基于特征增强的盾构掘进参数显式预测分析

盾构法施工时掘进参数的选取是保证盾构机安全、高效掘进的关键。为了实现掘进参数的预测与揭示参数之间的关联性,提出一种掘进参数的显式分析方法。依托实际的盾构隧道掘进数据,首先通过构造掘进参数的二次项以及耦合项进行特征增强;其次通过五折交叉验证和逐步线性回归相结合的方式获得最优特征子集;最终通过最优特征建立掘进参数的显式预测模型。研究结果表明：结合特征增强和特征选择可成功地将模型中的20个特征缩减到5个之内;建立的掘进参数预测模型,能良好反映真实值的变化趋势,且刀盘转速、土仓压力、总推进力、推进速度、螺机转速相对误差在15%以内,刀盘扭矩相对误差约21%,各参数误差基本可满足工况需求;此模型可定量揭示特征参数对预测量的影响程度,如对总推进力影响最大的特征是刀盘扭矩与推进速度的耦合项,其权重为1257,而推进速度和土仓压力受到多个独立参数组成的耦合项的共同影响。     

基于决策树模型的土压平衡盾构掘进参数预测研究

以洛阳地铁2号线为依托,建立了决策树模型、正向激励模型和随机森林模型来预测盾构掘进过程中的推进速度.通过实时采集盾构机的参数进行预处理,并分析参数之间的特征重要性指数,确定影响推进速度的关键性参数,然后对三种预测模型的预测结果进行比较.对比表明,随机森林预测模型预测推进速度的预测精度最高,可用于指导盾构施工过程的参数设置.     

基于BP神经网络的复合地层盾构掘进参数预测与分析

复杂地质条件下盾构机掘进参数的有效预测可以对盾构施工进行针对性的指导。基于深圳地铁11号线车公庙站～红树湾站和南山站～前海湾站两个区间Φ7m盾构施工现场监测的掘进参数,首先采用BP人工神经网络方法建立了复合地层条件下盾构掘进参数的预测模型|其次,以地层参数为输入组和盾构掘进参数为输出组,通过对数据样本进行训练,得到的输出值基本与原始数据一致,说明该预测模型具有很好的非线性映射能力;最后,采用盾构区间典型地段的地层参数,利用所建立的模型预测了复合地层条件下的盾构掘进参数,预测值与实际数据变化规律相近,平均误差在15%以内。本文建立的BP神经网络模型可用于复合地层条件下同类型盾构掘进参数的预测。     

基于TBM掘进性能的岩体分级及可掘性等级感知识别方法

传统的围岩分级方法难以适用于TBM掘进性能评价和预测,因此,建立一套适用于TBM施工的岩体可掘性分级系统并实现可掘性等级的准确感知识别。基于4条TBM隧道的152组岩体和机器数据建立的数据库,分析岩体参数与TBM可掘性评价指标的相关性,采用多目标决策方法 TOPSIS实现TBM隧道岩体可掘性分级。由于实际TBM掘进中岩体参数较难获取,利用单刀推力(Th)、净掘进速度(PR)、刀盘转速(RPM)以及贯入度(PRev)等TBM掘进参数对所提出的岩体可掘进性等级进行了感知识别。感知识别中,采用贝叶斯优化确定多种机器学习分类算法的最优超参数组合,实现最大感知识别准确率,比较不同算法对于岩体可掘性等级感知识别的适用性,并确定最优的感知识别方法。最后,基于吉林引松工程的实时TBM掘进数据,验证岩体可掘性分级及可掘性等级感知识别方法的有效性和准确性。研究成果可用于TBM平稳运行段岩体可掘性评估预测,为掘进参数优化和建立TBM智能化控制系统提供参考。     

基于LSTM的盾构掘进轨迹预测研究

盾构隧道建设过程中需要对盾构机的行进轨迹进行控制,使得盾构实际掘进线路与隧道设计轴线相吻合.文章利用长短期记忆循环神经网络(LSTM)处理时间序列数据的优势,提出了一种盾构机掘进轨迹预测模型.模型利用LSTM层代替传统神经网络中的隐含层,综合考虑了盾构操作参数、地质参数以及几何参数对盾构掘进轨迹误差的影响.结果 显示模型输出与实际测量值较为吻合,绝对误差总体上位于4mm之内,表明所提出的LSTM神经网络模型可以有效地对盾构行进路线进行预测.     

基于盾构机掘进参数对地表沉降影响敏感度的风险分析

为了研究软土地区土压平衡盾构机在穿越重要区域过程中,不同掘进参数影响下的地表沉降能否满足变形控制标准,针对建国道站至天津站站盾构掘进工程,分析盾构掘进参数与掘进速度的关系,并建立基于所确定的盾构掘进参数的三维有限元模型,分析对周围地层沉降的影响规律,通过空旷场地下的实测结果对数值分析进行验证。在此基础上,针对各参数在正常施工范围内的波动对地表沉降影响的敏感度进行分析,最后以盾构掘进过程中的关键掘进参数为底事件建立风险故障树并进行定量的风险评估。研究结果表明:盾构掘进速度不同时,各掘进关键参数的取值范围不同,各掘进参数之间具有一定的相关性;盾构机以接近30 mm/min的速度正常掘进的前提下,得出对地表沉降影响敏感度及风险值的致险因子排序,从而可对盾构掘进引起变形的精细化控制提供参考。     

饱和流塑状软土中盾构掘进上浮控制施工技术

在杭州地区饱和流塑状软土盾构掘进施工中,由于受盾构机及拖出盾尾管片的竖向浮力、超挖等因素影响易出现盾构机上浮现象。在分析引起盾构机上浮的原因后,围绕导致盾构机上浮的几个要素,通过优化推力分配和管片超前量等掘进参数,并采取二次双液补充注浆、设置纵向管片拉杆、附加盾构机配重等辅助措施,有效地控制了盾构机的上浮,很好地控制了成型隧道轴线。     

基于随机森林的复合地层盾构#br# 切口泥水压力预测与分析

盾构切口泥水压力是维持开挖面稳定最重要的因素。针对目前切口泥水压力与盾构掘进参数之间关系不明确的问题,基于Python提出了一种随机森林预测模型。以京张高铁清华园隧道工程为依托,考虑盾构几何参数、施工掘进参数以及泥水参数等因素的影响,通过网格搜索方法以及5折交叉验证法进行模型超参数调整,建立了盾构切口泥水压力的随机森林预测模型。研究结果显示随机森林模型对于复合地层盾构切口泥水压力预测具有较高的准确性与泛化性,在训练集与测试集的表现得分分别为0.963与0.946,结合RMSE和R2等各项数据的评估再次验证了该模型的预测能力。最后通过重要度分析得到对模型预测影响较大的参数分别为泥舱压力、埋深比、推力和扭矩。该研究可为同类条件下盾构开挖面切口泥水压力的预测提供一种有效的方法,对于泥水盾构开挖面的预测控制具有重要的现实意义。     

土压平衡盾构机在复合板岩地层掘进参数的确定

针对复合板岩地层的盾构工程,介绍了盾构机主要掘进参数的理论计算方法,合理确定盾构施工参数,确保盾构机在掘进中快速、平稳、安全地通过,为类似地层盾构隧道掘进施工提供相应的理论依据。     

复合地层盾构刀具磨损分析与预测

盾构机在复合地层中进行掘进时,刀具磨损严重,易发生偏磨,是制约土压平衡盾构机长距离高效掘进的重要因素。通过气压开仓和常压开仓技术,对深圳地铁9号线区间隧道盾构刀具进行现场磨损监测,分析了不同类型刀具在复合地层中的磨损规律,回归得到了不同类型刀具的磨损系数,并据此推算了盾构机在类似地层条件下的最远掘进距离。为准确预测盾构机掘进过程中刀具磨损情况,文章利用遗传算法优化BP神经网络模型对刀具磨损进行了分析,模型综合考虑了盾构机掘进速度、推力、扭矩等影响因素,预测结果与实测值接近。研究成果可为类似地层条件下的实际工程中盾构机的刀具磨损量、最长掘进距离等预测提供理论支持,对实际施工有一定指导作用。     

QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency

In recent years, tunnel boring machines (TBMs) have been widely used in tunnel construction. However, the TBM control parameters set based on operator experience may not necessarily be suitable for certain geological conditions. Hence, a method to optimize TBM control parameters using an improved loss function-based artificial neural network (ILF-ANN) combined with quantum particle swarm optimization (QPSO) is proposed herein. The purpose of this method is to improve the TBM performance by optimizing the penetration and cutterhead rotation speeds. Inspired by the regularization technique, a custom artificial neural network (ANN) loss function based on the penetration rate and rock-breaking specific energy as TBM performance indicators is developed in the form of a penalty function to adjust the output of the network. In addition, to overcome the disadvantage of classical error backpropagation ANNs, i.e., the ease of falling into a local optimum, QPSO is adopted to train the ANN hyperparameters (weight and bias). Rock mass classes and tunneling parameters obtained in real time are used as the input of the QPSO-ILF-ANN, whereas the cutterhead rotation speed and penetration are specified as the output. The proposed method is validated using construction data from the Songhua River water conveyance tunnel project. Results show that, compared with the TBM operator and QPSO-ANN, the QPSO-ILF-ANN effectively increases the TBM penetration rate by 14.85% and 13.71%, respectively, and reduces the rock-breaking specific energy by 9.41% and 9.18%, respectively.     

论隧洞掘进机施工中的不安全因素

结合新疆大坂隧洞工程近四年的TBM施工安全生产管理实践,针对工程施工特点和作业条件,具体分析了TBM施工中常见的不安全因素,为今后隧洞掘进机施工安全管理方面提供了借鉴。     

锦屏二级水电站长引水隧洞高地应力下开敞式硬岩隧道掘进机安全快速掘进技术研究

 对开敞式硬岩掘进机在锦屏二级引水隧洞群中的应用经验进行总结和梳理,首先针对锦屏二级水电站高地应力下开敞式硬岩隧道掘进机(TBM)设备选型及掘进参数的优化进行研究,合理确定设备结构及掘进参数;采用BEAM,TSP,地质雷达等手段综合开展掌子面前方超前地质预报,取得良好的预报效果,并制定相应的预处理措施。首次在国内水电工程中开展微震监测系统试验及应用,进行高地应力下岩爆风险预测预警研究,较为准确地预测了岩爆发生的大致范围,并开展一系列防岩爆支护措施的试验研究工作,取得较好的效果,有效降低岩爆风险和危害。最后,对如何实现TBM安全快速掘进进行综合分析研究,对今后开敞式TBM的选型和使用提供参考意见。     

Correlation of tunnel convergence with TBM operational parameters and chip size in the Ghomroud tunnel, Iran

Evaluating the impact of rock mass properties on a tunneling operation is crucial, especially when using a tunnel boring machine (TBM). It is an integral part of machine selection and performance prediction in the design and bidding stage. Monitoring and analysis of ground conditions during the construction is also essential to allow the operator to take precautionary measures in adverse geological conditions. This involves adjusting TBM operational parameters such as machine thrust and penetration to avoid potential problems caused by face collapse or excessive convergence and subsequent machine seizure that can cause long delays. Tunnel wall convergence is a function of rock mass characteristics, in situ stresses, size of excavation, and rate of penetration (ROP). It is one of the main factors in determining the use of shielded machines in deep rock tunnel projects. The case study of the Ghomroud water conveyance tunnel project, under construction by a double shield TBM, is used to examine the effect of rock mass parameters on tunnel convergence and hence on the need for over excavation and shield lubrication to avoid problems such as shield seizure. Results of a preliminary analysis of field observations show that the amount of the tunnel convergence can have a direct relationship with the percentage of powder and large rock fragments in the muck. In addition, tunnel convergence has shown a strong relationship with the TBM thrust/torque and rate of penetration (ROP). These relationships have been examined and the results of the analysis as well as the resulting formulas will be explained in this paper.     

Real-time rock mass condition prediction with TBM tunneling big data using a novel rock–machine mutual feedback perception method

Real-time perception of rock mass information is of great importance to efficient tunneling and hazard prevention in tunnel boring machines (TBMs). In this study, a TBM–rock mutual feedback perception method based on data mining (DM) is proposed, which takes 10 tunneling parameters related to surrounding rock conditions as input features. For implementation, first, the database of TBM tunneling parameters was established, in which 10,807 tunneling cycles from the Songhua River water conveyance tunnel were accommodated. Then, the spectral clustering (SC) algorithm based on graph theory was introduced to cluster the TBM tunneling data. According to the clustering results and rock mass boreability index, the rock mass conditions were classified into four classes, and the reasonable distribution intervals of the main tunneling parameters corresponding to each class were presented. Meanwhile, based on the deep neural network (DNN), the real-time prediction model regarding different rock conditions was established. Finally, the rationality and adaptability of the proposed method were validated via analyzing the tunneling specific energy, feature importance, and training dataset size. The proposed TBM–rock mutual feedback perception method enables the automatic identification of rock mass conditions and the dynamic adjustment of tunneling parameters during TBM driving. Furthermore, in terms of the prediction performance, the method can predict the rock mass conditions ahead of the tunnel face in real time more accurately than the traditional machine learning prediction methods.     

锦屏二级水电站TBM选型及施工关键技术研究

 锦屏二级水电站引水隧洞属于深埋特长隧洞,其中2条引水隧洞施工采用敞开式硬岩掘进机(TBM)进行施工。引水隧洞TBM开挖直径12.4 m,位列世界第二。引水隧洞穿越区域水文地质条件复杂,开挖中面临地下水预报及处理、通风、岩爆防治等三大关键技术问题。通过对大量文献资料和工程实例的研究,概述TBM近半个世纪的发展及其在隧道建设中的应用现状和主要问题。对锦屏二级水电站区域地质条件以及主要的工程地质问题进行分析,结合国内外已有的TBM施工经验,对锦屏二级TBM选型以及在施工中面临的超前地质预报、围岩稳定、高地应力及岩爆、突涌水、溶洞、有害气体、断层破碎带等不良地质条件所采用的施工方法进行分析研究,针对性地提出在各种不良地质条件的下的TBM施工对策,对锦屏二级水电站TBM施工提出建议,研究成果可供类似工程参考。     

深埋隧洞TBM掘进微震实时监测与特征分析

 针对深埋硬岩隧洞TBM掘进过程中开展微震实时监测存在的困难与不足,对现有微震监测技术进行优化与改进,并在锦屏II级水电站3#引水隧洞TBM施工洞段开展微震实时监测。监测结果表明：(1) TBM施工环境噪音复杂,但主要噪音特征明显,可通过建议的滤波方法有效滤除。(2) 围岩的微震活动和TBM掘进及掘进速率具有明显的关系,TBM掘进速率增加,围岩微震明显活跃;TBM掘进速率降低,围岩的微震活动明显降低;微震平静期发生在TBM检修期间,最活跃期发生在TBM检修后掘进4～6 h。(3) 一些岩爆发生前,微震事件和能量释放在空间上由随机离散状态变得相对集中,在时间上微震事件的数量和能量有一个迅速增加的趋势;视体积有突增趋势,能量指数有突然下降迹象。(4) 在深埋隧洞TBM掘进过程中进行微震实时监测,可以获得岩爆发生前的有效微震信息,获得岩爆发生前微震活动的演化特征与规律,为岩爆的发生提供较为准确的预警信息。因此,通过微震监测预测预报深埋隧洞TBM掘进过程中岩爆的发生是可行的。     

Tunneling in fault zones, Tuzla tunnel, Turkey

The Tuzla tunnel was excavated mainly in fault zones, shale and limestones using the conventional and shielded tunnel boring machine (TBM) methods. Fault zones in shales are brecciated and clayey, while those in limestones are of blocky structure. The rock mass rating, rock mass classification and support systems proposed for fault zones in Tuzla tunnel are insufficient for explaining the deformation and failure mechanisms encountered in the tunnel. In addition, dyke exposures, the fault-collapsed karstic system and groundwater also caused some problems during the excavation of the tunnel. The most important event relevant to fault zones in the Tuzla tunnel was the selection of a TBM. Before the excavation of the tunnel, the rock was determined to be of poor to fair quality. Therefore, tunneling with a TBM in rock of poor to fair quality was thought to be economic. However, during the excavation, fault zones with poor to very poor rock characteristics were encountered along an area comprising 70% of the tunnel length. The fault zones caused jamming of the TBM cutter and deviation from the tunnel alignment. In this respect, tunneling with the TBM method was quite problematic. Geotechnical problems encountered in the fault zones required special measures to be taken in the tunnel. With these measures, excavation and supporting of the tunnel were completed successfully by transforming heterogeneous conditions in the fault zones to homogeneous conditions in the tunnel impact area.     

基于模糊数学的TBM施工岩体质量分级研究

 根据隧道掘进机(TBM)施工进度将围岩分为施工条件好、施工条件较好、施工条件较差和施工条件差4个等级。利用模糊数学方法,采用岩石单轴抗压强度UCS和岩体完整性指标KV,分别建立UCS和KV关于TBM施工岩体质量4级分级的隶属度函数。采用单极性S形函数,分别构建UCS和KV的权重函数。这样,基于模糊数学的最大隶属度准则,就可以对TBM施工的岩体质量进行分级。同时,给出3个施工实例,演算表明,预测的结果与TBM施工实际相吻合,表明该分级方法简单而实用,具有很好的应用前景。     

锦屏二级水电站1#,3#引水隧洞TBM施工预测与施工效果对比分析

TBM施工预测在隧道施工方法选择、项目规划、施工规划、施工管理中发挥了很大的作用.针对锦屏二级水电站引水隧洞TBM施工,在获得隧洞沿线岩体参数及岩体所处环境参数的基础上,对TBM施工段的岩体进行分区分段,应用挪威科技大学(NTNU)TBM施工预测模型、岩体特性TBM施工预测模型对各段TBM掘进速度进行预测,应用NTNU...