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.

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.

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.

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.

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.

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.

Prediction of shield tunneling-induced ground settlement using machine learning techniques

Predicting the tunneling-induced maximum ground surface settlement is a complex problem since the settlement depends on plenty of intrinsic and extrinsic factors.This study investigates the efficiency and feasibility of six machine learning(ML)algorithms,namely,back-propagation neural network,wavelet neural network,general regression neural network(GRNN),extreme learning machine,support vector machine and random forest(RF),to predict tunneling?induced settlement.Field data sets including geological conditions,shield operational parameters,and tunnel geometry collected from four sections of tunnel with a total of 3.93 km are used to build models.Three indicators,mean absolute error,root mean absolute error,and coefficient of determination the(7?2)are used to demonstrate the performance of each computational model.The results indicated that ML algorithms have great potential to predict tunneling-induced settlement,compared with the traditional multivariate linear regression method.GRNN and RF algorithms show the best performance among six ML algorithms,which accurately recognize the evolution of tunneling-induced settlement.The correlation between the input variables and settlement is also investigated by Pearson correlation coefficient.

Prediction of TBM jamming risk in squeezing grounds using Bayesian and artificial neural networks

This study presents an application of artificial neural network(ANN)and Bayesian network(BN)for evaluation of jamming risk of the shielded tunnel boring machines(TBMs)in adverse ground conditions such as squeezing grounds.The analysis is based on database of tunneling cases by numerical modeling to evaluate the ground convergence and possibility of machine entrapment.The results of initial numerical analysis were verified in comparison with some case studies.A dataset was established by performing additional numerical modeling of various scenarios based on variation of the most critical parameters affecting shield jamming.This includes compressive strength and deformation modulus of rock mass,tunnel radius,shield length,shield thickness,in situ stresses,depth of over-excavation,and skin friction between shield and rock.Using the dataset,an ANN was trained to predict the contact pressures from a series of ground properties and machine parameters.Furthermore,the continuous and discretized BNs were used to analyze the risk of shield jamming.The results of these two different BN methods are compared to the field observations and summarized in this paper.The developed risk models can estimate the required thrust force in both cases.The BN models can also be used in the cases with incomplete geological and geomechanical properties.

A stiffness-matching based evaluation approach for compliance of mechanical systems in shield tunneling machines

As the most important performance,compliance of shield tunneling machines(STM) is defined as the capability to accommodate the sudden change of the load induced by the variable geological conditions during excavation.Owing to the different requirements of the compliant tasks,the existing methods in the robotic field cannot be utilized in the analysis and design of the mechanical system of shield tunneling machines.In this paper,based on the stiffness of the mechanical system and the equivalent contact stiffness of the tunnel face,the tunneling interface-matching index(IMI) is proposed to evaluate the compliance of the machine.The IMI is defined as a metric to describe the coincidence of the stiffness curves of the mechanical system and the tunnel face.Moreover,a tunneling case is investigated in the paper as an example to expound the validation of IMI and the analytical process.In conclusion,the IMI presented here can be served as an appraisement of the capability in conforming to the load fluctuation,and give instructions for the design of the thrust system of shield tunneling machines.

Risk identification and risk mitigation during metro station construction by enlarging shield tunnel combined with cut-and-cover method

Constructing a metro station by enlarging shield tunnels combined with a mining/cut-and-cover method provides a new method to solve the contradictions of construction time limits of shield tunnels and stations. As a new-style construction method, there are several specific risks involved in the construction process. Based on the test section of Sanyuanqiao station on Beijing metro line 10, and combined with the existing methods of risk identification at present, including a review of world-wide operational experience of similar projects, the study of generic guidance on hazards associated with the type of work being undertaken, and discussions with qualified and experienced staff from the project team, etc., the specific risks during the construction process of the metro station constructed by enlarging shield tunnels combined with the cut-and-cover method are identified. The results show that the specific risks mainly come from three construction processes which include constructing upper enclosure structures, excavating the soil between shield tunnels and demolishing shield segments. Then relevant risk mitigation measures are put forward. The results can provide references for scheme improvement and a comprehensive risk assessment of the new-style construction method.

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%.

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。

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.

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。

盾构机始发井深基坑力学和变形特性离心模型试验研究

为探究盾构机始发井深基坑开挖过程中周边土体的应力、变形和土压力分布规律,通过对工程原型进行概化,在典型土层条件下,采用停机-开挖-支护的模拟方式开展了方形基坑和圆形基坑两种方案共2组离心模型试验,从地连墙的弯矩和其后土体的水平位移、基坑外侧土压力分布以及地表沉降等角度,对比分析了两种盾构机始发井深基坑开挖过程的工程特性。结果表明:地面沉降随基坑开挖深度增大逐渐增加,形成沉降槽状;地连墙土压力变化呈非线性,随着基坑开挖不断深入的,离地表较近处的土压力逐渐变大,而深处的土压力则逐渐减小;在第5步开挖时2组模型地下连续墙水平位移均达到最大值,圆形基坑是方形基坑的1.4倍,同时地连墙的弯矩也达到最大值,圆形基坑比方形基坑小320 kN·m。研究成果可为深埋隧道深基坑的优化设计和开挖提供指导。

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。

小转弯曲线隧道TBM选型与掘进姿态调控方法

小转弯隧道施工时全断面隧道掘进机(full-section tunnel boring machine,TBM)的选型设计和掘进姿态控制具有特殊性,目前还缺乏通用的理论依据和指导方法。针对此问题,首先,对传统类型TBM小转弯选型进行研究,结合已有项目数据和几何模拟,确定传统类型TBM能适应的最小转弯半径。然后,对双盾敞开式TBM的推进系统和导向系统进行针对性设计,通过分析双盾敞开式TBM推进系统结构和实际施工,提出转弯时双盾敞开式TBM推进油缸内外侧行程差值的理论计算方法和施工过程中的姿态调控方法。最后,得出如下结论:1)当隧道转弯半径小于200 m时,敞开式TBM适应难度较大,需要采用双盾敞开式TBM;2)结合抚宁抽水蓄能电站项目实际施工情况,提出的双盾敞开式TBM的理论计算方法和姿态调控方法确保了转弯段隧道的轴线偏差在要求范围内。

盾构螺机后仓门液压系统仿真控制研究

针对传统盾构螺旋输送机后仓门紧急关闭过程中液压系统流量大小不稳定及油缸和蓄能器关键参数设置不合理而易引起液压冲击,造成机械部件损伤等缺陷。提出一种带压力补偿的插装式比例流量阀控制系统流量大小。通过分析其结构组成及原理,建立了该阀AMEsim模型,并对模型仿真特性曲线进行了分析,得出其有较好的流量稳定性,控制精度高、动态响应性好,且利用仿真环境建立了螺机后仓门液压系统的AMEsim模型,分析了系统中蓄能器与液压缸的关键参数对后仓门紧急关闭的影响规律,结合该影响规律及比例流量阀的工作特性,为螺机后仓门液压系统的进一步升级改造提供理论依据。

基于机器学习的盾构姿态预测模型与控制方法研究

为避免盾构轴线偏离引发衬砌管片错台、开裂等质量与安全问题,提出一种基于机器学习算法的盾构姿态智能预测模型与控制方法。以盾构掘进施工的实测数据为驱动,通过贝叶斯优化(BO)与支持向量回归(SVR)构建盾构姿态预测模型,挖掘施工参数-地层信息-盾构姿态三者间的非线性关系。结合模拟退火算法(SA)形成可控施工参数动态调整的盾构姿态控制方法,并将其应用于福州滨海快线南—三区间隧道的工程实践。主要结论如下:1)经数据预处理、特征筛选及BO超参数优化,基于SVR的盾构姿态预测模型具备优异的预测性能和泛化能力;2)结合SA算法进行可控施工参数调整时,需设置合理的优化规则,以确保所推荐的可控施工参数具备可操作性;3)将姿态控制方法应用于南—三区间后续掘进施工以辅助纠偏,盾尾垂直偏差在10环掘进过程中由45 mm减至18 mm,实现了连续稳定纠偏。

盾构机司机作业能力培评体系的构建及应用

构建盾构机司机作业能力培评体系,开展科学系统的专业培训和高标准考核,能够有效提高盾构机司机作业能力,降低盾构施工风险和减少事故发生。充分利用先进的技术手段,在国内首次将虚拟现实技术引入盾构机司机培评体系,专门开发培评VR系统,应用于培评盾构机司机作业能力,具有很好的引领和示范作用。