三节柔性盾构姿态的确定

现代隧道施工对盾构要求越来越高,传统的单节盾构渐渐难以满足,柔性更好的三节盾构机迅速兴起。目前相关文献多是研究单节盾构机姿态的计算方法。本文基于三维坐标转换模型、空间平面拟合以及最小二乘原理,介绍了通过观测一节盾构上三个棱镜坐标和各节盾构间千斤顶长度来确定三节柔性盾构实时姿态的一种算法。基于该算法编写的程序高效正确地完成了实例计算,验证了该算法的可行性和实用性。     

车载平台GPS姿态测量

本文简要介绍了GPS姿态测量技术的原理 ,包括姿态角的定义、坐标转换等 ,推导了姿态角的计算方法 ,并对其精度进行了分析 ,在此基础上重点讨论了对一特定姿态测量系统如何提高其精度的问题。在天线间长度已定、载波相位测量精度也已定的前提下 ,采用以天线间固定间距为约束条件的方法来提高所测姿态角的精度 ,并通过实际实验数据对此方法进行了检验 ,说明以天线间的固定间距为约束条件来提高姿态角的精度是必要和有效的     

地铁工程盾构机(TBM)测量定向的研究

在地铁工程的盾构机测量定向中,最主要的是确定出盾构机内棱镜在无仰俯无旋转情况下的局部坐标,然后根据实测数据就可以计算出盾构机头部、中部、尾部中心的三维坐标,计算出它们的偏差从而达到控制盾构机姿态的目的。     

基于全站仪和倾斜仪的盾构机实时姿态测量方法研究

介绍了国内外盾构机姿态测量技术水平,阐述了采用全站仪和倾斜仪进行盾构机实时姿态纠偏的思路,并结合实际案例论述了具体测量方法及原理。该方法的成功应用,有效提高了盾构法施工测量技术水平。     

等高点偏心测量法

通过测量建筑物墙面上两等高点的竖角及其方向,推算两等高点连线的坐标方位角,并在两点连线上设立偏心点并测量其坐标,根据等高点方位角和偏心点坐标可推算两等高点的坐标,指出该方法可部分解决待测房角点因不能立棱镜或视线受阻而无法测量的问题,且测量精度可以达到大比例测图精度要求。     

地铁盾构施工中盾构机姿态定位测量的研究

结合南京地铁一号线两个区间段地下隧道贯通的测量实践 ,简明地介绍了地铁建设中各种测量过程 ,并着重对盾构机姿态定位中的测量工作作了深入细致的研究 ,阐述了盾构机自动导向系统姿态定位测量的原理和方法 ,以及如何使用人工测量的方法来检核自动导向系统的准确性 ,分析了盾构机姿态定位检测的情况。     

盾构机自动导向系统的测量方法研究

介绍了利用激光标靶、全站仪和PLC构成盾构姿态测量系统获得测量数据的方法,利用这些测量数据求取盾构机的坐标位置和角度的算法,通过与隧道设计曲线的比较得到盾构机在水平方向和垂直方向上的位置和角度偏差值,使操作人员可以通过这些结果控制盾构机的掘进操作。     

盾构机姿态精度控制技术

随着盾构隧道掘进长度的增加,对施工控制测量的精度提出了更高的要求。结合北京市南水北调配套南干渠工程某标段施工经验,总结出影响盾构机姿态精度的主要因素,并提出了地面控制点(平面和高程)复核、竖井联系测量,洞内控制导线(水准点)的布设,自动测量系统站点、后视点,盾构机观测棱镜等关键环节的注意事项和误差分配原则,提高了盾构机姿态控制的精度。     

基于混合滤波算法的灭火机器人设计

为使两轮灭火机器人适应各类复杂的火灾环境,融合互补算法和卡尔曼算法对灭火机器人实时采集到的数据进行滤波处理,结合MATLAB软件建立姿态角计算模型,并对算法进行仿真分析,得到了静态试验数据和动态试验数据,并且对灭火机器人躲避障碍的性能进行了相关测试。研究结果表明,采用融合算法的灭火机器人姿态角能够实现较精确的控制,并较好地躲避障碍物。     

三维直角坐标转换在盾构引导中的运用

运用三维直角坐标转换,先求出盾构机轴线局部坐标系与实际三维空间坐标系两种坐标系的转换参数,然后再利用转换参数求出盾首中心和盾尾中心点的实际坐标,进而计算盾构机的姿态,该方法用于地铁盾构的引导测量,取得了令人满意的效果。     

Tunnel boring machine vibration-based deep learning for the ground identification of working faces

Tunnel boring machine (TBM) vibration induced by cutting complex ground contains essential information that can help engineers evaluate the interaction between a cutterhead and the ground itself. In this study, deep recurrent neural networks (RNNs) and convolutional neural networks (CNNs) were used for vibration-based working face ground identification. First, field monitoring was conducted to obtain the TBM vibration data when tunneling in changing geological conditions, including mixed-face, homogeneous, and transmission ground. Next, RNNs and CNNs were utilized to develop vibration-based prediction models, which were then validated using the testing dataset. The accuracy of the long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) models was approximately 70% with raw data; however, with instantaneous frequency transmission, the accuracy increased to approximately 80%. Two types of deep CNNs, GoogLeNet and ResNet, were trained and tested with time-frequency scalar diagrams from continuous wavelet transformation. The CNN models, with an accuracy greater than 96%, performed significantly better than the RNN models. The ResNet-18, with an accuracy of 98.28%, performed the best. When the sample length was set as the cutterhead rotation period, the deep CNN and RNN models achieved the highest accuracy while the proposed deep CNN model simultaneously achieved high prediction accuracy and feedback efficiency. The proposed model could promptly identify the ground conditions at the working face without stopping the normal tunneling process, and the TBM working parameters could be adjusted and optimized in a timely manner based on the predicted results.     

Utilizing rock mass properties for predicting TBM performance in hard rock condition

The key parameters on the estimation of tunnel-boring machine (TBM) performance are rock strength, toughness, discontinuity in rock mass, type of TBM and its specifications. The aim of this study is to both assess the influence of rock mass properties on TBM performance and construct a new empirical equation for estimation of the TBM performance. To achieve this aim, the database composed of actual measured TBM penetration rate and rock properties (i.e., uniaxial compressive strength, Brazilian tensile strength, rock brittleness/toughness, distance between planes of weakness, and orientation of discontinuities in rock mass) were established using the data collected from one hard rock TBM tunnel (the Queens Water Tunnel # 3, Stage 2) about 7.5 km long, New York City, USA. Intact rock properties were obtained from laboratory studies conducted at the Earth Mechanics Institute (EMI) in the Colorado School of Mines, CO, USA. Based on generated database, the statistical analyses were performed between available rock properties and measured TBM data in the field. The result revealed that rock mass properties have strong affect on TBM performance. It is concluded that TBM performance could be estimated as a function of rock properties utilizing new equation (r = 0.82).     

An evolutionary adaptive neuro-fuzzy inference system for estimating field penetration index of tunnel boring machine in rock mass

Field penetration index (FPI) is one of the representative key parameters to examine the tunnel boring machine (TBM) performance. Lack of accurate FPI prediction can be responsible for numerous disastrous incidents associated with rock mechanics and engineering. This study aims to predict TBM performance (i.e. FPI) by an efficient and improved adaptive neuro-fuzzy inference system (ANFIS) model. This was done using an evolutionary algorithm, i.e. artificial bee colony (ABC) algorithm mixed with the ANFIS model. The role of ABC algorithm in this system is to find the optimum membership functions (MFs) of ANFIS model to achieve a higher degree of accuracy. The procedure and modeling were conducted on a tunnelling database comprising of more than 150 data samples where brittleness index (BI), fracture spacing, α angle between the plane of weakness and the TBM driven direction, and field single cutter load were assigned as model inputs to approximate FPI values. According to the results obtained by performance indices, the proposed ANFIS_ABC model was able to receive the highest accuracy level in predicting FPI values compared with ANFIS model. In terms of coefficient of determination (R²), the values of 0.951 and 0.901 were obtained for training and testing stages of the proposed ANFIS_ABC model, respectively, which confirm its power and capability in solving TBM performance problem. The proposed model can be used in the other areas of rock mechanics and underground space technologies with similar conditions.     

Study on rock mass boreability by TBM penetration test under different in situ stress conditions

Rock mass boreability is a comprehensive parameter reflecting the interaction between rock mass and a tunnel boring machine (TBM). Many factors including rock mass conditions, TBM specifications and operation parameters influence rock mass boreability. In situ stress, as one of the important properties of rock mass conditions, has not been studied specifically for rock mass boreability in TBM tunneling. In this study, three sets of TBM penetration tests are conducted with different in situ stress conditions in three TBM tunnels of the Jinping II Hydropower Station. The correlation between TBM operation parameters collected during the tests and the rock mass boreability index is analyzed to reveal the influence of in situ stress on rock mass boreability and TBM excavation process. The muck produced by each test step is collected and analyzed by the muck sieve test. The results show that in situ stress not only influences the rock mass boreability but also the rock fragmentation process under TBM cutters. If the in situ stress is high enough to cause the stress-induced failure at the tunnel face, it facilitates rock fragmentation by TBM cutters and the corresponding rock boreability index decreases. Otherwise, the in situ stress restrains rock fragmentation by TBM cutters and the rock mass boreablity index increases. Through comparison of the boreability index predicted by the Rock Mass Characteristics (RMC) prediction model with the boreability index calculated from the penetration test results, the influence degree of different in situ stresses for rock mass boreability is obtained.     

基于TBM掘进参数和渣料特征的岩体质量指标辨识

在工程实践和前人研究成果的基础上,综合分析了TBM法隧洞的围岩地质条件对TBM掘进过程的影响,提出了一种基于TBM掘进参数和渣料特征的围岩质量指标(RMR)辨识方法。该方法一方面可将TBM的实时工作参数输入到回归公式或神经网络模型中,实现岩体质量指标的辨识;另一方面可通过分析TBM渣料特征得到岩体的镶嵌结构、不连续面状态以及地下水等地质信息,进而对岩体的地质力学强度(GSITBM)和质量指标(RMRTBM)进行估计。鉴于这两种方式的显著特征,建议在地质条件较好时优先采用基于掘进参数的辨识方法,地质条件较差时采用基于渣料特征的评价方法。在对TBM法隧洞围岩质量指标辨识研究的基础上,对岩体基本力学参数的估计方法进行了研究。     

基于耦合FDM-DEM方法的TBM滚刀最优化研究

为确定TBM盘型滚刀的最优布置,结合有限差分法(FDM)和离散元法(DEM)的优点,采用FDM-DEM耦合的数值模拟方法,对不同刀间距和贯入度条件下TBM双滚刀破岩过程进行三维动态仿真模拟,分析研究了不同滚刀配置对岩石破碎效果的影响。为了验证所提出方法的可行性和准确性,利用线性切割机(LCM)对科罗拉多红色花岗岩进行了切割试验,通过对比分析线性切割试验数据,验证了所提出方法的可行性。数值模拟结果表明：对于科罗拉多红色花岗,滚刀法向力和切向力会随着刀间距和贯入度比值的增加先减少后增大,当刀间距和贯入度的比值为20左右,滚刀法向力和切向力最小。线性切割试验和数值模拟结果均表明,当间距和贯入度的比值在17～20左右,岩石切割的比能量最低,此时TBM切割效率最高。文章提出方法的方法可以为TBM滚刀刀头配置提供重要依据。     

深部高应力隧道TBM护盾长度和推力计算方法研究

岩石隧道掘进机(TBM)法开挖长隧道是一种安全、快速、有效的隧道开挖方法,但TBM复杂高应力隧道掘进时易发生卡机事故,因此,TBM在设计之初应尽可能考虑地质环境的影响,降低TBM的卡机风险。通过分析高应力常规地层和高应力软弱破碎地层对TBM的影响,提出了高应力常规地层和高应力软弱破碎地层TBM卡机的两个判据。根据两个判据提出了考虑围岩力学参数的高应力隧道TBM护盾长度设计和推力设计理论计算方法,并给出了参数选取依据。最后依据西南地区某高应力隧道的实际围岩地质参数,计算分析了现有TBM设计的合理性。本研究可为TBM的盾体长度和推力设计计算提供围岩力学参数依据。     

煤矿长斜井TBM施工安全风险分析与评估

分析和辨识了煤矿长斜井TBM施工的风险因素,构建了二层级的风险评估指标体系。首先,对集对分析法中联系度的取值进行了改进;其次,采用熵权法对各个风险因素的权向量进行计算,构建了一种改进的集对分析法的煤矿长斜井TBM施工风险评估模型,研究了风险计算值与联系度的变化关系。最后,利用该模型对台格庙矿区煤矿长斜井(1^#、2^#实验井)TBM施工风险进行了预测与评估,给出了对风险偏好、不同的施工管理人员对风险的不同取值方法。研究表明该模型与方法在煤矿长斜井TBM施工风险分析中是有效的、实用的,而且也为煤矿长斜井TBM施工风险管控工作提供新的理论支持。     

基于改进的距离判别分析法的南水北调西线工程TBM施工围岩分级

 对马氏距离判别法和层次分析法存在的不足进行改进,将改进的距离判别分析法应用于南水北调西线工程TBM施工围岩分级中。根据TBM施工特点和相关研究成果,将TBM施工围岩分级标准定为4级。选用岩石强度、岩组特征、结构面间距、结构面与洞轴线夹角以及石英含量5项指标作为判别因子,以南水北调西线工程杜柯河-玛柯河段实例数据作为学习样本进行训练,建立TBM施工围岩分级的改进的距离判别分析模型,利用得到的线性判别函数对待判样本进行分级。最后,将改进的距离判别分析法得到的判定结果与传统马氏距离判别法、RTBM法以及RMR方法得到的判别结果进行对比分析,验证了改进的距离判别分析法的有效性。研究结果表明,改进的距离判别分析法具有预测精度高等优点,为TBM施工围岩分级提供了一种新的有效方法。     

An experimental research on the rock cutting process of the gage cutters for rock tunnel boring machine (TBM)

This paper proposed an experimental method to investigate the rock cutting process of TBM gage cutters based on the full-scale rotary cutting machine (RCM). The key point of this method is to reconstruct the RCM by inserting three wedges with angles of 10°, 20° and 30° respectively into the space between the cutter base and cutter box. As a result, the rock cutting process of gage cutters with tilt angles of 10°, 20° and 30° can be proceed. Using this method, rock cutting experiments were conducted with penetrations of 2 mm, 4 mm, 6 mm and 8 mm respectively. The testing results were analysed on the rock cutting force, rock debris dimension, specific energy and cutting surface profile, and it was found that: (1) the cutting forces and specific energy of the gage cutter were lower than those of the normal cutter respectively; and (2) the depth of the rock broken zone was smaller than the cutting depth. The testing results can also be used to validate corresponding numerical models and design the layout of gage cutters.