Search Results (59)

The main bearing of a tunnel boring machine (TBM) is a critical component of the main driving system that enables continuous excavation, and its performance is crucial for ensuring the safe operation of the TBM. Currently, there are few testing technologies for TBM main bearings, and a comprehensive testing and evaluation system has yet to be established. This study presents an experimental investigation using a self-developed, full-scale TBM main bearing test bench. Based on a representative load spectrum, both operational condition tests and life cycle tests are conducted alternately, during which the signals of the main bearing are collected. The observed vibration signals are weak, with significant vibration attenuation occurring in the large structural components. Compared with the test bearing, which reaches a vibration amplitude of 10 g in scale tests, the difference is several orders of magnitude smaller. To effectively utilize the selected evaluation indicators, the entropy weight method is employed to assign weights to the indicators, and a comprehensive analysis is conducted using grey relational analysis. This strategy results in the development of a comprehensive evaluation method based on entropy weighting and grey relational analysis. The main bearing performance is evaluated under various working conditions and the same working conditions in different time periods. The results show that the greater the bearing load, the lower the comprehensive evaluation coefficient of bearing performance. A multistage evaluation method is adopted to evaluate the performance and condition of the main bearing across multiple working scenarios. With the increase of the test duration, the bearing performance exhibits gradual degradation, aligning with the expected outcomes. The findings demonstrate that the proposed performance evaluation method can effectively and accurately evaluate the performance of TBM main bearings, providing theoretical and technical support for the safe operation of TBMs. Full article

Abrasive waterjet technology is a promising sustainable and green technology for cutting underground structures. Abrasive waterjet usage in demolition promotes sustainable and green construction practices by reduction of noise, dust, secondary waste, and disturbances to the surrounding infrastructure. In this study, a numerical framework based on a coupled Smoothed Particle Hydrodynamics (SPH)–Finite Element Method (FEM) algorithm incorporating the Riedel–Hiermaier–Thoma (RHT) constitutive model is proposed to investigate the damage mechanism of concrete subjected to abrasive waterjet. Numerical simulation results show a stratified damage observation in the concrete, consisting of a crushing zone (plastic damage), crack formation zone (plastic and brittle damage), and crack propagation zone (brittle damage). Furthermore, concrete undergoes plastic failure when the shear stress on an element exceeds 5 MPa. Brittle failure due to tensile stress occurs only when both the maximum principal stress (σ₁) and the minimum principal stress (σ₃) are greater than zero at the same time. The damage degree ($\chi$) of the concrete is observed to increase with jet diameter, concentration of abrasive particles, and velocity of jet. A series of orthogonal tests are performed to analyze the influence of velocity of jet, concentration of abrasive particles, and jet diameter on the damage degree and impact depth (h). The parametric numerical studies indicates that jet diameter has the most significant influence on damage degree, followed by abrasive concentration and jet velocity, respectively, whereas the primary determinant of impact depth is the abrasive concentration followed by jet velocity and jet diameter. Based on the parametric analysis, two optimized abrasive waterjet configurations are proposed: one tailored for rock fragmentation in tunnel boring machine (TBM) operations; and another for cutting reinforced concrete piles in shield tunneling applications. These configurations aim to enhance the efficiency and sustainability of excavation and tunneling processes through improved material removal performance and reduced mechanical wear. Full article

The Tunnel Boring Machine (TBM) method has gained attention as an eco-friendly tunneling technique, effectively reducing noise, vibration, and carbon emissions compared to conventional blasting methods. However, ground settlement and volume loss are inevitable during TBM excavation due to the deformation of the surrounding ground, which may even lead to ground collapse in severe cases. In this study, a Shield TBM model, validated using field data, was employed to perform numerical analyses on parameters such as tunnel diameter, ground elastic modulus, face pressure, and backfill pressure. Based on the simulation results, the influence of each parameter on settlement was evaluated, and a predictive model for estimating maximum settlement was developed. The proposed model was statistically validated using p-value assessment, variance inflation factor (VIF), coefficient of determination (R²), and residual analysis. Furthermore, the prediction model showed high agreement with the field data, yielding a prediction error of 8.25%. This study emphasizes the applicability of verified numerical modeling for accurately predicting ground settlement in Shield TBM tunneling and provides a reliable approach for settlement prediction under varying construction conditions. Full article

This study proposes an intelligent prediction framework integrating native sparse attention (NSA) with the Chen-Guan (CHG) algorithm to optimize tunnel boring machine (TBM) operations in heterogeneous geological environments. The framework resolves critical limitations of conventional experience-driven approaches that inadequately address the nonlinear coupling between the spatial heterogeneity of rock mass parameters and mechanical system responses. Three principal innovations are introduced: (1) a hardware-compatible sparse attention architecture achieving O(n) computational complexity while preserving high-fidelity geological feature extraction capabilities; (2) an adaptive kernel function optimization mechanism that reduces confidence interval width by 41.3% through synergistic integration of boundary likelihood-driven kernel selection with Chebyshev inequality-based posterior estimation; and (3) a physics-enhanced modelling methodology combining non-Hertzian contact mechanics with eddy field evolution equations. Validation experiments employing field data from the Pujiang Town Plot 125-2 Tunnel Project demonstrated superior performance metrics, including 92.4% ± 1.8% warning accuracy for fractured zones, ≤28 ms optimization response time, and ≤4.7% relative error in energy dissipation analysis. Comparative analysis revealed a 32.7% reduction in root mean square error (p < 0.01) and 4.8-fold inference speed acceleration relative to conventional methods, establishing a novel data–physics fusion paradigm for TBM control with substantial implications for intelligent tunnelling in complex geological formations. Full article

The TBM disc cutter, which is the main cutting tool of tunnel boring machines (TBMs), is replaced when it is excessively worn during the boring process. Disc cutters are usually monitored by workers at cutterhead chambers, and they check the status and wear of disc cutters. Manual measurement occasionally results in inaccurate measurement results. In order to overcome these limitations, real-time disc cutter monitoring techniques have been developed with different types of sensors. This study evaluates the distance measurement performance of an eddy-current sensor for measuring disc cutter wear via a series of laboratory experiments. This study focused on identifying the effects of various measurement environments on the sensor’s accuracy. The study considered conditions that the eddy-current sensor may encounter in shield TBM chambers, including air, water, slurry, and excavated muck. Experiments were conducted using both a small-scale disc cutter and a 17-inch full-scale disc cutter. The results indicate that the eddy-current sensor can accurately measure the distance to the disc cutter within a specific range and that its performance remains unaffected by different measurement environments. Full article

The thrust system, an important subsystem of a tunnel boring machine (TBM), primarily provides thrust force and adjusts TBM’s attitude in real time. In the tunneling process, only controlling the thrust speed causes pressure oscillations, increases soil deformation, and leads to surface subsidence or upheaval. Conversely, solely relying on pressure control causes fluctuations in speed, making it difficult to ensure that the deviation between the designed tunneling axis (DTA) and the actual tunneling axis (ATA) remains within the permissible range. Due to the increase in geological complexity and higher construction quality standards, primarily relying on single-mode speed or pressure control has become inadequate to meet operational demands. Therefore, to realize higher safety and precise trajectory tracking, it is necessary to ensure speed and pressure compound control for thrust systems. This paper proposes a novel adaptive sliding mode control (ASMC) strategy for thrust systems, which is composed of a proportional pressure relief valve (PPRV) and a proportional flow control valve (PFCV). Firstly, PPRV and PFCV are modeled as a second-order system and an ASMC is employed to control the pressure and speed. Next, to assess the performance of the ASMC controller, simulation experiments were conducted under various conditions, including speed regulation, sudden changed load, and disturbed load. The simulation results indicate that compared to the Proportion–Integral–Differential (PID) controller, the ASMC controller shows almost no overshoot in speed and pressure control during the initial stages, with the response time reduced by approximately 70%. During speed regulation process and sudden changed load process, the response time for both speed and pressure control is shortened by about 80%. In the disturbed load process, the ASMC controller maintains pressure stability. In conclusion, the ASMC controller significantly improves the response speed and stability of the thrust system, exhibiting better control performance under various operating conditions. Full article

In order to investigate the mechanism of ground settlement induced by shield tunnels better, this study proposes a novel simplified physical model testing method. In this physical model, double layer tubes with different materials are used to model the tunnel boring machine (TBM) and tunnel, respectively. When the outer tube in the experimental box is removed, the gap between the two different tubes can be utilized to reflect the ground settlement caused by TMB construction. Meanwhile, 3D image monitoring technology is introduced to collect ground settlement data for research on the mechanism of ground settlement induced by TBM construction. In order to validate the proposed testing method, firstly, the pilot experiment is performed; then, the obtained settlement curve obeys the Gaussian distribution, and the obtained settlement process is similar to that of the practical situation. Furthermore, based on the proposed testing method, an orthogonal experiment is designed to investigate the influences of the ground loss ratio, burial depth, and stratum condition on the ground settlement during the construction process. The results indicate that the ground loss ratio caused by the gap during construction excavation has a more significant impact than the tunnel burial depth and ground conditions. The findings in this study provide a quantitative guide for settlement monitoring during TBM construction, demonstrating that the ground loss ratio has the most significant impact on settlement (up to 28.7% deviation), while the effects of burial depth and stratum conditions are relatively minor (4.4% and 4.2% deviation, respectively). This method offers a practical and efficient approach for predicting and controlling ground settlement in TBM construction, which is of great importance in its practical application. Full article

To address the challenges in controlling a Tunnel Boring Machine (TBM) equipped with a V-type propulsion system during excavation, a digging attitude control strategy based on a nonlinear controller is introduced. First, the mathematical models of the V-type propulsion hydraulic system and the propulsion system’s attitude are developed, followed by an analysis of the system’s nonlinearities and susceptibility to strong disturbances. Second, a nonlinear control strategy tailored to the propulsion system’s characteristics is devised to regulate the digging attitude of the V-TBM. Finally, the proposed nonlinear control strategy is validated through comprehensive simulations and experimental evaluations. Simulation results demonstrate that the proposed nonlinear control strategy outperforms traditional PID control in attitude regulation performance. Field experiments reveal that the TBM achieves an average horizontal error of less than 20 mm and a vertical error of less than 22 mm in circular curve boring. This validates the strategy’s effectiveness in enabling rapid tracking and adjustment of the tunnel boring axis, meeting the stringent demands of small-radius curved boring. Full article

The accurate prediction of rock mass quality ahead of the tunnel face is crucial for optimizing tunnel construction strategies, enhancing safety, and reducing geological risks. This study developed three hybrid models using random forest (RF) optimized by moth-flame optimization (MFO), gray wolf optimizer (GWO), and Bayesian optimization (BO) algorithms to classify the surrounding rock in real time during tunnel boring machine (TBM) operations. A dataset with 544 TBM tunneling samples included key parameters such as thrust force per cutter (TFC), revolutions per minute (RPM), penetration rate (PR), advance rate (AR), penetration per revolution (PRev), and field penetration index (FPI), with rock classification based on the Rock Mass Rating (RMR) method. To address the class imbalance, the Borderline Synthetic Minority Over-Sampling Technique was applied. Performance assessments revealed the MFO-RF model’s superior performance, with training and testing accuracies of 0.992 and 0.927, respectively, and key predictors identified as PR, AR, and RPM. Additional validation using 91 data sets confirmed the reliability of the MFO-RF model on unseen data, achieving an accuracy of 0.879. A graphical user interface was also developed, enabling field engineers and technicians to make instant and reliable rock classification predictions, greatly supporting safe tunnel construction and operational efficiency. These models contribute valuable tools for real-time, data-driven decision-making in tunneling projects. Full article

During the excavation process of a Tunnel Boring Machine (TBM), the cutterhead exerts significant torque on the tunnel structure, which potentially causes torsional shear deformation at segment ring joints. Thus, examining the characteristics of torsional shear deformation and the shear-bearing performance of segment joints under construction torque is crucial for the design and safety of segment structures and the construction of TBM tunnels. To achieve this, a refined finite element model of the segment joints was developed to study their torsional shear resistance under varying axial forces and with or without mortise and tenon. Furthermore, the failure modes of bolts and the damage characteristics of segment concrete during torsional shear deformation are analyzed. The results show that the load-bearing process of torsional shear deformation in segment joints consists of three stages: development of the friction at the segment interface (Stage I), development of the bolt force (Stage II), and development of the mortise and tenon force (Stage III). It is noteworthy that axial force is the primary factor in enhancing the torsional shear resistance of the segmental joints. Moreover, as the torsional shear deformation increases, the contact and compression occur between the bolts and the segment bolt holes as well as between the mortise and tenon, leading to the yielding of the bolts and the failure of the concrete at the joints. Consequently, the segment concrete around the mortise and tenon and the bolt hole is prone to cracking and crushing. To prevent shear failure of the bolts, it is recommended that the rotational angle of segment be maintained at less than 0.045°. Full article

This study provides a theoretical foundation for optimizing tunnel boring machine (TBM) excavation parameters under diverse geological conditions, offering significant engineering value by enhancing construction efficiency and reducing costs. As the development of underground spaces advances, TBMs play a pivotal role in tunnel excavation. TBMs enhance safety in excavation by mechanically breaking rock, reducing the reliance on explosives, and the associated risks of blasts. The shield support minimizes surrounding rock collapse, advanced geological forecasting mitigates risks posed by complex geologies, and intelligent monitoring systems improve operational safety. To enhance TBM efficiency and safety, this study developed a 3D simulation model of rock breaking by disc cutters using the discrete element method. This study systematically examined the effects of excavation parameters, including disc-cutter diameter, cutter spacing, and penetration, on rock-breaking performance. The findings reveal, that as the disc-cutter diameter increases, the rolling force also increases, while the rock-breaking specific energy initially rises and then declines. The 19-inch disc cutter demonstrated a superior rock-breaking efficiency in conventional excavation operations. At a cutter spacing of 60 mm, the rock-breaking specific energy reached its lowest value, representing optimal efficiency. Furthermore, as the penetration increased, both the rolling force and rock fragmentation volume grew, whereas the specific energy decreased, further improving the rock-breaking efficiency. Full article

The accurate prediction of tunneling speed in tunnel boring machine (TBM) construction is the basis for the timely adjustment of the operating parameters of TBM equipment to ensure safe and efficient tunneling. In this paper, a multi-step prediction model of TBM tunneling speed based on the EWT-ICEEMDAN-SSA-LSTM hybrid model is proposed. Firstly, four datasets were selected under different geological conditions, and the original data were preprocessed using the binary discriminant function and the $3\sigma$ principle; secondly, the preprocessed data were decomposed using the empirical wavelet variation (EWT) to obtain several subseries and residual series; then, Intrinsic Computing Expressive Empirical Mode Decomposition With Adaptive Noise (ICEEMDAN) was used to perform further decomposition on residual sequences. Finally, several subsequences were fed into a Long Short-Term Memory (LSTM) network optimized by the Sparrow Search Algorithm (SSA) for multi-step training and prediction, and the predicted results of each subsequence were added up to obtain the final result. A comparison with existing models showed that the performance of the prediction method proposed in this paper is superior to other models. Of the four datasets, the average accuracy from the first step prediction to the fifth step prediction reached 99.06%, 98.99%, 99.07%, and 99.03%, respectively, indicating that the proposed method has high multi-step prediction performance and generalization ability. In this sense, this paper provides a reference for other projects. Full article

Segment assembly is one of the principal processes during tunnel construction using a tunnel boring machine (TBM). The segment erector is a robotic manipulator powered by a hydraulic system that assembles prefabricated concrete segments onto the excavated tunnel surface. In the case of a larger diameter, while the segment assembly has a more extensive range of motion, it also demands more control accuracy. However, the single-pump-based hydraulic system fails to meet the dual requirements. Therefore, this paper proposes a novel dual parallel-connected-pump hydraulic system consisting of a small displacement pump and a large displacement pump. On this basis, taking advantage of both the quick response and low dead zone of the small pump and the high flow range of the large pump, a two-level error allocation strategy is constructed to coordinate the two pumps and keep the motion error of segment assembly within a small range. Finally, comparative experiments were conducted, and the results show that the proposed scheme achieves the simultaneous high-level synchronization of the two pumps and high-precision and high-speed motion-tracking performance. Full article

Tunnel boring machines (TBMs) are essential for excavating metro tunnels, reducing disruptions to surrounding rock, and ensuring efficient progress. This study examines how machine learning (ML) models can predict key tunneling outcomes, focusing on making these predictions clearer. Specifically, the models aim to predict surface settlements (ground sinking) and the TBM’s penetration rate (PR) during the Athens Metro Line 2 extension to Hellinikon. For surface settlements, four artificial neural networks (ANNs) were developed, achieving an accuracy of over 79%, on average. For the TBM’s PR, both an XGBoost Regressor (XGBR) and ANNs performed consistently well, offering reliable predictions. This study emphasizes model transparency mostly. Using the SHapley Additive exPlanations (SHAP) library, it is possible to explain how models make decisions, highlighting key factors like geological conditions and TBM operating data. With SHAP’s Tree Explainer and Deep Explainer techniques, the study reveals which parameters matter most, making ML models less of a “black box” and more practical for real-world metro tunnel projects. By showing how decisions are made, these tools give decision-makers confidence to rely on ML in complex tunneling operations. Full article

In recent years, tunnel-boring machines (TBMs) have been widely applied in deep coal mining. Turning is an inevitable challenge in TBM tunneling, and a TBM turning roadway exhibits greater instability than a straight roadway, as engineering experience has indicated. This study aimed to explore the failure mechanism and evaluate the support performance of a deep-turning roadway. Several numerical models were established to investigate the deformation of the roadway, the stress distribution, and the failure zone of the surrounding rocks under different tunneling conditions. The results show that the tunneling depth influences the failure pattern of the turning roadway: deep tunneling with high in situ stress can cause asymmetric failure of the turning roadway, while shallow tunneling with low in situ stress does not. Moreover, the change in turning radius, namely the change in roadway geometry, does not influence the stability of the turning roadway. In addition, the support actions for both the straight and turning roadways do not differ significantly, and the amount of controlled deformation of the surrounding rocks is proportional to their natural deformation. Full article