Tunnel Boring Machine Research Articles

Creep Versus Consolidation in Tunnelling Through Squeezing Ground—Part A: Basic Time Effects

Although squeezing ground may undergo rapid convergences following tunnel excavation, its behaviour is often markedly time dependent due to creep or consolidation. The effects of creep and consolidation on shield tunnelling are comparatively evaluated in two companion papers, with the aim of demonstrating their qualitative similarities and distinctive features. The present, first paper investigates the basic time effects, placing focus on the time development of ground deformations and the complex interaction between ground, tunnel boring machine (TBM) and tunnel support during excavation and during construction standstills. The presented numerical simulations indicate several qualitative similarities between the two mechanisms of time dependency, in respect of the time development of ground deformations, the counter-intuitive behaviour of increasing shield loading with increasing rate of advance under certain conditions, as well as the thoroughly adverse effect of the additional time-dependent deformations taking place during construction standstills on the shield loading. However, they also underscore two prominent differences resulting from the fundamentally different nature of creep (a purely mechanical rheological process) and consolidation (a coupled hydromechanical process): first, the consistently more extensive plastic yielding in consolidating ground, which is partially associated with the seepage forces exerted by the pore water on the solid rock constituents. Second, the role of seepage forces as a potential destabilising agent, particularly for the tunnel face, which does not happen in the case of creep and may be critical for shield and cutterhead jamming. Building upon these investigations, the companion paper compares creep and consolidation with respect to the transferability of experiences about the required thrust force to tunnels of different diameter or to adjacent tunnels.

A Novel Identification Approach Using RFECV–Optuna–XGBoost for Assessing Surrounding Rock Grade of Tunnel Boring Machine Based on Tunneling Parameters

In order to solve the problem of the poor adaptability of the TBM digging process to changes in geological conditions, a new TBM digging model is proposed. An ensemble learning prediction model based on XGBoost, combined with Optuna for hyperparameter optimization, enables the real-time identification of surrounding rock grades. Firstly, an original dataset was established based on the TBM tunneling parameters under different surrounding rock grades based on the KS tunnel. Subsequently, the RF–RFECV was employed for feature selection and six features were selected as the optimal feature subset according to the importance measure of random forest features and used to construct the XGBoost identification model. Furthermore, the Optuna framework was utilized to optimize the hyperparameters of XGBoost and validated by applying the established TBM dataset of the KS Tunnel. In order to verify the applicability and efficiency of the proposed model in surrounding rock grade identification, the prediction results of five commonly used machine learning models, Optuna–XGBoost, Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Decision Tree (DT), XGBoost, and PSO–XGBoost, were compared and analyzed. The main conclusions are as follows: the feature selection method based on RF–RFECV improved the accuracy by 8.26%. Among the optimal feature subset, T was the most essential feature for the model’s input, while PR was the least important. The Optuna–XGBoost model proposed in this paper had higher accuracy (0.9833), precision (0.9803), recall (0.9813), and F1 score (0.9807) than other models and could be used as an effective means for the lithological identification of surrounding rock grade.

Automatic early warning of rockbursts from microseismic events by learning the feature-augmented point cloud representation

This study proposes an automatic early warning system for rockbursts in deeply buried tunnels using microseismic data. A novel machine learning model is introduced, which is the first to treats the microseismic event data as a high-dimensional point cloud. The model utilizes a fast approximated convolution (FAC) method to effectively and efficiently learn features from the raw event data with small model sizes. The model is trained and tested using microseismic data from the Hanjiang-To-Weihe water diversion project. Two competing models, a time series model and a traditional threshold model, are also constructed for comparison. Results show that the proposed model achieves reliable early warnings for 90.1% of rockbursts in the tunnel. This study presents a new and feasible rockburst prediction method that can be used independently or as a supplement to current assessment and management approaches for both tunnel boring machines (TBMs) and drilling- and blasting-excavated tunnels.

A Study on the Effects of Hob Temperature on the Rock-Breaking Characteristics of Sandstone Strata

To explore the effect of hob temperature on the rock-breaking characteristics of full-section tunnel boring machines (TBMs) in sandstone strata, high-temperature furnace heating experiments of sandstone and physical and mechanical experiments at room temperature and high temperatures were conducted to obtain the mechanical properties of sandstone at different temperatures. The mechanical properties at different temperatures were calibrated using PFC3D to obtain micro-mechanical and thermodynamic parameters and establish a rock-breaking model. The orthogonal experiments were used to establish the simulation experiments of rock breaking under different temperatures, confining pressure conditions, knife tip distances, and penetration degrees. The results show that the hob tip force is gradually increasing with an increase in the confining pressure. When below 600 °C, there is little temperature transfer from particle to particle as the temperature increases. At this time, with the two sides of the rock slag flaking, the hob knife tip force is the first to reduce. After 600 °C, with the expansion of the rock extrusion hob, the temperature rises on both sides; at this time, the hob tip force also increased. The hob tip force is minimal at a tip distance of 70 mm and an S/P of 14. As the surrounding pressure increases, the rock-breaking efficiency of the hobber decreases. The highest rock-breaking efficiency is achieved at 25 °C and 600 °C. The rock-breaking efficiency is highest when the knife tip distance is designed to be 70 mm, and when the S/P is 14. The three-dimensional constitutive analysis of rock-breaking particles showed that the increment caused by the hob temperature is mainly distributed in the normal force direction in the surrounding rock without any confining pressure, and the increment caused by the hob temperature exposed to the confining pressure occurs in all directions.

Effect of confining pressure on rock breaking by high-pressure waterjet-assisted TBM

High-pressure waterjet-assisted tunnel boring machine (WTBM) is an efficient method for improving the tunneling performance of a tunnel boring machine (TBM) and reducing the wear of its disc cutters in hard rock with high geostresses. Confining pressure directly affects the efficiency of rock breaking and the configuration of the disc cutters. In this study, we evaluated the effect of confining pressure on WTBM rock breaking by developing a self-designed and manufactured experimental system, including confining pressure loading, TBM disc-cutter penetration, and high-pressure waterjet. The macro fracture, acoustic emission (AE), peak normal force drop, and specific energy (SE) were analyzed for four different confining pressures (10, 20, 30, and 35 MPa). The results showed that the cutting depth of the waterjet increased linearly as the waterjet pressure increased and decreased with the gradual increase in the nozzle moving speed. The expansion and development of cracks formed rock debris, and the size of the rock fragments decreased with an increase in confining pressure. When the waterjet pressure was 280 MPa, the nozzle moving velocity was 800 mm/min and the kerf space was 75 mm, which indicated that the confining pressure, which was 23.16 MPa, minimized the cutting SE under this condition. However, regardless of the confining pressure, the maximum normal force of WTBM was less than that of a TBM, whereas the SE of WTBM was less than that of complete TBM cutting mode (CTCM). The average force drop and average drop rate of SE were approximately 25%, and 80%, respectively. The results of this study can inspire the design and mechanism of a TBM assisted by a high-pressure waterjet.

Seismic Isolation Materials for Bored Rock Tunnels: A Parametric Analysis

Most recent tunnel designs rely on more thorough analyses of the intricate rock interactions. The three principal techniques for excavating rock tunneling are drill-and-blast for complete or partial cross-sections, TBM only for circular cross-sections with full faces, and road header for small portions. Tunnel-boring machines (TBM) are being utilized to excavate an increasing number of tunnels. Newer studies have demonstrated that subterranean structures such as tunnels produce a variety of consequences during and after ground shaking, challenging the long-held belief that they are among the most earthquake-resistant structures. Consequently, engineering assessment has become crucial for these unique structures from both the geotechnical and structural engineering standpoints. The designer should evaluate the underground structure’s safety to ensure it can sustain various applied loads, considering both seismic loads and temporary and permanent static loads. This paper investigates how adding elastic, soft material between a circular tunnel and the surrounding rock affects seismic response. To conduct the study, Midas/GTS-NX was used to model the TBM tunnel and the nearby rock using the finite element (F.E.) method to simulate the soil–tunnel interactions. A time–history analysis of the El Centro (1940) earthquake was used to calculated the stresses accumulated in the tunnels during seismic episodes. Peak ground accelerations of 0.10–0.30 g, relative to the tunnel axis, were used for excitation. The analysis utilized a time step of 0.02 s, and the duration of the seismic event was set at 10 s. Numerical models were developed to represent tunnels passing through rock, with the traditional grout pea gravel vs. isolation layer. A parametric study determined how isolation material characteristics like shear modulus, Poisson’s ratio, and unit weight affect tunnel-induced stresses. In the meantime, this paper details the effects of various seismic isolation materials, such as geofoam, foam concrete, and silicon-based isolation material, to improve protection against seismic shaking. The analysis’s findings are discussed, and how seismic isolation affects these important structures’ performance and safety requirements is explained.

Investigation on Disaster Mechanism of Diversion Tunnel Induced by Gripper TBM in Hydrokarst Erosion Stratum and Engineering Measures

In gripper tunnel boring machine (TBM) tunneling through complex geological formations, the safe and efficient recovery from large-scale collapses remains a formidable challenge. In this study, we investigate the causes of a 1246 m3 collapse that occurred during the gripper TBM tunneling in the diversion tunnel in Xinjiang, China. Various techniques including TSP seismic waves, CFC advanced water exploration, laboratory point load tests and packer permeability tests were employed for thorough research. The examination discloses that the water softening in biotite-quartz schist in fractured zones contributes significantly to the loosening and dislocation of rock layers along joints. The gripper TBM’s cutterhead exacerbates this process through cutting action and vibrations, causing large-scale instability and eventual rock mass collapse. To tackle this engineering problem, we propose a three-step treatment scheme comprising “Reinforcement-Backfill-Re-excavation”. Furthermore, we propose a technique to handle TBM collapses by creating a “protective shell” within the cavity. The safety and feasibility of these proposed solutions were thoroughly validated through numerical simulations. Also, we utilized the Hoek-Brown theory and Rostami prediction formula to establish recommended values for the total thrust and total torque of the TBM during the collapsed section. The proposed treatment scheme and estimated parameters were successfully applied, resulting in a comprehensive solution from collapse handling to tunneling. This study offers valuable details on effectively managing large-scale collapses in gripper TBM tunneling, which can be useful for similar tunnel engineering and improve safety and efficiency.

Construction Mechanical Characteristics of TBM Pilot and Enlargement Method for Ventilation Tunnel of Wuhai Pumped Storage Power Station

Investigating the construction mechanics of a ventilation tunnel using the TBM (Tunnel Boring Machine) pilot and enlargement method with reliable rock mechanics parameters ensures the safety of on-site excavation operations. Leveraging the construction project of the ventilation tunnel at the Wuhai Pumped Storage Power Station, TGP sidewall forecasting was employed to explore the geological conditions within a 50 m range of the tunnel’s side. A systematic study of the construction mechanics of the TBM pilot and enlargement method was conducted, along with corresponding construction recommendations and engineering applications. This research indicates that sidewall forecasting can supplement the deficiencies in geological exploration reports, with excavation revealing conditions consistent with the forecast. Deformation at the interface, including the arch crown and sidewall, mainly concentrates during the construction phase from the completion of full-section excavation to the beginning of expansion. As the working face advances, the upper rock mass within the ventilation tunnel outline experiences tension, with stress concentration in the shoulder and bottom corner rock masses. The plastic zone before expansion primarily concentrates within the ventilation tunnel outline, shifting to the sidewall after expansion, with the left shoulder’s plastic zone depth slightly exceeding that of the right. The proposed method effectively ensures construction safety, and the research findings have valuable implications for similar projects.

Greenfield settlements due to tunnelling using tunnel boring machine (TBM) in layered soils: a parametric study

Greenfield settlements due to tunnelling using tunnel boring machine (TBM) in layered soils: a parametric study

Tunnel boring machine performance assessment and prediction applying hybrid artificial intelligence

A machine for tunnel boring machine (TBM) is recognized as productive equipment for tunnel construction. A dependable and precise tunnel boring machine’s performance (such as penetration rate (ROP)) prediction could reduce the cost and help choose the suitable construction method. Hence, this research develops new integrated artificial intelligence methods, i.e., biogeography-based multilayer perceptron neural network (BMLP) and biogeography-based support vector regression (BSVR), to forecast TBMPR. Using the biogeography-based optimization (BBO) algorithm aims to improve the developed model’s performance by determining the optimized neuron number of hidden layers for MLP models and the ideal values of the essential variables of SVR method. The results show that advanced methods can productively make a nonlinear relation among the ROP and its forecasters to obtain a satisfying forecast. Amongst the BMLP models with several hidden substrates, BM5L with five hidden substrates could attain the total ranking score (TRS) greatest rate, with root mean squared error (RMSE) and coefficient of determination (R2) equal to 0.017 and 0.9969. Simultaneously, the BSVR was the supreme model because of the fewer RMSE (0.00497 m/hr) and a larger R2 (0.999) compared with BMLP models. Overall, the acquired TRSs show that the BSVR outperforms the BMLP in terms of performance. As a consequence, the BSVR model may have been chosen as the suggested model if it had been able to accurately forecast the observed value even better than BM5L.

Vibration prediction and analysis of the main beam of the TBM based on a multiple linear regression model

The vibration of tunnel boring machine (TBM) is very difficult to monitor on sites, and related research on prediction methods is rare. Based on the field tunnelling test of a TBM in the Xinjiang Ehe project, the vibration information of the main beam of the TBM under different surrounding rock conditions is collected. The relationships among the tunnelling parameters, surrounding rock parameters and vibration parameters were studied. The results show that the penetration, cutter head speed, torque and thrust are important parameters affecting TBM vibration. In addition, the field penetration index and cutter head driving power index are significantly related to the root mean square of acceleration. Based on this, a multiple regression prediction model of TBM vibration is established. The model was verified and analysed via field projects, and the relative prediction error was less than 12%. This method can be used to predict the vibration of a TBM in real time through characteristic parameters without the use of a traditional monitoring system. This approach is highly important for determining the status of TBM equipment in real time.

Semi-Symmetrical, Fully Convolutional Masked Autoencoder for TBM Muck Image Segmentation

Deep neural networks are effectively utilized for the instance segmentation of muck images from tunnel boring machines (TBMs), providing real-time insights into the surrounding rock condition. However, the high cost of obtaining quality labeled data limits the widespread application of this method. Addressing this challenge, this study presents a semi-symmetrical, fully convolutional masked autoencoder designed for self-supervised pre-training on extensive unlabeled muck image datasets. The model features a four-tier sparse encoder for down-sampling and a two-tier sparse decoder for up-sampling, connected via a conventional convolutional neck, forming a semi-symmetrical structure. This design enhances the model’s ability to capture essential low-level features, including geometric shapes and object boundaries. Additionally, to circumvent the trivial solutions in pixel regression that the original masked autoencoder faced, Histogram of Oriented Gradients (HOG) descriptors and Laplacian features have been integrated as novel self-supervision targets. Testing shows that the proposed model can effectively discern essential features of muck images in self-supervised training. When applied to subsequent end-to-end training tasks, it enhances the model’s performance, increasing the prediction accuracy of Intersection over Union (IoU) for muck boundaries and regions by 5.9% and 2.4%, respectively, outperforming the enhancements made by the original masked autoencoder.

The Effect of Seismic Load on TBM Structures for Metro Line 3 in Hanoi, Vietnam

Metro line 3 in Hanoi is being constructed using tunnels, which may affect nearby constructions. In addition, Hanoi’s earthquakes also pose risks to this Tunnel Boring Machine (TBM) line. This paper studies the impact of TBM construction on the surrounding houses along the line, as well as the impact of earthquakes on this line. A 3D numerical model was developed to simulate the static and dynamic behavior of TBM at station 9 in Metro Line 3. The results show that the maximum settlement at the ground surface under seismic load is significantly reduced from 4.6 mm to 0.1 mm when using the jet grouting method.

An evaluation of cutting head design of current tunnel Boring machines and suggestions to improve machine Performance

ABSTRACT This paper evaluates cutterhead design of current tunnel boring machines (TBMs) in considerations with the actual cutting action of discs observed on the most common cutter heads. The discs on the current TBM cutter heads were found to predominantly perform asymmetrical cutting action, which generates excessive sideway forces impairing steering action of the machine. The effective s/p ratios related to actual cutting action of discs emerge to be significantly higher than those of the laboratory-determined values, and this consequently leads to adverse cyclic deepening effect. The gross lateral loads arising from excessive side forces were stated to be minimised if the adjacent discs are arranged to cut in symmetrical position by adopting appropriate cutterhead design methods suggested in this paper. Furthermore, a dynamic analysis was carried out in terms of reaction forces on cutter heads of different geometries, and it was indicated that flat face cutter heads have better steering abilities than conical or domed types, in mixed face conditions. Similar analysis on disc angular spacing also inferred that scattered type disc arrangements with equal angular spacing are preferable to cutterheads with spoke or star type lacing, in terms of cutterhead balance.

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.

Numerical Simulation Study on the Mechanical Properties of the Surrounding Rock in Tunnel Boring Machine Inclined Shaft

Numerical Simulation Study on the Mechanical Properties of the Surrounding Rock in Tunnel Boring Machine Inclined Shaft

A Novel Approach for Determining Cutting Geometry for TBM Using Full-scale Laboratory Linear Rock Cutting and PFC3D-Based Numerical Simulations

Efficient rock excavation requires an optimal s-p ratio, where adjacent cuts interact to completely plane the rock surface. Traditionally, the specific energy (SE) methodology has been used to determine this ratio, but its U-shaped curve is sometimes incomplete or absent, making it difficult to find the optimal s-p ratio. In this research, an alternative methodology is proposed. This methodology relies on the morphology of the excavated rock surface, specifically, the amounts of overbreaks. To evaluate the proposed methodology, full-scale rock cutting experiments were conducted in the laboratory. Numerical simulations were also conducted in 3D particle flow code (PFC3D) and the results were validated by the full-scale laboratory rock cutting experimental data. The study also involved conducting sensitivity analyses to evaluate the effectiveness of downscaling and upscaling the PFC3D-based model to obtain results for full-scale applications. Following the excavation, laser profilometry was employed to characterize the excavated rock surfaces, and the resulting profiles were used to calculate the volume of overbreaks. Plots of these volumes against the s-p ratio were generated to identify the optimal s-p ratio. The findings indicated the presence of a noticeable optimal s-p ratio in both the PFC3D-based simulations and full-scale laboratory experiments, which was not evident when the specific energy methodology was employed. This proposed methodology exhibits considerable potential and can prove beneficial in determining cutting geometry that is optimal for site-specifc and TBM-specific, as well as in the design and production of tunnel boring machines.

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.

Carbon Footprint Evaluation in Tunnels Excavated in Rock Using Tunnel Boring Machine (TBM)

The generation of CO2 is an important element in assessing the environmental impact generated in a tunnel construction project, making this knowledge very useful for evaluating different alternatives. In this study, an analysis of the carbon footprint has been carried out, including the main elements during the construction phase of a tunnel employing a tunnel boring machine (TBM). The research proposes several options for an easy and quick calculation of CO2 generation in a design phase. Its determination can be crucial for decision-making before and during the execution of any tunnel in the near future. The estimation models have been validated based on real case studies, defining the carbon footprint of each construction element. The proposed procedure can apply to any tunnel. However, it should be noted that it is an approximate analysis, and the limitations described in each section should be considered. The main CO2 generator found in the construction process is the lining element; the percentage varies between 50% in tunnels with smaller diameters (4–5 m) and 75% for tunnels with larger diameters (9–10 m), followed by the auxiliary elements, 16%, and the operation of the tunnel boring machine itself, 11.2%, while the other parts remain in a range between 1.3 and 5.7%. This knowledge makes it possible to define the aspects on which efforts should be focussed to reduce the carbon footprint of the tunnel construction process.

Predicting advance rate and cutter life in TBM tunnelling using evolutionary algorithm

ABSTRACT Tunnelling with a Tunnel Boring Machine (TBM) offers several advantages over conventional tunnelling methods when covering long distances, particularly regarding time, safety, and environmental impact. Prior to starting a TBM project, performance analysis is a crucial stage necessary to inform the initial investment decision and project cost estimation. However, this analysis requires extensive fieldwork and laboratory studies. The Advance Rate (AR), calculated from the Utilization Factor (U) – the ratio of excavation time to total project time – is a widely accepted measure of TBM performance. Total project time encompasses various parameters, including boring time, cutter inspection and replacement times, and constant factors like re-gripping, service, and maintenance times. In this study, we employed analytical methods, statistical analysis, and genetic programming approaches to evaluate the AR and thus enhance TBM performance analysis. Gamma tests were conducted to identify interrelated parameters in TBM operations. The results from these tests showed that the Applied Force per Cutter (FN) and penetration values (together forming the Field Penetration Index or FPI) are useful in estimating cutter consumption. While FN can be determined through laboratory tests, its field determination is challenging. We have developed several charts and a function derived from genetic programming to estimate the AR of a TBM. The study demonstrated that this function, formulated using genetic programming, possesses strong predictive capability and can significantly improve TBM performance analysis.