TBM岩体可掘性预测及其分级研究

全断面硬岩隧道掘进机（tunnel boring machine, TBM）对岩体条件极其敏感,且其前期投入较大,准确地评估岩体可掘性、预测TBM掘进性能对TBM隧道施工至关重要。基于来自中国、伊朗两国涵盖3种不同岩性的5条TBM施工引水隧洞约300组现场数据,以现场贯入度指数FPI为岩体可掘性评价指标,分析了岩石单轴抗压强度UCS、岩体完整性指数 、岩体主要结构面与洞轴线的夹角?、隧洞直径D等与岩体可掘性之间的关系;探讨了适用于岩体可掘性研究的岩体参数统一方法,进一步建立了精度较高的（相关系数为0.768）岩体可掘性经验预测方法。基于该预测方法,运用K中心聚类分析方法,将岩体可掘性分为6类,探讨了不同岩体可掘性条件下TBM平均单刀推力、刀盘转速分布规律,相应成果可为实际工程中TBM施工隧洞岩体可掘性评估、掘进参数的选择、施工进度的安排提供一定的指导。     

隧道掘进机施工条件下的隧洞围岩分类方法探讨

针对隧道掘进机(Tunnel Boring Machine--TBM)施工隧洞中的围岩分类问题,指出TBM施工条件下的隧洞围岩分类应针对围岩的可钻掘性,充分考虑影响TBM掘进效率的主要工程地质因素,提出了在《工程岩体分级标准》围岩稳定性基本分级的基础上,依据岩石的单轴抗压强度、岩石的耐磨性和岩体的完整性将TBM施工条件下的隧洞围岩分为A(好)、B(一般)、C(差)3个级别的围岩分类新方法。     

TBM岩体可掘性预测及其分级研究

全断面硬岩隧道掘进机（TBM）对岩体条件极其敏感,且其前期投入较大,准确地评估岩体可掘性、预测TBM掘进性能对TBM隧道施工至关重要。基于来自中国、伊朗两国涵盖三种不同岩性的5条TBM施工引水隧洞约300组现场数据,以现场贯入度指数FPI为岩体可掘性评价指标,分析了岩石单轴抗压强度UCS、岩体完整性指数K_(v)、岩体主要结构面与洞轴线的夹角α、隧洞直径D等与岩体可掘性之间的关系;探讨了适用于岩体可掘性研究的岩体参数统一方法,进一步建立了精度较高的（相关系数为0.768）岩体可掘性经验预测方法。基于该预测方法,运用K中心聚类分析方法,将岩体可掘性分为6类,探讨了不同岩体可掘性条件下TBM平均单刀推力、刀盘转速分布规律,相应成果可为实际工程中TBM施工隧洞岩体可掘性评估、掘进参数的选择、施工进度的安排提供一定的指导。     

利用PLSR-DNN耦合模型预测TBM净掘进速率

科学预测隧道掘进机(TBM)净掘进速率,对于隧道(洞)工程施工方法选择、施工进度安排以及成本估计具有重要意义。鉴于TBM施工过程具有高度非线性、模糊性和复杂性等特征,为提高TBM净掘进速率的预测精度和计算效率,采用偏最小二乘回归(PLSR)提取影响参数主成分,再利用深度神经网络(DNN)进行训练预测,提出了一种基于PLSR-DNN耦合方法的TBM净掘进速率预测模型。基于兰州水源地建设工程输水隧洞双护盾TBM施工实测数据,选择岩石单轴抗压强度、单轴抗拉强度、刀盘推力、刀盘转速、岩体完整性系数和岩石耐磨性指数,共6个影响参数,验证了模型预测的合理性,并对不同预测方法的拟合精度和预测精度进行了对比分析。研究结果表明:(1)偏最小二乘回归可有效克服自变量之间的多重共线性问题,将提取的主成分作为深度神经网络的输入层进行训练,简化了神经网络结构;(2)PLSR-DNN耦合预测模型避免了过拟合与拟合不足问题,具有收敛速度快,求解稳定和拟合精度高等特点;(3)PLSR-DNN耦合预测模型平均相对拟合误差2.96%,平均相对预测误差3.27%,其拟合精度和预测精度均明显高于偏最小二乘回归模型、BP神经网络模型以及支持向量回归(SVR)模型。     

关于TBM施工隧洞围岩分类方法的研究

本文针对TBM施工隧洞中的围岩分类问题，通过分析研究国内外大量TBM施工实例和一些现场及室内岩石力学试验结果，指出TBM施工条件下的隧洞围岩分类应针对围岩的可钻掘性，充分考虑影响TBM掘进效率的主要工程地质因素，提出了在《工程岩体分级标准》围岩稳定性基本分级的基础上，依据岩石的单轴抗压强度、岩石的耐磨性和岩体的完整性将TBM施工条件下的隧洞围岩分为A（好）、B（一般）、C（差）3个级别的围岩分类新方法，并将该方法应用于掌鸠河引水供水工程TBM施工段工程实践，取得了良好的效果。     

南水北调西线工程岩石中石英含量变化及其对TBM施工的影响

石英是各类岩石中广泛存在的一种矿物成分,石英颗粒的大小和含量不仅决定了岩石的强度、硬度和耐磨性,而且直接影响TBM施工的掘进效率。在南水北调西线工程区采集124块岩石样品进行了岩石薄片鉴定,在岩石碎屑粒度分析的基础上,得到了不同采样区域岩石中的石英含量分布特征。基于TBM施工特点,根据不同岩石中的石英含量统计结果,分析了石英含量分布规律对南水北调西线工程TBM施工围岩分类和掘进效率的影响。研究结果表明:(1)砂岩(含杂砂岩)、粉砂岩和板岩中的石英含量具有较明显的差异,砂岩(含杂砂岩)、粉砂岩的石英含量较高,其平均值分别为74.21%和80.41%; 板岩中的石英含量较低,平均值为9.36%。(2)砂岩(含杂砂岩)及粉砂岩中的石英含量一般大于60%,对TBM施工围岩分类的影响属于严重等级; 板岩中的石英含量一般小于30%,对TBM施工围岩分类的影响属于轻微-明显等级。(3)岩石中的石英含量越高,岩石硬度和耐磨性越高,刀具损耗越大,TBM施工掘进速度PR和施工速度AR越低。研究成果可为南水北调西线工程TBM优化选型、施工围岩分类以及施工进度预测提供参考依据。     

基于TBM掘进性能和适应性分析的围岩分级方法及应用

煤矿巷道变化的围岩地质条件影响着全断面岩石掘进机(Tunnel Boring Machine,TBM)的推广应用,准确评估煤矿岩体可掘性和岩层TBM适应性对TBM高效施工至关重要。基于对岩体参数和岩体可掘性指标的评价,采用优劣解距离法(TOPSIS)建立了岩体可掘性分级模型,并结合不同地质条件的[BQ]值和TBM利用率的相关性分析,提出了岩层适应性分级模型。以日掘进速度为判断指标,进行岩体可掘性和岩层适应性评估,建立了一套基于TBM施工性能的围岩综合分级方法,采用河南平顶山首山一矿底板瓦斯抽采巷道TBM掘进过程中的工程数据,对TBM围岩综合分级方法进行了现场应用。结果表明：在岩体可掘性等级为Ⅰ级,地层TBM适应性等级为3级的条件下,TBM施工巷道平均月进尺可达到400 m;当TBM利用率不足20%时,极有可能会出现卡机、出渣困难等现场问题。围岩综合分级方法通过利用自动采集的TBM掘进数据和围岩性质的综合分析,能够动态评估TBM在不同围岩地质条件下的施工性能,并为TBM掘进控制参数设计提供了理论依据。     

卵石土等效弹性模量理论预测模型初探

卵石土是由卵石和土组成的、处于土和岩体之间的天然地质体,但其宏观力学特性完全不同于土和岩体。通过对成都地铁1号线隧道掘进机（TBM）施工现场调查可知,卵石地层可导致TBM刀具过量磨损、刀盘堵塞等非正常损坏,极大地影响了TBM施工效率,是TBM掘进极其不利的一种典型混合工作面。主要研究卵石土变形性能,建立在均匀应变基础上,假设卵石为理想圆形或椭圆形,从理论上得到卵石土等效弹性模量的解析预测公式。并通过数值试验验证了理论预测模型的有效性。结果表明,此模型预测结果与试验数据比较接近,可用来初步判断卵石地层等效弹性模量,有助于理解并提高该地层中的TBM掘进效率。     

基于Monte Carlo-BP神经网络TBM掘进速度预测

预测隧道工程中TBM掘进速度,主要有完全经验的、半理论半经验的模型和人工智能等方法,所用参数均为确定性的,未考虑参数存在的随机性,故导致预测结果的不准确性。基于此,提出了Monte Carlo-BP神经网络TBM掘进速度预测模型,着重考虑了一些重要输入参数的随机性, 其中输入参数重要性的大小通过粗糙集进行计算排序。采用Monte Carlo产生随机数时,由于参量的样本数据的有限,分布函数均采用阶梯形经验分布函数。如果采用的数据是来自不同类型的 TBM,则应当考虑机器性能参数,并重新对参数重要性进行排序。实例计算表明,Monte Carlo-BP神经网络模型预测结果和实测值总体趋势和均值比较一致。     

基于TBM破岩数据的岩体条件深度学习表征方法

基于TBM施工数据进行围岩感知对保障TBM施工安全、提高施工效率至关重要,其中TBM掘进参数预测的准确率是检验围岩感知效果的重要依据.为此,以吉林引松工程TBM四标段为研究对象,选取TBM上升段破岩数据为输入特征X1,选择两个施工控制参数（刀盘转速和推进速度）为输入特征X2,构建卷积神经网络机器学习模型,对TBM掘进响应参数Y（刀盘扭矩和总推力）进行预测.按照学习对象的不同,分别构建了只学习稳定段掘进响应行为的点预测模型和同时学习上升段和稳定段掘进响应行为的线预测模型,结果表明：点预测模型无法描述控制参数对掘进响应参数的影响;线预测模型虽然可以描述控制参数对掘进响应参数的影响,但是对稳定段的掘进响应预测数值偏低.考虑到上述局限性的原因是稳定段行为样本数量只占总样本数量的9%,提出了一种通过调节损失函数的方法来提高稳定段行为样本的权重,显著提高了线预测模型的预测精度.改进后的结果表明：在TBM掘进参数预测中,应对整个掘进段的行为进行学习,并提高稳定段行为的权重,以便获得高精度的掘进响应参数预测模型.获得的模型能够为进一步的围岩感知和控制参数优化提供基础.     

A spatial estimation model for continuous rock mass characterization from the specific energy of a TBM

Summary  Basic principles of the theory of rock cutting with rolling disc cutters are used to appropriately reduce tunnel boring machine (TBM) logged data and compute the specific energy (SE) of rock cutting as a function of geometry of the cutterhead and operational parameters. A computational code written in Fortran 77 is used to perform Kriging predictions in a regular or irregular grid in 1D, 2D or 3D space based on sampled data referring to rock mass classification indices or TBM related parameters. This code is used here for three purposes, namely: (1) to filter raw data in order to establish a good correlation between SE and rock mass rating (RMR) (or tunnelling quality index Q) along the chainage of the tunnel, (2) to make prediction of RMR, Q or SE along the chainage of the tunnel from boreholes at the exploration phase and design stage of the tunnel, and (3) to make predictions of SE and RMR or Q ahead of the tunnel’s face during excavation of the tunnel based on SE estimations during excavation. The above tools are the basic constituents of an algorithm to continuously update the geotechnical model of the rock mass based on logged TBM data. Several cases were considered to illustrate the proposed methodology, namely: (a) data from a system of twin tunnels in Hong Kong, (b) data from three tunnels excavated in Northern Italy, and (c) data from the section Singuerlin-Esglesias of the Metro L9 tunnel in Barcelona. Correspondence: G. Exadaktylos, Department of Mineral Resources Engineering, Technical University of Crete, Chania, Greece     

Visualization of rock mass classification systems

A rock mass classification system is intended to classify and characterize the rock masses, provide a basis for estimating deformation and strength properties, supply quantitative data for mine support estimation, and present a platform for communication between exploration, design and construction groups. In most widely used rock mass classification systems, such as RMR and Q systems, up to six parameters are employed to classify the rock mass. Visualization of rock mass classification systems in multi-dimensional spaces is explored to assist engineers in identifying major controlling parameters in these rock mass classification systems. Different visualization methods are used to visualize the most widely used rock mass classification systems. The study reveals that all major rock mass classification systems tackle essentially two dominant factors in their scheme, i.e., block size and joint surface condition. Other sub-parameters, such as joint set number, joint space, joint surface roughness, alteration, etc., control these two dominant factors. A series two-dimensional, three-dimensional, and multi-dimensional visualizations are created for RMR, Q, Rock Mass index RMi and Geological Strength Index (GSI) systems using different techniques. In this manner, valuable insight into these rock mass classification systems is gained.     

隧洞围岩分类与洞径和超欠挖之间的关系研究

岩体的地质结构特征是影响隧洞围岩超欠挖的主要因素,正确评价岩体的质量,对岩体进行分类,并研究岩体质量与隧洞超欠挖之间的关系对分析和预测隧洞超欠挖具有重要的意义。对隧洞围岩的RMR分类和Q分类与超欠挖之间的关系进行了详细研究,研究了在同类岩体条件下不同开挖洞径与隧洞超欠挖之间的关系,并由此建立了RMR分类和Q分类之间的关系,得出围岩的RMR分类和Q分类与隧洞超欠挖呈线性关系和对数线性关系,根据超欠挖建立起来的RMR分类和Q分类之间的关系比较符合工程实际。最后研究了超欠挖与围岩分类及洞径之间的复相关关系。     

某电站地下厂房围岩质量综合分级

在大量现场调查、统计资料基础上，利用国际上通用的RMR分类、Q系统分类及我国的水电围岩分类方案，对某电站地下厂房围岩进行了岩体质量分级。三种围岩分类结果具有良好的一致性，围岩质量较好。     

Some Attempts for Estimating Rock Strength and Rock Mass Classification from Cutting Force and Investigation of Optimum Operation of Tunnel Boring Machines

Summary. Tunnel face and wall collapse are common during excavations performed by tunnel boring machines (TBMs) due to the difficulty of correctly identifying the properties of the excavated rock. This identification, however, can be simplified by using the cutting force to estimate rock strength, a method that has already proved quite successful in Japanese tunnel excavations. This paper summarizes knowledge relating to the cutting force obtained through tunnel excavation experience, and the relationship between rock strength and TBM operation is discussed. Although TBM operators rely on intuition to set the cutter head speed appropriately, this decision process represents a logical method of operation that takes advantage of the variable speed capability of the cutter head. Selection of appropriate support methods for the excavated face is also a critical issue in tunnel excavation. This selection process is based on the condition of the rock, which is difficult to determine quickly and accurately during tunnel excavation. The present paper uses the excavation of two tunnels to demonstrate that it is possible to assign rock mass classifications accurately based on rock strength when boring a uniform rock type. It is also shown that the rock mass can be classified from the rock strength normalized by the uniaxial compressive strength when boring through mixed rock types.     

Prediction model of tunnel boring machine performance by ensemble neural networks

The penetration rate of a tunnel boring machine (TBM) depends on many factors ranging from the machine design to the geological properties. Therefore it may not be possible to capture this complex relationship in an explicit mathematical expression. In this paper, we propose an ensemble neural network (ENN) to predict TBM performance. Based on site data, a four-parameter ENN model for the prediction of the specific rock mass boreability index is constructed. Such a neural-network-based model has the advantages of taking into account the uncertainties embedded in the site data and making appropriate inferences using very limited data via the re-sampling technique. The ENN-based prediction model is compared with a non-linear regression model derived from the same four parameters. The ENN model outperforms the non-linear regression model.