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.

A performance-based hybrid deep learning model for predicting TBM advance rate using Attention-ResNet-LSTM

The technology of tunnel boring machine(TBM)has been widely applied for underground construction worldwide;however,how to ensure the TBM tunneling process safe and efficient remains a major concern.Advance rate is a key parameter of TBM operation and reflects the TBM-ground interaction,for which a reliable prediction helps optimize the TBM performance.Here,we develop a hybrid neural network model,called Attention-ResNet-LSTM,for accurate prediction of the TBM advance rate.A database including geological properties and TBM operational parameters from the Yangtze River Natural Gas Pipeline Project is used to train and test this deep learning model.The evolutionary polynomial regression method is adopted to aid the selection of input parameters.The results of numerical exper-iments show that our Attention-ResNet-LSTM model outperforms other commonly-used intelligent models with a lower root mean square error and a lower mean absolute percentage error.Further,parametric analyses are conducted to explore the effects of the sequence length of historical data and the model architecture on the prediction accuracy.A correlation analysis between the input and output parameters is also implemented to provide guidance for adjusting relevant TBM operational parameters.The performance of our hybrid intelligent model is demonstrated in a case study of TBM tunneling through a complex ground with variable strata.Finally,data collected from the Baimang River Tunnel Project in Shenzhen of China are used to further test the generalization of our model.The results indicate that,compared to the conventional ResNet-LSTM model,our model has a better predictive capability for scenarios with unknown datasets due to its self-adaptive characteristic.

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.

3D Finite Element Analysis of TBM Water Diversion Tunnel Segment Coupled with Seepage Field

In most studies of tunnel boring machine(TBM)tunnelling, the groundwater pressure was not considered, or was simplified and exerted on the boundary of lining structure. Meanwhile, the leakage, which mainly occurs in the segment joints, was often ignored in the relevant studies of TBM tunnelling. Additionally, the geological models in these studies were simplified to different extents, and mostly were simplified as homogenous bodies. Considering the deficiencies above, a 3D refined model of the surrounding rock of a tunnel is firstly established using NURBS-TIN-BRe P hybrid data structure in this paper. Then the seepage field of the surrounding rock considering the leakage in the segment joints is simulated. Finally, the stability of TBM water diversion tunnel is studied coupled with the seepage simulation, to analyze the stress-strain conditions, the axial force and the bending moment of tunnel segment considering the leakage in the segment joints. The results illustrate that the maximum radial displacement, the minimum principal stress, the maximum principal stress and the axial force of segment lining considering the seepage effect are all larger than those disregarding the seepage effect.

Multi-degree-of-freedom coupling dynamic characteristic of TBM disc cutter under shock excitation

When the tunneling boring machine(TBM) cutterhead tunnels, the excessive vibration and damage are a severe engineering problem, thereby the anti-vibration design is a key technology in the disc cutter system. The structure of disc cutter contains many joint interfaces among cutter ring, cutter body, bearings and cutter shaft. On account of the coupling for dynamic contact and the transfer path among joint interface, mechanical behavior of disc cutter becomes extremely complex under the impact of heavy-duty, which puts forward higher requirements for disc cutter design. A multi-degree-of-freedom coupling dynamic model, which contains a cutter ring, a cutter body, two bearings and cutter shaft, is established, considering the external stochastic excitations, bearing nonlinear contact force, multidirectional mutual coupling vibration, etc. Based on the parameters of an actual project and the strong impact external excitations, the modal properties and dynamic responses are analyzed, as well as the cutter shaft and bearings' loads and load transmission law are obtained. Numerical results indicate the maximum radial and axial cutter ring amplitudes of dynamic responses are 0.568 mm and 0.112 mm; the maximum radial and axial vibration velocities are 41.1 mm/s and 38.9 mm/s; the maximum radial and axial vibration accelerations are 94.7 m/s2 and 58.6 m/s2; the maximum swing angle and angular velocity of cutter ring are 0.007° and 0.0074 rad/s, respectively. Finally, the maximum load of bearing roller is 40.3 k N. The proposed research lays a foundation for structure optimization design of disc cutter and cutter base, as well as model selection, modification and fatigue life of the cutter bearing.

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.

Examining the effect of adverse geological conditions on jamming of a single shielded TBM in Uluabat tunnel using numerical modeling

Severe shield jamming events have been reported during excavation of Uluabat tunnel through adverse geological conditions, which resulted in several stoppages at advancing a single shielded tunnel boring machine(TBM). To study the jamming mechanism, three-dimensional(3D) simulation of the machine and surrounding ground was implemented using the finite difference code FLAC3D. Numerical analyses were performed for three sections along the tunnel with a higher risk for entrapment due to the combination of overburden and geological conditions. The computational results including longitudinal displacement contours and ground pressure profiles around the shield allow a better understanding of ground behavior within the excavation. Furthermore, they allow realistically assessing the impact of adverse geological conditions on shield jamming. The calculated thrust forces, which are required to move the machine forward, are in good agreement with field observations and measurements. It also proves that the numerical analysis can effectively be used for evaluating the effect of adverse geological environment on TBM entrapments and can be applied to prediction of loads on the shield and preestimating of the required thrust force during excavation through adverse ground conditions.

Real-time prediction of rock mass classification based on TBM operation big data and stacking technique of ensemble learning

Real-time prediction of the rock mass class in front of the tunnel face is essential for the adaptive adjustment of tunnel boring machines(TBMs).During the TBM tunnelling process,a large number of operation data are generated,reflecting the interaction between the TBM system and surrounding rock,and these data can be used to evaluate the rock mass quality.This study proposed a stacking ensemble classifier for the real-time prediction of the rock mass classification using TBM operation data.Based on the Songhua River water conveyance project,a total of 7538 TBM tunnelling cycles and the corresponding rock mass classes are obtained after data preprocessing.Then,through the tree-based feature selection method,10 key TBM operation parameters are selected,and the mean values of the 10 selected features in the stable phase after removing outliers are calculated as the inputs of classifiers.The preprocessed data are randomly divided into the training set(90%)and test set(10%)using simple random sampling.Besides stacking ensemble classifier,seven individual classifiers are established as the comparison.These classifiers include support vector machine(SVM),k-nearest neighbors(KNN),random forest(RF),gradient boosting decision tree(GBDT),decision tree(DT),logistic regression(LR)and multilayer perceptron(MLP),where the hyper-parameters of each classifier are optimised using the grid search method.The prediction results show that the stacking ensemble classifier has a better performance than individual classifiers,and it shows a more powerful learning and generalisation ability for small and imbalanced samples.Additionally,a relative balance training set is obtained by the synthetic minority oversampling technique(SMOTE),and the influence of sample imbalance on the prediction performance is discussed.

Differentiation and analysis on rock breaking characteristics of TBM disc cutter at different rock temperatures

In order to study rock breaking characteristics of tunnel boring machine(TBM) disc cutter at different rock temperatures,thermodynamic rock breaking mathematical model of TBM disc cutter was established on the basis of rock temperature change by using particle flow code theory and the influence law of interaction mechanism between disc cutter and rock was also numerically simulated.Furthermore,by using the linear cutting experiment platform,rock breaking process of TBM disc cutter at different rock temperatures was well verified by the experiments.Finally,rock breaking characteristics of TBM disc cutter were differentiated and analyzed from microscale perspective.The results indicate the follows.1) When rock temperature increases,the mechanical properties of rock such as hardness,and strength,were greatly reduced,simultaneously the microcracks rapidly grow with the cracks number increasing,which leads to rock breaking load decreasing and improves rock breaking efficiency for TBM disc cutter.2) The higher the rock temperature,the lower the rock internal stress.The stress distribution rules coincide with the Buzin Neske stress circle rules: the maximum stress value is below the cutting edge region and then gradually decreases radiant around; stress distribution is symmetrical and the total stress of rock becomes smaller.3) The higher the rock temperature is,the more the numbers of micro,tensile and shear cracks produced are by rock as well as the easier the rock intrusion,along with shear failure mode mainly showing.4) With rock temperature increasing,the resistance intrusive coefficients of rock and intrusion power decrease obviously,so the specific energy consumption that TBM disc cutter achieves leaping broken also decreases subsequently.5) The acoustic emission frequency remarkably increases along with the temperature increasing,which improves the rock breaking efficiency.

Effects of discontinuities on penetration of TBM cutters

Based on the triaxial testing machine and discrete element method, the effects of embedded crack on rock fragmentation are investigated in laboratory tests and a series of numerical investigations are conducted on the effects of discontinuities on cutting characteristics and cutting efficiency. In laboratory tests, five propagation patterns of radial cracks are observed. And in the numerical tests, firstly, it is similar to laboratory tests that cracks ahead of cutters mainly initiate from the crushed zone, and some minor cracks will initiate from joints. The cracks initiating from crushed zones will run through the thinner joints while they will be held back by thick joints. Cracks tend to propagate towards the tips of embedded cracks, and minor cracks will initiate from the tips of embedded cracks, which may result in the decrease of specific area, and disturbing layers play as ‘screens', which will prevent cracks from developing greatly. The peak penetration forces, the consumed energy in the penetration process and the uniaxial compression strength will decrease with the increase of discontinuities. The existence of discontinuities will result in the decrease of the cutting efficiency when the spacing between cutters is 70 mm. Some modifications should be made to improve the efficiency when the rocks containing groups of discontinuities are encountered.

Influence of confining stress on fracture characteristics and cutting efficiency of TBM cutters conducted on soft and hard rock

Combined with numerical simulation, the influence of confining stress on cutting process, fracture conditions and cutting efficiencies of soft and hard rock has been conducted on the triaxial testing machine(TRW-3000) designed and manufactured in Central South University(China). Results are obtained by performing analysis on the fracture scopes of cement and granite plates,the characteristics of cutting force in cutting processes and the cutting efficiency. Firstly, the increase of latitude fracture scope and the decrease of longitude fracture scope are both more notable in the tests conducted on cement plates subjected to the increasing confining stresses; secondly, the increase tendency of peak penetration forces obtained from tests conducted on granite plates is more obvious, however, the increase tendencies of average penetration forces achieved from cement and granite plates are close to each other; thirdly, the cutting efficiency could be improved by increasing the spacing between cutters when the confining stress which acts on soft and hard rock increases in a certain degree, and the cutting efficiency of soft rock is more sensitive to the varying confining stresses.

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.

Reducing risk in long deep tunnels by using TBM and drill-and-blast methods in the same project-the hybrid solution

There are many examples of TBM tunnels through mountains, or in mountainous terrain, which have suffered the ultimate fate of abandonment, due to insufficient pre-investigation. Depth-of-drilling limitations are inevitable when depths approach or even exceed l or 2 km. Uncertainties about the geology, hydro-geology, rock stresses and rock strengths go hand-in-hand with deep or ultra-deep tunnels. Unfortunately, unexpected conditions tend to have a much bigger impact on TBM projects than on drill-and-blast projects. There are two obvious reasons. Firstly the circular excavation maximizes the tangential stress, making the relation to rock strength a higher source of potential risk. Secondly, the TBM may have been progressing fast enough to make probe-drilling seem to be unnecessary. If the stress-to-strength ratio becomes too high, or if faulted rock with high water pressure is unexpectedly encountered, the ＂unexpected events＂ may have a remarkable delaying effect on TBM. A simple equation explains this phenomenon, via the adverse local Q-value that links directly to utilization. One may witness dramatic reductions in utilization, meaning ultra-steep deceleration-of-the-TBM gradients in a log-log plot of advance rate versus time. Some delays can be avoided or reduced with new TBM designs, where belief in the need for probe-drilling and sometimes also pre-injection, have been fully appreciated. Drill-and-blast tunneling, inevitably involving numerous ＂probe-holes＂ prior to each advance, should be used instead, if investigations have been too limited. TBM should be used where there is lower cover and where more is known about the rock and structural conditions. The advantages of the superior speed of TBM may then be fully realized. Choosing TBM because a tunnel is very long increases risk due to the law of deceleration with increased length, especially if there is limited pre-investigation because of tunnel depth.

TBM Construction Process Simulation and Performance Optimization

Long tunnel excavation with tunnel boring ily affected by uncertainties and needs to be adjusted machine （TBM） is a complex and stochastic process. It is eas- according to specific geological conditions in different tunnel sections, which makes the construction scheduling and management difficult. Based on the rock mass classification, this paper estimates the penetration rate. Using the rate, a cyclic network simulation （CYCLONE） model of TBM boring system is established, and the advance rates under different geological conditions are determined. Then, the impact of different cutter head thrust, which is chosen in reasonable range according to previous experiences, on pro- ject schedule is analyzed. Moreover, the simulation model of mucking system is built to determine the number of muck trains and rail intersections reasonably, regarding the efficiency of muck loading and material transporting. Based on the interaction and interrelation between TBM boring system and mucking system, the combined CY- CLONE model for the entire tunneling process is established. Then reasonable construction schedule, the utilization rate of working resources, and the probability of project completion are obtained through the model programming. At last, a project application shows the feasibility of the presented method.

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.

复合地层小直径隧道掘进机掘进速度区间预测

合理准确预测隧道掘进机(tunnel boring machine,TBM)的掘进速度是实现TBM智能化控制的关键问题之一,复合地层小直径TBM施工的不确定性较常规地质条件更强,而传统预测方法对施工过程的不确定性考虑不足。在此通过引入区间预测方法,提出基于4种不同Bootstrap方法结合KELM-ANN模型的TBM掘进速度区间预测模型,并以南水北调安阳输水隧洞工程为例,选取142组工程实测数据验证区间预测模型的有效性。研究结果表明:基于Rademacher分布建立的模型预测结果优于其他3种方法,不仅可以得到较好的点预测结果,还可以构造出较为清晰可靠的区间将掘进速度实测值完全包络在内;随着置信水平的提高,区间可容纳的不确定性和风险也逐渐上升,通过变化区间宽度,能较好地量化和解释TBM施工过程中的不确定性因素对掘进速度的影响。研究结果可为TBM掘进性能预测和掘进参数优化提供参考。

基于PSO-LSSVM算法的隧道掘进机掘进参数预测方法

为了规避隧道掘进机(tunnel boring machine,TBM)掘进参数人为设定的主观性,提出了一种基于粒子群-最小二乘支持向量机算法(PSO-LSSVM)的TBM掘进参数预测方法。通过从海量TBM工程掘进数据中探寻参数变化规律,降低了TBM主司机设定掘进参数的主观性,辅助其合理选择掘进参数,有利于提高掘进效率、规避工程风险,经实验和工程数据验证,PSO-LSSVM算法通过对样本粒子全局迭代寻优来优化参数,提升了预测算法泛化能力和预测精度,对推力、扭矩和推进速度参数预测数值偏差满足要求,可辅助指导主司机设定掘进参数。

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.

Effect of confining pressure on rock breaking by high-pressure waterjet-assisted TBM

High-pressure waterjet-assisted tunnel boring machine(WTBM)is an efficient method for improving the tunneling performance of a tunnel boring machine(TBM)and reducing the wear of its disc cutters in hard rock with high geostresses.Confining pressure directly affects the efficiency of rock breaking and the configuration of the disc cutters.In this study,we evaluated the effect of confining pressure on WTBM rock breaking by developing a self-designed and manufactured experimental system,including confining pressure loading,TBM disc-cutter penetration,and high-pressure waterjet.The macro fracture,acoustic emission(AE),peak normal force drop,and specific energy(SE)were analyzed for four different confining pressures(10,20,30,and 35 MPa).The results showed that the cutting depth of the waterjet increased linearly as the waterjet pressure increased and decreased with the gradual increase in the nozzle moving speed.The expansion and development of cracks formed rock debris,and the size of the rock fragments decreased with an increase in confining pressure.When the waterjet pressure was 280 MPa,the nozzle moving velocity was 800 mm/min and the kerf space was 75 mm,which indicated that the confining pressure,which was 23.16 MPa,minimized the cutting SE under this condition.However,regardless of the confining pressure,the maximum normal force of WTBM was less than that of a TBM,whereas the SE of WTBM was less than that of complete TBM cutting mode(CTCM).The average force drop and average drop rate of SE were approximately 25%,and 80%,respectively.The results of this study can inspire the design and mechanism of a TBM assisted by a high-pressure waterjet.

Application of artificial neural networks to the prediction of tunnel boring machine penetration rate

Rate of penetration of a Tunnel Boring Machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project.This paper presents the results of a study into the application of an Artificial Neural Network(ANN) technique for modeling the penetration rate of tunnel boring machines.A database,including actual,measured TBM penetration rates,uniaxial compressive strengths of the rock,the distance between planes of weakness in the rock mass and rock quality designation was established.Data collected from three different TBM projects(the Queens Water Tunnel,USA,the Karaj-Tehran water transfer tunnel,Iran,and the Gilgel Gibe II hydroelectric project,Ethiopia).A five-layer ANN was found to be optimum,with an architecture of three neurons in the input layer,9,7 and 3 neurons in the first,second and third hidden layers,respectively,and one neuron in the output layer.The correlation coefficient determined for penetration rate predicted by the ANN was 0.94.