Vote-Based Feature Selection Method for Stratigraphic Recognition in Tunnelling Process of Shield Machine

Shield machines are currently the main tool for underground tunnel construction. Due to the complexity and variability of the underground construction environment, it is necessary to accurately identify the ground in real-time during the tunnel construction process to match and adjust the tunnel parameters according to the geological conditions to ensure construction safety. Compared with the traditional method of stratum identifcation based on staged drilling sampling, the real-time stratum identifcation method based on construction data has the advantages of low cost and high precision. Due to the huge amount of sensor data of the ultra-large diameter mud-water balance shield machine, in order to balance the identifcation time and recognition accuracy of the formation, it is necessary to screen the multivariate data features collected by hundreds of sensors. In response to this problem, this paper proposes a voting-based feature extraction method (VFS), which integrates multiple feature extraction algorithms FSM, and the frequency of each feature in all feature extraction algorithms is the basis for voting. At the same time, in order to verify the wide applicability of the method, several commonly used classifcation models are used to train and test the obtained efective feature data, and the model accuracy and recognition time are used as evaluation indicators, and the classifcation with the best combination with VFS is obtained. The experimental results of shield machine data of 6 diferent geological structures show that the average accuracy of 13 features obtained by VFS combined with diferent classifcation algorithms is 91%;among them, the random forest model takes less time and has the highest recognition accuracy, reaching 93%, showing best compatibility with VFS. Therefore, the VFS algorithm proposed in this paper has high reliability and wide applicability for stratum identifcation in the process of tunnel construction, and can be matched with a variety of classifer algorithms. By combining 13 features selected from shield machine data features with random forest, the identifcation of the construction stratum environment of shield tunnels can be well realized, and further theoretical guidance for underground engineering construction can be provided.

From tunnel boring machine to tunnel boring robot: perspectives on intelligent shield machine and its smart operation

Advances in intelligent shield machines reflect an evolving trend from traditional tunnel boring machines(TBMs)to tunnel boring robots(TBRs).This shift aims to address the challenges encountered by the conventional shield machine industry arising from construction environment and manual operations.This study presents a systematic review of intelligent shield machine technology,with a particular emphasis on its smart operation.Firstly,the definition,meaning,contents,and development modes of intelligent shield machines are proposed.The development status of the intelligent shield machine and its smart operation are then presented.After analyzing the operation process of the shield machine,an autonomous operation framework considering both stand-alone and fleet levels is proposed.Challenges and recommendations are given for achieving autonomous operation.This study offers insights into the essence and developmental framework of intelligent shield machines to propel the advancement of this technology.

Geological information prediction for shield machine using an enhanced multi-head self-attention convolution neural network with two-stage feature extraction

Due to the closed working environment of shield machines,the construction personnel cannot observe the construction geological environment,which seriously restricts the safety and efficiency of the tunneling process.In this study,we present an enhanced multi-head self-attention convolution neural network(EMSACNN)with two-stage feature extraction for geological condition prediction of shield machine.Firstly,we select 30 important parameters according to statistical analysis method and the working principle of the shield machine.Then,we delete the non-working sample data,and combine 10 consecutive data as the input of the model.Thereafter,to deeply mine and extract essential and relevant features,we build a novel model combined with the particularity of the geological type recognition task,in which an enhanced multi-head self-attention block is utilized as the first feature extractor to fully extract the correlation of geological information of adjacent working face of tunnel,and two-dimensional CNN(2dCNN)is utilized as the second feature extractor.The performance and superiority of proposed EMSACNN are verified by the actual data collected by the shield machine used in the construction of a double-track tunnel in Guangzhou,China.The results show that EMSACNN achieves at least 96%accuracy on the test sets of the two tunnels,and all the evaluation indicators of EMSACNN are much better than those of classical AI model and the model that use only the second-stage feature extractor.Therefore,the proposed EMSACNN achieves high accuracy and strong generalization for geological information prediction of shield machine,which is of great guiding significance to engineering practice.

Attention-based LSTM predictive model for the attitude and position of shield machine in tunneling

Shield machine may deviate from its design axis during excavation due to the uncertainty of geological environment and the complexity of operation.This study therefore introduced a framework to predict the attitude and position of shield machine by combining long short-term memory(LSTM)model with attention mechanism.The data obtained from the Wuhan Rail Transit Line 6 project were utilized to verify the feasibility of the proposed method.By adding the attention mechanism into the LSTM model,the proposed model can focus more on parameters with higher weights.Sensitivity analysis based on Pearson correlation coefficient was conducted to improve the prediction efficiency and reduce the irrelevant input parameters.Compared with LSTM model,LSTM-attention model has higher accuracy.The mean value of coefficient of determination(R^(2))increases from 0.625 to 0.736,and the mean value of root mean square error(RMSE)decreases from 3.31 to 2.24.The proposed LSTM-attention model can provide an effective prediction for attitude and position of shield machine in practical tunneling engineering.

Regulating bulkhead pressure of EPB shield machines through DEM modeling and data mining

Proper regulation of the earth pressure on the bulkhead of earth-pressure balanced(EPB)shield tunneling machines is significant to ensure safe construction.This study proposes a procedure for regulating the bulkhead pressure by combining numerical simulations and data mining,and applies the procedure to a metro line constructed in sandy pebble stratum using EPB shield.Firstly,the relationship between the bulkhead pressure and the pressure on the tunnel face is carefully obtained from discrete element modeling,and the required supporting earth pressure is derived by considering the arching effect.Secondly,aided with the machine learning method,a model is constructed for predicting the average bulkhead pressure per ring according to the operational parameters(i.e.,the average driving speed and the rotation speed of the screw conveyor).Given the target value of the bulkhead pressure,the optimal values of the operational parameters are obtained from the model.In addition,an autoregressive moving average stochastic process model is developed to monitor the real-time fluctuation of the bulkhead pressure and guide the actions taken in time to avoid dramatic fluctuations.The results indicate that the pressure difference between the tunnel face and the bulkhead is considerable,and the consideration of the arching effect can avoid overestimating the bulkhead pressure.A combination of the machine learning model and the stochastic process model provides a plausible performance in regulating the bulkhead pressure around the target value without dramatic fluctuation.

A novel LSTM-autoencoder and enhanced transformer-based detection method for shield machine cutterhead clogging

Shield tunneling machines are paramount underground engineering equipment and play a key role in tunnel construction.During the shield construction process,the“mud cake”formed by the difficult-to-remove clay attached to the cutterhead severely affects the shield construction efficiency and is harmful to the healthy operation of a shield tunneling machine.In this study,we propose an enhanced transformer-based detection model for detecting the cutterhead clogging status of shield tunneling machines.First,the working state data of shield machines are selected from historical excavation data,and a long short-term memory-autoencoder neural network module is constructed to remove outliers.Next,variational mode decomposition and wavelet transform are employed to denoise the data.After the preprocessing,nonoverlapping rectangular windows are used to intercept the working state data to obtain the time slices used for analysis,and several time-domain features of these periods are extracted.Owing to the data imbalance in the original dataset,the k-means-synthetic minority oversampling technique algorithm is adopted to oversample the extracted time-domain features of the clogging data in the training set to balance the dataset and improve the model performance.Finally,an enhanced transformer-based neural network is constructed to extract essential implicit features and detect cutterhead clogging status.Data collected from actual tunnel construction projects are used to verify the proposed model.The results show that the proposed model achieves accurate detection of shield machine cutterhead clogging status,with 98.85%accuracy and a 0.9786 F1 score.Moreover,the proposed model significantly outperforms the comparison models.

Modelling the performance of EPB shield tunnelling using machine and deep learning algorithms

This paper introduces an intelligent framework for predicting the advancing speed during earth pressure balance(EPB)shield tunnelling.Five artificial intelligence(AI)models based on machine and deep learning techniques-back-propagation neural network(BPNN),extreme learning machine(ELM),support vector machine(SVM),long-short term memory(LSTM),and gated recurrent unit(GRU)-are used.Five geological and nine operational parameters that influence the advancing speed are considered.A field case of shield tunnelling in Shenzhen City,China is analyzed using the developed models.A total of 1000 field datasets are adopted to establish intelligent models.The prediction performance of the five models is ranked as GRU>LSTM>SVM>ELM>BPNN.Moreover,the Pearson correlation coefficient(PCC)is adopted for sensitivity analysis.The results reveal that the main thrust(MT),penetration(P),foam volume(FV),and grouting volume(GV)have strong correlations with advancing speed(AS).An empirical formula is constructed based on the high-correlation influential factors and their corresponding field datasets.Finally,the prediction performances of the intelligent models and the empirical method are compared.The results reveal that all the intelligent models perform better than the empirical method.

Towards autonomous and optimal excavation of shield machine:a deep reinforcement learning-based approach

Autonomous excavation operation is a major trend in the development of a new generation of intelligent tunnel boring machines(TBMs).However,existing technologies are limited to supervised machine learning and static optimization,which cannot outperform human operation and deal with ever changing geological conditions and the long-term performance measure.The aim of this study is to resolve the problem of dynamic optimization of the shield excavation performance,as well as to achieve autonomous optimal excavation.In this study,a novel autonomous optimal excavation approach that integrates deep reinforcement learning and optimal control is proposed for shield machines.Based on a first-principles analysis of the machine-ground interaction dynamics of the excavation process,a deep neural network model is developed using construction field data consisting of 1.1 million samples.The multi-system coupling mechanism is revealed by establishing an overall system model.Based on the overall system analysis,the autonomous optimal excavation problem is decomposed into a multi-objective dynamic optimization problem and an optimal control problem.Subsequently,a dimensionless multi-objective comprehensive excavation performance measure is proposed.A deep reinforcement learning method is used to solve for the optimal action sequence trajectory,and optimal closed-loop feedback controllers are designed to achieve accurate execution.The performance of the proposed approach is compared to that of human operation by using the construction field data.The simulation results show that the proposed approach not only has the potential to replace human operation but also can significantly improve the comprehensive excavation performance.

Pressure Regulation for Earth Pressure Balance Control on Shield Tunneling Machine by Using Adaptive Robust Control

Most current studies about shield tunneling machine focus on the construction safety and tunnel structure stability during the excavation. Behaviors of the machine itself are also studied, like some tracking control of the machine. Yet, few works concern about the hydraulic components, especially the pressure and flow rate regulation components. This research focuses on pressure control strategies by using proportional pressure relief valve, which is widely applied on typical shield tunneling machines. Modeling of a commercial pressure relief valve is done. The modeling centers on the main valve, because the dynamic performance is determined by the main valve. To validate such modeling, a frequency-experiment result of the pressure relief valve, whose bandwidth is about 3 Hz, is presented as comparison. The modeling and the frequency experimental result show that it is reasonable to regard the pressure relief valve as a second-order system with two low corner frequencies. PID control, dead band compensation control and adaptive robust control（ARC） are proposed and simulation results are presented. For the ARC, implements by using first order approximation and second order approximation are presented. The simulation results show that the second order approximation implement with ARC can track 4 Hz sine signal very well, and the two ARC simulation errors are within 0.2 MPa. Finally, experiment results of dead band compensation control and adaptive robust control are given. The results show that dead band compensation had about 30° phase lag and about 20% off of the amplitude attenuation. ARC is tracking with little phase lag and almost no amplitude attenuation. In this research, ARC has been tested on a pressure relief valve. It is able to improve the valve＇s dynamic performances greatly, and it is capable of the pressure control of shield machine excavation.

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.

Synchronous shield tunnelling technology combining advancement and segment fabrication:Principle,verification and application

Through the active control of shield thrust system oil pressures,a synchronous shield tunnelling technology combining advancement and segment fabrication was proposed.The key to this technology was to completely exploit the additional stroke of the hydraulic jacks generated by the axial insertion of a key block to assemble the segments.Taking the tunnelling project of the Shanghai Airport Railway Link Line as a demonstration project,this paper uses a large model test platform for this synchronous technology.A single-ring construction process along a straight line was simulated,in which the theoretical external loads were implemented on the shield machine.The feasibility and reliability of this synchronous technology were evaluated considering the accuracy of total thrust force control,maintenance of tunnelling speed and shield postures and segment compression.The effectiveness of the redistribution principle for the missing thrust force due to the withdrawal of cylinders located in the segment fabrication region was discussed.Furthermore,some other interesting phenomena were observed in the practical application in addition to the model test.This technology primarily realizes synchronous assembly by improving the control of the propulsion system of conventional shield machines without any transformation of the shield machine’s primary structure,segment patterns or excavation method.The proposed method has good adaptability and a low construction cost,which will be beneficial for future long-distance tunnelling projects.

Heat Treatment Properties of 42CrMo Steel for Bearing Ring of Varisized Shield Tunneling Machine

The heat treatment properties of 42CrMo steel for bearing ring of varisized shield tunneling machine were investigated by optical microscope （OM）, scanning electron microscope （SEM）, transmission electron microscope （TEM）, and impact tests. The addition of 0.03 wt% C into 42CrMo steel can increase the hardness. But it reduces the impact energy by 46 J because of the appearance of coarser carbides in the matrix and the carbides along the austenite grain boundary. The addition of 0.40 wt% Mn into 42CrMo steel can improve hardenability. However, the toughness of steel is also reduced by 26 J mainly because of the coarsening of carbides and the strengthening of matrix. Both hardenability and toughness of 42CrMo steel can be improved by adding 1.49 wt% Ni and reducing 0.32 wt% Cr. The depth of hardening layer can be raised to 45 mm, and the impact energy at -20 ℃ is 120 J. Thus, it is concluded that a good combination of hardness, hardenability, and toughness of 42CrMo steel can be achieved by alloying with adding some content of C and Ni. Detailed content of C and Ni should be on the requirements of heat treatment properties of steel for bearing ring of varisized shield tunneling machine.