Effects of data smoothing and recurrent neural network(RNN)algorithms for real-time forecasting of tunnel boring machine(TBM)performance

Tunnel boring machines(TBMs)have been widely utilised in tunnel construction due to their high efficiency and reliability.Accurately predicting TBM performance can improve project time management,cost control,and risk management.This study aims to use deep learning to develop real-time models for predicting the penetration rate(PR).The models are built using data from the Changsha metro project,and their performances are evaluated using unseen data from the Zhengzhou Metro project.In one-step forecast,the predicted penetration rate follows the trend of the measured penetration rate in both training and testing.The autoregressive integrated moving average(ARIMA)model is compared with the recurrent neural network(RNN)model.The results show that univariate models,which only consider historical penetration rate itself,perform better than multivariate models that take into account multiple geological and operational parameters(GEO and OP).Next,an RNN variant combining time series of penetration rate with the last-step geological and operational parameters is developed,and it performs better than other models.A sensitivity analysis shows that the penetration rate is the most important parameter,while other parameters have a smaller impact on time series forecasting.It is also found that smoothed data are easier to predict with high accuracy.Nevertheless,over-simplified data can lose real characteristics in time series.In conclusion,the RNN variant can accurately predict the next-step penetration rate,and data smoothing is crucial in time series forecasting.This study provides practical guidance for TBM performance forecasting in practical engineering.

Tunnelling performance prediction of cantilever boring machine in sedimentary hard-rock tunnel using deep belief network

Evaluating the adaptability of cantilever boring machine(CBM) through in-depth excavation and analysis of tunnel excavation data and rock mass parameters is the premise of mechanical design and efficient excavation in the field of underground space engineering.This paper presented a case study of tunnelling performance prediction method of CBM in sedimentary hard-rock tunnel of Karst landform type by using tunneling data and surrounding rock parameters.The uniaxial compressive strength(UCS),rock integrity factor(Kv),basic quality index([BQ]),rock quality index RQD,brazilian tensile strength(BTS) and brittleness index(BI) were introduced to construct a performance prediction database based on the hard-rock tunnel of Guiyang Metro Line 1 and Line 3,and then established the performance prediction model of cantilever boring machine.Then the deep belief network(DBN) was introduced into the performance prediction model,and the reliability of performance prediction model was verified by combining with engineering data.The study showed that the influence degree of surrounding rock parameters on the tunneling performance of the cantilever boring machine is UCS > [BQ] > BTS >RQD > Kv > BI.The performance prediction model shows that the instantaneous cutting rate(ICR) has a good correlation with the surrounding rock parameters,and the predicting model accuracy is related to the reliability of construction data.The prediction of limestone and dolomite sections of Line 3 based on the DBN performance prediction model shows that the measured ICR and predicted ICR is consistent and the built performance prediction model is reliable.The research results have theoretical reference significance for the applicability analysis and mechanical selection of cantilever boring machine for hard rock tunnel.

Vibrations induced by tunnel boring machine in urban areas: In situ measurements and methodology of analysis

Excavation with tunnel boring machine(TBM)can generate vibrations,causing damages to neighbouring buildings and disturbing the residents or the equipment.This problem is particularly challenging in urban areas,where TBMs are increasingly large in diameter and shallow in depth.In response to this problem,four experimental campaigns were carried out in different geotechnical contexts in France.The vibration measurements were acquired on the surface and inside the TBMs.These measurements are also complemented by few data in the literature.An original methodology of signal processing is pro-posed to characterize the amplitude of the particle velocities,as well as the frequency content of the signals to highlight the most energetic bands.The levels of vibrations are also compared with the thresholds existing in various European regulations concerning the impact on neighbouring structures and the disturbance to local residents.

Design Theory of Full Face Rock Tunnel Boring Machine Transition Cutter Edge Angle and Its Application

At present, the inner cutters of a full face rock tunnel boring machine (TBM) and transition cutter edge angles are designed on the basis of indentation test or linear grooving test. The inner and outer edge angles of disc cutters are characterized as symmetric to each other with respect to the cutter edge plane. This design has some practical defects, such as severe eccentric wear and tipping, etc. In this paper, the current design theory of disc cutter edge angle is analyzed, and the characteristics of the rock-breaking movement of disc cutters are studied. The researching results show that the rotational motion of disc cutters with the cutterhead gives rise to the difference between the interactions of inner rock and outer rock with the contact area of disc cutters, with shearing and extrusion on the inner rock and attrition on the outer rock. The wear of disc cutters at the contact area is unbalanced, among which the wear in the largest normal stress area is most apparent. Therefore, a three-dimensional model theory of rock breaking and an edge angle design theory of transition disc cutter are proposed to overcome the flaws of the currently used TBM cutter heads, such as short life span, camber wearing, tipping. And a corresponding equation is established. With reference to a specific construction case, the edge angle of the transition disc cutter has been designed based on the theory. The application of TBM in some practical project proves that the theory has obvious advantages in enhancing disc cutter life, decreasing replacement frequency, and making economic benefits. The proposed research provides a theoretical basis for the design of TBM three-dimensional disc cutters whose rock-breaking operation time can be effectively increased.

Wear Analysis of Disc Cutters of Full Face Rock Tunnel Boring Machine

Wear is a major factor of disc cutters’ failure. No current theory offers a standard for the prediction of disc cutter wear yet. In the field the wear prediction method commonly used is based on the excavation length of tunnel boring machine（TBM） to predict the disc cutter wear and its wear law, considering the location number of each disc cutter on the cutterhead（radius for installation）; in theory, there is a prediction method of using arc wear coefficient. However, the preceding two methods have their own errors, with their accuracy being 40% or so and largely relying on the technicians’ experience. Therefore, radial wear coefficient, axial wear coefficient and trajectory wear coefficient are defined on the basis of the operating characteristics of TBM. With reference to the installation and characteristics of disc cutters, those coefficients are modified according to penetration, which gives rise to the presentation of comprehensive axial wear coefficient, comprehensive radial wear coefficient and comprehensive trajectory wear coefficient. Calculation and determination of wear coefficients are made with consideration of data from a segment of TBM project（excavation length 173 m）. The resulting wear coefficient values, after modification, are adopted to predict the disc cutter wear in the follow-up segment of the TBM project（excavation length of 5621 m）. The prediction results show that the disc cutter wear predicted with comprehensive radial wear coefficient and comprehensive trajectory wear coefficient are not only accurate（accuracy 16.12%） but also highly congruous, whereas there is a larger deviation in the prediction with comprehensive axial wear coefficient（accuracy 41%, which is in agreement with the prediction of disc cutters’ life in the field）. This paper puts forth a new method concerning prediction of life span and wear of TBM disc cutters as well as timing for replacing disc cutters.

Performance characteristics of tunnel boring machine in basalt and pyroclastic rocks of Deccan traps–A case study

A12.24km long tunnel between Maroshi and Ruparel College is being excavated by tunnel boring machine(TBM)to improve the water supply system of Greater Mumbai,India.In this paper,attempt has been made to establish the relationship between various litho-units of Deccan traps,stability of tunnel and TBM performances during the construction of5.83km long tunnel between Maroshi and Vakola.The Maroshi–Vakola tunnel passes under the Mumbai Airport and crosses both runways with an overburden cover of around70m.The tunneling work was carried out without disturbance to the ground.The rock types encountered during excavation arefine compacted basalt,porphyritic basalt,amygdaloidal basalt pyroclastic rocks with layers of red boles and intertrappean beds consisting of various types of shales Relations between rock mass properties,physico-mechanical properties,TBM specifications and the cor responding TBM performance were established.A number of support systems installed in the tunne during excavation were also discussed.The aim of this paper is to establish,with appropriate accuracy the nature of subsurface rock mass condition and to study how it will react to or behave during under ground excavation by TBM.The experiences gained from this project will increase the ability to cope with unexpected ground conditions during tunneling using TBM.

Real-time rock mass condition prediction with TBM tunneling big data using a novel rock-machine mutual feedback perception method

Real-time perception of rock mass information is of great importance to efficient tunneling and hazard prevention in tunnel boring machines(TBMs).In this study,a TBM-rock mutual feedback perception method based on data mining(DM) is proposed,which takes 10 tunneling parameters related to surrounding rock conditions as input features.For implementation,first,the database of TBM tunneling parameters was established,in which 10,807 tunneling cycles from the Songhua River water conveyance tunnel were accommodated.Then,the spectral clustering(SC) algorithm based on graph theory was introduced to cluster the TBM tunneling data.According to the clustering results and rock mass boreability index,the rock mass conditions were classified into four classes,and the reasonable distribution intervals of the main tunneling parameters corresponding to each class were presented.Meanwhile,based on the deep neural network(DNN),the real-time prediction model regarding different rock conditions was established.Finally,the rationality and adaptability of the proposed method were validated via analyzing the tunneling specific energy,feature importance,and training dataset size.The proposed TBM-rock mutual feedback perception method enables the automatic identification of rock mass conditions and the dynamic adjustment of tunneling parameters during TBM driving.Furthermore,in terms of the prediction performance,the method can predict the rock mass conditions ahead of the tunnel face in real time more accurately than the traditional machine learning prediction methods.

Hybrid ensemble soft computing approach for predicting penetration rate of tunnel boring machine in a rock environment

This study implements a hybrid ensemble machine learning method for forecasting the rate of penetration(ROP) of tunnel boring machine(TBM),which is becoming a prerequisite for reliable cost assessment and project scheduling in tunnelling and underground projects in a rock environment.For this purpose,a sum of 185 datasets was collected from the literature and used to predict the ROP of TBM.Initially,the main dataset was utilised to construct and validate four conventional soft computing(CSC)models,i.e.minimax probability machine regression,relevance vector machine,extreme learning machine,and functional network.Consequently,the estimated outputs of CSC models were united and trained using an artificial neural network(ANN) to construct a hybrid ensemble model(HENSM).The outcomes of the proposed HENSM are superior to other CSC models employed in this study.Based on the experimental results(training RMSE=0.0283 and testing RMSE=0.0418),the newly proposed HENSM is potential to assist engineers in predicting ROP of TBM in the design phase of tunnelling and underground projects.

An evolutionary adaptive neuro-fuzzy inference system for estimating field penetration index of tunnel boring machine in rock mass

Field penetration index(FPI) is one of the representative key parameters to examine the tunnel boring machine(TBM) performance.Lack of accurate FPI prediction can be responsible for numerous disastrous incidents associated with rock mechanics and engineering.This study aims to predict TBM performance(i.e.FPI) by an efficient and improved adaptive neuro-fuzzy inference system(ANFIS) model.This was done using an evolutionary algorithm,i.e.artificial bee colony(ABC) algorithm mixed with the ANFIS model.The role of ABC algorithm in this system is to find the optimum membership functions(MFs) of ANFIS model to achieve a higher degree of accuracy.The procedure and modeling were conducted on a tunnelling database comprising of more than 150 data samples where brittleness index(BI),fracture spacing,α angle between the plane of weakness and the TBM driven direction,and field single cutter load were assigned as model inputs to approximate FPI values.According to the results obtained by performance indices,the proposed ANFISABC model was able to receive the highest accuracy level in predicting FPI values compared with ANFIS model.In terms of coefficient of determination(R^(2)),the values of 0.951 and 0.901 were obtained for training and testing stages of the proposed ANFISABC model,respectively,which confirm its power and capability in solving TBM performance problem.The proposed model can be used in the other areas of rock mechanics and underground space technologies with similar conditions.

Soft ground tunnel lithology classification using clustering-guided light gradient boosting machine

During tunnel boring machine(TBM)excavation,lithology identification is an important issue to understand tunnelling performance and avoid time-consuming excavation.However,site investigation generally lacks ground samples and the information is subjective,heterogeneous,and imbalanced due to mixed ground conditions.In this study,an unsupervised(K-means)and synthetic minority oversampling technique(SMOTE)-guided light-gradient boosting machine(LightGBM)classifier is proposed to identify the soft ground tunnel classification and determine the imbalanced issue of tunnelling data.During the tunnel excavation,an earth pressure balance(EPB)TBM recorded 18 different operational parameters along with the three main tunnel lithologies.The proposed model is applied using Python low-code PyCaret library.Next,four decision tree-based classifiers were obtained in a short time period with automatic hyperparameter tuning to determine the best model for clustering-guided SMOTE application.In addition,the Shapley additive explanation(SHAP)was implemented to avoid the model black box problem.The proposed model was evaluated using different metrics such as accuracy,F1 score,precision,recall,and receiver operating characteristics(ROC)curve to obtain a reasonable outcome for the minority class.It shows that the proposed model can provide significant tunnel lithology identification based on the operational parameters of EPB-TBM.The proposed method can be applied to heterogeneous tunnel formations with several TBM operational parameters to describe the tunnel lithologies for efficient tunnelling.

Theoretical prediction of wear of disc cutters in tunnel boring machine and its application

Predicting the cutter consumption and the exact time to replace the worn-out cutters in tunneling projects constructed with tunnel boring machine(TBM) is always a challenging issue. In this paper, we focus on the analyses of cutter motion in the rock breaking process and trajectory of rock breaking point on the cutter edge in rocks. The analytical expressions of the length of face along which the breaking point moves and the length of spiral trajectory of the maximum penetration point are derived. Through observation of rock breaking process of disc cutters as well as analysis of disc rock interaction, the following concepts are proposed: the arc length theory of predicting wear extent of inner and center cutters, and the spiral theory of predicting wear extent of gage and transition cutters. Data obtained from5621 m-long Qinling tunnel reveal that among 39 disc cutters, the relative errors between cumulatively predicted and measured wear values for nine cutters are larger than 20%, while approximately 76.9% of total cutters have the relative errors less than 20%. The proposed method could offer a new attempt to predict the disc cutter's wear extent and changing time.

Load-sharing Characteristic of Multiple Pinions Driving in Tunneling Boring Machine

The failure of the key parts, such as gears, in cutter head driving system of tunneling boring machine has not been properly solved under the interaction of driving motors asynchronously and wave tunneling torque load. A dynamic model of multi-gear driving system is established considering the inertia effects of driving mechanism and cutter head as well as the bending-torsional coupling. By taking into account the nonlinear coupling factors between ring gear and multiple pinions, the influence for meshing angle by bending-torsional coupling and the dynamic load-sharing characteristic of multiple pinions driving are analyzed. Load-sharing coefficients at different rotating cutter head speeds and input torques are presented. Numerical results indicate that the load-sharing coefficients can reach up to 1.2-1.3. A simulated experimental platform of the multiple pinions driving is carried out and the torque distributions under the step load in driving shaft of pinions are measured. The imbalance of torque distribution of pinions is verified and the load-sharing coefficients in each pinion can reach 1.262. The results of simulation and test are similar, which shows the correctness of theoretical model. A loop coupling control method is put forward based on current torque master slave control method. The imbalance of the multiple pinions driving in cutter head driving system of tunneling boring machine can be greatly decreased and the load-sharing coefficients can be reduced to 1.051 by using the loop coupling control method. The proposed research provides an effective solution to the imbalance of torque distribution and synchronous control method for multiple pinions driving of TBM.

Excavation of underground research laboratory ramp in granite using tunnel boring machine: Feasibility study

Underground research laboratory(URL)plays an important role in safe disposal of high-level radioactive waste(HLW).At present,the Xinchang site,located in Gansu Province of China,has been selected as the final site for China’s first URL,named Beishan URL.For this,a preliminary design of the Beishan URL has been proposed,including one spiral ramp,three shafts and two experimental levels.With advantages of fast advancing and limited disturbance to surrounding rock mass,the tunnel boring machine(TBM)method could be one of the excavation methods considered for the URL ramp.This paper introduces the feasibility study on using TBM to excavation of the Beishan URL ramp.The technical challenges for using TBM in Beishan URL are identified on the base of geological condition and specific layout of the spiral ramp.Then,the technical feasibility study on the specific issues,i.e.extremely hard rock mass,high abrasiveness,TBM operation,muck transportation,water drainage and material transportation,is investigated.This study demonstrates that TBM technology is a feasible method for the Beishan URL excavation.The results can also provide a reference for the design and construction of HLW disposal engineering in similar geological conditions.2020 Institute of Rock and Soil Mechanics,Chinese Academy of Sciences.Production and hosting by Elsevier B.V.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

A real-time prediction method for tunnel boring machine cutter-head torque using bidirectional long short-term memory networks optimized by multi-algorithm

Based on data from the Jilin Water Diversion Tunnels from the Songhua River(China),an improved and real-time prediction method optimized by multi-algorithm for tunnel boring machine(TBM)cutter-head torque is presented.Firstly,a function excluding invalid and abnormal data is established to distinguish TBM operating state,and a feature selection method based on the SelectKBest algorithm is proposed.Accordingly,ten features that are most closely related to the cutter-head torque are selected as input variables,which,in descending order of influence,include the sum of motor torque,cutter-head power,sum of motor power,sum of motor current,advance rate,cutter-head pressure,total thrust force,penetration rate,cutter-head rotational velocity,and field penetration index.Secondly,a real-time cutterhead torque prediction model’s structure is developed,based on the bidirectional long short-term memory(BLSTM)network integrating the dropout algorithm to prevent overfitting.Then,an algorithm to optimize hyperparameters of model based on Bayesian and cross-validation is proposed.Early stopping and checkpoint algorithms are integrated to optimize the training process.Finally,a BLSTMbased real-time cutter-head torque prediction model is developed,which fully utilizes the previous time-series tunneling information.The mean absolute percentage error(MAPE)of the model in the verification section is 7.3%,implying that the presented model is suitable for real-time cutter-head torque prediction.Furthermore,an incremental learning method based on the above base model is introduced to improve the adaptability of the model during the TBM tunneling.Comparison of the prediction performance between the base and incremental learning models in the same tunneling section shows that:(1)the MAPE of the predicted results of the BLSTM-based real-time cutter-head torque prediction model remains below 10%,and both the coefficient of determination(R^(2))and correlation coefficient(r)between measured and predicted values exceed 0.95;and(2)the incremental learning method is suitable for realtime cutter-head torque prediction and can effectively improve the prediction accuracy and generalization capacity of the model during the excavation process.

Tunnel boring machine vibration-based deep learning for the ground identification of working faces

Tunnel boring machine(TBM) vibration induced by cutting complex ground contains essential information that can help engineers evaluate the interaction between a cutterhead and the ground itself.In this study,deep recurrent neural networks(RNNs) and convolutional neural networks(CNNs) were used for vibration-based working face ground identification.First,field monitoring was conducted to obtain the TBM vibration data when tunneling in changing geological conditions,including mixed-face,homogeneous,and transmission ground.Next,RNNs and CNNs were utilized to develop vibration-based prediction models,which were then validated using the testing dataset.The accuracy of the long short-term memory(LSTM) and bidirectional LSTM(Bi-LSTM) models was approximately 70% with raw data;however,with instantaneous frequency transmission,the accuracy increased to approximately 80%.Two types of deep CNNs,GoogLeNet and ResNet,were trained and tested with time-frequency scalar diagrams from continuous wavelet transformation.The CNN models,with an accuracy greater than 96%,performed significantly better than the RNN models.The ResNet-18,with an accuracy of 98.28%,performed the best.When the sample length was set as the cutterhead rotation period,the deep CNN and RNN models achieved the highest accuracy while the proposed deep CNN model simultaneously achieved high prediction accuracy and feedback efficiency.The proposed model could promptly identify the ground conditions at the working face without stopping the normal tunneling process,and the TBM working parameters could be adjusted and optimized in a timely manner based on the predicted results.

Utilizing partial least square and support vector machine for TBM penetration rate prediction in hard rock conditions

Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accuracy of prediction models employing partial least squares(PLS) regression and support vector machine(SVM) regression technique for modeling the penetration rate of TBM. To develop the proposed models, the database that is composed of intact rock properties including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and peak slope index(PSI), and also rock mass properties including distance between planes of weakness(DPW) and the alpha angle(α) are input as dependent variables and the measured ROP is chosen as an independent variable. Two hundred sets of data are collected from Queens Water Tunnel and Karaj-Tehran water transfer tunnel TBM project. The accuracy of the prediction models is measured by the coefficient of determination(R2) and root mean squares error(RMSE) between predicted and observed yield employing 10-fold cross-validation schemes. The R2 and RMSE of prediction are 0.8183 and 0.1807 for SVMR method, and 0.9999 and 0.0011 for PLS method, respectively. Comparison between the values of statistical parameters reveals the superiority of the PLSR model over SVMR one.

Technical innovation in the “Beishan No. 1” hard rock tunnel boring machine for high-gradient spiral tunnels

1 Project overview The“Beishan No.1”,the world’s first-ever hard rock Tunnel Boring Machine(TBM)tailored for high-gradient spiral tunnels,constitutes a pivotal element within the Beishan Underground Research Laboratory(URL)initiative in China.Beishan URL,the first URL for geological disposal of high level radioactive waste(HLW)in China,is a national key construction project listed in the“13th Five-Year Plan”.In 2019,subsequent to receiving approval from the China Atomic Energy Authority,the Beijing Research Institute of Uranium Geology(BRIUG),serving as the project’s owner,initiated its construction.This underground facility,categorized as a“third generation”URL for HLW disposal,i.e.,area-specific URL,was located in Beishan,Jiuquan City,Gansu Province,China,following more than three decades of rigorous research on site selection.

HSP超前探测技术在煤矿TBM掘进巷道中的应用研究

随着全断面掘进机TBM(Tunnel Boring Machine)逐渐应用于煤矿岩巷掘进,对不良地质构造进行超前准确快速预测的需求日益迫切。通过对主动源地震波超前探测方法的特点和TBM破岩震源超前探测技术的适用性进行分析,结合煤矿巷道地质和生产条件,提出了适用于煤矿巷道TBM掘进的HSP超前探测方法。以河南平顶山首山一矿TBM掘进底板瓦斯治理巷道为工程背景,选用防爆硬件一体化设计的探测仪器在煤矿巷道中进行应用。构建了空间型观测方式对煤矿巷道近水平煤线进行探测,优化了双护盾TBM掘进巷道狭小空间检波器阵列式布置参数;基于时频分析、互相关干涉处理、反射与散射联合反演等方法处理原始信号并进行探测结果成像。研究表明:采用空间型观测方式可实现与巷道小角度斜交煤线的识别,设计震源与首检波器间距离为15 m时最优。通过时频分析提取有效信号,利用互相关干涉法获取虚拟震源道和反射特征曲线,并结合反射与散射联合反演成像得到探测区域地层反射能量分布图,能够较准确地推测得到围岩存在的不良地质构造。通过比较现场开挖揭露情况与探测结果发现两者吻合度较高,表明HSP超前探测方法可实现掘进工作面前方100 m范围内超前无损地质预测,有助于提高煤矿岩巷TBM掘进速度。

天山胜利隧道敞开式TBM钢管片支护作用效果研究

针对TBM穿越断层破碎带地层洞壁围岩松动掉块严重的问题,依托乌尉天山胜利隧道,采用数值模拟的方法,通过对比研究传统支护方式与新型钢管片支护方式下围岩变形和围岩塑性区情况,研究钢管片支护的作用效果;同时,为说明不同支护的适用条件,采用现场调研方法开展不同支护方式下施工效率和经济性对比分析。结果表明:1)断层破碎带地层中,当围岩出露护盾后,变形量与塑性区范围增大,围岩破坏程度加剧,以锚杆和钢拱架为主的传统支护方式无法较好地控制围岩变形和塑性区发展。2)断层破碎带地层中,钢管片支护较传统支护的围岩累计沉降值降低53%,围岩变形收敛快,能更好地控制围岩变形和发展。3)钢管片支护较传统支护的围岩塑性区范围小,其中边墙的最终塑性区缩小44%,且钢管片支护可实现早封闭、及时支护,塑性区均匀,更有利于围岩的稳定。4)一般地层中,传统支护方式的经济性和效率更佳;断层破碎带地层中,钢管片支护方式可提供安全作业空间,减小钢拱架+喷锚支护作业量,较传统支护经济性和施工效率更佳。

深部金属矿山巷道TBM掘进技术应用现状及研究进展

未来10年,我国将有30%以上金属矿山开采深度达到或超过千米。深部金属矿山开采面临高地应力、高地温、地层多变、采掘强扰动等复杂地质环境,给巷道掘进带来严峻挑战。传统钻爆法巷道掘进非连续作业、效率低、灾害频发,难以满足建设需求,采用隧道掘进机(TBM)进行机械化连续掘进是深部金属矿山巷道建设的未来发展方向。然而,因深部金属矿山独有工程地质特点和巷道掘进需求,TBM技术在深部金属矿山巷道掘进中仍存在一些技术难题和挑战。本文首先总结了TBM技术在深部金属矿山巷道掘进中的三个应用难点:1)缺乏适用于深部金属矿山巷道的TBM适应性选型设计理论;2)缺乏适用于深部金属矿山巷道的TBM高效掘进技术;3)缺乏适用于深部金属矿山巷道的TBM掘进灾害监测预警与防控技术。然后,从六个方面介绍了深部金属矿山巷道TBM掘进技术应用的研究进展:适应性选型与设计技术、刀盘刀具高效破岩理论、掘进性能精准预测与评价方法、小半径转弯和倾斜巷道掘进技术、掘进灾害智能预警与防控技术、智能决策和辅助驾驶技术。上述研究成果为推动TBM掘进技术在深部金属矿山巷道中的广泛应用,实现深部金属矿山巷道快速掘进指明了发展方向。