Search

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 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]

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]

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]

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]

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 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.)

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]

During shield machine tunneling, the earth pressure in the sealed cabin must be kept balanced to ensure construction safety. As there is a strong nonlinear coupling relationship among the tunneling parameters, it is difficult to control the balance between the amount of soil entered and the amount discharged in the sealed cabin. So, the control effect of excavation face stability is poor. For this purpose, a coordinated optimization control method of shield machine based on dynamic fuzzy neural network (D-FNN) direct inverse control is proposed. The cutter head torque, advance speed, thrust, screw conveyor speed and earth pressure difference in the sealed cabin are selected as inputs, and the D-FNN control model of the control parameters is established, whose output are screw conveyor speed and advance speed at the next moment. The error reduction rate method is introduced to trim and identify the network structure to optimize the control model. On this basis, an optimal control system for earth pressure balance (EPB) of shield machine is established based on the direct inverse control method. The simulation results show that the method can optimize the control parameters coordinately according to the changes of the construction environment, effectively reduce the earth pressure fluctuations during shield tunneling, and can better control the stability of the excavation surface. [ABSTRACT FROM AUTHOR]

Geotechnical and tunneling aspects during the construction of the U5 in Frankfurt am Main The new district Europaviertel in Frankfurt is developed, and will be connected to the public transport system. Therefore, two tunnel tubes of 7.1 m in diameter and approximately 840 m in length will be excavated with an EPB‐TBM. The ARGE U5 Europaviertel, PORR and Stump, were selected by the client, the Stadtbahn Europaviertel Projektbaugesellschaft (SBEV), to build the tunnels. The start for the tunnel drive of the south tube was in August 2019. The project has challenging subsoil conditions. Especially the Frankfurt Clay with chalk and sandstone layers is important but also the western part, directly after the start of each tunnel drive, is challenging due to the heterogeneous subsoil conditions, too. [ABSTRACT FROM AUTHOR]

Mud and gravel deposition often occurs in circulation systems of pumps. In this paper, a liquid-solid coupling method combining CFD and DEM is proposed to study two-phase flow behavior of water, The flow characteristics of water-clay mud and the motion characteristics of gravel were simulated under different discharge pipe bending radii and geometry shape conditions. Numerical results showed that for a circular cross section pipe with a fixed bending radius, the collision times between the gravel particles and the pipe wall decreased with the increase of the bending radius. [ABSTRACT FROM AUTHOR]

The Zhanjiang Bay Sea-crossing Tunnel is the first phase of an ambitious plan of the Golden Triangle Economic Zone in southwestern China and passes underneath the deepest artificial shipping channel with the highest level in Asia. The tunnel is a world-record extralong and small-diameter corridor constructed using an uninterrupted single-end shield tunneling method in subsea soft ground under ultrahigh hydraulic pressure for water conveyance. This case study first highlights the engineering challenges of constructing the sea-crossing shield tunnel in subsea soft ground under ultrahigh hydraulic pressure. A series of key techniques are then investigated and some innovations are proposed to address the engineering challenges in the following four key aspects of the sea-crossing shield tunneling process: (a) optimal design of segmental linings; (b) adaptive reformation of the shield machine; (c) structural construction of deep vertical shafts; and (d) supporting techniques of long-distance advancing. On the basis of the field monitoring and numerical analyses, it is concluded that the implemented key techniques ensure the successful management and control of the engineering challenges in terms of optimizing the segmental lining, selecting the shield machine and constructing the vertical working shaft during the sea-crossing shield tunneling process with limited geological investigation data available under submarine conditions. [ABSTRACT FROM AUTHOR]

Earth pressure balanced (EPB) shield machines are large and complex mechanical systems and have been widely applied to tunnel engineering. Tunnel face stability evaluation is very important for EPB shield machines to avoid ground settlement and guarantee safe construction during the tunneling process. In this paper, we propose a novel earth pressure field modeling approach to evaluate the tunnel face stability of large and complex EPB shield machines. Based on the earth pressures measured by the pressure sensors on the clapboard of the chamber, we construct a triangular mesh model for the earth pressure field in the chamber and estimate the normal vector at each measuring point by using optimization solution and projection Delaunay triangulation, which can reflect the change situation of the earth pressures in real time. Furthermore, we analyze the characteristics of the active and passive earth pressure fields in the limit equilibrium states and give a new evaluation criterion of the tunnel face stability based on Rankine's theory of earth pressure. The method validation and analysis demonstrate that the proposed method is effective for modeling the earth pressure field in the chamber and evaluating the tunnel face stability of EPB shield machines. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central South University 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.)

Earth pressure balance (EPB) shield has been widely used in underground construction. The excavation face stability is crucial to avoid the accidents caused by EPB shield tunneling, so that it is very important to propose an effective control method for the earth pressure balance in sealed cabin. Considering the problem that stable automatic control of the earth pressure in shield's sealed cabin is difficult, an optimal control method of the earth pressure is proposed based on action-dependent heuristic dynamic programming (ADHDP), which can realize online autonomous learning and adaptive control in tunneling process. According to Bellman's principle of optimality, the cost function with respect to the sealed cabin's earth pressure is given. In addition, the action network and critic network of ADHDP controller are constructed. The critic network approximates the cost function and feeds error back into the action network. With the goal of minimizing the cost function, the action network utilizes the critic network's error to optimize screw conveyor speed. The simulation results show that the earth pressure controller based on ADHDP can realize the earth pressure balance control, and the control process is steadier. Moreover, ADHDP controller has good dynamic performance and anti-interference ability. • Action-dependent heuristic dynamic programming (ADHDP) is used to control earth pressure balance in sealed cabin. • Based on Bellman's principle of optimality, ADHDP controller for earth pressure balance is designed. • ADHDP controller can realize earth pressure balance control, and the control process is steadier. • The proposed method has shorter adjusting time and strong anti-interference ability. [ABSTRACT FROM AUTHOR]

• The efficiency enhancement method of abrasive water jet slit-assisted shield cutter cutting reinforced concrete was investigated. • Revealed the efficiency enhancement mechanism of abrasive water jet slit-assisted shield cutters. • The main influencing factors affecting the effectiveness of abrasive water jet slit-assisted shield cutters were discussed. To improve the efficiency and safety of shield machines cutting reinforced concrete (RC) obstacles, using an abrasive waterjet (AWJ)-assisted shield cutter to cut RC is considered a feasible solution. To explore the effect of an AWJ on a shell cutter cutting RC, laboratory experiments and numerical simulation methods were used to compare the breaking performance and damage mechanism of a shell cutter cutting RC with and without a slit. The results showed that the waterjet pre-cutting slit could effectively reduce the cutter force of the shell cutter and promote the cutting of steel bars. The reduction rate of the cutter force by the waterjet slit increased and then decreased with an increase in the penetration degree. When the penetration degree was 1.5 mm, the waterjet cutting slit had the best reduction effect on the cutting force. An optimal critical value was found for the waterjet cutting slit of the steel bar; when the waterjet slit was larger than ½ of the diameter of the steel bar, the steel bar was prone to tensile fracture under the cutting action. The waterjet pre-cutting slit reduced the residual cross-sectional area and stress distribution of the steel bar, which promoted the bending and tensile fracture of the steel bar and thus improved the cutting efficiency. Furthermore, construction recommendations for the AWJ-assisted shield cutting of RC obstacles were identified. The findings presented in this paper can provide useful reference information for the design and construction of AWJ shield machines. [ABSTRACT FROM AUTHOR]

• It proposes a geological adaptive operating parameters decision-making method. • It also optimizes parameters of screw machine rotation speed and soil bin pressure. • A D-ResNet network is proposed to predict tunneling speed and cutterhead torque. • Optimization time of our method is much lower than existing optimization methods. • Our decision-making method effectively balance efficiency and energy consumption. The performance of shield machine is sensitive to geological conditions and its operation depends on driver experience. Based on D-ResNet network and improved inertia weight adaptive constrained particle swarm optimization algorithm (IWACPSO), this study proposes an adaptive operating parameters decision-making method for shield machine considering geological environment. First, a novel D-ResNet network is proposed to establish the mapping models between geological type, operating parameters and tunneling speed, cutterhead torque, respectively. Then, we design the evaluation indicators of tunneling performance considering construction efficiency and considering both construction efficiency and energy consumption, respectively, and build the mapping model between operating parameters and performance evaluation indicators. On this basis, the optimization equation with operation parameters as control variables and improving tunneling performance as the goal is established. In order to solve the decision results of optimal operating parameters under various geological types, we present an enhanced inertia weight adaptive constrained particle swarm optimization algorithm. Finally, the proposed D-ResNet and IWACPSO-based decision-making method are verified by the actual construction data of subway project in Singapore. The results show that the R2 values of the proposed D-ResNet network for predicting the tunneling speed and cutterhead torque reach 0.98 and 0.96, respectively, which are much better than AdaBoost, support vector regression, multiple linear regression and deep neural network. The IWACPSO-based decision-making method could accurately solve the optimal solution of operating parameters, and the optimization time is much lower than that of ant colony algorithm, genetic algorithm and exhaustive method. Moreover, operation parameter decision-making method considering construction efficiency increased the tunneling speed by 61.02% on average, and the operation parameter decision-making method considering construction efficiency and energy consumption increased the tunneling speed by 59.55% on average, while the tunneling specific energy decreased by 20.50% on average compared with the former. [ABSTRACT FROM AUTHOR]

Earth pressure in sealed chamber is affected by multisystem and multifield coupling during shield tunneling process, so it is difficult to establish a mechanism earth pressure control model. Therefore, a data-driven modeling method of earth pressure in sealed chamber is proposed, which is based on parallel least squares support vector machine optimized by parallel cooperative particle swarm (parallel cooperative particle swarm optimization-partial least squares support vector machine). The vectors are parallel studied according to different hierarchies firstly, then the initial classifiers are updated by using cross-feedback method to retrain the vectors, and finally the vectors are merged to obtain the support vectors. The parameters of least squares support vector machine are optimized by the parallel cooperative particle swarm optimization, so as to predict quickly for large-scale data. Finally, the simulation experiment is carried out based on in-site measured data, and the results show that the method has high computing efficiency and prediction accuracy. The method has directive significance for engineering application. [ABSTRACT FROM AUTHOR]

Owing to the complex fault information features in the operation of complex large-scale electromechanical systems, the existing semi-artificial state detection method is unable to obtain fast, accurate, or reliable diagnosis results, which causes significant loss to engineering construction projects. To solve this problem, a fault diagnosis method for a shield machine system based on an extension neural network is proposed. Using a matter-element model to describe the fault characteristic information, the upper and lower bounds of the classical domain of the characteristic index are used as the double weights of the neural network. Then, a fault diagnosis is made using the extension distance as the measuring tool, the algorithm structure and flow are constructed, the steps of the algorithm are given, and the effectiveness of the method is verified using tunnel boring machine as an example. The simulation results show that this method simplifies the network structure, improves the convergence speed, and obtains consistent diagnostic results. This research can provide a new method and idea for solving the problems of time, and manpower consumption, as well as the cost, of a fault diagnosis of a semi-automatic shield machine system. [ABSTRACT FROM AUTHOR]

• The proposed method constructs physical models for various hydraulic components and investigates the fault mechanisms of shield machine hydraulic systems to provide theoretical basis for fault injection. • relying solely on the physical models themselves does not guarantee perfect match with the actual physical systems. To eliminate the uncertainties arising from the actual operating conditions of hydraulic systems, a physical model correction method based on cosine similarity is proposed to adjust the sensitive parameters of the physical models and ensure their validity. • Fault injection is then performed based on statistically collected high-frequency fault records. • A new validation method is introduced, using simulated data as the training set and measured data as the test set, to demonstrate the effectiveness of the proposed method. The hydraulic system of shield machine is critical component, and any faults in this system can have severe consequences on construction progress and even pose risks to the safety of construction personnel. However, the high manufacturing and construction costs of shield machines, as well as the use of highly reliable hydraulic components, make it challenging to obtain a large number of fault samples that accurately reflect the actual operating conditions of the hydraulic system. This limitation is one of the major obstacles to the practical application of intelligent fault diagnosis in shield machines. Furthermore, due to the large scale and complex structure of the hydraulic system in shield machines, few scholars have studied the bottleneck that restricts the fault diagnosis of the hydraulic system of the shield machine. To address these challenges, this paper proposes physical model-driven approach for fault diagnosis in shield machine hydraulic systems, aiming to overcome the lack of fault feature information during the fault diagnosis process. Firstly, physical models for various hydraulic components of the shield machine hydraulic system are established, and the relationship between the physical models and fault characteristics is investigated to provide a theoretical basis for fault injection. Then, a physical model correction method based on cosine similarity is proposed. Using recorded high-frequency fault data from shield machine hydraulic systems, fault injection is performed to obtain simulated data that effectively reflects the actual fault states and normal states of the hydraulic system. Finally, using the simulated data as training samples and measured data as test samples, various artificial intelligence diagnosis models, such as Support Vector Machines (SVM), Extreme Learning Machines (ELM), and Convolutional Neural Networks (CNN), are employed for diagnostic classification. The classification results demonstrate the feasibility and effectiveness of the physical model-driven approach for fault diagnosis in shield machine hydraulic systems. Furthermore, this approach exhibits strong universality and scalability, providing a solution for fault diagnosis scenarios with limited or even no fault samples. [ABSTRACT FROM AUTHOR]

In order to reduce the construction risk caused by human operation error and improve the geological adaptive ability of the shield machine, an autonomous intelligent control method is proposed for shield machine within the framework of interaction–judgment–decision based on Deep Deterministic Policy Gradient (DDPG) deep reinforcement learning in this study. Due to the strong nonlinear relationship between the shield machine's tunneling parameters, this research builds a deep reinforcement learning environment using mechanism model of sealed cabin pressure. DDPG agent model of the shield machine is established to replace the shield machine to interact and train with the geological environment. By minimizing the difference between the target pressure setting value and the sealed cabin pressure value, the dynamic balance between the sealed cabin pressure and the pressure on the excavation surface is realized, and the best strategy is obtained. Through real-time interaction with the geological environment, the method in this paper can dynamically adjust the tunneling parameters, accurately control the sealed cabin pressure, and has a strong geological adaptive ability. By realizing the intelligent decision-making of the tunneling parameters, it greatly improves the independent decision-making ability of the shield machine system, reduces the inaccuracy of human operation, and provides an effective guarantee for the efficient and safe operation of the shield machine. This study applies deep reinforcement learning technology to the control field of earth pressure balance shield machine, promotes AI technology, and provides a new idea for the development of AI construction technology in engineering field. [ABSTRACT FROM AUTHOR]

• A novel approach for adjusting the shield's attitude and position during tunneling was proposed. • A hybrid deep learning prediction framework including comprehensive feature evaluation method, EEMD, CA-CNN and GRU-TA is established. • The test is carried out with the field data collected from the railway in Shanghai, China. • The proposed model is significantly superior to other models in prediction accuracy and prediction efficiency. • The contributions of CA and TA in the model are revealed. In constructing long-distance shield tunnels, it is a difficult challenge to maintain the tunneling trajectory consistent with the design tunnel axis. The accurate prediction of the attitude and position during tunneling can reap the advantage of optimizing the tunneling operation parameters in advance leading to the best tunneling trajectory. This study investigates a framework based on a hybrid deep learning model for attitude and position prediction of the shield machine. This prediction framework contains comprehensive feature evaluation method, ensemble empirical mode decomposition (EEMD), convolutional neural network (CNN), and gate recurrent unit (GRU). The introduction of channel attention and temporal attention in CNN and GRU further strengthens the spatial and temporal feature extraction ability of the model. The performance of the prediction framework is verified through a case study with data collected from the Shanghai urban railway tunnel section. Results reveal that the proposed model with dual attention significantly outperforms other models in prediction accuracy and speed. The bias of feature data can be alleviated by introducing channel attention, and using temporal attention can capture long-distance temporal feature data. The model can support shield construction safety by adjusting the operation parameters, and an application example is used to demonstrate the feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

• An analytical approach was developed to investigate the effects of slurry clogging. • The workface deformation caused by the reverse circulation of slurry was analyzed. • The ultimate volume deviation ratio of slurry was proposed to alarm the collapse. • The validity of the analytical approach was verified by the field measurements. The pressurized slurry is the essential part of the slurry pressure balanced shield (SPBS) for stabilizing the working face and transporting the excavated soil. If clogging occurs in case of adverse ground conditions or equipment failure, a series of consequences will happen, such as sudden changes in slurry pressure, massive ground loss, and deformation or even collapse at tunnel workface. The present study reviewed and summarized the clogging mechanism of the slurry shield under different ground conditions. Then, based on the law of conservation of mass and Bernoulli's Equation, an analytical approach was developed to investigate the effects of clogging on the slurry velocity and pressure along with the deformation of tunnel workface. Furthermore, a case study and field measurements were conducted to validate the analytical method. The significant findings drawn from the study can be summarized as follows: (1) The clogging of a slurry shield imposes severe effects on the slurry pressure balancing mechanism of shield tunneling. It can occur in both cohesive and non-cohesive ground with different formation mechanisms. (2) During the initial clogging, the slurry pressure above the spring line increases, and that below the spring line decreases. With the clogging deteriorating, the slurry velocity drops considerably at the top of the deposit area and escalates near the discharging port. The presence and increase of deposit lead to a sharp rise of slurry pressure in the slurry chamber, especially in the clogging region. (3) The reverse circulation of the slurry system (RCSS) is useful to clear the clogging. Still, the slurry deposits at the bottom will obstruct the slurry reverse flow, resulting in inward deformation of the tunnel face and a drastic increase in overcutting volume. (4) The ultimate volume deviation ratio of the slurry was proposed as a useful index to alarm the potential risk of collapse induced by the RCSS. (5) The timely adoption of RCSS is strongly suggested when any abnormal changes in the slurry pressure or the ultimate volume deviation ratio are observed for the first time. [ABSTRACT FROM AUTHOR]

The maximum ground surface settlement prediction is a complex problem as the settlement depends on plenty of intrinsic and extrinsic factors. To obtain the approximate range of the settlement, a hybrid prediction dataset including the geological and construction parameters is built using spatial and temporal series according to the sampling methods. The settlement prediction task is transformed into a multi-modal and multi-variate series prediction task. Hence, a spatial-temporal fusion network (STF-Network) is proposed. The spatial-temporal fusion mechanism is firstly designed to establish the spatial-temporal fusion map, which makes spatial and temporal series interact earlier. Then, the 3D residual unit structure is designed to capture the features of temporal series and spatial-temporal fusion map, and two fully-connected layers are established to capture the spatial structural information. Finally, the final output is merged by the three components. The experimental results for STF-Network demonstrate the superiority over state-of-the-art methods. • Establishing a hybrid prediction dataset using spatial and temporal series • Designing a spatial-temporal fusion network (STF-Network) for settlement prediction • Employing STFM make spatial and temporal series interact earlier • Designing 3D-ResUnit structure based on the periodicity of the temporal series data [ABSTRACT FROM AUTHOR]

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

In order to avoid safety accidents caused by the unbalance of earth pressure in the chamber during the tunneling process of shield tunneling machine, it is very crucial to make a scientific and accurate dynamic prediction of earth pressure change. Therefore, a hybrid deep learning model is built by using discrete wavelet transform (DWT), one-dimensional convolution neural network (1DCNN) and long-short term memory (LSTM), implementing the scientific prediction of multipoint earth pressure variation trend in this paper. The DWT is introduced to reduce noise in training data, reduce training cost, and improve training accuracy; the 1DCNN is employed to extract features quickly and reduce the training time of LSTM; in order to further improve the prediction performance of the model, the Ranger optimizer is used to optimize the LSTM, which not only improves the prediction accuracy, but also makes the training process of the prediction model more stable. Finally, the validity of the model is verified based on the actual construction data. The results show that the overall and local earth pressure variation trends in the sealed chamber are clearly visible, which can provide decision-making basis for the shield machine to realize automatic, intelligent and safe excavation construction. • The hybrid deep learning is used to construct the prediction of earth pressure. • Range optimizer is used in LSTM to further improve the prediction performance. • The model in this paper has strong practicability and high prediction accuracy. • The prediction method of earth pressure variation trend is proposed. [ABSTRACT FROM AUTHOR]