierarchy process comprehensive evaluation method for health monitoring data of shield tunnels[J]. Journal of Disaster Prevention and Mitigation Engineering, 2015, 35(6):769-776.
[105] GETULI V, CAPONE P, BRUTTINI A, et al. On-demand generation of as-built infrastructure information models for mechanised tunnelling from TBM data:A computational design approach[J]. Automation in Construction, 2021, 121:103434.
[106] CHEN Z, ZHANG Y, LI J, et al. Diagnosing tunnel collapse sections based on TBM tunneling big data and deep learning:A case study on the Yinsong Project, China[J]. Tunnelling and Underground Space Technology, 2020, 108:103700.
[107] 陈卫忠, 李长俊, 曾灿军, 等. 大型水下盾构隧道结构健康监测系统的构建与应用[J]. 岩石力学与工程学报, 2018, 37(1):1-13. CHEN Weizhong, LI Changjun, ZENG Canjun, et al. Establishment and application of structural health monitoring system for large shield tunnel[J]. Chinese Journal of Rock Mechanics&Engineering, 2018, 37(1):1-13.
[108] SHI M, ZHANG L, SUN W, et al. A fuzzy c-means algorithm guided by attribute correlations and its application in the big data analysis of tunnel boring machine[J]. Knowledge-Based Systems, 2019, 182:104859.
[109] SONG X, SHI M, WU J, et al. A new fuzzy c-means clustering-based time series segmentation approach and its application on tunnel boring machine analysis[J]. Mechanical Systems and Signal Processing, 2019, 133:106279.