Boring Machine Research Articles

The influence of a Tunnel Boring Machine on a building in Sofia City Center, located above the route of the third Metro line

The construction of the third line of Sofia underground railway (Sofia Metro) used to be one of the major priorities of the geotechnical engineers in the Bulgarian capital for a period of at least two years. Since the first and second lines pass under the existing streets and parks, the third line is designed to go under some buildings erected in the last century. These buildings have been part of the architectural cityscape for years. Such is the case described in the present paper. The underground railway is designed to pass under a building in the Sofia city center. It is a five-story skeletal building, constructed on single and strap foundations. The purpose of the study is to establish whether the settlement due to the passing of the Tunnel Boring Machine (TBM) will adversely affect the structure of the building. The solution is for a single plane task. Data given in the manual of the TBM and the subsoil geotechnical properties are used in order to define the behavior of the soil massif. After the calculations have been made, the relative settlement of the building is determined to be much smaller (1/1662) than the allowable one (1/500). Then the maximum relative settlement is calculated to be 1/1054, which is higher than the average settlement for the whole building, but much less than the allowable. The calculations and comparisons are confirmed in practice.

Tunnel Seismic Detection for Tunnel Boring Machine by Joint Active and Passive Source Method and Imaging Advanced Prediction

Abstract Tunnel seismic advanced prediction method enables the detection of anomalous bodies in front of the tunnel face and the reduction of tunnel construction risk. The active source and the passive source detection methods are two commonly used for imaging advanced prediction of tunnel boring machine (TBM). Although both methods achieve good results, each method still has room for improvement. The active detection method is not conducive to long-distance detection because the sources are generally far away from the anomalous bodies and the received signals are weak. The passive detection method usually produces results of low resolution and with limited forecast precision. Since the active and passive source methods are applied separately, the geophone utilization rate is low and the cost is high. Moreover, both methods have low signal-to-noise ratio and low imaging resolution for single detection data. To overcome the above drawbacks, this article proposes a joint TBM tunnel seismic detection method and an imaging advanced prediction method to detect anomalous bodies in medium and long distances. The advanced prediction employs multiple stacking schemes to enhance the reflection wave energy of the anomalous bodies and suppress the interference wave. The proposed method integrates the active source data into the passive source method to extract the P- and S-waves. The joint active and passive source method further incorporates multiple stack schemes to achieve much higher resolution imaging and provides more accurate detection results. Moreover, the proposed joint method allows multiple utilization of the geophone and reduces the cost. Simulation results are presented for performance verification.

Effectiveness analysis of a novel rectangular tunnel boring machine with planetary transmission for box jacking

Traditional rectangular tunnel boring machines have low tunneling efficiency, poor soil mixing effects, which has hindered the construction of long-distance and large-section box jacking projects. To overcome these limitations, a new type of full-face excavation boring machine was developed based on a planetary transmission mechanism. This machine achieves full-face excavation by utilizing three eccentric cutter heads that revolve around both the central axis of the cutter plate and their individual shafts. The design methodology, feasibility, and principles of this new mechanism were introduced. Furthermore, a successful construction project of an underground highway passage in Japan was presented as a case study to demonstrate the design and application of this tunnel boring machine. The case details include the excavation face support mechanism, optimal cutter-head layout, obstacle removal strategies, and methods for reducing jacking resistance. Monitoring data from the project verified the applicability, reliability, and overall engineering performance of the rectangular shield machine developed using the planetary mechanism. This research demonstrates that the planetary transmission mechanism-based boring machine offers superior performance in terms of ground settlement and tunneling speed, potentially providing a comprehensive solution to the challenges in the development of rectangular shield machines for box jacking projects.

Study on gas explosion propagation law in excavation roadway with TBM

Gas explosion is one of the five major hazards in mines, with about 36% of such incidents occurring in the excavation working face. Therefore, to investigate the impact of gas explosion propagation laws within excavation roadways, we conducted a simulation analysis of parameters such as peak of explosion overpressure, peak rate of pressure rise, and flame propagation speed in the presence of a Tunnel Boring Machine (TBM). In the presence of the TBM, the explosion overpressure approximately doubles, and the flame propagation speed also greatly increases, exacerbating the explosion hazard. Thus, when investigating gas explosion laws within excavation roadways, the presence of a TBM emerges as a significant factor that cannot be overlooked. Furthermore, an analysis of the effects of methane concentration and gas accumulation length on explosion parameters was conducted. The results indicate that when the flame passes through the TBM baffle, the average flame propagation speed increases the most when the methane concentration is 9.5%, increasing by about 6 times. In addition, as the gas accumulation length increases, both explosion overpressure and flame propagation speed gradually increase. Additionally, TBM has a certain impact on flame propagation and methane dissipation.

Machine Learning Approach for Rock Mass Classification with Imbalanced Database of TBM Tunnelling in Himalayan Geology

AbstractThe geological condition of the Himalayan region is very complex and challenging. So far, empirical and analytical approaches for rock mass characterization have been a common practice in the Himalayas. Due to the limitations of input parameters and governing equations in design practices, rock mass characterization in tunnel boring machine (TBM) excavated tunnels is crucial. This research introduces robust machine learning (ML) approaches to predict rock mass quality conditions in complex geological environments, leveraging a large database of TBM parameters and rock mass rating (RMR) values. To do so, a total of 6879 stable phase TBM cycle data were collected from 12 km long tunnel in Nepal. The pre-processed parameters were randomly split into a training set (80%) and a testing set (20%). Seven individual classifiers consisting of logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), k-nearest neighbor (KNN), extreme gradient boosting (XGBoost), and bagging, and stacking ensemble classifier were exploited with optimal hyperparameters. The comprehensive assessment carried out has shown that the ensemble classifier gave highest overall accuracy as compared to other individual classifiers. More importantly, the synthetic minority over-sampling technique (SMOTE) performs better to handle the imbalanced database, while the RF and stacking classifier demonstrated the best prediction performance with accuracy of 92%. Moreover, for the minority rock mass class, the RF shows better performance compared to stacking classifier. The authors emphasize that the effective application of ML-based data-driven approach shows substantial potential for rock mass characterization in TBM tunnelling.

Prediction of TBM cutter wear in heterogeneous ground under high ambient pressure

To prevent tunnel face collapse in heterogeneous ground, the applied face pressure in the excavation chamber can be increased to stabilise the tunnel face, which may hinder cutter rotation during tunnel boring machine (TBM) tunnelling. This study proposes a TBM cutter wear prediction model that considers the impact of the variation in the cutter normal force on the cutterhead owing to the high applied face pressure and low penetration rate. First, the cutter motion mode in heterogeneous ground is investigated. The cutter is found to slide when cutting soft rock based on an analysis of the field data. The evolution process of the cutter wear morphology under heterogeneous ground conditions is summarised. A method for calculating the cutter normal force when the cutter slides on the tunnel face is proposed by referring to the cutting mechanism of the drag bits. The cutter hardness is measured on-site using a portable hardness tester. A semi-empirical model considering the variation in the cutter normal force on the cutterhead is proposed to predict the cutter wear in heterogeneous ground. Wear prediction is compared with other predictive models and field data for validation, and the discrepancies between the different models are discussed. The results show that the proposed model is effective for predicting TBM cutter wear under complex geological conditions.

Investigation of the Influence of Cutter Geometry on the Cutting Forces in Soft–Hard Composite Ground by Tunnel Boring Machine Cutters

Tunnel Boring Machines (TBMs) are integral to modern underground engineering construction, offering enhanced safety and efficiency. However, TBMs often face challenges in complex geological conditions, such as composite strata, resulting in reduced advancement speed and increased cutter wear. This study investigates the rock-breaking characteristics of TBM disc cutters in composite strata through numerical simulations using the Particle Flow Code (PFC) 5.0 software. Focusing on the Jinan Metro Line 6, the research analyzes cutter forces, rock crack propagation, and the impact of cutter edge shapes on rock-breaking efficiency. The discrete element method (DEM) is employed to simulate microscopic behaviors of rocks, providing insights into crack formation, expansion, and failure. This study’s findings reveal that cutter design and operational parameters can significantly influence cutter lifespan and efficiency. By modifying cutter spacing and penetration depth, enhancing rock-breaking efficiency, and grouting softer layers, TBMs can maintain effective excavation in composite strata. The study establishes a comprehensive understanding of the interplay between TBM cutters and complex geological conditions, offering actionable strategies to enhance TBM performance and mitigate cutter damage.

Numerical analysis of relative density effects on shallow TBM tunnel excavation: Ground behavior and principal strains at surface in greenfield conditions

Tunnel construction in urban areas may result in ground deformations that pose a risk to existing buildings and infrastructure; thus, accurate prediction of these induced ground deformations during the design phase is crucial. The paper focuses on the effects of sandy soil relative density on the ground deformations induced by tunnels excavated with Tunnel Boring Machines (TBMs). The study utilizes the Finite Element Method (FEM) and the NorSand model to simulate the behavior of a sandy ground. The validity of the FEM modeling approach is established by comparing predictions with results from six centrifuge tunnel tests from the literature. The centrifuge tests were performed on sand at different relative densities, tunnel diameters, and tunnel depths. The parameters for the NorSand model were determined based on laboratory tests. Only the state parameter was modified to achieve the desired relative density in the numerical simulations. The effects of relative density observed in centrifuge tests (Franza et al., 2019) have been numerically reproduced with no further adjustments of the model parameters. The rich outputs from the numerical models enabled an in-depth investigation of tunnel behavior, yielding new insights into how tunnels respond under varying relative densities, depths, and diameters. A comprehensive analysis of the induced ground deformations caused by shallow tunnels in sandy ground and the potential to damage buildings is included.

EMNet: An ensemble deep learning approach for geological condition detection in tunnel excavation

This paper established a novel ensemble deep learning method for the identification of the geological condition encountered during tunnel boring machine (TBM) operation. By integrating multiple MobileNet base models with information fusion through the Dempster-Shafer theory (DST), the proposed method is able to provide reliable identification of the geological condition based on the images of the excavated mucks on TBM’s conveyor belt. Shapley additive explanations (SHAP) analysis is further presented for model interpretation and understanding of the key features in the images. A case study using data collected from Singapore’s Circle Line 6 tunnel construction projects is conducted to verify the effectiveness of the proposed method. The results indicate that (1) The proposed method detects the encountered geological condition with high accuracy; (2) EMNet outperforms all the base MobileNet models; (3) SHAP analysis demonstrates that surface texture could be the key feature that assists the model for the classification. The developed method contributes to the state of knowledge in proposing a novel ensemble method for reliable image identification and the state of practice in proposing a useful tool to identify the encountered geological condition automatically.

TBM Advanced Geological Prediction via Ellipsoidal Positioning Velocity Analysis

Traditional seismic wave-based tunnel advanced geological forecasting techniques are primarily designed for drill and blast method construction tunnels. However, given the fast excavation speed and limited prediction space in tunnel boring machine (TBM) construction tunnels, traditional methods have significant technical limitations. This study analyzes the characteristics of different types of TBM construction tunnels and, considering the practical construction conditions, identifies an effective observation system and data acquisition method. To address the challenges in advanced forecasting for TBM construction tunnels, a method of ellipsoid positioning velocity analysis, which takes into account the constraints of three-component data directions, is proposed. Based on the characteristics of the advanced forecasting observation system, this method compares the maximum values on the spatial isochronous ellipsoidal surface to determine the average velocity of the geological layer rays, thereby enabling accurate inversion of the spatial distribution ahead. Utilizing numerical simulation, a model for the advanced detection of typical unfavorable geological formations is established by obtaining the wave field response characteristics of seismic waves in three-dimensional space, and the velocity structure of the model is retrieved through this velocity analysis method. In the engineering example, the fracture property, water content, and weathering degree of the surrounding rock are predicted accurately.

Analysis of the Influence of Rock Mechanics Variables on the Stability of Tunnel Surrounding Rock Based on Engineering Mathematics Applications

Tunnels are essential infrastructure elements, and it is critical to maintain their stability for both operation and safety. Using engineering techniques, this study examines the correlation between rock mass motorized characteristics and tunnel surrounding rock stability. This study utilizes the multi-sensor monitoring data of the surrounding rock mechanical characteristics and tunnel support structure collected during tunnel boring machine construction as its research object. The integrated cuckoo search optimized Upgraded dynamic convolutional neural network (ICSO-UDCNN) has been utilized for predicting the tunnel parameters. In general, the surrounding rock’s hardness correlates with its level, which in turn determines how quickly tunnels are being excavated. There is a stronger correlation of 98\% between the field penetration index (FPI) variables of the rock’s characteristic slope along the conditions surrounding the tunnel. The most significant factor influencing its deformation is the surrounding rock’s mechanical characteristics. For engineers and other decision-makers engaged in tunnel design, building, and maintenance, the study’s findings add a greater understanding of the variables affecting tunnel stability. This research provides an establishment for enhancing security protocols, lowering hazards related to tunneling operation, and optimizing tunnel engineering techniques by quantitatively evaluating the influence of rock mass mechanical factors on solidity.

Automated position control of tunnel boring machine during excavation using deep reinforcement learning

Position control of tunnel boring machines (TBM) is critical to ensure precise construction and meets design specifications. However, the high dependence on human intervention largely affects the efficiency and reliability of the operations due to slow response, inconsistent judgment and high risk of errors. Targeting to automate the position control of the TBM to enable it to the planned route in a more efficient and reliable manner, this research proposes a deep reinforcement learning (DRL) method considering spatial-temporal dynamics to perform automated real-time TBM operations. The DRL algorithm is embedded with a time-series deep learning model to simulate the working environment with dynamic time-space variations. The whole process is coupled with the designed reward and evaluation metrics for model enhancement. To showcase the viability and applicability of the suggested approach, a project from Singapore is utilized as an illustrative example. The outcomes indicate that the suggested method exhibits robust capabilities in forecasting and controlling TBM positions, with an R2 of 0.86 and 0.92 in forecasting the horizontal and vertical deviations of the TBM tail three steps ahead and an overall improvement of 50.7 %. The proposed method is capable of providing an optimal strategy to intelligently forecast and control the TBM deviation. The novelty of this method comes from the feasible integration of time series prediction and DRL to simulate the spatial-temporal dynamics along with TBM excavation and obtain a position control model for optimal tunneling strategy, contributing to the effective facilitation of TBM tunneling to improve efficiency and reliability.

TBM adaptability analysis in small-radius curved tunnels in Kurkar strata in Israel

In shield tunnelling, both alignment and geological conditions not only influence the performance of the tunnel boring machine (TBM) but also affect disturbance of the strata. Based on an analysis of the challenges posed by alignment and geological conditions, a targeted design was implemented for a TBM for small-radius curved tunnels in the Kurkar strata in Israel. The adaptability of the TBM was verified through a comprehensive analysis of strata disturbance, variations in boring parameters, tunnel alignment and cutter wear. The results indicated that, to accommodate small-radius curves, the TBM could be adapted by shortening the shield length, setting articulation cylinders and configuring overcutting tools. To adapt to the Kurkar strata, 2.5‰ of foaming agent (CLB F5/M) can be added, along with appropriately increasing the soil chamber pressure. The TBM parameters can be set as follows: total thrust 20 000–24 000 kN, cutterhead rotation speed 1.3–1.4 rpm, TBM advance rate reaching 30–45 mm/min and cutterhead torque ranging from 2500–3500 kN.m. Double-disc cutters should be avoided in the Kurkar strata, especially in central regions of the cutterhead.

Microwave-assisted TBM cutter for efficient hard rock fracturing in high stress environments

Elucidating the effects and fundamental mechanisms of microwave-assisted mechanical excavation under high initial stress conditions is of paramount importance for enhancing the efficiency of deep resource extraction. In this study, indentation experiments were conducted on microwave-damaged rock under initial stress conditions using a tunnel boring machine (TBM) for the first time. By integrating acoustic emission, digital image correlation, and the discrete element method, we conducted a comprehensive analysis of the multifaceted effects of microwave irradiation and initial stress on rock fracturing. The rock-breaking efficiency was evaluated based on the volume of broken rock and the energy consumption. The indentation failure of the sample can be divided into three stages: microfracture closure, elastic deformation, and unstable crack propagation. The microwave irradiation reduced the peak load during the indentation process and simultaneously reduced the brittleness of the specimen. The experimental and simulation results jointly demonstrated the existence of an initial stress threshold in the rock fracturing process. When the initial stress is below the threshold, it suppresses the extension of rock fractures, which is unfavorable for rock fragmentation. When the initial stress exceeds the threshold, stress-induced rock failure occurs, which promotes rock fragmentation. A notable observation is that microwave irradiation alters the initial stress threshold of the rock, where a higher microwave power correlates with a lower initial stress threshold. This indicates that the optimal parameters for microwave equipment must be reconsidered when the initial stress changes. Methods for optimizing rock breakage at initial stress were suggested and examined.

Intelligent decision-making for TBM tunnelling control parameters using multi-objective optimization

In tunnel construction, tunnel boring machine (TBM) tunnelling typically relies on manual experience with sub-optimal control parameters, which can easily lead to inefficiency and high costs. This study proposed an intelligent decision-making method for TBM tunnelling control parameters based on multi-objective optimization (MOO). First, the effective TBM operation dataset is obtained through data preprocessing of the Songhua River (YS) tunnel project in China. Next, the proposed method begins with developing machine learning models for predicting TBM tunnelling performance parameters (i.e. total thrust and cutterhead torque), rock mass classification, and hazard risks (i.e. tunnel collapse and shield jamming). Then, considering three optimal objectives, (i.e., penetration rate, rock-breaking energy consumption, and cutterhead hob wear), the MOO framework and corresponding mathematical expression are established. The Pareto optimal front is solved using DE-NSGA-II algorithm. Finally, the optimal control parameters (i.e., advance rate and cutterhead rotation speed) are obtained by the satisfactory solution determination criterion, which can balance construction safety and efficiency with satisfaction. Furthermore, the proposed method is validated through 50 cases of TBM tunnelling, showing promising potential of application.

Environmental simulation and refrigeration demand analysis for shield tunnel construction in hot summer and cold winter regions

The thermal hazards generated during the tunnel boring machine construction process significantly impact the physical and mental health of personnel. A field test was conducted on the tunnel of Metro Line 1 in Changsha, China, where the average temperature in the work zone reached up to 33.16 °C, and the air velocity was generally below 0.2 m/s. These conditions were unfavorable for ventilation and heat dissipation. In this paper, a full-scale CFD simulation model was established to simulate the construction environment and analyze the cooling demand. The results indicated that increasing the fresh air velocity could lower the temperature in the work zone; however, this effect was limited. When the fresh air temperature exceeded a certain threshold, it actually raised the average temperature of the work zone. Consequently, there is a need for supplementary cooling of the fresh air during the hot summer months. At a fresh air velocity of 15 m/s, the maximum temperature in the work zone was recorded at 27.8 °C when the fresh air was cooled down to 22.5 °C through auxiliary cooling measures. This approach effectively controlled the temperature in the work zone, reduced flow dead zones, and significantly improved air distribution and heat dissipation. Additionally, it ensured the physical and mental well-being of the construction workers and the safe operation of the equipment. This study provides valuable guidance for managing thermal environments in underground tunnel construction.

Identification of cutter flat wear for rock TBMs using vibration signal of shield

Identification of cutter wear status is one of the critical issues for the successful operation of tunnel boring machines (TBM). This study proposed a novel model for predicting the locations of cutters experiencing flat wear, leveraging measured vibration signals from the TBM shield. Field tests were conducted to collect vibration acceleration data using accelerometers. This paper outlines the equipment and setup for monitoring vibration acceleration and develops procedures for signal processing. Analysis of vibration acceleration patterns reveals distinct characteristics corresponding to varying numbers and locations of cutters with flat wear. A linear relationship can be identified between the frequency of different vibration acceleration peak intervals and the extent of flat wear on the cutters (number and location of wear cutters). The proposed model is then applied to a TBM tunnelling section in Guangzhou, China, and validated using on-site wear data. This method offers real-time insight into the potential need for cutter replacement during tunnelling operations in challenging hard rock conditions.

Novel multifractal-based classification model for the quality grades of surrounding rock within tunnels

Understanding the variation patterns of tunnel boring machine (TBM) operational parameters is crucial for assessing engineering geological conditions and quality grades of surrounding rock within tunnels. Studying the multifractal characteristics of the TBM operational parameters can help identify the patterns, but the relevant research has not yet been explored. This paper proposed a novel classification model for quality grades of surrounding rock in TBM tunnels based on multifractal analysis theory. Initially, the statistical characteristics of eight TBM cycle data with different grades of surrounding rock were explored. Subsequently, the method of calculating and analyzing the multifractal characteristic parameters of the TBM operational data was deduced and summarized. The research results showed that the TBM operational parameters of cutterhead torque, total thrust, advance rate, and cutterhead rotation speed have significant multifractal characteristics. Its multifractal dimension, midpoint slope of the generalized fractal spectrum, and singularity strength range can be used to evaluate the surrounding rock grades of the tunnel. Finally, a novel classification model for the tunnel surrounding rocks based on the multifractal characteristic parameters was proposed using the multiple linear regression method, and the model was verified through four TBM cycle data containing different surrounding rock grades. The results showed that the proposed multifractal-based classification model for tunnel surrounding rocks has high accuracy and applicability. This study not only achieves multifractal feature representation and surrounding rock classification for TBM operational parameters but also holds the potential for adaptive adjustment of TBM operational parameters and automated tunneling applications.

Convolutional Neural Network for Predicting the Performance of a Tunnel Boring Machine

Convolutional Neural Network for Predicting the Performance of a Tunnel Boring Machine

Study on the key factors affecting the performance of shield scrapers in gravelly soil strata

Gravelly soil strata exhibit heterogeneity and nonlinearity in their physical and mechanical properties, leading to volatile fluctuations of shield scraper force. Understanding the performance of shield scrapers in gravelly soils is significant for the safe and efficient excavation of tunnel boring machines. This paper conducts unconsolidated-undrained triaxial tests to obtain the mechanical properties of gravelly soils with gravel content ranging from 0 % to 30 %. The process of shield scrapers cutting through gravelly soils is analyzed by combining the varying mechanical properties of gravelly soils with the limit equilibrium analysis method. Subsequently, modified models for predicting the scraper force and specific energy in gravelly soils are established. Based on these models, the impact of key factors on the scraper performance is analyzed. Laboratory experiments are further performed on a rotary test bench to validate these models. The experimental results demonstrate that the horizontal cutting force and specific energy of shield scrapers in gravelly soils can be predicted with mean average errors of 8.8 % and 7.3 %, respectively, with the errors of all predicted values falling within ±20 % of the experimental results. These established models can serve as useful references for the structural and operational design of shield scrapers in gravelly soil strata.