Tunnel Boring Machine Construction Research Articles

Sustainability of underground infrastructure – Part 1: Digitalisation-based carbon assessment and baseline for TBM tunnelling

Alongside the transition towards sustainable and renewable infrastructure and energy production, tunnelling projects that adopt tunnel boring machines (TBM) continue to increase in size and complexity. Life Cycle Assessment (LCA) methodologies have a significant impact on the Architecture, Engineering and Construction (AEC) industry, with a shift in focus to cost and carbon management across the infrastructure lifecycle through the use of digitalisation techniques and sustainability rating systems. This paper aims to develop a systematic framework to enhance the sustainability of underground infrastructure in the early design and construction stages, aiming for “build clever and efficiently”. This framework integrates digitalisation, carbon assessment standards, numerical modelling, and optimisation techniques to assess carbon emissions and establish benchmarks for construction products and processes. The approach uses parametric models to define tunnelling-related entities such as geological formations, TBM, and segments, designed with dimensional constraints that allow for adaptive modelling and interactions. Enabled by building information modelling (BIM) through parametric models enriched with domain properties, module-based carbon accounting is adopted to evaluate the footprint of material- and asset-level key emitters with functional units. Leveraging established databases, baselines for segmental lining designs and carbon reduction strategies are established. Through project-driven case studies, the correlations among geotechnical conditions, tunnel dimensions, and TBM operating parameters are developed to quantify TBM carbon emissions. A prototype programme demonstrates the framework's application on typical metro and road tunnels at different levels of details (LoD). This paper serves as a guideline for conducting embodied carbon assessments specifically tailored to TBM tunnelling, acknowledging the substantial contribution of the TBM construction process, which is controlled by geotechnical conditions.

Data-driven intelligent prediction of TBM surrounding rock and personalized evaluation of disaster-inducing factors

With the widespread application of tunnel boring machines (TBM) in underground tunnel construction, the accurate and real-time identification of geological indicators has become a key factor for ensuring construction safety. In this study, we introduced the nomogram method into the field of civil engineering for the first time to predict surrounding rock grades. Against the backdrop of a major water conservancy project in China, an in-depth analysis of construction data from the ascending phase was conducted to establish the relationship between surrounding rock grade predictions and key construction parameters. Compared with traditional models, the nomogram method has shown advantages in terms of prediction accuracy. Furthermore, by providing a personalized scoring mechanism for specific construction data, the nomogram method not only achieves the real-time prediction of rock grades but also clearly reveals the dynamic relationships between input parameters and rock grades, clarifying the entire prediction process. We identified the main factors related to rock grade prediction among TBM construction parameters, including the slurry conveyor belt speed, jacking pressure, cutterhead torque, cutterhead thrust, thrust pressure, and actual rotation speed. The training process revealed that the nomogram prediction model achieved optimal performance with seven input parameters, balancing accuracy and training time. According to the prediction results, the nomogram model’s area under the curve index is 0.92, providing greater accuracy than the traditional random forest model (0.89). Therefore, this study enhances the transparency and interpretability of model predictions by providing personalized assessments of construction parameters for predicting surrounding rock grades, overcoming the black-box issue of existing prediction methods and offering a novel and effective tool for rock grade prediction.

An empirical method for estimating TBM penetration rate using tunnelling specific energy

Establishing a suitable tunnel boring machine (TBM) performance estimation model is benefit to the machine type selection, project scheduling and budgeting. Although various models have been developed to predict TBM performance, most of these models use a relatively small specific database, which is difficult to be applied to engineering practice. In this study, some equations for estimating TBM penetration rate (PR) applicable to different geological conditions are proposed. Firstly, a TBM construction performance database containing 159 tunnel sections is built according to four different tunnel projects originating from China, which covers rock mass properties, TBM specification parameters and performance information. By eliminating the effects of some TBM specification and operating parameters, a normalized energy index is proposed for assessing the rock mass boreability. On this basis, a series of statistical analysis, i.e. linear multiple regression (LMR) and non-linear multiple regression (NLMR) are used to develop some empirical equations for estimating the PR by employing rock mass properties and TBM specification parameters, and more than 84% of the PR can be accurately predicted. Finally, to further evaluate the prediction performance of these equations, they are compared and discussed with fifteen published typical models, and the results proved that the models developed in this study are more reasonable and reliable. It should be noted that the input parameters of these equations are readily available at the project planning stage, thus they are very simple and practical for designers and constructors.

대심도 Shield TBM 터널의 IoT 스마트 안전관리시스템 구축에 관한 연구

The purpose of this study is to establish a smart safety management system during deep-seated Shield TBM(Tunnel Boring Machine)tunnel construction. During this work, the site is continuously managed and controlled audio-visually to identify and discover unexpected situations in advance. This study aims to contribute to preventing accidents during TBM (Tunnel Boring Machine) construction while performing efficient work by responding accurately. The system is composed of turn gates and vascular recognizers that can control workers'access, smart tags for tracking workers' location in real-time, emergency call systems, Workers motion sensor to detecting workers who are not moving, monitoring through CCTV cameras, PTZ (Pan Tilt Zoom), DOME, sensing systems detecting four major gases (02, CO, H2S, CH4) that can cause serious accident, loudspeakers, amplifiers, and two-way interphones, etc.
 As a result of this study, the IoT Smart Safety Management System for the Shield TBM Tunnel Construction in the deep underground construction site has been materialized to establish the Safety Management Control Center, safety management system for TBM Construction Machinery, tunnel internal safety management system, network system, and can make immediate response in case of emergency. This system can guarantee safety of workers who provide labor within the poor working environment of deep underground spaces. The IoT-based smart safety management system established in this study is expected to contribute a IoT to preventing industrial accidents at the site of underground deep-seated TBM tunnel construction (earth pressure type and Slurry shield TBM or Open TBM) over 40m in the future.

On-site measurement and environmental impact of vibration caused by construction of double-shield TBM tunnel in urban subway

The vibration generated during the construction of subway tunnels with double-shield tunnel boring machine (TBM) has a significant impact on the environment, which has caused multiple complaints from residents. Taking a double-shield TBM tunnel project as the background, vibration measurements were conducted by installing vibration sensors on-site. By combining theoretical methods—such as normalization, polynomial fitting prediction, and gray correlation analysis—the vibration characteristics, impact range on the environment, and factors affecting the vibration of TBM construction were studied. The key research results included: (1) The amplified zone of X and Y vibration acceleration occurred on the left-hand side of the tunnel from 3.15 to 13.85 m, but rapidly decayed away from the amplification zone. (2) The impact range of TBM vibrations on residential areas at night and during the day was studied according to the official “Urban Regional Environmental Vibration Standard” and it was found to be larger at night than during the day. (3)The main factors affecting the TBM vibration level was studied—including the cutter-head torque, TBM thrust, cutter-head speed, penetration, field penetration index (FPI) and so on. In summary, when the double-shield TBM construction tunnel is adjacent to residential areas, the vibration generated exceeds the national standard limit. In order to reduce the impact of TBM vibration on residential areas, excavation parameters such as cutter head torque, TBM thrust, cutter head speed, and penetration should be appropriately reduced.

A data driven real-time perception method of rock condition in TBM construction

In tunnel boring machine (TBM) construction, the presence of collapsible rock mass (CRM) can lead to accidents such as collapse and jamming. This study presents a novel CRM early warning strategy based on real-time TBM rock fragmentation data to improve safety and efficiency in CRM conditions. The strategy includes a qualitative classification model and a quantitative probability model for CRM identification. The results indicate that the distribution dissimilarity index β effectively reflect the significance of variables across CRM and non-CRM datasets. Various parameters, including TPI, FPI, WR, and AF, show discriminatory ability between CRM and non-CRM samples. In particular, the CRM-weighted index, which combines the strengths of the individual indices, achieves a distributional dissimilarity index of 1.05, significantly higher than any of the individual indices. The qualitative classification model proves effective in identifying samples from collapse areas, demonstrating ability to identify samples located in adverse geological condition. The quantitative model shows that the probability of CRM is generally higher in adverse geological area samples, particularly in zones where collapse has occurred, with a CRM probability is approaching 1. The proposed strategy provides accurate early warnings to prevent collapse accidents and represents a practical approach to improving the safety and efficiency.

Rock fragmentation indexes reflecting rock mass quality based on real-time data of TBM tunnelling

Perception of rock condition (RC) is a challenge in tunnel boring machine (TBM) construction due to lack of space and time to observe and detect RC. To overcome this problem, this study aims to extract a new rock fragmentation index (RFI) that can reflect RC from real-time rock fragmentation data of the TBM. First, a comprehensive review of existing rock fragmentation models is conducted, which leads to some candidate RFIs that can reflect RC. Next, these candidate RFIs are investigated using data from 12,237 samples from a well-monitored tunnel boring process of the TBM in a 20,198 m tunnel. Further, a new RFI system is recommended as the parameter involving the optimal models. Finally, a preliminary study of the relationship between these RFIs and RC is carried out, and it is shown that these RFIs can reflect RC to a large extent. In the TBM boring process, these RFIs can be extracted from real-time TBM fragmentation data and used to predict the RC in the field. Therefore, the challenge of RC perception is solved with this new RFI system. The new RFI system offers significant potential for the real-time rock classification, prediction of the surrounding rock collapse potential, and selection of control parameters or support measures during TBM construction. This will be the key to improving TBM construction performance.

Key Technology of TBM Excavation in Soft and Broken Surrounding Rock

Tunnel Boring Machine (TBM), as the most advanced construction equipment for rock tunnels, has been widely used in railway tunnel engineering, water conservancy tunnel engineering, urban rail engineering, coal mine roadway engineering, and other fields. However, the disadvantage of TBM lies in its poor adaptability to complex geological conditions, resulting in a significant reduction in tunneling efficiency and even major accidents due to improper TBM construction measures, especially in weak and fractured strata, which pose a great challenge to TBM construction. Therefore, it is necessary to study the key technologies of TBM construction in weak and fractured strata in order to improve the efficiency of TBM construction. Based on the background of the TBM construction of the Gaoligongshan Tunnel on the Southwest Darui Railway, this paper provides a reference for safe and efficient TBM excavation in weak and fractured areas through research on equipment, excavation parameter control, and construction measures.

A Study of Vibration Reduction Effects of TBM and NATM Parallel Construction Methods according to Tunnel Depth and Size

The new Austrian tunnelling method (NATM) and tunnel boring machine (TBM) are methods for excavating underground spaces. The NATM construction method has noise and vibration problems, and excavation using TBM has the disadvantage of high initial cost and is limited by ground conditions. A parallel TBM and NATM construction method has been developed to overcome these limitations. However, research on the TBM and NATM parallel construction method is lacking, and it is crucial to identify the applicability of the TBM and NATM parallel construction method in advance by applying it to various ground and blasting conditions. In this study, based on the vibration measurement data of the excavation site using the NATM construction method, the vibration measurement values generated when excavating the top surface using the existing NATM construction method and the TBM and NATM parallel construction methods were compared. Lastly, the vibration reduction effect of the two construction methods was analysed according to the tunnel excavation depth and tunnel size through 3D numerical analysis. As the depth of tunnel excavation increases, the vibration at the ground surface is reduced by up to 83%. In addition, as the ratio of the size of the blasting surface with NATM and the excavation surface with TBM is closer to 1.0, the vibration decreases.

Numerical simulation of ground surface settlement of underpass building in tunnel boring machine double-line tunnels

Ground surface settlement under subway tunnel construction is one of the main sources of urban ground surface settlement, which may threaten the safety of existing buildings. Therefore, it is necessary to study the influence of subway tunnel construction on the safety and stability of surface subsidence. In the current study, the ground settlement originating from the operation of a tunnel-boring machine (TBM) while excavating a double-line tunnel that underpasses existing buildings is studied. To this end, numerical simulations were carried out and experimental data were analyzed. The boring parameters for the underpass interval section were derived by simulating the construction process and analyzing the TBM parameters in a real project. Then, the influence of thrust force, grouting pressure, and frictional force was considered on the surface settlement. For spatial locations between the existing buildings and the tunnel in the underpass area, the width of the ground surface settlement caused by different excavation sequences of the TBM two-line tunnel was analyzed. The obtained results demonstrate that the performed calculations are consistent with the monitoring data. It is found that the width and depth of the ground surface settlement trough are affected by the distance between the existing buildings and the TBM construction tunnel. However, the settlement location is independent of the excavated sequence but depends on the location of the TBM tunnel and the existing building.

Developing hybrid ELM-ALO, ELM-LSO and ELM-SOA models for predicting advance rate of TBM

Accurate prediction of TBM performance is very important for efficient completion of TBM construction tunnel project. This paper aims to predict the advance rate (AR) of tunnel boring machine (TBM) using three hybrid models by combining three swarm intelligence optimization algorithm (Ant Lion Optimizer (ALO), Loin swarm optimization (LSO) and Seagull optimization algorithm (SOA)) and the Extreme learning machine (ELM) model, namely ELM-ALO, ELM-LSO and ELM-SOA model respectively. The dataset consists of 1, 286 samples from the Pahang Selangor Raw Water Transfer (PSRWT) tunnel project in Malaysia, and containing six parameters (rock quality designation (RQD), uniaxial compressive strength (UCS), rock mass rating (RMR), Brazilian tensile strength (BTS), thrust force per cutter (TFC) and revolution per minutes (RPM)) from rock mass and machines. In order to evaluate the prediction performance of different hybrid models, the root mean square error (RMSE), the mean absolute error (MAE), the mean square error (MSE), the determination coefficient (R2), the sum of square error (SSE), and the variance accounted for (VAF) were adopted as the performance indicators. The results show that ELM-LSO is the best model to predict AR. Sensitivity analysis shows the importance of all considerations to AR. TFC, RPM and RMR are the three most important parameters. But this is not absolute, more parameters need to be taken into account in AR prediction. Meanwhile, the ELM-LSO model proposed in this paper can be used as a new method to predict AR.

The analysis of TBM cutter stress under different construction environment

Abstract As the country’s underground tunnels continue to expand, underground construction technology has been developed rapidly. In order to meet the development needs of construction technology in China, Tunnel Boring Machine (TBM) has made great progress due to its own advantages. In this paper, feed amount of disc cutter on rock crushing under complex working conditions is considered. The mechanism of rock fragmentation is analyzed. The force between disc cutter and the rock is studied. The accuracy of the model is verified by setting up a TBM rock fragmentation experiment platform. This study provides technical support for TBM construction parameters under different working conditions.

Hard-Rock TBM Thrust Prediction Using an Improved Two-Hidden-Layer Extreme Learning Machine

It is difficult for tunnel boring machine (TBM) operators to respond for safe and high-efficient construction without accurate reference parameters such as the TBM thrust. A new hybrid model (MRFO-AT-TELM) combining an improved two-hidden-layer extreme learning machine (AT-TELM) and manta ray foraging optimization (MRFO) algorithm is proposed to predict TBM thrust with 12 selected input featuring parameters. The affine transformation (AT) activation function is used to improve the performance of TELM. Input weights and bias of AT-TELM are optimized using the MRFO algorithm. The performance of the proposed model is validated with TBM construction data collected from the Yin-Song Project in China and compared with other models. Input data of the first 30, 60, and 90 seconds of the rising period are analyzed. Results show that the proposed model is superior to the other models and with 90-second data as input outperforms that with 30 and 60-seconds data. The proposed model and the selected input features are validated in a new project. The thrust prediction model can be embedded into the TBM construction intelligence system and thus help improve construction efficiency.

A new transient electromagnetic prospecting method in TBM tunnel environment

Tunnel boring machine (TBM) construction is a safe and efficient tunnel construction method at this stage, which contributes to the essential role of ahead prospecting methods, an essential prerequisite for ensuring the safety of TBM construction. Due to the high interference in the TBM tunnel environment, it is difficult to effectively apply multiple ahead prospecting methods. The existing geophysical methods are restricted because of the extremely small prospecting space in the TBM tunnel environment. Also, the TBM tunnel environment is required to introduce high-resolution detection methods. This paper proposes an ahead prospecting method suitable for TBM tunnel, using the simulation of three-dimensional vector finite element. Specifically, this prospecting method uses transient electromagnetic electrical radiation sources to emit and receives the Ex. Besides, the full field apparent resistivity imaging method is used to perform imaging interpretation of the adverse geological structures in front of the tunnel face in a semi-quantitative manner, to obtain the prospecting results required for the ahead prospecting in TBM tunnel.

Prediction of Geological Parameters during Tunneling by Time Series Analysis on In Situ Data.

A tunnel boring machine (TBM) is a type of heavy load equipment that is widely used in underground tunnel construction. The geological conditions in the tunneling process are decisive factors that directly affect the control of construction equipment. Because TBM tunneling always takes place underground, the acquisition of geological information has become a key issue in this field. This study focused on the internal relationships between the sequential nature of tunnel in situ data and the continuous interaction between equipment and geology and introduced the long short-term memory (LSTM) time series neural network method for processing in situ data. A method for predicting the geological parameters in advance based on TBM real-time state monitoring data is proposed. The proposed method was applied to a tunnel project in China, and the R2 of the prediction results for five geological parameters are all higher than 0.98. The performance of the LSTM was compared with that of an artificial neural network (ANN). The prediction accuracy of the LSTM was significantly higher compared with that of the ANN, and the generalization and robustness of LSTM are also better than those of ANN, which indicates that the proposed LSTM method could extract the sequence properties of the in situ data. The rule of equipment-geology interaction was reflected by increasing the memory structure of the model through the introduction of the “gate” concept, and the accurate prediction of geological parameters during tunneling was realized. Additionally, the influence of time window and distance of prediction on the model is discussed. The proposed method provides a new approach toward obtaining geological information during TBM construction and also provides a certain reference for the effective analysis of the in situ data with sequence properties.

Prediction of TBM Operational Parameters Using an Integrated Data Mining Framework

Tunnel Boring Machine (TBM) is widely used in tunnel construction because of its high safety, less effect on surroundings and rapid excavation speed. However, its performance is highly dependent on the adjustment of operational parameters during tunnelling. In many cases, the project delays even the accidents take place due to the mis-operation of inexperienced drivers. To improve the adaptability of a TBM in complex geological conditions, this study proposes an integrated data mining framework to perform near real-time prediction of two operational parameters including thrust and torque. The integrated framework provides a set of data processing methods including data cleaning, partition of full tunnelling cycles, feature extracting and model establishment. These data mining methods were applied to analyse the in-situ data of a water conveyance project. The results showed that the proposed framework performed well in predicting those two parameters with the determination coefficient R2 all exceeding 0.9, which illustrated the feasibility of using the proposed framework to assist driving in TBM construction.

Overview of Tunnel Boring Machine Dynamic Balance Construction

The concept of Tunnel Boring Machine (TBM) dynamic balance construction has been put forward here, i.e. 5S TBM construction. Among them, Suitability refers to the matching degree between TBM equipment and geological conditions, the matching degree between TBM equipment and construction scheme, the dynamic adjustment ability of TBM in the face of different geological conditions, and the induction and analysis of geological exploration results. Sustainability refers to TBM construction is continuous, not interrupted by uncontrollable factors, the operation of each process is continuous and the connection is compact, the speed of each stage is basically balanced. And the maintenance time, the number of tool changes and the frequency of tool changes are basically the same. Speedy refers to under the same geological conditions, maintain a consistent and rapid tunneling speed. When the geological condition is good, the equipment completion rate and tunneling footage maintain efficient, while for bad geological conditions, the tunneling footage and support time maintain relatively efficient. And with a reasonable material transport scheme, reasonable slag discharge scheme, reasonable support scheme, equipment integrity rate and pure tunneling time utilization ratio. Safety refers to the safety of staffs and TBM equipment in the process of construction and maintenance of equipment, standardized operation of each process, advanced detection of geological prediction. Safeguarded of environmental protection refers to TBM equipment itself and excavated chambers and tunnels should be kept clean. Long working conditions need ventilation, cooling, noise removal, vibration reduction measures, sanitary conditions measures. Furthermore, combined with the II bid of Water Supply Project in Jilin, the TBM construction has been realized with the average monthly intact rate of 94.7%, the highest reached to 98.06%, the average utilization rate of 56.85%, the maximum utilization rate of 75.6%, and the average monthly footage of more than 600 meters. It shows that the operation method of TBM dynamic balance construction has been well verified in this project.

Mechanical Aspects of Construction of New TBM Tunnel Under Existing Structures

It is inevitable for the construction of subways to pass under the existing structures in the city; this will cause settlement of the existing structures and affect their safety. The soil is a complex mixture, and hence it is difficult to determine the damage caused by construction using tunnel boring machine (TBM) method to the stress balance of the surrounding soil. Normally, settlement and uplift of the surface are caused during tunneling. Therefore, large-scale 3D simulations are needed to study the interaction between mechanically driven tunnel construction and the surrounding soil to estimate the expected settlement and related damage risks to the existing structures, especially in tunnel construction in urban areas. Qingdao Metro Line 1 in the Haixiao section (Xiaocunzhuang and Haiboqiao stations) was taken up as the research object to study the trend and degree of the effect of TBM tunnel construction using the numerical simulation software Midas GTS NX. The numerical simulation results and measured data were used for the reliability analysis to study the influence of different digging speeds and pressures in TBM construction on ground subsidence, and the effect of vibrations generated at the tunnel face by the TBM cutter head on the ground structure. It is shown that when other factors remain unchanged, the faster the driving speed, the smaller the ground settlement; the higher the surrounding rock pressure, the smaller the ground settlement. The vibration of the ground structure caused by the TBM cutter head complies with the relevant specifications. During construction, the driving parameters should be selected suitably depending on the available budget.

Automatic segmentation of TBM muck images via a deep-learning approach to estimate the size and shape of rock chips

Real-time muck analysis is of great importance for assisting tunnel boring machines (TBMs) in intelligent tunneling. Typically, muck images are characterized by low contrast, large appearance differences, and object overlap, posing a great challenge to image segmentation. In this study, a deep learning-based approach, composed of a dual UNet with multi-scale inputs and side-output (MSD-UNet) and a post-processing algorithm, was proposed to solve the automatic segmentation of muck images and estimate the size and shape of rock chips. The MSD-UNet used a dual structure with two decoders to segment the regions and boundaries of rock chips in a unified network, aiming to solve the overlapping problem of rock chips by introducing the boundary information. It also integrated a multi-scale input and side outputs to enhance low-level image features and supervise the training of early layers of the network, respectively. An integrated loss function based on generalized dice loss was developed to solve the class imbalance problem. The multi-radius erosion and seed filling algorithms were employed to further separate the connected chips in the post-processing. To evaluate the effectiveness of the method, a dataset containing various muck images collected from a TBM construction site was set up, and the MSD-UNet was trained and tested. Experimental results showed that the segmentation using the proposed approach outperformed those of using U-Net and comparable conventional methods. It achieved the highest F1-score of 0.867 and 0.640 on the region and boundary task respectively, and an average Hausdorff distance of 3.59 mm for the rock chip instance. The proposed approach can process a 2,048 × 2,048 image in about 4 s and can nearly meet the requirement of real-time TBM muck image analysis.

Diagnosing tunnel collapse sections based on TBM tunneling big data and deep learning: A case study on the Yinsong Project, China

The Yinsong Water Diversion Project in China’s northeast region contains a 20 km long tunnel section, which was drilled by a tunnel boring machine (TBM) and monitored in real time to generate continuously measured data. During the tunnel construction, 18 tunnel wall collapse failures were documented. This study reviews the boring performance of the TBM during these failures based on the field-collected data for the penetration rate, cutterhead rotation speed, torque, and thrust force, which were obtained at a 1-second interval. The main task in this study includes the development of a time series forecasting (TSF) approach combined with a deep belief network (DBN) that predicts the torque associated with a given penetration rate and rotation speed through a parameter called the drilling efficiency index, TPI, in a neural network. This study begins with a pilot case of a tunnel collapse that eventually caused the TBM construction to be abandoned. In this case, the TSF&DBN algorithm clearly identifies the deviations of the observed TPI values from those given by the neural network, indicating the successful prediction of an unfavorable geological condition. In addition, the cases of 13 other collapse sections are reviewed; of these, 11 clearly exhibit similar performance to the pilot case, whereas the remaining 2 provide no sufficient indications to exclude possible unstable roofs. This preliminary study shows that a systematic and high-quality TBM performance database can be useful in diagnosing adverse geological conditions in conjunction with the proper use of big data and machine learning techniques.