Tunnel Boring Machine Tunneling Research Articles

Analysis and Enlightenment on the Relationships between Two Kinds of Cutter Life Evaluation Indexes and Installation Radius: A Case Study

Accurate evaluation of cutter life at different cutter positions on the cutter head is helpful to determine the time of cutter change and reduce the time of cutter wear measurement, which is of great significance to improve the tunneling efficiency of tunnel boring machine (TBM) projects. Unfortunately, there is no unified cutter life evaluation index now. The field data of cutter wear are collected from a section of a long TBM tunneling water conveyance tunnel in China. Two kinds of cutter life evaluation indexes (based on the radial wear extent of cutter rings and replacement number of cutter rings) are selected and the variation rule between these two kinds of indexes with cutter installation radius is statistically analyzed. The results show that the regression relationships between the two kinds of cutter life evaluation indexes and installation radius mainly present linear functions and quadratic functions. Those regression relationships are affected by factors such as wear type, installation angle, cutter spacing, influence width, and allowable limit wear extent of cutter rings. Considering the calculation accuracy of the evaluation index, the actual working conditions of the disc cutter, and ignoring the influence of tunnel diameter, it is recommended to preferentially choose the radial wear extent of cutter rings per unit rolling distance as the evaluation index of cutter life. The research results can provide a reference for the selection of cutter life evaluation index, prediction of disc cutter life at different cutter positions, and establishment of cutter life prediction mode.

An integrated determination method of optimal supporting time against TBM jamming

TBM (Tunnel Boring Machine) jamming and support failure occur frequently with inappropriate supporting time, particularly when TBM crosses adverse geology. Given this, an integrated determination method of optimal supporting time against TBM jamming was proposed. In this paper, the causes and failure mechanism of large deformation in the tunnel were revealed through X-ray diffraction (XRD) measurement of the rocks. The frictional resistance of TBM shield and the stress of support under different supporting time and measures were investigated by numerical simulation. It was shown that early installation of the support will lead to excessive load on the support, resulting in support failure. Contrarily, delayed installation of the support will lead to excessive tunnel deformation, inducing TBM shield jamming. The optimal supporting time was determined by comprehensively analyzing the critical deformation of TBM jamming and ultimate stress of support. The deformation of surrounding rock in the range of 31.6 mm ∼ 39.9 mm was the optimal support time in this study. Installation support with an optimal supporting time can ensure the safety of TBM and support simultaneously. The range of optimal supporting time decreases continuously with the weakening of support parameters. Specifically, the support measures (bolt + steel arch, shotcrete + bolt) cannot simultaneously ensure the safety of the TBM and support. The research results can effectively guide the prevention of jamming and support design in TBM tunnels.

TBM tunneling strata automatic identification and working conditions decision support

It has become an important research topic to ensure the safe and efficient tunnel boring machine (TBM) tunneling in the construction of long distance and deep buried tunnels. This study aims to construct an assistant decision support system that integrates surrounding rock classification identification and tunneling parameters prediction optimization, providing quantitative decision guidance for driving TBM for main drivers. For this purpose, this study takes a TBM diversion tunnel project in Xinjiang as the research background, collects 8633 m length TBM on-site tunneling data, and constructs a TBM tunneling data sample database; The DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm was improved using kernel density estimation to achieve TBM tunnel surrounding rock classification based on tunneling performance and automatic identification. Compared with the traditional DBSCAN model, the Sk parameter average decreased by 30.0%, and the Ku parameter average increased by 181.9%; Adopting the opposition-based learning strategy and β-chaotic sequence method improved the MOGWO (multi-objective grey wolf optimization) algorithm to achieve optimal decision-making of TBM tunneling parameters under 12 working conditions. Compared to the traditional MOGWO model, the Pareto optimality solved by the improved MOGWO model increasing the improvement efficiency of the original value and sample mean by 14.82% and 11.46%, respectively. The on-site application results indicate that the assistant decision support system proposed in this study can provide construction guidance for TBM main drivers under different working conditions, improving the tunneling efficiency and safety of TBM.

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.

Improving Tunnel Boring Machine Tunneling Performance by Investigating the Particle Size Distribution of Rock Chips and Cutter Consumption

The construction environment of deep rock tunnels is complex, and effectively enhancing tunnel boring machine (TBM) tunneling efficiency is paramount. Increasing rock-breaking efficiency and minimizing cutter consumption are essential strategies for improving TBM tunneling efficiency. Selecting suitable tunneling parameters is crucial for enhancing rock-breaking efficiency and reducing cutter consumption. Existing research on the optimization of the ratio of maximum cutter spacing to penetration (Smax/P) based on field-measured data is limited, and few studies compare and analyze the relationship between SE, CI, and the Smax/P ratio separately. Consequently, this study determined optimal tunneling parameters for various types of surrounding rock and construction environments, aiming to more accurately optimize TBM tunneling performance during construction processes based on on-site construction data. This study conducted a comparative analysis of specific energy (SE) and the coarseness index (CI). Under both working conditions, the quadratic fitting coefficients of the CI are 4.2% and 10.6% higher than those of the SE, respectively, with the CI selected to represent the particle size distribution of rock chips. Finally, taking into account both the correlations between the CI and the ratio of maximum cutter spacing to penetration (Smax/P), as well as cutter consumption and the Smax/P ratio, an optimization method for the TBM tunneling parameter was established under both dry and saturated conditions. The research findings indicate that cutter consumption exhibits an exponential increase with a higher rock Cerchar Abrasivity Index (CAI); it initially decreases as the Smax/P ratio increases and subsequently increases in both dry and saturated conditions. Instead, the CI demonstrates an initial increase and subsequent decrease as the Smax/P ratio increases. Maximizing rock-breaking efficiency and minimizing cutter consumption are crucial for improving tunneling performance. In saturated conditions, the corresponding optimal Smax/P ratio ranges are 7.055–8.319 for soft rock, 8.606–8.931 for medium–hard rock, and 13.50–14.00 for hard rock, and these optimal ranges under dry conditions are 8.495–9.457, 10.972–12.169, and 16.5–17.5 for the same rock types. This study provides optimal Smax/P ratio ranges for TBM tunneling, thereby significantly enhancing tunneling efficiency.

Effect of advance rate on excess pore water pressure induced by TBM tunnelling in saturated sand

The stability of tunnel face can be significantly influenced by the excess pore water pressure induced by tunnel boring machine (TBM) tunnelling in saturated sand. This article aims to investigate the effect of TBM advance rate on the variation of such excess pore water pressure. Numerical and analytical solutions are given for the situations of TBM advance rate faster and slower than the slurry infiltration velocity, respectively. It appears that the excess pore water pressure at the tunnel face maintains constant with time when the TBM advance rate exceeds the slurry infiltration velocity and the gradient close to the TBM decreases with distance. In the opposite situation, there is a temporal decrease in excess pore water pressure at the tunnel face, accompanied by a more gradual spatial drop in pressure. In addition, the gradient close to the TBM remains constant. Notably, a larger extent of excess pore water pressures is present in the latter situation, augmenting the risk of a blow-out during tunnelling. Comparative analysis of field measurements and theoretical calculations indicates that the conditions at Green Heart Tunnel align with the latter situation. Furthermore, for the latter situation, a slower TBM advance rate will result in a higher discharge and thus a higher pore water pressure.

Enhancing ground classification models for TBM tunneling: Detecting label errors in datasets

Tunnel Boring Machine (TBM) construction, particularly with closed-face TBMs, faces uncertainties due to the inability of the operator to directly observe the ground ahead. These uncertainties can lead to time delays, cost overruns, and accidents. While supervised machine learning techniques have been used to predict geology from TBM sensor data, their performance drops significantly when applied to other projects, indicating poor generalization. To ensure accurate results and improved generalization to future data, supervised learning models require high-quality, well-labeled data which is not usually the case for TBM datasets. This paper addresses the issue of “noisy” labels in TBM datasets, which human operators and engineers often label with varying interpretations. A data-centric framework was adapted and applied to an Earth Pressure Balance Machines (EPBM) tunnel dataset to detect and identify these mislabeled datapoints. The framework's outputs were validated using two techniques and apply several methods to clean the dataset. The best-performing method was selected for the test set. The paper concludes by discussing the limitations of the proposed method, the challenges encountered, and future research directions in this area.

Prediction of rock mass classification in tunnel boring machine tunneling using the principal component analysis (PCA)–gated recurrent unit (GRU) neural network

Prediction of rock mass classification in tunnel boring machine tunneling using the principal component analysis (PCA)–gated recurrent unit (GRU) neural network

АНАЛИТИЧЕСКИЙ ПОДХОД К ОПРЕДЕЛЕНИЮ ПРОДОЛЬНОГО СМЕЩЕНИЯ СУЩЕСТВУЮЩЕГО СБОРНОГО ТОННЕЛЯ ПРИ ВЫЕМКЕ КОТЛОВАНА ПО ВСЕЙ ДЛИНЕ

In the realm of urban construction employing excavation techniques, safeguarding existing underground structures from detrimental consequences arising from surface construction operations poses a formidable challenge. The reduction of loads due to excavation activities can induce unintended responses, potentially jeopardizing subterranean infrastructure, particularly high-safety-demanding structures like Tunnel Boring Machine (TBM) tunnels. This article introduces an uncomplicated method for ascertaining the axial displacement of TBM tunnels amidst concurrent surface excavation activities. Primarily, the approach entails the identification of stress variations encountered during soil excavation at the tunnel face. Subsequently, employing the solutions derived for the determination of tunnel deformation subjected to concentrated loads, the deformation incurred by the tunnel due to alterations in excavation-induced stress is quantified. The analytical outcomes are meticulously juxtaposed against results generated from a three-dimensional computational model. The comparative analysis demonstrates that the displacement values and axial deviations calculated using the proposed analytical method exhibit only marginal disparities of 4,3% and 1%, respectively, when compared to those obtained through finite element analysis. This study underscores the efficient predictive capabilities of the analytical method in assessing tunnel deformations, enabling a preliminary estimation of critical parameters associated with the excavation pit. These findings have significant implications for mitigating adverse impacts on existing subterranean infrastructure in densely populated urban areas.

Grouped machine learning methods for predicting rock mass parameters in a tunnel boring machine‐driven tunnel based on fuzzy C‐means clustering

AbstractTo guarantee safe and efficient tunneling of a tunnel boring machine (TBM), rapid and accurate judgment of the rock mass condition is essential. Based on fuzzy C‐means clustering, this paper proposes a grouped machine learning method for predicting rock mass parameters. An elaborate data set on field rock mass is collected, which also matches field TBM tunneling. Meanwhile, target stratum samples are divided into several clusters by fuzzy C‐means clustering, and multiple submodels are trained by samples in different clusters with the input of pretreated TBM tunneling data and the output of rock mass parameter data. Each testing sample or newly encountered tunneling condition can be predicted by multiple submodels with the weight of the membership degree of the sample to each cluster. The proposed method has been realized by 100 training samples and verified by 30 testing samples collected from the C1 part of the Pearl Delta water resources allocation project. The average percentage error of uniaxial compressive strength and joint frequency (Jf) of the 30 testing samples predicted by the pure back propagation (BP) neural network is 13.62% and 12.38%, while that predicted by the BP neural network combined with fuzzy C‐means is 7.66% and 6.40%, respectively. In addition, by combining fuzzy C‐means clustering, the prediction accuracies of support vector regression and random forest are also improved to different degrees, which demonstrates that fuzzy C‐means clustering is helpful for improving the prediction accuracy of machine learning and thus has good applicability. Accordingly, the proposed method is valuable for predicting rock mass parameters during TBM tunneling.

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.

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.

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.

Water saturation effects on rock abrasivity and TBM tunneling efficiency

Tunnel boring machine (TBM) tunneling efficiency is influenced by surrounding rocks, TBM design, and interaction between the TBM cutter and rocks. Using a case study from a water conveyance tunnel project located in northwestern China and involving five different lithologies (metamorphic andesite, sandstone, granite, quartz schist, and quartz diorite), this work investigates the water saturation effects on Cerchar abrasivity index (CAI) and its correlations with other physical and mechanical rock properties and TBM disc cutter and energy consumption. The results show that water saturation reduces CAI for all examined rocks except for the quartz diorite at the 90 % confidence level. CAI of saturated rocks (CAIsat) has statistically better correlations with weighted Mohs hardness value of rock minerals (Ha), equivalent quartz content (EQC), and uniaxial compressive strength (UCS), than CAI of dry rocks (CAIdry). The in-situ construction data analyses suggest that the average disc cutter consumption increases exponentially with the rise of CAI values in both dry and saturated conditions, and the water saturation effects significantly reduce the disc cutter consumption within medium-hard and hard surrounding rocks. Both disc cutter and specific energy consumption have a quadratic relationship with the ratio of the maximum cutter spacing to penetration depth (Smax/P), and water saturation can significantly shift the quadratic relationships. Optimal Smax/P ranges for minimizing the disc cutter and energy consumption are recommended for different rocks under dry and saturated conditions. The findings from this study can support estimating cutter and energy consumption with considerations of groundwater effects.

K-means-based heterogeneous tunneling data analysis method for evaluating rock mass parameters along a TBM tunnel

Rapid and accurate judgment of the rock mass condition is the key to guaranteeing the safety and efficiency of tunnel boring machine (TBM) tunneling. This paper proposes a method for evaluating rock mass parameters based on K-means clustering, grouping tunneling areas according to the values of TBM tunneling parameters. A dataset including rock mass and TBM tunneling data is treated by logistic normalization and principal component analysis (PCA), and large volumes of tunneling data with different features are transformed into appropriate volumes of dimensionless data. K-means clustering is used, samples are grouped according to the values of tunneling data, and the specific ranges as defined by clustering are regarded as the unified evaluated results of each group. Based on the C1 part of the Pearl Delta water resources allocation project, 100 training samples and 30 testing samples were field-collected, and the proposed method was realized by the training samples and verified by the testing samples. The evaluation accuracies of uniaxial compressive strength (UCS), and joint frequency (Jf) were 90%, and 86.7% respectively, demonstrating that the evaluation had acceptable values, and the proposed method was greatly helpful for judging rock conditions.

Transfer learning for collapse warning in TBM tunneling using databases in China

Tunnel boring machine (TBM) has been widely used for tunnelling in harsh environments with deeply embedded tunnels and hard rocks. The collapse of surrounding rocks seriously jeopardizes the safety of the machine and the driver. Prediction of TBM tunneling response is vitally important to alert the hazard in advance, which is still a challenge topic, especially for a new-constructing tunnel where scarce data are available. This study proposes a transfer learning model that is capable to predict TBM tunneling responses for a new project using the boring data obtained from the other existing tunnels. The key problem of transfer learning model, i.e., what to transfer, is first studied based on the big data obtained from Yin-song project and Yin-chao project in China. Then, when to transfer is investigated by comparing with the machine learning model that developed using only the data in the new tunnel. Finally, possible collapses in the new Yin-chao project are analyzed using the TBM responses that obtained from the transfer learning model. Results show that the types of transferred TBM parameters significantly affect the prediction accuracy. A filter procedure is proposed to determine the number and types of parameters to be transferred. The predicted accuracy for the torque and thrust reach 93 % and 95 %, respectively, for the first 100 steps from Ch. 66060 m to Ch. 65366 m in the new Yin-chao project when the most important 10 parameters are used in the transfer learning model. The predicted accuracy of the transfer learning model is better than that of the machine learning model in the early stage of the new tunnelling, especially in the first 2000 steps. The proposed model shows superior capability in prediction of the TBM responses and in collapse warning for a new tunnelling project, which paves the way for an automatic construction of TBM.

Experimental and numerical studies on the shear mechanical behavior of rock joints under normal vibration loads

Jointed rock masses of roadways in underground coal mines are heavily disturbed by rock-cutting vibrations from Tunnel Boring Machines (TBMs), posing significant challenges in controlling the deformation and damage of the surrounding rock. To investigate the mechanical behavior of rock joints under normal vibration loads, a series of laboratory vibration experiments were conducted with varying frequencies and amplitudes, based on in-situ vibration characteristics during TBM rock-cutting. Subsequently, the relative damage coefficient Dv and relative shear strength ratio Rτ were proposed to quantify the damage degree of rock joints. The findings revealed a non-linear growth of rock joints damage with increasing vibration frequency and amplitude. The vibration frequency and amplitude are categorized into three and two stages, respectively, depending on the damage of rock joints. Additionally, the vibration failure mechanism of rock joints was elucidated using the discrete element method. It was observed that the failure of weakly joints is controlled by both the “filler-rock” interfaces and “filler-filler”, while strongly joints fail solely due to the “filler-rock” interfaces, confirming the agreement with laboratory experiments. Moreover, effective grouting reinforcement was proven to significantly enhance the vibration resistance of in-situ jointed rock masses, preventing vibration collapse resulting from TBM tunneling disturbance.

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.

Nitrogen Dioxide Gas Levels in TBM Tunnel Construction with Diesel Locomotives Based on Directive 2017/164/EU

Directive 2017/164/EU proposed a drastic reduction of nitrogen monoxide (NO) and nitrogen dioxide (NO2) levels, thereby fortifying the health protection framework within the mining industry. Despite the commendable record of non-road emissions standards (Stage IV and V) in continuing to reduce NOx emissions, concerns remain about compliance with the directive’s strict limits, particularly in demanding tunnels and mining fields. To illustrate this problem, this study undertakes a comprehensive assessment of the practical feasibility surrounding the implementation of these proposed limits in a 6.2 internal diameter tunnel-boring machine (TBM) tunnel constructed with Stage III emission locomotives. The results cast light upon the formidable challenges entailed in achieving strict compliance with the envisioned limits, with a substantial number of measurements notably surpassing these thresholds, primarily concerning NO2 emissions from Stage III engines. To address these challenges, this study highlights the key role of moving to Stage IV-V locomotives or introducing electric locomotives to effectively reduce NOx emissions, ensure compliance with the directive, and avoid delays in tunnel construction.