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.

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.

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.

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.

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.

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.

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。

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。

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。

基于随机森林声波曲线构建的盾构机掘进路线预测

近年来,煤矿井下为了提高生产效率,将盾构机应用到巷道掘进中。地质条件是决定盾构机工作效率的重要因素,盾构机施工围岩以石灰岩、砂岩等中硬岩最为有利,因此拟掘进巷道位置的岩性预测成为提高盾构机工作效率的核心问题。目前地震反演是岩性预测的重要手段,阳泉新景区块由于部分声波测井曲线缺失,对波阻抗反演产生了影响,因此,如何利用已有的测井曲线生成缺失的声波测井曲线以及如何利用盾构机工作行进中的岩性揭露数据不断更新前方岩性分布预测结果成为当前亟待解决的关键。首先对测井曲线进行标准化等预处理,针对声波测井曲线缺失问题,引入随机森林回归算法,利用密度(Density)、自然伽马(GR)、视电阻率(Res)和自然电位(SP)4种测井曲线作为特征变量,建立曲线回归预测模型,预测声波(P-wave)测井曲线。在分析声波曲线和伽马曲线原理的基础上,进行拟声波曲线构建并做拟声波反演,在反演数据体上解释K7砂岩的顶和底界面,将解释得到的地震解释层位信息从时间域数据转换到深度域,经验证实预测结果与揭露数据符合率较高。同时,结合盾构机工作行进中的岩性揭露数据对目标巷道剖面的K7砂体分布预测进行了多次更新迭代,精细刻画了巷道位置K7砂岩的分布情况,为盾构机掘进路线优选提供了有效指导。

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。

基于粒子群优化的盾构推进参数预测算法

为了提高传统机器学习模型在盾构推进参数方面的预测精度,提出了基于粒子群优化(PSO)的混合模型算法。以推进油缸推力和位置预测为例,阐述了PSO优化的混合模型构建流程。以粒子群优化模型为基础,分别建立混合多层感知机(MLP)油缸推力模型、混合长短期记忆(LSTM)人工神经网络油缸推力预测模型,以及混合LSTM油缸位置预测模型。以实际工程数据为例,完成多种混合预测模型的构建,并与RF、XGBoost等传统模型对比,验证所提方法和模型的有效性和先进性。实验表明:经过PSO优化的模型准确率均有不同程度的提升,本文提出的基于粒子群优化模型对指导掘进参数调控、辅助操作人员掘进等具有一定的工程价值。

单胞结构对盾构机复合材料刀圈增强体抗压性能的影响

为明确WC陶瓷颗粒单胞结构对盾构机滚刀刀圈蜂窝结构增强体的抗压性能的影响,利用绘图软件对体积分数为36%WC颗粒(四边形、六边形、八边形)的滚刀刀圈蜂窝结构增强体进行三维空间结构设计,通过ANSYS有限元模拟仿真分析不同单胞结构增强体的抗压性能。结果表明:WC颗粒体积分数为36%,四边形单胞结构增强体的极限压缩强度为123MPa、六边形的极限压缩强度为125 MPa、八边形的极限压缩强度为121 MPa,体积分数为36%的正六边形蜂窝结构增强体的抗压性能最优。

盾构法联络通道中提高切削效率的管片弱化研究

盾构法联络通道施工中因洞口处临时管片难以切削,施工中存在掘进周期长、盾机姿态难控制、涌水涌砂风险高等问题,但是关于此问题解决方案的研究目前还很缺乏。因此,本文以天津某地铁盾构法联络通道项目为背景,在保证结构施工及使用期间安全性的基础上,从弱化临时管片的角度出发,提出了3种弱化方式,分别为降低待切管片砼强度等级、减少玻璃纤维筋布筋量以及管片打孔,并利用有限元数值模拟对弱化的可能性及影响进行了研究。结果表明:(1)在3种弱化方式下,特殊衬砌环内力变化规律均为切削区内力减小,钢管片区内力增大,普通衬砌环的内力小幅度提高;(2)临时管片砼强度由C40降到C20后对衬砌环内力影响在15%以内,其中钢管片区内力增加量在5%以内;(3)主筋量减少70%以内对管片内力影响在2%以内;(4)管片内侧打孔使切削区弯矩降低30%左右,孔径与孔深尺寸相同变化量情况下,孔径对于内力影响大于孔深;(5)不同弱化方式弱化后的结构内力均在极限承载范围之内,3种弱化方式均可采用,从施工便利性和安全性角度来看,临时管片砼强度降低方案为最优方案。