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.

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.

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.

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.

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.

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.

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.

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。

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。

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。

基于聚类与Transformer算法的上软下硬地层盾构滚刀正常磨损预测

盾构法是隧道施工的主流方法,广泛应用于软土地层与复合地层。盾构机在上软下硬地层中掘进时,准确预测滚刀的磨损以便及时更换滚刀是确保施工安全与效率的关键。基于机器学习中的无监督kmeans聚类算法、有监督Transformer算法以及遗传算法,提出了一种根据刀盘前方地层条件、滚刀布设以及施工参数预测滚刀正常磨损的方法。kmeans聚类算法用于实时分析当前盾构施工参数间的关系,从而划分不同施工状态,并为不同施工状态分配不同的磨损修正系数作为Transformer模型的输入参数。根据地层条件及施工参数相关关系确定磨损修正系数初始值的大小,借助遗传算法对磨损修正系数进行优化。Transformer算法中,以地层条件、施工参数、滚刀安装半径和切削距离以及kmeans聚类得到的磨损修正系数作为输入参数,以滚刀磨损量作为输出参数,并由遗传算法对模型的超参数进行优化。所使用的数据采集自某大直径泥水平衡盾构隧道,所穿越地层条件包含全断面软土、上软下硬地层以及全断面硬岩,样本集包含217条数据。聚类结果所揭示的施工状态与滚刀的磨损速度具有明显的对应关系,预测模型的滚刀磨损平均预测误差为3.6mm,占滚刀换刀磨损量40.0mm的9%。

中国盾构隧道工程关键技术的新进展综述

从研究背景与思路、机制/原理、材料/装备、技术论证、试验研究、工程应用等方面介绍中国大直径盾构掘进主轴承事故分析及国产主轴承的自主研发、盾构隧道工程安全高效掘进渣土适配性改良技术及配套材料、盾尾安全保障核心材料研发、盾构隧道新型管片连接技术、城市隧道三模掘进机关键技术等5个方面关键技术的最新进展情况。认为:1)自主研制的超大型盾构机用直径8.01 m主轴承大型套圈纯净度、大型滚子纯净度、大型套圈疲劳性能、大型滚子硬度均匀性、套圈材料淬透性和淬硬层深度、保持架焊缝疲劳寿命和拉断力均优于进口水平;构建了真实对数曲线大型滚子设计方法,突破了高精度大型滚子加工技术,处于国际先进水平,高精度加工和装配保证了零件尺寸精度及形位公差均满足设计要求;通过产品验收、装机应用论证和工程应用,表明应用该大直径主轴承不会产生接触疲劳强度不足和微动引起的疲劳磨损,可用于超大直径盾构隧道工程建设。2)针对黏性、富水砂性和硬岩等复杂地层,以改善岩土环境为核心,研发出了高配向密度的聚合型泡沫剂、高分子抗黏剂、喷涌防止剂和耐磨抑尘剂等环保型系列渣土改良剂,形成了盾构泥饼、喷涌和磨损原位改良主动防控技术,建立了通用地层适配性盾构渣土动态改良技术体系,保障了盾构安全高效掘进。3)针对盾构掘进过程中的盾尾密封失效难题,研发了“黏-稠-流”有机统一的新型盾尾密封油脂,温度敏感性低(黏温系数0.4)、抗水压能力强(最高抗水压3.5 MPa),整体性能提高1.3倍,为盾尾安全提供了有力保障;针对盾构同步注浆浆液泌水、离析和地层沉降难题,研发出了同步注浆充填剂,优化了惰性充填浆液配比,30 min零泌水,实现了壁后惰性充填,保障了管片壁后注浆质量和地层沉降精细化控制。4)新型管片连接技术采用环缝插入+纵缝滑入的连接形式,可以高标准地控制隧道接缝张开量和错台量,管片制作及拼装精度高,可降低接缝防水难度,提升成型隧道的质量,无需设置手孔,无需人工紧固作业,自动化程度高,施工效率高,可节约施工成本及改善洞内拼装作业环境。5)研制出的土压+泥水+TBM三模掘进机能够满足复杂多变、软硬不均复合地层等几乎所有地层的掘进施工,且可在不停机、不拆装设备的前提下实现掘进模式的快速转换,在TBM掘进模式下螺旋输送机+泥浆管道可协同排渣,解决了富水硬岩地层掘进排渣难的问题,机械化和智能化程度高,安全可靠,施工效率高,可改善工人作业环境,节省工期和成本。中国盾构隧道建设取得的巨大成就,推动了中国乃至世界盾构隧道技术的发展和进步,但仍时有安全事故发生,需从装备、材料、技术、管理等方面进一步研究和突破。在今后较长的时间内,中国盾构隧道(尤其是大直径盾构隧道)仍将处于高速建设发展期,面临的建设条件将越来越复杂,技术难度和挑战也将越来越大。要实现盾构隧道建设的快速、安全、健康,需处理好盾构隧道技术领域的关键问题,实现盾构隧道关键核心技术的突破。上述关键技术仍需不断完善并加强推广和应用,以促进中国盾构隧道建设向高适应性、高智能、高可靠性、高效益、高质量、高安全性和低能耗方向发展,形成适合于中国“土壤”的盾构隧道新技术。

盾构机主驱动单唇形密封圈密封性能研究

以典型工况下盾构机主驱动单唇形密封圈为研究对象,利用单轴拉伸试验得到密封圈丁腈橡胶材料的应力-应变曲线,确定Yeoh三阶模型的材料参数;建立单唇形密封圈的二维轴对称有限元模型,研究介质压力、压差、摩擦因数和温度对其密封性能的影响规律。研究表明:介质压力主要影响最大接触应力,随着介质压力的增加,最大接触应力呈线性增加;介质压差主要影响最大接触应力和接触长度,随着介质压差的增加,最大接触应力先线性增加然后基本保持不变,而接触长度呈非线性增加;温度变化对密封性能的影响可以忽略;在考虑的工况条件下,单唇形密封圈唇口与旋转轴接触处产生的最大接触应力始终大于介质压力,密封性能良好。