Effects of data smoothing and recurrent neural network(RNN)algorithms for real-time forecasting of tunnel boring machine(TBM)performance

Tunnel boring machines(TBMs)have been widely utilised in tunnel construction due to their high efficiency and reliability.Accurately predicting TBM performance can improve project time management,cost control,and risk management.This study aims to use deep learning to develop real-time models for predicting the penetration rate(PR).The models are built using data from the Changsha metro project,and their performances are evaluated using unseen data from the Zhengzhou Metro project.In one-step forecast,the predicted penetration rate follows the trend of the measured penetration rate in both training and testing.The autoregressive integrated moving average(ARIMA)model is compared with the recurrent neural network(RNN)model.The results show that univariate models,which only consider historical penetration rate itself,perform better than multivariate models that take into account multiple geological and operational parameters(GEO and OP).Next,an RNN variant combining time series of penetration rate with the last-step geological and operational parameters is developed,and it performs better than other models.A sensitivity analysis shows that the penetration rate is the most important parameter,while other parameters have a smaller impact on time series forecasting.It is also found that smoothed data are easier to predict with high accuracy.Nevertheless,over-simplified data can lose real characteristics in time series.In conclusion,the RNN variant can accurately predict the next-step penetration rate,and data smoothing is crucial in time series forecasting.This study provides practical guidance for TBM performance forecasting in practical engineering.

Vibrations induced by tunnel boring machine in urban areas: In situ measurements and methodology of analysis

Excavation with tunnel boring machine(TBM)can generate vibrations,causing damages to neighbouring buildings and disturbing the residents or the equipment.This problem is particularly challenging in urban areas,where TBMs are increasingly large in diameter and shallow in depth.In response to this problem,four experimental campaigns were carried out in different geotechnical contexts in France.The vibration measurements were acquired on the surface and inside the TBMs.These measurements are also complemented by few data in the literature.An original methodology of signal processing is pro-posed to characterize the amplitude of the particle velocities,as well as the frequency content of the signals to highlight the most energetic bands.The levels of vibrations are also compared with the thresholds existing in various European regulations concerning the impact on neighbouring structures and the disturbance to local residents.

3D Finite Element Simulation of Tunnel Boring Machine Construction Processes in Deep Water Conveyance Tunnel

Applying stiffness migration method,a 3D finite element mechanical model is established to simulate the excavation and advance processes.By using 3D nonlinear finite element method,the tunnel boring machine(TBM) excavation process is dynamically simulated to analyze the stress and strain field status of surrounding rock and segment.The maximum tensile stress of segment ring caused by tunnel construction mainly lies in arch bottom and presents zonal distribution.The stress increases slightly and limitedly in the course of excavation.The maximum and minimum displacements of segment,manifesting as zonal distribution,distribute in arch bottom and vault respectively.The displacements slightly increase with the advance of TBM and gradually tend to stability.

HSP超前探测技术在煤矿TBM掘进巷道中的应用研究

随着全断面掘进机TBM(Tunnel Boring Machine)逐渐应用于煤矿岩巷掘进,对不良地质构造进行超前准确快速预测的需求日益迫切。通过对主动源地震波超前探测方法的特点和TBM破岩震源超前探测技术的适用性进行分析,结合煤矿巷道地质和生产条件,提出了适用于煤矿巷道TBM掘进的HSP超前探测方法。以河南平顶山首山一矿TBM掘进底板瓦斯治理巷道为工程背景,选用防爆硬件一体化设计的探测仪器在煤矿巷道中进行应用。构建了空间型观测方式对煤矿巷道近水平煤线进行探测,优化了双护盾TBM掘进巷道狭小空间检波器阵列式布置参数;基于时频分析、互相关干涉处理、反射与散射联合反演等方法处理原始信号并进行探测结果成像。研究表明:采用空间型观测方式可实现与巷道小角度斜交煤线的识别,设计震源与首检波器间距离为15 m时最优。通过时频分析提取有效信号,利用互相关干涉法获取虚拟震源道和反射特征曲线,并结合反射与散射联合反演成像得到探测区域地层反射能量分布图,能够较准确地推测得到围岩存在的不良地质构造。通过比较现场开挖揭露情况与探测结果发现两者吻合度较高,表明HSP超前探测方法可实现掘进工作面前方100 m范围内超前无损地质预测,有助于提高煤矿岩巷TBM掘进速度。

深部金属矿山巷道TBM掘进技术应用现状及研究进展

未来10年,我国将有30%以上金属矿山开采深度达到或超过千米。深部金属矿山开采面临高地应力、高地温、地层多变、采掘强扰动等复杂地质环境,给巷道掘进带来严峻挑战。传统钻爆法巷道掘进非连续作业、效率低、灾害频发,难以满足建设需求,采用隧道掘进机(TBM)进行机械化连续掘进是深部金属矿山巷道建设的未来发展方向。然而,因深部金属矿山独有工程地质特点和巷道掘进需求,TBM技术在深部金属矿山巷道掘进中仍存在一些技术难题和挑战。本文首先总结了TBM技术在深部金属矿山巷道掘进中的三个应用难点:1)缺乏适用于深部金属矿山巷道的TBM适应性选型设计理论;2)缺乏适用于深部金属矿山巷道的TBM高效掘进技术;3)缺乏适用于深部金属矿山巷道的TBM掘进灾害监测预警与防控技术。然后,从六个方面介绍了深部金属矿山巷道TBM掘进技术应用的研究进展:适应性选型与设计技术、刀盘刀具高效破岩理论、掘进性能精准预测与评价方法、小半径转弯和倾斜巷道掘进技术、掘进灾害智能预警与防控技术、智能决策和辅助驾驶技术。上述研究成果为推动TBM掘进技术在深部金属矿山巷道中的广泛应用,实现深部金属矿山巷道快速掘进指明了发展方向。

地铁施工物化阶段TBM区间碳排放核算与减排

目前,国内外对土建工程碳排放的研究主要集中在住宅、办公建筑领域,对地铁建设工程碳排放的研究较少。为计量地铁TBM区间土建工程物化阶段碳排放,识别其排放特点,运用碳排放系数法建立了包含TBM区间土建实体、建造配套设备、运输施工机具、周转材料、劳动力在内的系统边界,将物化阶段划分为建材及预构件生产、建材及预构件运输、现场施工三个环节对碳排放进行计量。对碳排放因子进行了定义选取,构建了TBM区间各环节碳排放的计量模型并对青岛地铁典型TBM工段进行了案例分析,得到物化阶段三个环节的碳排放强度分别为4875.87 tCO_(2)/km、49.54 tCO_(2)/km、1780.04 tCO_(2)/km,总碳排放强度为6705.45 tCO_(2)/km。通过情景假设法,得出地铁TBM区间再生建材的使用可以带来1.21%~2.43%的减排效益。

小断面土石组合地质条件下TBM施工围岩可掘性分级识别

围岩可掘性分级以及识别研究对隧道掘进机(TBM)高效率施工及智能化控制意义重大。依托南水北调安阳市西部调水工程TBM施工实际数据,利用掘进性能综合指标单位贯入度推力(FPI)、单位贯入度扭矩(TPI)建立了小断面土石组合地质条件下TBM施工围岩可掘性分级标准;提出了PCA-RF模型对围岩可掘性分级进行识别,并与BP、SVR和RF模型进行了比较讨论。结果表明:①建立的小断面土石组合围岩TBM施工可掘性分级标准是适用的,克服了土石组合围岩下传统围岩分类方法的局限性;②小断面土石组合围岩TBM施工可掘性分级PCA-RF识别模型的识别准确率达到了98.3%,高于BP、SVR和RF模型,可以满足工程施工需要。

煤矿复杂地层TBM掘进巷道新型装配式支护结构工程实践

TBM(Tunnel Boring Machine)全断面掘进机已在煤矿深井巷道掘进工程中成功应用,取得了显著的社会经济效益。但仍存在影响其进一步推广应用的技术瓶颈。针对当前支护结构与支护技术难以同时满足支护效率、支护强度和施工成本方面的要求,TBM难以充分发挥其速度优势的技术瓶颈,研究团队研发了新型钢管片型装配式支护结构。开展了新型装配式支护结构大比尺模型试验,获得新型支护结构在受载条件下变形破坏规律,根据试验结果优化新型支护结构型式。基于新型钢管片支护下TBM掘进巷道数值模拟评估新型支护结构可靠性。最后开展工业性试验,现场验证新型钢管片式支护结构用于煤矿TBM掘进巷道的可行性。试验结果表明,巷道围岩最大拉应变为803×10-6,钢管片支护结构变形量小于1 mm。新型支护结构安装速度快,可在90 min内掘进1.5 m并完成一环管片的安装工作,显著提高了TBM掘进巷道的支护强度和TBM掘进作业线的地层适应性。为未来进一步提高煤矿巷道掘进速度、保障煤矿采掘接替提供技术参考。

渗流-应力耦合作用下穿断层破碎带TBM输水隧洞结构安全研究

为分析敞开式隧道掘进机(TBM)穿越断层破碎带深埋长输水隧洞围岩的稳定性及其支护结构的安全性,依托东庄水利枢纽北线输水隧洞工程,采用ABAQUS软件建立隧洞开挖过程渗流-应力耦合三维动态施工仿真模型,研究了隧洞开挖支护过程中断层破碎带处围岩的稳定性和支护结构的受力特性及其变化规律。结果表明:隧洞围岩由于卸载作用其孔隙度最大值较初始状态增大了0.88%,渗透系数最大值较初始状态增大了2.59%;隧洞围岩孔隙水压力随开挖支护过程先下降—再平缓—最后回升至稳定;围岩塑性区出现在沿径向1 m范围内,等效塑性应变极值出现在围岩腰线处;锚杆应力在衬砌进行支护时达到峰值,其最大值为182.90 MPa;衬砌内、外缘均处于受压状态,衬砌环向应力值随开挖支护过程先出现最大值,随后略微减小至稳定,其值在6.66~11.92 MPa范围内;衬砌的变形整体上表现为向内收缩,收缩量从顶拱和底拱处向腰线处逐渐减小,其值在0.67~1.35 mm范围内;随着排水量的增加,围岩最大径缩量逐渐增大,衬砌外水压力折减系数逐渐减小。研究结果可为穿断层破碎带TBM隧洞工程结构设计及其安全施工和运营提供参考依据。

Design Theory of Full Face Rock Tunnel Boring Machine Transition Cutter Edge Angle and Its Application

At present, the inner cutters of a full face rock tunnel boring machine (TBM) and transition cutter edge angles are designed on the basis of indentation test or linear grooving test. The inner and outer edge angles of disc cutters are characterized as symmetric to each other with respect to the cutter edge plane. This design has some practical defects, such as severe eccentric wear and tipping, etc. In this paper, the current design theory of disc cutter edge angle is analyzed, and the characteristics of the rock-breaking movement of disc cutters are studied. The researching results show that the rotational motion of disc cutters with the cutterhead gives rise to the difference between the interactions of inner rock and outer rock with the contact area of disc cutters, with shearing and extrusion on the inner rock and attrition on the outer rock. The wear of disc cutters at the contact area is unbalanced, among which the wear in the largest normal stress area is most apparent. Therefore, a three-dimensional model theory of rock breaking and an edge angle design theory of transition disc cutter are proposed to overcome the flaws of the currently used TBM cutter heads, such as short life span, camber wearing, tipping. And a corresponding equation is established. With reference to a specific construction case, the edge angle of the transition disc cutter has been designed based on the theory. The application of TBM in some practical project proves that the theory has obvious advantages in enhancing disc cutter life, decreasing replacement frequency, and making economic benefits. The proposed research provides a theoretical basis for the design of TBM three-dimensional disc cutters whose rock-breaking operation time can be effectively increased.

TBM隧道岩石可掘性评价指标及其应用研究综述

TBM(Tunnel Boring Machine)掘进施工成本中,刀具的磨损占比很高,这通常是由于TBM的刀具设计与岩石强度及硬度不匹配。因此需要对TBM隧道岩石进行可掘性评估,为刀具设计及施工参数确定提供依据。岩石可掘性评价在TBM围岩分级、掘进效率预测等方面十分重要,是评估TBM施工效率与成本的关键,但隧道TBM开挖过程中岩石可掘性的评价通常相当复杂,需要考虑许多参数,且至今国内外对可掘性这一指标尚未有标准化定义。通过文献检索,本文针对可掘性指标的定义,可掘性的研究应用等方面研究进展进行综述。结合已有的研究成果,对TBM可掘性未来的研究方向进行了展望。

Theoretical prediction of wear of disc cutters in tunnel boring machine and its application

Predicting the cutter consumption and the exact time to replace the worn-out cutters in tunneling projects constructed with tunnel boring machine(TBM) is always a challenging issue. In this paper, we focus on the analyses of cutter motion in the rock breaking process and trajectory of rock breaking point on the cutter edge in rocks. The analytical expressions of the length of face along which the breaking point moves and the length of spiral trajectory of the maximum penetration point are derived. Through observation of rock breaking process of disc cutters as well as analysis of disc rock interaction, the following concepts are proposed: the arc length theory of predicting wear extent of inner and center cutters, and the spiral theory of predicting wear extent of gage and transition cutters. Data obtained from5621 m-long Qinling tunnel reveal that among 39 disc cutters, the relative errors between cumulatively predicted and measured wear values for nine cutters are larger than 20%, while approximately 76.9% of total cutters have the relative errors less than 20%. The proposed method could offer a new attempt to predict the disc cutter's wear extent and changing time.

Load-sharing Characteristic of Multiple Pinions Driving in Tunneling Boring Machine

The failure of the key parts, such as gears, in cutter head driving system of tunneling boring machine has not been properly solved under the interaction of driving motors asynchronously and wave tunneling torque load. A dynamic model of multi-gear driving system is established considering the inertia effects of driving mechanism and cutter head as well as the bending-torsional coupling. By taking into account the nonlinear coupling factors between ring gear and multiple pinions, the influence for meshing angle by bending-torsional coupling and the dynamic load-sharing characteristic of multiple pinions driving are analyzed. Load-sharing coefficients at different rotating cutter head speeds and input torques are presented. Numerical results indicate that the load-sharing coefficients can reach up to 1.2-1.3. A simulated experimental platform of the multiple pinions driving is carried out and the torque distributions under the step load in driving shaft of pinions are measured. The imbalance of torque distribution of pinions is verified and the load-sharing coefficients in each pinion can reach 1.262. The results of simulation and test are similar, which shows the correctness of theoretical model. A loop coupling control method is put forward based on current torque master slave control method. The imbalance of the multiple pinions driving in cutter head driving system of tunneling boring machine can be greatly decreased and the load-sharing coefficients can be reduced to 1.051 by using the loop coupling control method. The proposed research provides an effective solution to the imbalance of torque distribution and synchronous control method for multiple pinions driving of TBM.

基于图像分析的TBM掘进参数与岩碴特征关系研究

岩碴图像分析是隧道掘进机(tunnelboringmechine,TBM)智能掘进中实时破岩效率评价和掘进参数优化的重要手段。为了研究掘进参数与岩碴图像特征的关系,依托青岛地铁6号线创石区间TBM施工段,在一段较完整花岗岩地层中开展了分步推力掘进试验,并通过搭载的岩碴图像分析系统对岩碴图像进行采集和分析。结果表明,岩碴图像的中位粒径d50、最大粒径dmax和粗糙度指数CI与推力、扭矩和贯入度之间均表现出正相关,与现场贯入指数FPI和工程比能SE之间均呈现出良好的负相关。当推力超过破岩临界值后,推力的提升将显著提高岩碴的d50和CI,而只有当推力进一步增大到一定水平后dmax才显著增大。此外,随着推力的提升,大岩片的二维形状逐渐呈现出长条形,其平均长短轴比也将增加,但是推力超过一定水平后,大岩片长短轴将保持不变。上述研究结果表明,岩碴粒径和形状参数均反映出破岩效率,可作为实时TBM掘进参数优化的评价指标。

基于VMD与加权RF的TBM掘进速度预测SHAP解释模型

为较准确地实现TBM掘进速度(PR)的预测,构建一套基于加权随机森林(RF)结合变分模态分解(VMD)的集成学习预测模型。模型建立过程中,通过收集来自KS隧洞与兰州水源地输水隧洞中涵盖不同岩性下的数据,利用VMD对数据进行4次模态分解,在保留数据特性的同时去除最高频噪音;采用SHAP对未加权传统RF从模型贡献角度进行特征度量,以此实现未加权传统RF加权,并使用RFECV与网格搜索对加权RF进行特征遴选、超参数优化;通过实际工程对模型的性能进行验证,基于SHAP理论对模型从全局与局部进行解释。结果表明:1)所建模型预测精度较高,其在测试集上的均方根误差(MSE)、平均绝对误差(MAE)与决定系数(R^(2))分别为0.0649(m/h)^(2)、0.1875 m/h、0.9254。2)在实际工程的验证中,模型取得了MSE=0.0503(m/h)^(2)、MAE=0.1613 m/h、R^(2)=0.9505的性能表现,精度理想,且性能均高于常用的深度神经网络、支持向量回归、未加权传统RF。3)经过VMD处理可有效提升PR的预测精度,处理后的模型在测试集上MSE、MAE、R^(2)分别提升了82.50%、59.00%、33.25%。4)岩石单轴抗压强度是精准预测PR时最重要的因素,地质参数在预测中的交互性明显优于掘进参数。预测分析重要洞段的PR时,需结合全局与局部2个角度进行综合分析。

Performance characteristics of tunnel boring machine in basalt and pyroclastic rocks of Deccan traps–A case study

A12.24km long tunnel between Maroshi and Ruparel College is being excavated by tunnel boring machine(TBM)to improve the water supply system of Greater Mumbai,India.In this paper,attempt has been made to establish the relationship between various litho-units of Deccan traps,stability of tunnel and TBM performances during the construction of5.83km long tunnel between Maroshi and Vakola.The Maroshi–Vakola tunnel passes under the Mumbai Airport and crosses both runways with an overburden cover of around70m.The tunneling work was carried out without disturbance to the ground.The rock types encountered during excavation arefine compacted basalt,porphyritic basalt,amygdaloidal basalt pyroclastic rocks with layers of red boles and intertrappean beds consisting of various types of shales Relations between rock mass properties,physico-mechanical properties,TBM specifications and the cor responding TBM performance were established.A number of support systems installed in the tunne during excavation were also discussed.The aim of this paper is to establish,with appropriate accuracy the nature of subsurface rock mass condition and to study how it will react to or behave during under ground excavation by TBM.The experiences gained from this project will increase the ability to cope with unexpected ground conditions during tunneling using TBM.

Excavation of underground research laboratory ramp in granite using tunnel boring machine: Feasibility study

Underground research laboratory(URL)plays an important role in safe disposal of high-level radioactive waste(HLW).At present,the Xinchang site,located in Gansu Province of China,has been selected as the final site for China’s first URL,named Beishan URL.For this,a preliminary design of the Beishan URL has been proposed,including one spiral ramp,three shafts and two experimental levels.With advantages of fast advancing and limited disturbance to surrounding rock mass,the tunnel boring machine(TBM)method could be one of the excavation methods considered for the URL ramp.This paper introduces the feasibility study on using TBM to excavation of the Beishan URL ramp.The technical challenges for using TBM in Beishan URL are identified on the base of geological condition and specific layout of the spiral ramp.Then,the technical feasibility study on the specific issues,i.e.extremely hard rock mass,high abrasiveness,TBM operation,muck transportation,water drainage and material transportation,is investigated.This study demonstrates that TBM technology is a feasible method for the Beishan URL excavation.The results can also provide a reference for the design and construction of HLW disposal engineering in similar geological conditions.2020 Institute of Rock and Soil Mechanics,Chinese Academy of Sciences.Production and hosting by Elsevier B.V.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

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.

北山1号TBM刀盘破岩振动影响参数分析

隧道掘进机在掘进过程中,刀盘破碎岩石会产生振动,刀盘振动对TBM机械与施工会造成负面影响。依托甘肃北山地下实验室斜坡道工程TBM施工段,在北山1号TBM上搭载振动监测系统,通过跟踪现场施工和开展掘进试验,收集研究区段的隧道设计工况信息、围岩信息、掘进参数与刀盘振动信息。开展不同隧道设计工况、围岩类别、掘进参数对刀盘破岩振动的影响分析。研究结果表明:1)隧道转弯段与直线段的TBM刀盘振动强度、主振频率具有一定差异,转弯段TBM刀盘振动强度更大、主要频率更高;2)不同围岩类别下,转弯段刀盘振动主振频率差异较大,而直线段差异较小;3)随着推力、转速的提高,刀盘的振动强度增大,但刀盘三轴振动主振频率不发生改变。

Hybrid ensemble soft computing approach for predicting penetration rate of tunnel boring machine in a rock environment

This study implements a hybrid ensemble machine learning method for forecasting the rate of penetration(ROP) of tunnel boring machine(TBM),which is becoming a prerequisite for reliable cost assessment and project scheduling in tunnelling and underground projects in a rock environment.For this purpose,a sum of 185 datasets was collected from the literature and used to predict the ROP of TBM.Initially,the main dataset was utilised to construct and validate four conventional soft computing(CSC)models,i.e.minimax probability machine regression,relevance vector machine,extreme learning machine,and functional network.Consequently,the estimated outputs of CSC models were united and trained using an artificial neural network(ANN) to construct a hybrid ensemble model(HENSM).The outcomes of the proposed HENSM are superior to other CSC models employed in this study.Based on the experimental results(training RMSE=0.0283 and testing RMSE=0.0418),the newly proposed HENSM is potential to assist engineers in predicting ROP of TBM in the design phase of tunnelling and underground projects.