Developing new equations for TBM performance prediction in carbonate-argillaceous rocks: a case history of Nowsood water conveyance tunnel

Nowsood water conveyance tunnel is 49 km long and has been designed for transferring 70 m³/s water from Sirvan river southward to Dashte Zahab plain in the west of Iran. This long tunnel has been divided into three sections, namely 1A, 1B and 2. By April 2008, about 5.3 km of the lot 2 of this project, with a total length of 26 km, were excavated by a double-shield TBM. The bored section of tunnel passed through different geological units of three main formations of the Zagross mountain ranges which mainly consist of weak to moderately strong argillaceous-carbonate rocks. This paper will offer an overview of the project, concentrating on the TBM operation, and review the results of the field performance of the machine. Also results of statistical analyses to evaluate correlation of TBM performance parameters with rock mass characteristics will be discussed. The results of machine performance analysis indicated that there are strong relationships between geomechanical parameters and TBM performance parameters in this particular project. In this research some empirical equations and a chart have been developed to estimate TBM performance parameters in similar cases based on common rock mass properties.     

Estimation of the performance of the tunnel boring machine (TBM) using uniaxial compressive strength and rock mass rating classification (RMR)–A case study from the Deccan traps,India

The competency of any TBM in any geological condition is determined by a rock or rock mass breakage process. A 12.24 km long tunnel between Maroshi and Ruparel College was excavated by Brihanmumbai municipal corporation (BMC) to improve water supply system of greater Mumbai, India, using open-type hard rock tunnel boring machines (TBMs). In this paper an attempt has been made to establish the relationship between rock mass characteristics i.e. RMR and UCS of the Deccan trap rocks and TBMs performance characteristics for 5.83 km long Maroshi–Vakola tunnel section of the Maroshi–Ruparel college tunnel project. To analyze the effect of variable rock mass conditions on the TBM performance, the operating parameters i.e. thrust force, torque and RPM of the machine, were recorded and intact rock strength was determined. The effect of rock mass properties on machine penetration rate (PR) and the relation with other operational parameters were analyzed. The rock strength affects the rock behaviour under compression. When the rolling cutters indent the rock, the stress exerted must be higher than the rock strength i.e.; the rock strength is directly relevant to the performance of TBM. Studies show that the penetration rate decreases with increase in uniaxial compressive strength (UCS). The comparison of measured penetration rate with empirical model developed by Graham, in which, the penetration rate is computed using UCS and average thrust per cutter, showed good agreement with coefficient of determination (R²), i.e. 0.97. The study shows that the TBM performance was maximum in rock mass rating (RMR) range from 40 to 75, while slower penetration was recorded both in very poor and very good rock masses.     

Advanced prediction of tunnel boring machine performance based on big data

Predicting the performance of a tunneling boring machine is vitally important to avoid any possible accidents during tunneling boring.The prediction is not straightforward due to the uncertain geological conditions and the complex rock-machine interactions.Based on the big data obtained from the 72.1 km long tunnel in the Yin-Song Diversion Project in China,this study developed a machine learning model to predict the TBM performance in a real-time manner.The total thrust and the cutterhead torque during a stable period in a boring cycle was predicted in advance by using the machine-returned parameters in the rising period.A long short-term memory model was developed and its accuracy was evaluated.The results show that the variation in the total thrust and cutterhead torque with various geological conditions can be well reflected by the proposed model.This real-time predication shows superior performance than the classical theoretical model in which only a single value can be obtained based on the single measurement of the rock properties.To improve the accuracy of the model a filtering process was proposed.Results indicate that filtering the unnecessary parameters can enhance both the accuracy and the computational efficiency.Finally,the data deficiency was discussed by assuming a parameter was missing.It is found that the missing of a key parameter can significantly reduce the accuracy of the model,while the supplement of a parameter that highly-correlated with the missing one can improve the prediction.     

Applying Optimized Support Vector Regression Models for Prediction of Tunnel Boring Machine Performance

One of the main factors in the effective application of a tunnel boring machine (TBM) is the ability to accurately estimate the machine performance in order to determine the project costs and schedule. Predicting the TBM performance is a nonlinear and multivariable complex problem. The aim of this study is to predict the performance of TBM using the hybrid of support vector regression (SVR) and the differential evolution algorithm (DE), artificial bee colony algorithm (ABC), and gravitational search algorithm (GSA). The DE, ABC and GSA are combined with the SVR for determining the optimal value of its user defined parameters. The optimization implementation by the DE, ABC and GSA significantly improves the generalization ability of the SVR. The uniaxial compressive strength (UCS), average distance between planes of weakness (DPW), the angle between tunnel axis and the planes of weakness (α), and intact rock brittleness (BI) were considered as the input parameters, while the rate of penetration was the output parameter. The prediction models were applied to the available data given in the literature, and their performance was assessed based on statistical criteria. The results clearly show the superiority of DE when integrated with SVR for optimizing values of its parameters. In addition, the suggested model was compared with the methods previously presented for predicting the TBM penetration rate. The comparative results revealed that the hybrid of DE and SVR yields a robust model which outperforms other models in terms of the higher correlation coefficient and lower mean squared error.     

Full-scale linear cutting test in Chongqing Sandstone and the comparison with field TBM excavation performance

In this study, determination of some machine parameters and performance prediction for tunnel boring machine (TBM) are conducted based on laboratory rock cutting test. Firstly, laboratory full-scale linear cutting test is carried out using 432-mm CCS (constant cross section) disc cutter in Chongqing Sandstone. Then, the input parameters for TBM cutterhead design are extracted; some TBM specifications are determined and then compared to the manufactured values. Finally, laboratory full-scale linear cutting test results are compared with the field TBM excavation performance data collected in Chongqing Yangtze River Tunnel. Results show that laboratory full-scale linear cutting test results, combined with some engineering considerations, can be used for the preliminary and rough design of TBM machine capacity. Meanwhile, combined with some modification factors, it can also well predict the field TBM excavation performance.

     

深埋长隧道TBM施工关键问题探讨

针对深埋长隧道开挖所面临的高水压、高地压、高地温、大变形、难支护等问题,分析总结传统钻爆法开挖与支护技术、全断面隧道掘进机（TBM）施工技术、TBM导洞扩挖技术应用中的优劣,TBM导洞扩挖法为深埋长隧道开挖提供了新的设计思路。由于深埋长隧道的建设环境与浅埋隧道建设环境存在显著差异,TBM施工将面临3个关键问题--岩爆问题、卡盾（大变形）问题和未准确探测前方地质而发生的施工事故（涌水、突泥等）。为揭示TBM施工过程中卡盾的存在性,分别针对某一特定地质条件下深埋软、硬岩TBM施工进行理论分析和数值模拟研究。结果表明,软岩地层TBM施工发生卡盾,而硬岩完整地层TBM施工未发生卡盾。     

Modelling of rock temperatures for deep alpine tunnel projects

The construction of deep railway tunnels requires the prediction of natural temperatures at depth. Geothermal data for the Alps are presented and principles of previously employed methods to predict temperatures, using Andreae's analytical approach, are discussed. We then use a finite element numerical model based on pure conduction to calculate temperatures at depth. This method allows rock heterogeneity and anisotropy to be taken into account.This model is applied to the Maurienne-Ambin tunnel project, a 55 km long tunnel between St-Jean-de-Maurienne (France) and Susa (Italy), which will be the longest tunnel for the planned TGV (high speed train) Lyon-Torino link. Data from several deep boreholes (10 total, with 3>1000 m) are used to provide essential parameters for the model, i.e.:

双护盾TBM施工隧洞综合超前地质预报方法研究

由于受护盾、管片及电磁干扰的影响,地质素描、炸药激振地震法、电磁法等超前地质预报方法在双护盾TBM施工中无法使用。根据双护盾TBM技术特点,以CCS水电站引水隧洞为工程背景,提出了以地质分析法、物探法、和超前钻探等为主的综合超前地质预报方法。综合超前地质预报采用"由粗到细、点面结合"的原则。地质分析法包括隧洞沿线地质分析、施工地质观察、岩渣及掘进参数分析等,不占用TBM掘进时间,成本低,可全洞段采用。物探法包括ISIS地震法和BEAM电法。物探法和超前钻探占用TBM掘进时间,且预报成本较高。因此,应根据预报精度、预报成本及是否占用掘进时间综合权衡后,确定采用何种预报方法。基于综合超前地质预报结果,针对不良地质条件,提出了相应的处理措施。研究结果表明,综合超前地质方法符合双护盾TBM施工特点,能有效识别掌子面前方的不良地质条件,同时可为工程应对措施提供基础支撑,从而有效避免或降低不良地质条件的影响。     

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.     

基于AHP和模糊综合评判的TBM施工风险评估

岩石隧洞建设中面临很大和众多的风险,利用TBM施工的深埋长隧洞受多种不确定因素影响,具有随机性和模糊性,目前的研究方法难以对其进行准确定量分析。通过深入分析影响TBM施工的风险因素,建立了TBM施工风险综合评价指标体系。基于风险影响因素的层次性,提出了TBM施工风险二级模糊综合评判计算模型,并利用层次分析法(AHP)确定各级因素权重,利用模糊集法确定隶属函数,划分了风险接受等级。以南水北调西线工程深埋长隧洞TBM施工为例,应用二级模糊综合评判计算模型对该工程TBM施工风险进行分析,计算结果表明,该方法是合理性实用的。其理论、方法、思路和结论可供同类工程借鉴。     

软土地层TBM开挖面支护压力计算模型及可视化

隧道掘进机（TBM）近年来在世界范围内得到了广泛应用,通常通过完全充满压力仓的泥土或泥浆来支护开挖面。但在较差的地层和水力条件下,开挖面失稳时有发生。事实上,TBM开挖面的支护压力的大小直接决定了施工安全及地表变形。基于所建立的开挖面支护压力计算模型,并考虑复合地层下土体分层带来的影响,通过计算机编程方法,建立了界面友好、使用便捷的开挖面支护压力可视化计算平台(TBM Studio);并结合阿拉斯加隧道、钱江隧道工程实例进行了不同模型结果的验证分析,给出了各模型计算结果的差异性;讨论了软土复合地层条件下,土体自稳性对开挖面稳定的影响,认为软土地层中定量确定有效支护压力和水头高度至关重要,研究为正确评价TBM开挖面稳定性提供了相应的计算模型。     

开敞式TBM过类泥石流不良地质洞段施工处理技术

开敞式TBM在掘进过程中,往往会遇到断层破碎带,目前业界已经有相应的处理方式,但是在遇到类泥石流洞段时,单独依靠目前TBM自身条件及已有的处理方式很难实现顺利通过,需要采取特殊的施工处理措施相互配合才能通过。本文依托吉林引松供水项目三标段类泥石流不良地质洞段的处理,形成了一套完整的开敞式TBM过类泥石流不良地质洞段施工处理技术,即在碎块石夹杂断层泥段采用超前管棚支护,在类泥石流不良地质洞段采用堵水灌浆加固技术并配以喷锚喷网 钢拱 模筑混凝土的联合支护技术。工程实践验证了该技术的可行性。     

Applications of NTNU/SINTEF Drillability Indices in Hard Rock Tunneling

Drillability indices, i.e., the Drilling Rate Index? (DRI), Bit Wear Index? (BWI), Cutter Life Index? (CLI), and Vickers Hardness Number Rock (VHNR), are indirect measures of rock drillability. These indices are recognized as providing practical characterization of rock properties used in the Norwegian University of Science and Technology (NTNU) time and cost prediction models available for hard rock tunneling and surface excavation. The tests form the foundation of various hard rock equipment capacity and performance prediction methods. In this paper, application of the tests for tunnel boring machine (TBM) and drill and blast (D&B) tunneling is investigated and the impact of the indices on excavation time and costs is presented.     

A Completely 3D Model for the Simulation of Mechanized Tunnel Excavation

For long deep tunnels as currently under construction through the Alps, mechanized excavation using tunnel boring machines (TBMs) contributes significantly to savings in construction time and costs. Questions are, however, posed due to the severe ground conditions which are in cases anticipated or encountered along the main tunnel alignment. A major geological hazard is the squeezing of weak rocks, but also brittle failure can represent a significant problem. For the design of mechanized tunnelling in such conditions, the complex interaction between the rock mass, the tunnel machine, its system components, and the tunnel support need to be analysed in detail and this can be carried out by three-dimensional (3D) models including all these components. However, the state-of-the-art shows that very few fully 3D models for mechanical deep tunnel excavation in rock have been developed so far. A completely three-dimensional simulator of mechanised tunnel excavation is presented in this paper. The TBM of reference is a technologically advanced double shield TBM designed to cope with both conditions. Design analyses with reference to spalling hazard along the Brenner and squeezing along the Lyon–Turin Base Tunnel are discussed.     

Sheared shale response to deep TBM excavation

Phien-wej, N. and Cording, E.J., 1991. Sheared shale response to deep TBM excavation. Eng. Geol., 30: 371–391.

Ravelling and squeezing of sheared shale of Stillwater Tunnel caused severe problems in tunneling with a tunnel boring machine (TBM) that led to termination of the contract. The tunnel was finally holed through with two TBM's specially designed for squeezing ground. Although the shale mass in all geological conditions exhibited time-dependent response, significant squeezing was confined to sheared shale with large amounts of clay gouge infill, wherein creep of the clay gouge was the prime mechanism controlling the ground response. However, when the tunnel face was advanced at a slow rate, the observed ground squeezing in the early period was largely induced by the effect of stress change from face advance, not the creep. Ground ravelling was very significant in sheared shale due to the high degree of fissuring and fracturing of this thinly bedded shale. Failure of the first TBM resulted mainly from the incompatibility of the shield design with the sheared shale. The shield was too long and stiff and had variable diameters. Extensive observation and instrumentation programs of the project provided valuable information on rapid mechanized tunneling in heavy ground.     

Study of the influence of geotechnical parameters on the TBM performance in Tehran–Shomal highway project using ANN and SPSS

Alborz twin tunnel along with an exploratory or service tunnel between the two main tunnels, are the longest tunnels section in Tehran–Shomal highway with 6.3 km length. The service tunnel is designed to be used for geological investigations, ventilation, transportation during the construction of main tunnels, water drainage, ground improvement by grouting, and emergency exit. An open tunnel boring machine (TBM) of Wirth Company was used to drive this service tunnel. With regard to the fact that in such mechanized tunneling projects, performance of the TBMs is of the most importance, which affects the economy and timing of the projects; on the other hand, geotechnical conditions of the region play a significant role in this respect, this effect was investigated during this study. In this study, two main elements of the TBM performance including the rate of penetration and utilization factor were investigated using artificial neural network and Statistical Package for Social Sciences. It is shown that geotechnical conditions have considerable effect on the rate of penetration. Whereas, utilization is largely affected by management and non-rock mass-related parameters including delays, wasted times, maintenance, labor, etc. With regard to the available data, four parameters including uniaxial compressive strength (UCS), friction angle, Poisson’s ratio, and cohesion were selected to be studied. Based on assessments conducted using these approaches, the rate of effectiveness of four selected parameters on penetration rate, in a descending order, was as follows: UCS, friction angle, Poisson’s ratio, and cohesion. For increasing utilization, it was concluded that minimizing time delays by good management is the most effective way. Furthermore, with regard to the relative error percentages and the coefficient of correlation of the input and output data, it was concluded that the method artificial neural network yields more reliable results than the statistical approach.     

New equations for predicting the field penetration index of tunnel boring machines in fractured rock mass

The Queens Water Tunnel No. 3, stage 2 having 7.5-km length and 7-m diameter, is excavated by a high-power tunnel boring machine (TBM) underneath Brooklyn and Queens area for distributing freshwater throughout the New York City, USA. This paper offers a review of the project by considering the TBM performance and rock mass interaction. Using the individual cutter force, intact, and mass rock properties, TBM performance by means of field penetration index (FPI) was predicted and compared with actual results obtained in the field. Further, the study involves statistical analysis of the laboratory and field data including machine, intact, and mass rock properties to develop new empirical equations to estimate FPI. It is stated that the FPI, also converted to the rate of penetration, could be estimated utilizing intact and mass rock properties together with cutter force for similar type of rocks with correlation coefficient of 0.88.     

A prediction model for important mineral resources in China

Prediction models for mineral resources provide an analytical foundation and method to express the results of resource evaluations. The project “China National Mineral Resources Assessment Initiative” was conducted during 2006–2013, with the aim to determine the location, quantity, and quality of 25 important mineral resources occurring at depths of <1 km. There are currently 80 integrated prediction models on the scale of III–level metallogenic belts in use across China. The Huangshaping Pb–Zn polymetallic deposit, Hunan province, China, is used as a case study to establish methods and processes for developing a mineral resource prediction model that would be used for exploration targeting. The construction of prediction models requires the development of a classification scheme for the proposed prediction method appropriate for the prediction area. An initial metallogenic model is quantitatively transformed to a prospecting model, and then a prediction model. The incorporation of additional methodology, analysis of a comprehensive geological database, and correlation of asymmetric information between the well–explored typical deposit area and regional prediction area, yield an integrated prediction model. This paper also discusses the prediction modeling theory, and presents 12 models used for mineral assessments.     

TBM在不良地质洞条件下的施工技术

TBM掘进机以其快速、高效、安全、优质等优点越来越广泛地被应用于隧洞开挖施工中,尤其更适用于深埋超长隧洞,然而在不良地质洞段中TBM掘进缓慢,甚至有卡刀可能,反而不如钻爆法灵活,这就需要根据围岩性状采取特殊技术处理措施,辅以监控量测手段对支护方案进行验证、调整支护措施、修正设计参数等。结合辽宁大伙房输水隧洞工程,总结了在不良地质洞条件下的超前地质预报方法、不良地质段处理措施以及围岩变形监测方法。     

雪峰山公路隧道的超前预测预报研究

雪峰山隧道为上海－瑞丽国道主干线湖南省邵阳－怀化高速公路上最大的控制性工程,全长约7 km,最大埋深约850 m,为典型的越岭长大公路隧道。采用不同方法,对该隧道的断层破碎带、塌方、岩爆及大变形等地质灾害和围岩稳定性问题进行了超前预测预报研究。研究以地质分析为基础,运用了TSP、地质雷达、赤平投影和关键块体检索法、断层错动机制解、室内岩石力学试验、数值模拟以及隧道内地质观测分析等方法对前方各类地质信息开展超前预报,取得了较好的效果,并成功地预测预报了断层F2的出现及无强烈岩爆灾害的可能性,对安全施工提供了保证,可为今后类似工程借鉴。