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

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

Sliding mode robust controller for automatic rectification of shield machine

According to the actual engineering problem that the precise load model of shield machine is difficult to achieve,a design method of sliding mode robust controller oriented to the automatic rectification of shield machine was proposed. Firstly,the nominal load model of shield machine and the ranges of model parameters were obtained by the soil mechanics parameters of certain geological conditions and the messages of the self-learning of shield machine by tunneling for previous segments. Based on this rectification mechanism model with known ranges of parameters,a sliding mode robust controller was proposed. Finally,the simulation analysis was developed to verify the effectiveness of the proposed controller. The simulation results show that the sliding mode robust controller can be implemented in the attitude rectification process of the shield machine and it has stronger robustness to overcome the soil disturbance.

Machine learning-based automatic control of tunneling posture of shield machine

For a tunnel driven by a shield machine,the posture of the driving machine is essential to the construction quality and environmental impact.However,the machine posture is controlled by the experienced driver of shield machine by setting hundreds of tunneling parameters empirically.Machine learning(ML)algorithm is an alternative method that can let the computer to learn from the driver’s operation and try to model the relationship between parameters automatically.Thus,in this paper,three ML algorithms,i.e.multi-layer perception(MLP),support vector machine(SVM)and gradient boosting regression(GBR),are improved by genetic algorithm(GA)and principal component analysis(PCA)to predict the tunneling posture of the shield machine.A set of the parameters for shield tunneling is extracted from the construction site of a Shanghai metro.In total,53,785 pairwise data points are collected for about 373 d and the ratio between training set,validation set and test set is 3:1:1.Each pairwise data point includes 83 types of parameters covering the shield posture,construction parameters,and soil stratum properties at the same time.The test results show that the averaged R2 of MLP,SVM and GBR based models are 0.942,0.935 and 0.6,respectively.Then the automatic control for the posture of shield tunnel is illustrated with an application example of the proposed models.The proposed method is proved to be helpful in controlling the construction quality with optimized construction parameters.

Motion control of thrust system for shield tunneling machine

The thrust hydraulic system of the prototype shield machine with pressure and flow compound control scheme was introduced. The experimental system integrated with proportional valves for study was designed. Dynamics modeling of multi-cylinder thrust system and synchronous control design were accomplished. The simulation of the synchronization motion control system was completed in AMESim and Matlab/Simulink software environments. The experiment was conducted by means of master/slave PID with dead band compensating flow and conventional PID regulating pressure. The experimental results show that the proposed thrust hydraulic system and its control strategy can meet the requirements of tunneling in motion and posture control for the shield machine, keeping the non-synchronous error within ±3 mm.

Numerical simulation of freezing effect and tool change of shield machine with a frozen cutterhead

A shield machine with freezing function is proposed in order to realize tool change operation at atmospheric pressure. Furthermore, the transformation project of freezing cutterhead and tool change maintenance method are put forward. Taking the shield construction of Huanxi Power Tunnel as an example, a numerical analysis of the freezing cutter head of the project was carried out. The results show that when the brine temperature is-25 °C, after 30 d of freezing, the thickness of the frozen wall can reach 0.67 m and the average temperature drops to-9.9 °C. When the brine temperature is-30 °C, after 50 d of freezing, the thickness of the frozen wall can reach 1.01 m and the average temperature drops to-12.4 °C. If the thickness of the frozen wall is 0.5 m and the average temperature is-10 °C, as the design index of the frozen wall, the brine temperature should be lower than-28 °C to meet the excavation requirements in 30 d. Analyzing the frozen wall stress under 0.5 m thickness and-10 °C average temperature condition, the tensile safety factor and compressive safety factor are both greater than 2 at the most dangerous position, which can meet the tool change requirements for shield construction.

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

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

Study on Magnetic Shielding for Performance Improvement of Axial-Field Dual-Rotor Segmented Switched Reluctance Machine

A category of permanent-magnet-shield(PM-shield)axial-field dual-rotor segmented switched reluctance machines(ADS-SRMs)are presented in this paper.These topologies are featured by using the magnetic material to shield the flux leakage in the stator and rotor parts.Besides,the deployed magnets weaken the magnetic saturation in the iron core,thus increasing the main flux.Hence,the torque-production capability can be increased effectively.All the PM-shield topologies are proposed and designed based on the magnetic equivalent circuit(MEC)model of ADS-SRM,which is the original design deploying no magnet.The features of all the PM-shield topologies are compared with the original design in terms of the magnetic field distributions,flux linkages,phase inductances,torque components,and followed by their motion-coupled analyses on the torque-production capabilities,copper losses,and efficiencies.Considering the cost reduction and the stable ferrite-magnet supply,an alternative proposal using the ferrite magnets is applied to the magnetic shielding.The magnet demagnetization analysis incorporated with the thermal behavior is performed for further verification of the motor performance.

Energy saving analysis of segment positioning in shield tunneling machine considering assembling path optimization

A motion parameter optimization method based on the objective of minimizing the total energy consumption in segment positioning was proposed for segment erector of shield tunneling machine. The segment positioning process was decomposed into rotation, lifting and sliding actions in deriving the energy calculation model of segment erection. The work of gravity was taken into account in the mathematical modeling of energy consumed by each actuator. In order to investigate the relationship between the work done by the actuator and the path moved along by the segment, the upward and downward directions as well as the operating quadrant of the segment erector were defined. Piecewise nonlinear function of energy was presented, of which the result is determined by closely coupled components as working parameters and some intermediate variables. Finally, the effectiveness of the optimization method was proved by conducting a case study with a segment erector for the tunnel with a diameter of 3 m and drawing comparisons between different assembling paths. The results show that the energy required by assembling a ring of segments along the optimized moving path can be reduced up to 5%. The method proposed in this work definitely provides an effective energy saving solution for shield tunneling machine.

Load Sharing Performance of the Main Drive System in the Shield Machine and Improvement of Control Method

Shield machine is the major technical equipment badly in need in national infrastructure construction. The service conditions of shield machine are extremely complex. The driving interface load fluctuation caused by geological environment changes and multi field coupling of stress field may lead into imbalance of redundant drive motors output torque in main driving system. Therefore, the shield machine driving synchronous control is one of the key technologies of shield machine. This paper is in view of the shield machine main driving synchronous control, achieving the system＇s adaptive load sharing. From the point of view of cutterhead load changes, nonlinear factors of mechanical transmission mechanism and the control system synchronization performance, the authors analyze the load sharing performance of shield machine main drive system in the event of load mutation. The paper proposes a data-driven synchronized control method applicable to the main drive system. The effectiveness of the method is verified through simulation and experimental methods. The new method can make the system synchronization error greatly reduced, thus it can effectively adapt to load mutation, and reduce shaft broken accident.

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

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

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

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

Prediction of surface settlement caused by synchronous grouting during shield tunneling in coarse-grained soils:A combined FEM and machine learning approach

This paper presents a surrogate modeling approach for predicting ground surface settlement caused by synchronous grouting during shield tunneling process.The proposed method combines finite element simulations with machine learning algorithms and introduces an intelligent optimization algorithm to invert geological parameters and synchronous grouting variables,thereby predicting ground surface settlement without conducting numerous finite element analyses.Two surrogate models based on the random forest algorithm are established.The first is a parameter inversion surrogate model that combines an artificial fish swarm algorithm with random forest,taking into account the actual number and distribution of complex soil layers.The second model predicts surface settlement during synchronous grouting by employing actual cover-diameter ratio,inverted soil parameters,and grouting variables.To avoid changes to input parameters caused by the number of overlying soil layers,the dataset of this model is generated by the finite element model of the homogeneous soil layer.The surrogate modeling approach is validated by the case history of a large-diameter shield tunnel in Beijing,providing an alternative to numerical computation that can efficiently predict surface settlement with acceptable accuracy.

Flexible healable electromagnetic-interference-shielding bioelastichydrogel nanocomposite for machine learning-assisted highlysensitive sensing bioelectrode

The prosperous evolution of conductive hydrogel-based skin sensors is attract-ing tremendous attention nowadays.Nevertheless,it remains a great challenge to simultaneously integrate excellent mechanical strength,desirable electrical conduc-tivity,admirable sensing performance,and brilliant healability in hydrogel-based skin sensors for high-performance diagnostic healthcare sensing and wearable human-machine interface,as well as robust photothermal performance for promptly intelligent photothermal therapy followed by the medical diagnosis and superior electromagnetic interference(EMI)shielding performance for personal protec-tion.Herein,aflexible healable MXene hydrogel-based skin sensor is prepared through a delicate combination of MXene(Ti_(3)C_(2)T_(x))nanosheets network with the polymeric network.The as-prepared skin sensor is featured with significantly enhanced mechanical,conducting,and sensing performances,along with robust self-healability,good biocompatibility,and reliable injectability,enabling ultrasensitive human motion monitoring and teeny electrophysiological signals sensing.As a fron-tier technology in artificial intelligence,machine learning can facilitate to efficiently and precisely identify the electromyography signals produced by various human motions(such as variablefinger gestures)with up to 99.5%accuracy,affirming the reliability of the machine learning-assisted gesture identification with great poten-tial in smart personalized healthcare and human-machine interaction.Moreover,the MXene hydrogel-based skin sensor displays prominent EMI shielding performance,demonstrating the great promise of effective personal protection.

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

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

Discrete element simulation of mechanical characteristic of conditioned sands in earth pressure balance shield tunneling

The discrete element method (DEM) was used to simulate the flow characteristic and strength characteristic of the conditioned sands in the earth pressure balance (EPB) tunneling. In the laboratory the conditioned sands were reproduced and the slump test and the direct shear test of the conditioned sands were implemented. A DEM equivalent model that can simulate the macro mechanical characteristic of the conditioned sands was proposed,and the corresponding numerical models of the slump test and the shear test were established. By selecting proper DEM model parameters,the errors of the slump values between the simulation results and the test results are in the range of 10.3%-14.3%,and the error of the curves between the shear displacement and the shear stress calculated with the DEM simulation is 4.68%-16.5% compared with that of the laboratory direct shear test. This illustrates that the proposed DEM equivalent model can approximately simulate the mechanical characteristics of the conditioned sands,which provides the basis for further simulation of the interaction between the conditioned soil and the chamber pressure system of the EPB machine.

Optimal design of structural parameters for shield cutterhead based on fuzzy mathematics and multi-objective genetic algorithm

In order to improve the strength and stiffness of shield cutterhead, the method of fuzzy mathematics theory in combination with the finite element analysis is adopted. An optimal design model of structural parameters for shield cutterhead is formulated,based on the complex engineering technical requirements. In the model, as the objective function of the model is a composite function of the strength and stiffness, the response surface method is applied to formulate the approximate function of objective function in order to reduce the solution scale of optimal problem. A multi-objective genetic algorithm is used to solve the cutterhead structure design problem and the change rule of the stress-strain with various structural parameters as well as their optimal values were researched under specific geological conditions. The results show that compared with original cutterhead structure scheme, the obtained optimal scheme of the cutterhead structure can greatly improve the strength and stiffness of the cutterhead, which can be seen from the reduction of its maximum equivalent stress by 21.2%, that of its maximum deformation by 0.75%, and that of its mass by 1.04%.

Research on the abrasive wear resistance of YDCrMoV coating produced by CO_2 shielded flux-cored wire surfacing

Caterpillar construction machines play an important part in many fields such as hydraulic and electric engineering, the construction of highway, for its particular of structure. The walking system of caterpillar construction machines is always under the condition of three-body abrasive wear. The abrasive wear of walking system is very severe, which always results in damages of components or structures of walking system of caterpillar construction machines. It is very important to repair the walking system by cladding technique. The abrasive wear properties of four kinds coatings produced by the shielded flux-cored wire surfacing for the repair of the damaged components of walking system of caterpillar construction machines have been studied experimentally on an MLS-23 type wet sand rubber wheel abrasive tester. The surfaces morphologies of the abrasively worn specimens and their microstructures are investigated by transmitting electron microscopy (TEM) after wear testing. Results show that the wear mechanism of cladding metals of the flux-cored wire No.1 and the No.2 is micro-cutting while that of the No.3 and the No.4 is micro-ploughing. The four kinds of flux-cored wire coatings present great potential applications for the repairing of caterpillar construction machines in the Three Gorges Engineering.

机器学习方法在盾构隧道工程中的应用研究现状与展望

随着盾构隧道工程信息化水平的提升,隧道掘进设备作业过程监测技术日益完善,记录的工程数据蕴含了掘进设备内部信息及其与外部地层的相互作用关系。机器学习因其数据分析能力强,无需先验的理论公式和专家知识,相较于传统的建模统计分析方法具有更大的应用空间。通过机器学习方法对收集的信息与数据进行深度挖掘并分析其内在联系,有助于提升盾构隧道工程建设的效率和安全保障水平。简述机器学习方法的基本原理,总结和分析机器学习方法在盾构工程中的应用研究状况,综述基于机器学习的盾构设备状态分析、盾构设备性能预测、围岩参数反演、地表变形预测和隧道病害诊断等5个方面的进展,并分析当前研究的不足。最后,分析盾构隧道工程向智能化方向发展需重点攻克的难题。

基于多域物理信息神经网络的复合地层隧道掘进地表沉降预测

复合地层中盾构掘进诱发地表沉降的准确预测是隧道工程安全建设与施工决策的关键问题。基于隧道施工诱发地层变形机制构建隧道收敛变形与掘进位置的联系,并将其耦合至深度神经网络(deep neural network,简称DNN)框架,建立了预测盾构掘进诱发地层变形的物理信息神经网络(physics-informed neural network,简称PINN)模型。针对隧道上覆多个地层的地质特征,提出了多域物理信息神经网络(multi-physics-informed neural network,简称MPINN)模型,实现了在统一的框架内对不同地层的物理信息分区域表达。结果表明:MPINN模型高度还原了有限差分法的计算结果,可以准确预测复合地层中隧道开挖诱发的地表沉降;由于融入了物理机制,MPINN模型对隧道施工诱发地表沉降的问题具有普适性,可应用于不同地质和几何条件下隧道诱发地表沉降的预测;基于工程实测数据,提出的MPINN模型准确预测了监测断面的地表沉降曲线,可为复合地层下盾构掘进过程中地表沉降的预测预警提供参考。

基于机器学习的盾构隧道地表沉降曲线智能预测方法

联合机器学习模型和启发式智能优化算法,提出盾构隧道开挖地表沉降曲线智能预测方法。首先,建立盾构隧道开挖数值分析模型,在考虑等代层弹性模量、土体弹性模量、隧道半径、土体摩擦角、黏聚力影响的基础上,构建了1680组不同工况影响的地表沉降曲线数据库;然后,分析地层和衬砌力学参数、隧道几何参数对地表沉降曲线的影响规律,采用Peck函数对获得的地表沉降槽曲线进行拟合,获得对应工况下地表最大沉降和沉降槽宽度系数;最后,采用粒子群优化算法(particle swarm optimization,PSO)分别优化4种机器学习方法即多层感知机(multi-layer perceptron,MLP)、极限学习机(extreme learning machine,ELM)、随机森林(random forest,RF)和支持向量回归(support vector regression,SVR)的超参数或随机数,建立了4种盾构隧道开挖有限元模拟代理模型,预测了盾构隧道地表沉降曲线,并对模型的预测结果、预测误差和评价指标进行了对比分析,结果表明PSO-SVR模型在训练和测试过程中性能最佳。建立的盾构隧道地表沉降智能预测方法具有较高的计算精度及计算效率,能合理高效地预测地表沉降曲线分布规律。