The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combi...The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combines numerical simulation with machine learning techniques to explore this issue.It presents a summary of special-shaped tunnel geometries and introduces a shape coefficient.Through the finite element software,Plaxis3D,the study simulates six key parameters—shape coefficient,burial depth ratio,tunnel’s longest horizontal length,internal friction angle,cohesion,and soil submerged bulk density—that impact uplift resistance across different conditions.Employing XGBoost and ANN methods,the feature importance of each parameter was analyzed based on the numerical simulation results.The findings demonstrate that a tunnel shape more closely resembling a circle leads to reduced uplift resistance in the overlying soil,whereas other parameters exhibit the contrary effects.Furthermore,the study reveals a diminishing trend in the feature importance of buried depth ratio,internal friction angle,tunnel longest horizontal length,cohesion,soil submerged bulk density,and shape coefficient in influencing uplift resistance.展开更多
A typical Whipple shield consists of double-layered plates with a certain gap.The space debris impacts the outer plate and is broken into a debris cloud(shattered,molten,vaporized)with dispersed energy and momentum,wh...A typical Whipple shield consists of double-layered plates with a certain gap.The space debris impacts the outer plate and is broken into a debris cloud(shattered,molten,vaporized)with dispersed energy and momentum,which reduces the risk of penetrating the bulkhead.In the realm of hypervelocity impact,strain rate(>10^(5)s^(-1))effects are negligible,and fluid dynamics is employed to describe the impact process.Efficient numerical tools for precisely predicting the damage degree can greatly accelerate the design and optimization of advanced protective structures.Current hypervelocity impact research primarily focuses on the interaction between projectile and front plate and the movement of debris cloud.However,the damage mechanism of debris cloud impacts on rear plates-the critical threat component-remains underexplored owing to complex multi-physics processes and prohibitive computational costs.Existing approaches,ranging from semi-empirical equations to a machine learningbased ballistic limit prediction method,are constrained to binary penetration classification.Alternatively,the uneven data from experiments and simulations caused these methods to be ineffective when the projectile has irregular shapes and complicate flight attitude.Therefore,it is urgent to develop a new damage prediction method for predicting the rear plate damage,which can help to gain a deeper understanding of the damage mechanism.In this study,a machine learning(ML)method is developed to predict the damage distribution in the rear plate.Based on the unit velocity space,the discretized information of debris cloud and rear plate damage from rare simulation cases is used as input data for training the ML models,while the generalization ability for damage distribution prediction is tested by other simulation cases with different attack angles.The results demonstrate that the training and prediction accuracies using the Random Forest(RF)algorithm significantly surpass those using Artificial Neural Networks(ANNs)and Support Vector Machine(SVM).The RF-based model effectively identifies damage features in sparsely distributed debris cloud and cumulative effect.This study establishes an expandable new dataset that accommodates additional parameters to improve the prediction accuracy.Results demonstrate the model's ability to overcome data imbalance limitations through debris cloud features,enabling rapid and accurate rear plate damage prediction across wider scenarios with minimal data requirements.展开更多
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 technique...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.展开更多
For a tunnel driven by a shield machine,the posture of the driving machine is essential to the construction quality and environmental impact.However,the machine posture is controlled by the experienced driver of shiel...For a tunnel driven by a shield machine,the posture of the driving machine is essential to the construction quality and environmental impact.However,the machine posture is controlled by the experienced driver of shield machine by setting hundreds of tunneling parameters empirically.Machine learning(ML)algorithm is an alternative method that can let the computer to learn from the driver’s operation and try to model the relationship between parameters automatically.Thus,in this paper,three ML algorithms,i.e.multi-layer perception(MLP),support vector machine(SVM)and gradient boosting regression(GBR),are improved by genetic algorithm(GA)and principal component analysis(PCA)to predict the tunneling posture of the shield machine.A set of the parameters for shield tunneling is extracted from the construction site of a Shanghai metro.In total,53,785 pairwise data points are collected for about 373 d and the ratio between training set,validation set and test set is 3:1:1.Each pairwise data point includes 83 types of parameters covering the shield posture,construction parameters,and soil stratum properties at the same time.The test results show that the averaged R2 of MLP,SVM and GBR based models are 0.942,0.935 and 0.6,respectively.Then the automatic control for the posture of shield tunnel is illustrated with an application example of the proposed models.The proposed method is proved to be helpful in controlling the construction quality with optimized construction parameters.展开更多
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 microsco...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.展开更多
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 o...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.展开更多
The thrust hydraulic system of the prototype shield machine with pressure and flow compound control scheme was introduced. The experimental system integrated with proportional valves for study was designed. Dynamics m...The thrust hydraulic system of the prototype shield machine with pressure and flow compound control scheme was introduced. The experimental system integrated with proportional valves for study was designed. Dynamics modeling of multi-cylinder thrust system and synchronous control design were accomplished. The simulation of the synchronization motion control system was completed in AMESim and Matlab/Simulink software environments. The experiment was conducted by means of master/slave PID with dead band compensating flow and conventional PID regulating pressure. The experimental results show that the proposed thrust hydraulic system and its control strategy can meet the requirements of tunneling in motion and posture control for the shield machine, keeping the non-synchronous error within ±3 mm.展开更多
According to the actual engineering problem that the precise load model of shield machine is difficult to achieve,a design method of sliding mode robust controller oriented to the automatic rectification of shield mac...According to the actual engineering problem that the precise load model of shield machine is difficult to achieve,a design method of sliding mode robust controller oriented to the automatic rectification of shield machine was proposed. Firstly,the nominal load model of shield machine and the ranges of model parameters were obtained by the soil mechanics parameters of certain geological conditions and the messages of the self-learning of shield machine by tunneling for previous segments. Based on this rectification mechanism model with known ranges of parameters,a sliding mode robust controller was proposed. Finally,the simulation analysis was developed to verify the effectiveness of the proposed controller. The simulation results show that the sliding mode robust controller can be implemented in the attitude rectification process of the shield machine and it has stronger robustness to overcome the soil disturbance.展开更多
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 sh...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.展开更多
A shield machine with freezing function is proposed in order to realize tool change operation at atmospheric pressure. Furthermore, the transformation project of freezing cutterhead and tool change maintenance method ...A shield machine with freezing function is proposed in order to realize tool change operation at atmospheric pressure. Furthermore, the transformation project of freezing cutterhead and tool change maintenance method are put forward. Taking the shield construction of Huanxi Power Tunnel as an example, a numerical analysis of the freezing cutter head of the project was carried out. The results show that when the brine temperature is-25 °C, after 30 d of freezing, the thickness of the frozen wall can reach 0.67 m and the average temperature drops to-9.9 °C. When the brine temperature is-30 °C, after 50 d of freezing, the thickness of the frozen wall can reach 1.01 m and the average temperature drops to-12.4 °C. If the thickness of the frozen wall is 0.5 m and the average temperature is-10 °C, as the design index of the frozen wall, the brine temperature should be lower than-28 °C to meet the excavation requirements in 30 d. Analyzing the frozen wall stress under 0.5 m thickness and-10 °C average temperature condition, the tensile safety factor and compressive safety factor are both greater than 2 at the most dangerous position, which can meet the tool change requirements for shield construction.展开更多
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 o...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.展开更多
A category of permanent-magnet-shield(PM-shield)axial-field dual-rotor segmented switched reluctance machines(ADS-SRMs)are presented in this paper.These topologies are featured by using the magnetic material to shield...A category of permanent-magnet-shield(PM-shield)axial-field dual-rotor segmented switched reluctance machines(ADS-SRMs)are presented in this paper.These topologies are featured by using the magnetic material to shield the flux leakage in the stator and rotor parts.Besides,the deployed magnets weaken the magnetic saturation in the iron core,thus increasing the main flux.Hence,the torque-production capability can be increased effectively.All the PM-shield topologies are proposed and designed based on the magnetic equivalent circuit(MEC)model of ADS-SRM,which is the original design deploying no magnet.The features of all the PM-shield topologies are compared with the original design in terms of the magnetic field distributions,flux linkages,phase inductances,torque components,and followed by their motion-coupled analyses on the torque-production capabilities,copper losses,and efficiencies.Considering the cost reduction and the stable ferrite-magnet supply,an alternative proposal using the ferrite magnets is applied to the magnetic shielding.The magnet demagnetization analysis incorporated with the thermal behavior is performed for further verification of the motor performance.展开更多
A motion parameter optimization method based on the objective of minimizing the total energy consumption in segment positioning was proposed for segment erector of shield tunneling machine. The segment positioning pro...A motion parameter optimization method based on the objective of minimizing the total energy consumption in segment positioning was proposed for segment erector of shield tunneling machine. The segment positioning process was decomposed into rotation, lifting and sliding actions in deriving the energy calculation model of segment erection. The work of gravity was taken into account in the mathematical modeling of energy consumed by each actuator. In order to investigate the relationship between the work done by the actuator and the path moved along by the segment, the upward and downward directions as well as the operating quadrant of the segment erector were defined. Piecewise nonlinear function of energy was presented, of which the result is determined by closely coupled components as working parameters and some intermediate variables. Finally, the effectiveness of the optimization method was proved by conducting a case study with a segment erector for the tunnel with a diameter of 3 m and drawing comparisons between different assembling paths. The results show that the energy required by assembling a ring of segments along the optimized moving path can be reduced up to 5%. The method proposed in this work definitely provides an effective energy saving solution for shield tunneling machine.展开更多
Shield machine is the major technical equipment badly in need in national infrastructure construction. The service conditions of shield machine are extremely complex. The driving interface load fluctuation caused by g...Shield machine is the major technical equipment badly in need in national infrastructure construction. The service conditions of shield machine are extremely complex. The driving interface load fluctuation caused by geological environment changes and multi field coupling of stress field may lead into imbalance of redundant drive motors output torque in main driving system. Therefore, the shield machine driving synchronous control is one of the key technologies of shield machine. This paper is in view of the shield machine main driving synchronous control, achieving the system＇s adaptive load sharing. From the point of view of cutterhead load changes, nonlinear factors of mechanical transmission mechanism and the control system synchronization performance, the authors analyze the load sharing performance of shield machine main drive system in the event of load mutation. The paper proposes a data-driven synchronized control method applicable to the main drive system. The effectiveness of the method is verified through simulation and experimental methods. The new method can make the system synchronization error greatly reduced, thus it can effectively adapt to load mutation, and reduce shaft broken accident.展开更多
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 ...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.展开更多
Compared with traditional feedback control,predictive control can eliminate the lag of pose control and avoid the snakelike motion of shield machines.Therefore,a shield pose prediction model was proposed based on dyna...Compared with traditional feedback control,predictive control can eliminate the lag of pose control and avoid the snakelike motion of shield machines.Therefore,a shield pose prediction model was proposed based on dynamic modeling.Firstly,the dynamic equations of shield thrust system were established to clarify the relationship between force and movement of shield machine.Secondly,an analytical model was proposed to predict future multistep pose of the shield machine.Finally,a virtual prototype model was developed to simulate the dynamic behavior of the shield machine and validate the accuracy of the proposed pose prediction method.Results reveal that the model proposed can predict the shield pose with high accuracy,which can provide a decision basis whether for manual or automatic control of shield pose.展开更多
Although machine learning models have achieved high enough accuracy in predicting shield position deviations,their“black box”nature makes the prediction mechanisms and decision-making processes opaque,leading to wea...Although machine learning models have achieved high enough accuracy in predicting shield position deviations,their“black box”nature makes the prediction mechanisms and decision-making processes opaque,leading to weaker explanations and practicability.This study introduces a novel explainable deep learning framework comprising the Informer model with enhanced attention mechanisms(EAMInfor)and deep learning important features(DeepLIFT),aimed at improving the prediction accuracy of shield position deviations and providing interpretability for predictive results.The EAMInfor model attempts to integrate channel attention,spatial attention,and simple attention modules to improve the Informer model's performance.The framework is tested with the four different geological conditions datasets generated from the Xiamen metro line 3,China.Results show that the EAMInfor model outperforms the traditional Informer and comparison models.The analysis with the DeepLIFT method indicates that the push thrust of push cylinder and the earth chamber pressure are the most significant features,while the stroke length of the push cylinder demonstrated lower importance.Furthermore,the variation trends in the significance of data points within input sequences exhibit substantial differences between single and composite strata.This framework not only improves predictive accuracy but also strengthens the credibility and reliability of the results.展开更多
At present, in the process of urban construction, there is no lack of various types of shield machine application, because of its higher requirements for installation technology, so it is of practical significance to ...At present, in the process of urban construction, there is no lack of various types of shield machine application, because of its higher requirements for installation technology, so it is of practical significance to analyze the downhole installation process and matters needing attention. Taking the downhole installation technology of Herrick shield machine as an example, this paper briefly expounds the downhole installation technology of shield machine.展开更多
基金Guangzhou Metro Scientific Research Project(No.JT204-100111-23001)Chongqing Municipal Special Project for Technological Innovation and Application Development(No.CSTB2022TIAD-KPX0101)Science and Technology Research and Development Program of China State Railway Group Co.,Ltd.(No.N2023G045)。
文摘The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combines numerical simulation with machine learning techniques to explore this issue.It presents a summary of special-shaped tunnel geometries and introduces a shape coefficient.Through the finite element software,Plaxis3D,the study simulates six key parameters—shape coefficient,burial depth ratio,tunnel’s longest horizontal length,internal friction angle,cohesion,and soil submerged bulk density—that impact uplift resistance across different conditions.Employing XGBoost and ANN methods,the feature importance of each parameter was analyzed based on the numerical simulation results.The findings demonstrate that a tunnel shape more closely resembling a circle leads to reduced uplift resistance in the overlying soil,whereas other parameters exhibit the contrary effects.Furthermore,the study reveals a diminishing trend in the feature importance of buried depth ratio,internal friction angle,tunnel longest horizontal length,cohesion,soil submerged bulk density,and shape coefficient in influencing uplift resistance.
基金supported by National Natural Science Foundation of China(Grant No.12432018,12372346)the Innovative Research Groups of the National Natural Science Foundation of China(Grant No.12221002).
文摘A typical Whipple shield consists of double-layered plates with a certain gap.The space debris impacts the outer plate and is broken into a debris cloud(shattered,molten,vaporized)with dispersed energy and momentum,which reduces the risk of penetrating the bulkhead.In the realm of hypervelocity impact,strain rate(>10^(5)s^(-1))effects are negligible,and fluid dynamics is employed to describe the impact process.Efficient numerical tools for precisely predicting the damage degree can greatly accelerate the design and optimization of advanced protective structures.Current hypervelocity impact research primarily focuses on the interaction between projectile and front plate and the movement of debris cloud.However,the damage mechanism of debris cloud impacts on rear plates-the critical threat component-remains underexplored owing to complex multi-physics processes and prohibitive computational costs.Existing approaches,ranging from semi-empirical equations to a machine learningbased ballistic limit prediction method,are constrained to binary penetration classification.Alternatively,the uneven data from experiments and simulations caused these methods to be ineffective when the projectile has irregular shapes and complicate flight attitude.Therefore,it is urgent to develop a new damage prediction method for predicting the rear plate damage,which can help to gain a deeper understanding of the damage mechanism.In this study,a machine learning(ML)method is developed to predict the damage distribution in the rear plate.Based on the unit velocity space,the discretized information of debris cloud and rear plate damage from rare simulation cases is used as input data for training the ML models,while the generalization ability for damage distribution prediction is tested by other simulation cases with different attack angles.The results demonstrate that the training and prediction accuracies using the Random Forest(RF)algorithm significantly surpass those using Artificial Neural Networks(ANNs)and Support Vector Machine(SVM).The RF-based model effectively identifies damage features in sparsely distributed debris cloud and cumulative effect.This study establishes an expandable new dataset that accommodates additional parameters to improve the prediction accuracy.Results demonstrate the model's ability to overcome data imbalance limitations through debris cloud features,enabling rapid and accurate rear plate damage prediction across wider scenarios with minimal data requirements.
基金funded by“The Pearl River Talent Recruitment Program”in 2019(Grant No.2019CX01G338),。
文摘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.
基金supported by the National Natural Science Foundation of China(Grant Nos.52130805 and 51978516)Scientific Program of Shanghai Science and Technology Committee(Grant No.20dz1202200).
文摘For a tunnel driven by a shield machine,the posture of the driving machine is essential to the construction quality and environmental impact.However,the machine posture is controlled by the experienced driver of shield machine by setting hundreds of tunneling parameters empirically.Machine learning(ML)algorithm is an alternative method that can let the computer to learn from the driver’s operation and try to model the relationship between parameters automatically.Thus,in this paper,three ML algorithms,i.e.multi-layer perception(MLP),support vector machine(SVM)and gradient boosting regression(GBR),are improved by genetic algorithm(GA)and principal component analysis(PCA)to predict the tunneling posture of the shield machine.A set of the parameters for shield tunneling is extracted from the construction site of a Shanghai metro.In total,53,785 pairwise data points are collected for about 373 d and the ratio between training set,validation set and test set is 3:1:1.Each pairwise data point includes 83 types of parameters covering the shield posture,construction parameters,and soil stratum properties at the same time.The test results show that the averaged R2 of MLP,SVM and GBR based models are 0.942,0.935 and 0.6,respectively.Then the automatic control for the posture of shield tunnel is illustrated with an application example of the proposed models.The proposed method is proved to be helpful in controlling the construction quality with optimized construction parameters.
基金supported by the National High Technology Research and Development Program of China (No. 2012AA03A503)
文摘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.
基金the National Key Research and Development Program of China(Nos.2020YFF0218004 and 2020YFF0218003)the National Natural Science Foundation of China(No.52105074)。
文摘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.
基金Project(50425518) supported by National Outstanding Youth Foundation of China Project(2007CB714004) supported by National Basic Research Program of China
文摘The thrust hydraulic system of the prototype shield machine with pressure and flow compound control scheme was introduced. The experimental system integrated with proportional valves for study was designed. Dynamics modeling of multi-cylinder thrust system and synchronous control design were accomplished. The simulation of the synchronization motion control system was completed in AMESim and Matlab/Simulink software environments. The experiment was conducted by means of master/slave PID with dead band compensating flow and conventional PID regulating pressure. The experimental results show that the proposed thrust hydraulic system and its control strategy can meet the requirements of tunneling in motion and posture control for the shield machine, keeping the non-synchronous error within ±3 mm.
基金Project(2007CB714006) supported by the National Basic Research Program of China
文摘According to the actual engineering problem that the precise load model of shield machine is difficult to achieve,a design method of sliding mode robust controller oriented to the automatic rectification of shield machine was proposed. Firstly,the nominal load model of shield machine and the ranges of model parameters were obtained by the soil mechanics parameters of certain geological conditions and the messages of the self-learning of shield machine by tunneling for previous segments. Based on this rectification mechanism model with known ranges of parameters,a sliding mode robust controller was proposed. Finally,the simulation analysis was developed to verify the effectiveness of the proposed controller. The simulation results show that the sliding mode robust controller can be implemented in the attitude rectification process of the shield machine and it has stronger robustness to overcome the soil disturbance.
基金supported by the National Natural Science Foundation of China(No.52105074)the Open Project of State Key Laboratory of Shield Machine and Boring Technology(No.SKLST-2021-K02),China。
文摘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.
基金Project(2014FJ1002)supported by the Science and Technology Major Project of Hunan Province,ChinaProject(2012AA041803)supported by National High Technology Research and Development Program of China。
文摘A shield machine with freezing function is proposed in order to realize tool change operation at atmospheric pressure. Furthermore, the transformation project of freezing cutterhead and tool change maintenance method are put forward. Taking the shield construction of Huanxi Power Tunnel as an example, a numerical analysis of the freezing cutter head of the project was carried out. The results show that when the brine temperature is-25 °C, after 30 d of freezing, the thickness of the frozen wall can reach 0.67 m and the average temperature drops to-9.9 °C. When the brine temperature is-30 °C, after 50 d of freezing, the thickness of the frozen wall can reach 1.01 m and the average temperature drops to-12.4 °C. If the thickness of the frozen wall is 0.5 m and the average temperature is-10 °C, as the design index of the frozen wall, the brine temperature should be lower than-28 °C to meet the excavation requirements in 30 d. Analyzing the frozen wall stress under 0.5 m thickness and-10 °C average temperature condition, the tensile safety factor and compressive safety factor are both greater than 2 at the most dangerous position, which can meet the tool change requirements for shield construction.
基金Supported by National Natural Science Funds of China(Grant No.51275451)National Basic Research Program of China(973 Program,Grant No.2013CB035404)+1 种基金Science Fund for Creative Research Groups of National Natural Science Foundation of China(Grant No.51221004)National Hi-tech Research and Development Program of China(863 Program,Grant No.2013AA040203)
文摘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.
基金the National Natural Science Foundation of China under Grant 51807094。
文摘A category of permanent-magnet-shield(PM-shield)axial-field dual-rotor segmented switched reluctance machines(ADS-SRMs)are presented in this paper.These topologies are featured by using the magnetic material to shield the flux leakage in the stator and rotor parts.Besides,the deployed magnets weaken the magnetic saturation in the iron core,thus increasing the main flux.Hence,the torque-production capability can be increased effectively.All the PM-shield topologies are proposed and designed based on the magnetic equivalent circuit(MEC)model of ADS-SRM,which is the original design deploying no magnet.The features of all the PM-shield topologies are compared with the original design in terms of the magnetic field distributions,flux linkages,phase inductances,torque components,and followed by their motion-coupled analyses on the torque-production capabilities,copper losses,and efficiencies.Considering the cost reduction and the stable ferrite-magnet supply,an alternative proposal using the ferrite magnets is applied to the magnetic shielding.The magnet demagnetization analysis incorporated with the thermal behavior is performed for further verification of the motor performance.
基金Project(51305328)supported by the National Natural Science Foundation of ChinaProject(2012AA041803)supported by the NationalHigh Technology R&D Program of China+1 种基金Project(GZKF-201210)supported by the Open Fund of State Key Laboratory of Fluid Power Transmission and Control of Zhejiang University,ChinaProject(2013M532031)supported by the China Postdoctoral Science Foundation
文摘A motion parameter optimization method based on the objective of minimizing the total energy consumption in segment positioning was proposed for segment erector of shield tunneling machine. The segment positioning process was decomposed into rotation, lifting and sliding actions in deriving the energy calculation model of segment erection. The work of gravity was taken into account in the mathematical modeling of energy consumed by each actuator. In order to investigate the relationship between the work done by the actuator and the path moved along by the segment, the upward and downward directions as well as the operating quadrant of the segment erector were defined. Piecewise nonlinear function of energy was presented, of which the result is determined by closely coupled components as working parameters and some intermediate variables. Finally, the effectiveness of the optimization method was proved by conducting a case study with a segment erector for the tunnel with a diameter of 3 m and drawing comparisons between different assembling paths. The results show that the energy required by assembling a ring of segments along the optimized moving path can be reduced up to 5%. The method proposed in this work definitely provides an effective energy saving solution for shield tunneling machine.
文摘Shield machine is the major technical equipment badly in need in national infrastructure construction. The service conditions of shield machine are extremely complex. The driving interface load fluctuation caused by geological environment changes and multi field coupling of stress field may lead into imbalance of redundant drive motors output torque in main driving system. Therefore, the shield machine driving synchronous control is one of the key technologies of shield machine. This paper is in view of the shield machine main driving synchronous control, achieving the system＇s adaptive load sharing. From the point of view of cutterhead load changes, nonlinear factors of mechanical transmission mechanism and the control system synchronization performance, the authors analyze the load sharing performance of shield machine main drive system in the event of load mutation. The paper proposes a data-driven synchronized control method applicable to the main drive system. The effectiveness of the method is verified through simulation and experimental methods. The new method can make the system synchronization error greatly reduced, thus it can effectively adapt to load mutation, and reduce shaft broken accident.
基金Supported by National Natural Science Foundation of China and Shanxi Coalbased Low Carbon Joint Fund(Grant No.U1910211)National Natural Science Foundation of China(Grant Nos.51975024 and 52105044)National Key Research and Development Project(Grant No.2019YFC0121700).
文摘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.
基金Project(2023JBZY030)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(U1834208)supported by the National Natural Science Foundation of China。
文摘Compared with traditional feedback control,predictive control can eliminate the lag of pose control and avoid the snakelike motion of shield machines.Therefore,a shield pose prediction model was proposed based on dynamic modeling.Firstly,the dynamic equations of shield thrust system were established to clarify the relationship between force and movement of shield machine.Secondly,an analytical model was proposed to predict future multistep pose of the shield machine.Finally,a virtual prototype model was developed to simulate the dynamic behavior of the shield machine and validate the accuracy of the proposed pose prediction method.Results reveal that the model proposed can predict the shield pose with high accuracy,which can provide a decision basis whether for manual or automatic control of shield pose.
基金supported by the National Natural Science Foundation of China(Grant Nos.52378392,52408356)the Foal Eagle Program Youth Top-notch Talent Project of Fujian Province,China(Grant No.00387088).
文摘Although machine learning models have achieved high enough accuracy in predicting shield position deviations,their“black box”nature makes the prediction mechanisms and decision-making processes opaque,leading to weaker explanations and practicability.This study introduces a novel explainable deep learning framework comprising the Informer model with enhanced attention mechanisms(EAMInfor)and deep learning important features(DeepLIFT),aimed at improving the prediction accuracy of shield position deviations and providing interpretability for predictive results.The EAMInfor model attempts to integrate channel attention,spatial attention,and simple attention modules to improve the Informer model's performance.The framework is tested with the four different geological conditions datasets generated from the Xiamen metro line 3,China.Results show that the EAMInfor model outperforms the traditional Informer and comparison models.The analysis with the DeepLIFT method indicates that the push thrust of push cylinder and the earth chamber pressure are the most significant features,while the stroke length of the push cylinder demonstrated lower importance.Furthermore,the variation trends in the significance of data points within input sequences exhibit substantial differences between single and composite strata.This framework not only improves predictive accuracy but also strengthens the credibility and reliability of the results.
文摘At present, in the process of urban construction, there is no lack of various types of shield machine application, because of its higher requirements for installation technology, so it is of practical significance to analyze the downhole installation process and matters needing attention. Taking the downhole installation technology of Herrick shield machine as an example, this paper briefly expounds the downhole installation technology of shield machine.
