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Scientific and precise control of tunnelling parameters is of utmost importance during the construction of shield machines. Given the complexity of the working environment, manual operation is highly prone to causing safety accidents. Therefore, achieving intelligent control of the shield machine is crucial. Based on this, this paper proposes a geological adaptive intelligent control method of earth pressure balance shield machine using the Deep Deterministic Policy Gradient (DDPG) algorithm as the framework, with Actor-Critic as the basis. Firstly, DDPG agent is constructed to replace the screw conveyor control system as the main body of strategy implementation. Secondly, an environmental model is established by utilizing the mechanism model between the sealed cabin pressure and the screw conveyor speed. The real-time sealed cabin pressure, target pressure, and pressure error serve as the state space, while the screw conveyor speed is used as the action space. A combined reward function is set based on safety and accuracy. Finally, the Actor network interacts with the environment under the supervision of the reward function and Critic network. Successful training is achieved when the cumulative reward value is maximized, resulting in the output of optimal control strategy. In this paper, the method dynamically regulates the screw conveyor speed by interacting with the geological environment, to realize the precise control of the sealed cabin pressure and ensure the dynamic balance between sealed cabin pressure and excavation face pressure. The test results show that this method has a good control effect on the sealed cabin pressure under various geological conditions, and can complete 72 kinds of soil transition tasks. It has strong soil adaptability and can respond well to the dynamic changes of soil conditions. This approach enhances the intelligence of the shield machine, mitigating inaccuracies attributed to human operation, which provides a guarantee of safe shield machine operation, whilst exhibiting valuable engineering applications. [ABSTRACT FROM AUTHOR]

Purpose: In response to the problem that the third-stage sun gear of the shield machine three-stage planetary reducer is prone to tooth wear and seriously affects the meshing force, the influence of tooth wear of the third-stage sun gear on the dynamic characteristic is explored. Methods: First, a healthy dynamic theoretical model of a three-stage planetary reducer is established, and the theoretical meshing force is obtained. The simulation analysis of the meshing force is conducted using Adams software, and the correctness of the simulation model is verified by comparing the theoretical meshing force. A fault dynamic model of the three-stage planetary reducer is established under different wear depths of the third-stage sun gear tooth, and the influences of tooth wear depth on the all stages meshing forces are analyzed. Results: The research results indicate that the errors of average values between the simulation results in a healthy state and the theoretical meshing forces are less than 7%. The frequency-domain diagrams of all stages meshing forces under the wear condition of the third-stage sun gear tooth show low-frequency amplitudes, and the low-frequency amplitudes increase with the increase of wear depth. The wear of the third-stage sun gear tooth has a negligible impact on the other stages meshing forces. Conclusions: The research results can provide a theoretical basis for the operation status detection and fault diagnosis of the shield machine three-stage planetary reducer. [ABSTRACT FROM AUTHOR]

Copyright of Lubrication Engineering (0254-0150) is the property of Editorial Office of LUBRICATION ENGINEERING and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Zhejiang University: Science A is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the construction project of composite stratum in the Guangzhou subway tunnel, the frequent occurrence of chordal eccentric wear of disk cutters has become a major problem restricting the smooth progress of the project. According to the disk cutter rock breaking mechanism, the wear morphology of the chordal eccentric wear disk cutter, the mechanical properties of the cutter ring material, the chemical element composition, and the observation of metallographic structure were comprehensively analyzed, and the causes of the chordal eccentric wear of the cutter were determined in combination with maintenance methods. The results of the mechanical property test show that the chemical element composition of the cutter ring material complies with the 4Cr5MoSiV1 steel standard and also has high hardness and good toughness. The metallographic structure is fine and is not prone to brittle fracture and overload. On this basis, combined with the cutter maintenance report, it is determined that the reason for the occurrence of chordal eccentric wear caused by the non-rotation of the disk cutter is the mismatch between the setting of starting torque and the excavation parameters. According to the critical condition for chordal eccentric wear of the cutter, some suggestions are put forward for the adjustment of the tunneling parameters to avoid the occurrence of chordal eccentric wear disk cutter during excavation. [ABSTRACT FROM AUTHOR]

During the construction process of tunnels, the cutterhead of shield tunneling machines may get clogged due to clay adhesion, which may seriously affect the efficiency of the project. Therefore, finding an intelligent diagnosis method to detect the clogging status is of great importance. In this study, a deep residual network-based method for diagnosing cutterhead clogging on shield tunneling machines is proposed. First, working state data of the shield tunneling machine is screened out, and parameters reflecting the clogging state are selected for further analysis. After eliminating extreme outliers, an empirical formula is proposed to label the data. At the same time, several time-domain features of the selected excavation parameters within every five minutes are extracted. These features are then fed into the proposed model as the input data to realize clogging detection. Because the original dataset is unbalanced, the combination of f1-score and accuracy is used to evaluate the performance of the proposed model. The results show that the accuracy of the proposed algorithm reaches 95.71%, which is 1.21%, 2.84%, 9.84%, 6.04%, and 0.86% higher than the support vector machine-based, random forest-based, AdaBoost-based, extreme gradient boosting-based and deep neural network-based methods. The f1 score of the proposed model is 0.923, which is also 0.038, 0.042, 0.269, 0.169 and 0.02 higher than those compared methods. Therefore, the proposed deep residual network-based method can accurately detect cutterhead clogging conditions. [ABSTRACT FROM AUTHOR]

During tunneling process, it is of critical importance to dynamically adjust operation parameters of shield machine due to changes of geological conditions. Cutterhead torque is one of the key load parameters, and its accurate prediction could adjust operational parameters including cutterhead rotational speed and tunneling speed in advance and avoid potential cutterhead jamming. Based on operation and state data collected by the monitoring system, we propose a residual-convolutional-LSTM neural network (RCLSTMNet) for forecasting cutter head torque in shield machine. On the basis of correlation analysis, parameters closely related to cutter head torque are selected as inputs by employing cosine similarity, which significantly reduces input dimension. Convolutional-LSTM neural network is fused and constructed for extracting deep useful features, while residual network module is utilized to avoid gradient disappearing and improve regression performance. Comparisons with recent data-driven cutterhead torque prediction methods are made on the actual engineering datasets, which demonstrate the presented RCLSTMNet outperforms the other data driven models in most cases. Moreover, the predicted curves of cutterhead torque using the proposed RCLSTMNet coincide with the actual curves much better than predicted curves using the other models. Meanwhile, the highest and average accuracy of RCLSTMNnet reach 98.1% and 95.6%, respectively. [ABSTRACT FROM AUTHOR]

Aiming at the lack of accuracy and effectiveness of the current shield machine speed prediction method, the study proposes to improve the residual network and combine this improved algorithm with the surrounding rock category prediction model to construct the underground shield machine digging speed prediction model. With an average accuracy of 87.4%, an F1 value of 0.86, and an accuracy of 0.84, the study’s prediction model of surrounding rock categories was determined to be valid and superior to the other compared models. The effectiveness of the improved residual algorithm constructed by the study was verified, and it was found to have a better fit to the actual values, with a maximum deviation error value of 4.6 mm/min and a root mean square error of 1.835, which was lower than the other comparative algorithms. The empirical analysis of the underground shield machine digging speed prediction model constructed by the study revealed that the area under the line of the work characteristic curve of the subjects was 0.74, and the F1 value was 0.35, and the accuracy was as high as 84.6%, which was significantly better than that of other comparative models. The shield machine digging speed prediction model, which is based on an enhanced residual network built in the study, performs better than other comparison models, according to the results, which can serve as a theoretical guide for the digital management of coal mine output.

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 (R2) 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.

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.

The real-time control and prediction of attitude and moving trajectory in shield tunneling is challenging, especially in the small radius of curvature. The present study focuses on developing a method for predicting the attitude of a shield based on a new model that incorporates a wavelet transform (WT) into a random forest (RF) deep learning method. Daubechies (dbN) is selected among several wavelet bases to denoise the original tunneling parameters in the time and frequency domains, and optimal wavelet parameters for different input variables are obtained. The proposed WT–RF model was implemented and validated by a case study of the Shizimen Tunnel in Zhuhai, China. Sorted by the correlation coefficient between the input parameters and the output parameters, eight cases with different input parameter combinations were also selected for comparison. The results revealed that the developed wavelet signal denoiser-RF model can successfully predict the shield attitude parameters and that using a suitable combination of input parameters can greatly improve the prediction accuracy of the model. The number of input parameters is not a key factor for determining the prediction accuracy of the model, and adding less relevant input parameters will greatly reduce the prediction performance of the model. [ABSTRACT FROM AUTHOR]

This study aims to develop a rational theoretical model for cutterhead-soil interaction. The cutterhead-soil interaction mechanism is divided into two components: the cutting action of the cutter on the soil and the extrusion of the cutterhead on the soil. By enhancing the Mckyes–Ali model, we analyze and deduce the force state of the cutter during shield tunneling, obtaining a calculation method for determining the force on the cutter. Additionally, we conduct an in-depth analysis of the extrusion effect of the cutterhead on the soil during shield tunneling, utilizing the fundamental solution of the Kelvin problem. Based on these theoretical calculations, we validate the tunneling thrust and cutterhead torque of the shield using our self-developed multi-functional large-scale shield tunneling test platform. The test results demonstrate that the tunneling thrust and cutterhead torque derived from the established cutterhead-soil interaction model in this paper are relatively close to the experimental monitoring values. This provides a theoretical foundation for establishing reasonable shield tunneling loads.

Abstract 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 identification based on staged drilling sampling, the real-time stratum identification 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 identification 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 classification models are used to train and test the obtained effective feature data, and the model accuracy and recognition time are used as evaluation indicators, and the classification with the best combination with VFS is obtained. The experimental results of shield machine data of 6 different geological structures show that the average accuracy of 13 features obtained by VFS combined with different classification 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 identification in the process of tunnel construction, and can be matched with a variety of classifier algorithms. By combining 13 features selected from shield machine data features with random forest, the identification of the construction stratum environment of shield tunnels can be well realized, and further theoretical guidance for underground engineering construction can be provided.

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 identification based on staged drilling sampling, the real-time stratum identification 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 identification 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 classification models are used to train and test the obtained effective feature data, and the model accuracy and recognition time are used as evaluation indicators, and the classification with the best combination with VFS is obtained. The experimental results of shield machine data of 6 different geological structures show that the average accuracy of 13 features obtained by VFS combined with different classification 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 identification in the process of tunnel construction, and can be matched with a variety of classifier algorithms. By combining 13 features selected from shield machine data features with random forest, the identification of the construction stratum environment of shield tunnels can be well realized, and further theoretical guidance for underground engineering construction can be provided. [ABSTRACT FROM AUTHOR]

Copyright of Lubrication Engineering (0254-0150) is the property of Editorial Office of LUBRICATION ENGINEERING and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Lubrication Engineering (0254-0150) is the property of Editorial Office of LUBRICATION ENGINEERING and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Tunnel Construction / Suidao Jianshe (Zhong-Yingwen Ban) is the property of Tunnel Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes a modified process analysis method that combines with the input–output method for carbon emissions assessment in slurry shield tunnel construction. The method was applied to analyze the carbon emissions generated during the construction procedures of a slurry shield tunnel. The results indicate that the carbon emissions from building materials account for the majority of the total emissions, while those from the shield machine and construction procedure are relatively small. In addition, BP and CNN-LSTM neural network models were established to validate the accuracy of the calculation results with model error of 0.1031. Finally, recommendations for reducing carbon emissions in the construction course of slurry shield tunnels are provided. [ABSTRACT FROM AUTHOR]

To investigate the vibration response law of station structure vibration induced by shield machine passing through the underground diaphragm wall of the existing station, the vibration caused by the shield cutter head cutting the underground diaphragm wall was measured by the method of field measurement, and the impact of the vibration was evaluated. The research results showed that the main frequency of the background vibration was 30.8 Hz in the stop state, and the main frequency of the vibration generated by the cutter head was 89.3 Hz in the process of cutting the wall. Compared with the background vibration, the peak value and effective value of the vibration acceleration generated by the cutting wall increased at least one order of magnitude. The vibration wave transmitted to the station was significantly attenuated, and the maximum attenuation rate of the acceleration peak value can reach nearly 98.35%. Except for 31.5 Hz, the maximum vibration level of each frequency band of the station measurement point were all smaller than the standard control value. When the shield tunneling through the underground diaphragm wall, the maximum response value of the vibration speed was 1.3 mm/ s, which was less than the standard value given by domestic and foreign specifications. [ABSTRACT FROM AUTHOR]

Aiming at solving the problems of huge cost, long cycle and high risky during manual cutter changing process for shield machine, a cutter changing robot was designed to adapt to its narrow and complex space, the workspace and motion path of cutter changing robot arm were studied. Firstly, by analyzing the internal space and cutter layout characteristic, the configuration of the robot arm was proposed, and the dimension of each rod was designed using multi-objective optimization method. The kinematic model of the robot arm was established by using the D-H model method to analyze its reachable working space. While the inverse kinematic model of the robot arm was solved by the Newton-Raphson iterative method to complete the trajectory planning of the arm for all cutter locations, where the relationship between the target position and the joint variables was established. The motion stability of the robot was verified by virtual simulation and test. The results show that the working space of the designed robot arm meets the cutter changing requirements that all the cutter position can be reached. Moreover, the power output of the drive is smooth, which can transport the cutter weighing up to 250 kg. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Lubrication Engineering (0254-0150) is the property of Editorial Office of LUBRICATION ENGINEERING and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Lubrication Engineering (0254-0150) is the property of Editorial Office of LUBRICATION ENGINEERING and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Taking a running tunnel of Shanghai Railway Airport connecting line as a demonstration application project, the synchronous technology combining shield tunneling with segment assembling is proposed based on the active control on the oil pressure of the shield thrust system, which can solve the problem of the long construction period produced by a single shield machine employed in a long-distance shield tunnel project. The principle is to make full use of the extra stroke of propulsion cylinders generated by the axial insertion of the key block to assemble segments, and the theoretical operation time of a single ring can be reduced by 31.6%. Then, a large model test platform for this synchronization technology is established to verify its feasibility and reliability, and the redistribution method of the missing thrust force is introduced during the synchronous process, which is verified by the model test. The test results show that the actuators of the shield machine respond quickly, and the errors of the cylinder pressures and total thrust force of the propulsion system are controlled within ±2%. The attitude deviation of the shield machine is controlled within ±6 mm, and the error range of the driving speed is -2 to 4 mm/min. The segments are safe with the designed overburden thickness of 33 m, and the compressive safety coefficient reaches 1.68.

During the construction of tunnel using shield method, the rock breaking performance of hob is one of the key factors that determines the successful construction of subway tunnel. In this study, a mechanics model of shield hob based on the Colorado School of Mines (CSM) model and a discrete element model of shield hob based on particle flow code (PFC) are established on the basis of the shield construction of ultrahigh hard rock section from Agricultural and Forestry University Station to Hongtang Station of Fuzhou Metro Line No. 5. These models are used to reveal the rock breaking law under different hob types (single-edged hob, double-edged hob and edge hob) and numbers (single hob, double hobs, three hobs and full hobs). Laboratory test results show that the discrete element model based on PFC has high accuracy. Numerical simulation results show that the vertical force and rolling force of the single-edged hob and double-edged hob increase with the increase in the distance between the installation position of the hob and the centre of the cutter, whereas that of the edge hob are less affected. With the increase in the number of hobs, the hard rock gradually forms a through joint from a single crack, and the vertical force and torque of a single hob decrease. Comparative results of the theoretical and numerical simulation show that the maximum errors of vertical force and torque are 10.9% and 47.3%, respectively, and they are 11.3% and 37.5% of the rated value of No. 552 shield machine of China Railway. These values meet the engineering requirements and can provide reference for shield selection and shield tunnel construction in the future. [ABSTRACT FROM AUTHOR]

Copyright of China Rubber Industry is the property of Editorial Office of China Rubber Industry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Achieving multivariate long-distance forecasting of shield machine tunneling parameters remains a challenge due to the huge number of tunneling parameters and the complexity of the variation pattern. To solve this problem, a long-distance forecasting method called Adaptive Variational Mode Decomposition and Multi-Stage Stabilized Transformer-based (AVMD-MST) for multiple tunneling parameters is proposed. It uses adaptive VMD and normalization to stabilize the pre-processed tunneling parameters, and the stabilized transformer is designed to establish relationships between historical and future data. The results on two different projects show that the MAPE of the proposed method decreases on average by 12.31%–36.8% for the 180th step predictions compared to the state-of-the-art algorithms. Therefore, the idea of stabilization-prediction-inverse stabilization can achieve high precision multi-variable long-distance forecasting. In the future, geological information overcasting will be carried out on the basis of the multivariate long-distance forecasting model, which can help the driver to determine the tunneling strategy. • A long-distance forecasting method for multiple tunneling parameters is presented. • Adaptive VMD method is presented to determine optimal number of decomposition modes. • An inverse stabilization attention calculation is proposed to retain information. • AVMD-MST could output all tunneling parameters for a specified step ahead at once. • Forecasting errors of AVMD-MST are much smaller than those of existing methods. [ABSTRACT FROM AUTHOR]

To mitigate the potential hazards of shield tunneling misalignment (STM) caused by tunneling posture deviation, a method for optimizing operational parameters tailored to tunneling posture adjustment is developed. This paper presents a generative adversarial network (GAN) framework that incorporate a conditional generative adversarial network (CGAN) and two distinct discriminators (WGAN and Path GAN) to enhance the performance of the multi operational parameter generator. Based on engineering data, comparative experiments are designed to investigate the impact of the feature extraction methods, discriminators, and training strategies on the generation performance. Research has shown that an optimal generator scheme, comprising independent convolutional neural networks (CNNs), a summation feature fusion strategy, and a shared decoder, achieves remarkable performance with an MAE of 0.009, RMSE of 0.012, and average error scope of 0.073. Applications of the model confirm its ability to provide optimization suggestions for shield tunneling posture adjustments in engineering scenarios. • A new method for adjusting the operational parameters of the shield is proposed. • The method has been validated through the data collected from the railway. • Obtained a multi-parameter generator with high accuracy. • The influence of different factors on the performance of the generator are analyzed. [ABSTRACT FROM AUTHOR]

Shield thrust is a critical operational parameter during shield driving, which is of vital significance for adjusting operational parameters and ensuring efficient and safe propulsion of shield tunneling machine. In this paper, a novel hybrid prediction model (CLM) combining attention mechanism, convolutional neural networks (CNN) and Bi-directional long short-term memory (BiLSTM) network is proposed for shield thrust prediction. Correlation analysis based on Maximal Information Coefficient (MIC) between the thrust and other parameters is first conducted to select optimal parameters and reduce input dimension. An attention mechanism is introduced into CNN to distinguish the importance of different features, with the convolution layer and pooling layer further extracting dimension features of the data. Then, a BiLSTM neural network integrating first attention layer is employed to extract time-varying characteristics of the data, with a second attention layer added to capture important time information. Field data during shield cutting bridge piles are investigated to support and validate the effectiveness and superiority of the proposed method. Results show that the proposed CLM model are general enough to avoid overfitting problems and have good performance at prediction. The predicted value match reasonably well the monitoring data, with coefficient of determination ( $\text{R}^{2}$ ) equaling to 0.85, root mean square error (RMSE) equaling to 0.05, mean absolute error (MAE) equaling to 0.02. The CLM model in this paper can accurately predict the thrust even under complicated construction conditions, which provides reference for similar industrial application.

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Zhejiang University: Science A is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Tunnel Construction / Suidao Jianshe (Zhong-Yingwen Ban) is the property of Tunnel Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to solve the problems such as the kinematics modeling of the shield machine propulsion system and the difficulty of forward solution， the simplified equivalent model method is adopted to construct a simplified model of the shield machine propulsion system， and the parallel shield propulsion system of n-SPS is simplified to 4-SPS equivalent parallel propulsion mechanism and construct its kinematics model. According to the established equivalent mechanism kinematics model， a neural network-Newton hybrid algorithm is used to solve the shield tunneling pose， and the solution domain search function of the neural network is used to predict the initial value and substitute the prediction result into the Newton iteration method for calculation and solution. The pros and cons of the single neural network predicted value and the calculated results of the BP neural network-Newton iterative hybrid algorithm are analyzed and compared. The study found that the establishment of the equivalent model of the shield machine propulsion system can greatly simplify the complexity of kinematics modeling， and the use of hybrid algorithms to solve the kinematics positive solution has higher accuracy， which can improve the control accuracy of the shield machine excavation attitude， thereby improving the tunneling tunnel quality.

The use of subway tunnel engineering technology has become more professional and refined with the growth of society and the advancement of science and technology. The initial construction process of a subway tunnel shield is the most critical part of the entire engineering system. Shield launching period construction is the most prone to accidents in the shield construction process, directly related to the smooth through the shield tunnel. The line 1 of Ho Chi Minh (HCM) Metro is the first subway line, the full length of 19.7 km, the underground road length of 2.6 km from km 0 + 615 to km 2 + 360, from Ben Thanh market, and then through the Sai Gon river and 14 station (including 3 underground stations and 11 elevated stations), reach Suoi Tien park and is located in Long Binh area station, underground building blocks including Ben Thanh market station to Opera House station interval, Opera House station, Opera House station to Ba Son station interval. This paper selects Shield launching period of Opera House station to Ba Son shaft interval as an example, analyze the key construction technology, construction control parameters and launching considerations of shield machine.

It is easy to cause safety accidents due to the low precision and poor effect of earth pressure balance (EPB) control during shield machine tunneling process at present. So a data-driven optimization control method for earth pressure balance in sealed cabin of shield machine is proposed. Firstly, the earth pressure prediction model of four pressure monitoring points in the sealed cabin is established by using least squares support vector machine (LSSVM) method, and the penalty coefficient C and kernel parameters σ are optimized by particle swarm optimization (PSO). Then, an optimization function is established with the minimum sum of multi-point earth pressure prediction errors as the target. The optimal solution is obtained by using the fruit fly optimization algorithm (FOA) to solve the optimal screw conveyor speed, so as to realize the balance control of the earth pressure in sealed cabin. Finally, the simulation experiments are carried out based on field construction data. The results show that the method has great performance such as higher accuracy of calculation and better control effect, which can control the excavation face of shield machine more steady. [ABSTRACT FROM AUTHOR]

Shield machine is a complex large-scale tunneling equipment with multiple systems and driving sources. In order to improve the accuracy of fault diagnosis for shield machine, a method based on the combination of reverse feature elimination (RFE) and extreme learning machine (ELM) is proposed. For the characteristics of shield machine operation data with many dimensions and large quantity, the RFE method is introduced to reduce the dimension of the data, eliminate the redundant dimension and remove the correlation between features. To improve the accuracy and efficiency of fault diagnosis, the ELM neural network classifier model is built based on the extremely learning mechanism for fault diagnosis of shield machine. The simulation results based on the field construction data show that this method improves the accuracy of fault diagnosis of shield machine significantly and has good engineering application value. [ABSTRACT FROM AUTHOR]

The geological condition is complicated and changeable, and difficult to predict accurately, so the shield tunneling construction will face great risks, even lead to serious safety accidents. Real-time and accurate geological identification and prediction is an important guarantee for the safe construction of shield machine, which is also an important problem of shield technology. Based on the real-time tunneling control parameters of shield machine construction, an unsupervised data tree K-Means cluster method is proposed in this paper. Firstly, the correlation between shield tunneling parameters and geological types is analyzed to determine the main tunneling parameters which are greatly affected by geological changes. Secondly, the unsupervised data tree is used to optimize the K value of K-Means cluster algorithm, and then a geological recognition cluster algorithm based on main tunneling parameters is constructed. Finally, a simulation experiment is carried out based on the field construction data, and the accuracy of geological identification is 100%. The results show that the method can accurately identify geological categories and provide decision support for effective parameter regulation of shield machine. [ABSTRACT FROM AUTHOR]

Cutter-changing robot is a new product to automatically replace the cutter in shield machine, whose performance is affected by multiple criteria with diversity, multiplicity and correlation, which is a complex multiple criteria decision-making problem with limited and fuzzy available information. This paper proposes a comprehensive performance evaluation model including the performance indicator hierarchy, weight determination, measurement and assessment methods of indicators and sensitivity analysis. Fuzzy Analytical Hierarchy Process (FAHP) is applied to identify the construction requirements from external shield machine and environment and design characteristics in the whole cutter-changing process, weight for 3 first-level indicators and 14 s-level indicators are determined. The objective evaluation indexes measured by instrumentation and subjective evaluation indexes quantified by fuzzy logic are normalized within the range of 0–1. The scores and proportion of assessment grade for each indicator at each level are obtained through Fuzzy Comprehensive Evaluation (FCE). Furthermore, the sensitivity of weight and evaluation data on performance evaluation result are deeply analyzed. The applicability and validity of the proposed approach were demonstrated by a case study of a cutter-changing robot made by China Railway Construction Heavy Industry Co., Ltd. According to the result, operational capacity is the most important criteria in the first level, and cutter grasping ability is the most important indicator in the second level. Path planning ability is the weakest indicators. Cutter grasping ability and time of replacement are the most critical affecting factors. The proposed method not only helps the designers capture the weak indicators accurately and improve the design quality, but also provides engineers with a holistic perspective to the cutter-changing robot before actual application. [ABSTRACT FROM AUTHOR]

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