Tunneling Machine Research Articles

Research on the application of a hybrid intelligent algorithm in reducing the negative response of shield tunneling construction

Traditional shield project control relies on manual monitoring and management, which suffers from poor reliability, lag, and other defects. In this paper, a hybrid algorithm based on Bayesian optimization (BO), light gradient enhancer (LGBM), and nondominated sorting genetic algorithm-III (NSGA-III) is proposed to realize intelligent control of shield construction safety. The BO-LGBM-NSGA-III is a multiobjective optimization algorithm that takes cutterhead wear and shield energy consumption as objective functions and the nonlinear mapping function of surface settlement and construction parameters as fitness functions. Based on the Pareto distribution of the optimized backing structure construction parameters, reasonable limits of the control parameters of the shield tunneling machine are obtained. The conclusions are as follows. (1) The R2 of the test set for surface subsidence prediction is 0.916, and the RMSE is 2.655. (2) The proposed method can achieve multiobjective optimization, and the surface settlement, cutter head wear, and shield energy consumption are optimized by 49.92%, 24.38%, and 41.88%, respectively. (3) By using this method, both prediction and optimization can be realized, and the optimal control scheme can be obtained. The negative impact of shield tunneling construction has been significantly reduced.

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.

Airtightness failure analysis of filter cake during shield tunneling machine hyperbaric intervention

Airtightness failure analysis of filter cake during shield tunneling machine hyperbaric intervention

Identification of Disc Cutter Wear via Operation Parameters Combined With Vibration Data: A Case Study

ABSTRACTThis paper proposed an approach to estimate disc cutter wear utilizing a combination of multiple operational parameters and vibration data collected during shield tunneling operations. The incorporation of vibration signals, notably those originating from acceleration sensors mounted on the back plate of the soil chamber, has markedly enhanced the accuracy of the model. Time‐frequency domain features were extracted through analysis methods such as Fast Fourier Transform (FFT), Short‐Time Fourier Transform (STFT), and Continuous Wavelet Transform (CWT). A predictive model utilizing vibration and shield operation parameters was developed using the XGBoost algorithm, and a deep GoogLeNet Convolutional Neural Network (CNN) was trained on time‐frequency graphs from the CWT. In addition, this study also investigated the impact of signal duration on wavelet image information and model accuracy. In the Huang‐Shang Intercity Railway Project, the approach effectively assessed disc cutter wear during tunneling operations and dynamically optimized the operational parameters of the shield tunnel machine through predictive analysis.

LightGBM integration with modified data balancing and whale optimization algorithm for rock mass classification

The accurate prediction of uneven rock mass classes is crucial for intelligent operation in tunnel-boring machine (TBM) tunneling. However, the classification of rock masses presents significant challenges due to the variability and complexity of geological conditions. To address these challenges, this study introduces an innovative predictive model combining the improved EWOA (IEWOA) and the light gradient boosting machine (LightGBM). The proposed IEWOA algorithm incorporates a novel parameter l for more effective position updates during the exploration stage and utilizes sine functions during the exploitation stage to optimize the search process. Additionally, the model integrates a minority class technique enhanced with a random walk strategy (MCT-RW) to extend the boundaries of minority classes, such as Classes II, IV, and V. This approach significantly improves the recall and F1-score for these rock mass classes. The proposed methodology was rigorously evaluated against other predictive algorithms, demonstrating superior performance with an accuracy of 94.74%. This innovative model not only enhances the accuracy of rock mass classification but also contributes significantly to the intelligent and efficient construction of TBM tunnels, providing a robust solution to one of the key challenges in underground engineering.

The Effects of Different Ultrasonic Composite Surface Modifications on the Properties of H13 Steel for Shield Tunnel Machine Cutter Ring

Tunnel boring machines (TBMs) are exposed to the impact of the ground shattering force and the friction of sandstone during excavation work, and are prone to wear and breakage, and other failures. Traditional heat treatment processes cannot simultaneously achieve the required high-energy composite structure of hard external and tough internal properties for cutter rings, leading to inadequate wear resistance and impact toughness under working conditions. This study utilizes H13 steel as the base material, and based on a study of carburizing, nitriding, and ultrasonic impact processes for H13 steel analyzing the effects of different high-energy composite modification processes on the hardness distribution, microstructure, and residual stress of H13 steel, the mechanisms by which high-energy composite modification processes affect the wear resistance and impact resistance of H13 steel are revealed. The results indicate that the wear amount and impact toughness of the sample subjected to carburizing and ultrasonic surface rolling composite strengthening were 1.9 mg and 27.34 J/cm2, demonstrating the best wear and impact resistance. This combination of properties allows the H13 steel cutter ring to achieve the optimal overall performance in terms of wear resistance and impact resistance.

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.

Research on work-stress recognition for deep ground miners based on depth-separable convolutional neural network

In deep mine production operations, the challenging operating environment intensifies the workload and pressure on coal miners. Long-term exposure to high-intensity operating pressure can seriously impact the physical and mental health of miners, leading to unsafe behaviors and accidents. To identify the pressure of miners' operations, this paper examines various driving scenarios, such as the deep-well tunneling machine cutting the wall and opening the alley, the shoveling machine shoveling ore, and the pickup truck driver transporting. The paper randomly collects facial images of miners during each operation using an explosion-proof CCD camera to obtain the facial expression characteristic data of miners. The Ferface2013 facial expression dataset was used to establish the dataset. The depth separable convolutional neural network MiniXception was used for training and to output the classification results of the pressure degree of deep shaft miners. A MiniXception-based miners' operating pressure recognition model was established. The training time, precision, recall, F1 score, and classification accuracy confusion matrix were selected. The study evaluated the effectiveness of the recognition model by measuring its training time, precision, recall, F1 score, and classification accuracy confusion matrix. The results indicate that the model has a correct recognition rate of 88% for the pressure state, 91% for the pleasure state, and 74% for the normal state. The overall accuracy of the model is 0.843. Therefore, the MiniXception recognition model is suitable for recognizing the pressure of miners' operations in deep mines. This can meet practical needs and is useful for preventing major accidents in mines, managing on-site safety, and managing safety in non-hazardous areas. It has important theoretical and practical significance.

Modeling of reinforced-concrete cutting with shield rippers using FEM-DEM-coupling method

With the increase in the number of underground structures, it is common to encounter reinforced concrete (RC) pile foundations of existing buildings or bridges during shield machine tunneling in densely constructed urban areas. The cutting of RC piles with the shield machines is a challenging task due to cutter damage and cutting efficiency issues. In this paper, the interaction between a ripper and an RC was studied based on the FEM-DEM coupling method. A three-dimensional transient dynamic model of two typical rippers (angular ripper and curved ripper) cutting RC piles was established, respectively. In the FEM-DEM coupling model, the mechanical behavior of concrete was described via DEM, and the impact of steel bars in concrete was considered via FEM. In addition, an experimental verification was conducted to validate the effectiveness of the numerical results. The damage characteristics of the steel bars and concrete, and the force patterns of the rippers were analyzed. The influence of the geometric parameters of the ripper, including blade width, blade angle, and blade anterior angle on the cutting performance was also investigated through a parametric study. Some recommendations are made for the geometric design of rippers used for cutting reinforced concrete. The results of this study could provide some references for cutting RC obstacles with a shield machine.

Construction Technology and Application of TBM in Shengli Tunnel of Tianshan Mountain under poor geological conditions

When a tunnel boring machine (TBM) excavates in poor geological conditions like fracture zones, soft rock with high ground stress, hard rock with high ground stress, and water inrush ground, severe problems such as surrounding rock collapse, convergence, rock burst and water and mud outburst are usually encountered. These problems existed in the Tianshan Shengli Tunnel project in Xinjiang, China. Several methods and techniques were adopted to reduce construction risks and improve construction efficiency. The seismic wave reflection long-distance geological forecasting method was employed to forecast and predict these adverse geological conditions. The pre-reinforcement construction method was employed on the surrounding broken rocks in front of the tunnel face to lower the risks of tunnel collapse and machine jamming. A combined steel arch was applied to prevent tunnel arch collapse. An artificial cap method was applied to prevent the jamming caused by high-ground stress soft rock. Measures such as spraying water on the tunnel face and drilling stress holes were employed to prevent rock bursts caused by high-ground stress in hard rock. Implementing the advanced pipe shed method during water influx, sudden mud, and other working conditions reduced the risk of groundwater damage to equipment and lowered risks to personnel safety. The successful application of these techniques can provide a reference for TBM tunnel construction in similar ground conditions.

RESEARCH ON CONSTRUCTION TECHNOLOGY OF LARGE-DIAMETER SHIELD TUNNEL IN SHALLOW COVER SECTION OF RED STRATA GEOLOGY

This paper is based on the construction of the Haizhuwan Tunnel in Guangzhou, which passes through predominantly mudstone and sandstone red strata geology. Excavation is carried out using a slurry balance shield tunneling machine with both atmospheric pressure and pressurized cutterheads, and real-time excavation parameters recorded by the shield equipment are collected and statistically analyzed. Discovery: In the initial shallow-buried excavation section, the total thrust of the shield machine increased as the tunneling distance increased. By continuously adjusting the excavation parameters during construction, the shield machine gradually tended towards a stable operating state, leading to a gradual reduction in the range of thrust fluctuations. East Line: After excavating the reinforcement area, the increase in cutterhead torque is significant, leading to a more pronounced adaptation process for the shield machine. The cutterhead torque fluctuates more on rings 20 to 65 on the East Line, showing greater variability. This situation is related to the wear of the cutterhead and the condition of the shield machine equipment on the East Line. West Line: After excavating the reinforcement area, the cutterhead torque is initially reduced, with a slow and continuous increase in torque from rings 20 to 65, eventually stabilizing at around 12 MN·m. Throughout the excavation process, the shield machine on the East Line maintained a relatively high excavation speed within the reinforced soil at the face, with an average of 7.76 mm/min. As the machine excavated beyond the reinforcement area, the overall excavation speed of the shield machine slowed down. The excavation speed from ring 21 to ring 65 was primarily controlled between 2 to 6 mm/min, with an average of 4.49 mm/min, showing a relatively stable overall change.

Experimental study on granule trajectory tracking for the EPB shield tunneling in sandy cobble stratum

An investigation into the movement characteristics of granules during Earth Pressure Balance (EPB) shield tunneling contributes significantly to understanding the interaction between the shield machine and the soil. This understanding also enables researchers to gain deeper insights into the cutting and guiding effects of the shield cutterhead on the soil, thus providing a scientific basis for optimizing shield tunneling parameters and cutterhead designs. To achieve this, indoor model shield tunneling experiments were conducted using independently developed shield tunneling equipment and spatial positioning devices developed by our research group. The research investigated the spatial movement trajectories, radial diffusion, and axial movement characteristics of granules at the face subjected to the action of a spoke-type cutterhead. Furthermore, the study proposes the use of average diffusion degree and movement rates of granules as metrics for evaluating the cutting and steering performance of the cutterhead. The influence of cutterhead rotational speed and advance rate on the movement characteristics of granules was further explored based on these two indicators. The study’s findings reveal the following: (1) Granules at the face predominantly exhibit a spiral movement trajectory when the EPB shield tunneling machine operates in a dynamic equilibrium state. (2) The cutting and steering performance of the cutterhead’s various areas is enhanced progressively from the center to the outer edge, as evidenced by a gradual decrease in the average diffusion degree of granules and an increase in the average axial movement rate (AMR). (3) The AMR and average diffusion degree of granules are influenced by both the rotational speed of the cutterhead and the advance rate of the shield machine. Optimal matching of the rotational speed and advance rate maximizes the average AMR of granules, minimizes the average diffusion degree, reduces the torque on the cutterhead to its lowest point, and consequently, optimizes the efficiency of shield tunneling. This research presents a novel perspective and methodology for understanding the movement characteristics of granules during shield tunneling operations. The outcomes of this study provide valuable insights for the structural design of shield machine cutterheads and the optimization of tunneling parameters.

Multi-step intelligent prediction of shield machine position attitude on the basis of BWO-CNN-LSTM-GRU

Abstract Realizing automatic control of shield machine tunneling attitude is a challenging problem. Realizing multi-step intelligent prediction for attitude and position is an important prerequisite for solving this problem in the tunneling process with complex and varied geological environments. In this paper, a multi-step intelligent predictive scheme based on beluga whale optimization-convolutional neural network-Long Short-term memory-gated recurrent unit (BWO-CNN-LSTM-GRU) is proposed for shield machine position attitude. First, Pearson correlation analysis is utilized to determine the input feature variables from the construction data and temporalize the input features. Subsequently, CNN-LSTM-GRU predictive models are established for the six positional parameters, separately. Among them, CNN performs feature extraction on the input variables, and LSTM-GRU realizes the predictions for the target positional parameters. In the end, the optimization of the convolutional layer dimension, the number of convolutional layers, iterations, the learning rate, the number of neurons in the LSTM layer and GRU layer of each position predictive model is performed on the basis of BWO, separately, and the best hyperparameters found are built into a BWO-CNN-LSTM-GRU position predictive model, which realizes the multi-step intelligent predictions for the shield machine’s position. The proposed approach is examined by utilizing the Beijing Metro Line 10. The results show that the predictive deviation of the position predictive model is within 3 mm, and the positional trajectory points obtained on the basis of the predicted values and the 3D coordinate system are highly coincident with the actual trajectory points. Therefore, the approach provides a more accurate predictive result for shield attitude and position and can provide a decision-making scheme for further realizing the coordinated autonomous control of shield machine.

Estimation of foam (surfactant) consumption in earth pressure balance tunnel boring machine using statistical and soft-computing methods

The use of foam, as the most economical soil conditioning technique, in earth pressure balance tunnel boring machine (EPB-TBM) tunneling projects has significant effects on operation efficiency, excavation cost, and operation time. This study mainly focuses on developing models to predict the foam (surfactant) consumption. For this purpose, five empirical models are developed based on a database containing 11048 datasets of real-time foam consumption from three EPB-TBM tunneling projects in Iran. This database includes the most effective machine operational parameters and soil geomechanical properties on the foam consumption. Multiple linear regression analysis, multiple non-linear regression analysis, M5Prime decision tree, artificial neural network, and least squares support vector machine techniques are used to construct the models. To evaluate the performance of developed models, three performance evaluation criteria (including normalized root mean square error, variance account for, and coefficient of determination) are used based on the training and testing datasets. The results show that the developed models have high performance and their validity is guaranteed according to the testing dataset. Furthermore, the M5Prime model, which demonstrates the best performance compared to other models, is applied to predict the foam consumption in 19 excavation rings of Kohandezh station in Isfahan metro, Iran. After conducting an excavation operation in this station and comparing the results, it was found that the M5Prime model accurately predicts foam consumption with an average error of less than 13%. Therefore, the developed models, particularly M5Prime model, can be confidently applied in EPB-TBM tunneling projects for predicting foam consumption with a low error rate.

Adaptive VMD and multi-stage stabilized transformer-based long-distance forecasting for multiple shield machine tunneling parameters

Achieving multivariate long-distance forecasting of shield machine tunneling parameters remains a challenge due to the huge number of tunneling parameters and the complexity of the variation pattern. To solve this problem, a long-distance forecasting method called Adaptive Variational Mode Decomposition and Multi-Stage Stabilized Transformer-based (AVMD-MST) for multiple tunneling parameters is proposed. It uses adaptive VMD and normalization to stabilize the pre-processed tunneling parameters, and the stabilized transformer is designed to establish relationships between historical and future data. The results on two different projects show that the MAPE of the proposed method decreases on average by 12.31%–36.8% for the 180th step predictions compared to the state-of-the-art algorithms. Therefore, the idea of stabilization-prediction-inverse stabilization can achieve high precision multi-variable long-distance forecasting. In the future, geological information overcasting will be carried out on the basis of the multivariate long-distance forecasting model, which can help the driver to determine the tunneling strategy.

Research on Rock-Breaking Characteristics of Cutters and Matching of Cutter Spacing and Penetration for Tunnel Boring Machine

Tunnel boring machine (TBM) tunnel construction in composite strata relies heavily on understanding the rock-breaking characteristics of TBM cutters and optimizing cutter spacing and penetration. Utilizing a full-scale rock rotary cutting machine (RCM), this study conducted rock-breaking tests with disc cutters under varying rolling radii. An analysis of rock debris shape and cutter behavior provided insights into rock-breaking mechanisms. Two main types of rock fragments were identified, with both shear and compression failure observed during cutter–rock interactions. The influence of the rolling radius and cutter spacing on cutter forces was analyzed, along with numerical modeling using the particle flow method. Optimal cutter selection in soft–hard composite strata should prioritize cutter force, with the greatest force required in hard rock. Cutter force increases with penetration, while the force difference between cutters decreases with reduced cutter spacing. These findings offer practical guidance for efficient rock-breaking in composite geological formations during tunnel construction.

Research on Tunnel Boring Machine Tunnel Water Disaster Detection and Radar Echo Signal Processing

This study focused on the detection of water inrush in tunnels excavated by full-section hard rock tunnel boring machines (TBMs) and employed ground penetrating radar methods for conducting research on radar signal processing algorithms. The research demonstrates that conventional techniques are inadequate for eliminating the interference of TBM equipment on radar signal propagation. This study employs a radar antenna array method for signal transmission, utilizing a wavelet double-threshold filtering algorithm and wave propagation theory to suppress clutter. These methods exhibit strong signal reception capabilities and are effective in eliminating 13.1% of the direct wave components. The adoption of a novel, efficient radar signal imaging algorithm simplifies the imaging process. Results of verification indicate that the synthetic aperture algorithm, enhanced with cross-correlation calculation, yields the optimal imaging effect. This investigation, which was conducted in conjunction with the construction of a diversion tunnel in a specific region, has confirmed the applicability of the ground penetrating radar method for the detection of water inrush in TBM tunnels by conducting a comparative analysis of the direct wave removal algorithm and the integration of the optimal imaging algorithm. The innovative application of ground penetrating radar within TBM tunnels, along with a targeted technology to mitigate signal interference from metal equipment, has led to the selection of an appropriate algorithm for both signal processing and imaging. This approach offers a novel solution for the detection of water source disasters in TBM tunnels.

Introducing a new rock abrasivity index using a scaled down disc cutter

Rock abrasivity influences wear of cutting tools and consequently, performance of mechanized tunneling machines. Several methods have been proposed to evaluate rock abrasivity in recent decades, each one has its own advantages. In this paper, a new method is introduced to estimate wear of disc cutters based on rock cutting tests using scaled down discs (i.e. 54 and 72 ​mm diameter). The discs are made of H13 steel, which is a common steel type in producing real-scale discs, with hardness of 32 and 54 HRC. The small-scale linear rock cutting machine and a new abrasion test apparatus, namely University of Tehran abrasivity test machine, are utilized to perform the tests. Tip width of the worn discs is monitored and presented as the function of the accumulated test run to classify the rock abrasion. Abrasivity tests show that by increasing the uniaxial compressive strength (UCS) of the rock samples, wear rate is doubled gradually that reveals the sensitivity of the test procedure to the main parameters affecting the abrasivity of hard rocks. For the rocks with the highest UCS, the normal wear stops after performing 5 to 10 rounds of the tests, and then, deformation of the disc tip is detectable. Two abrasivity indices are defined based on the abrasivity tests results and their correlations with Cerchar Abrasivity Index (CAI) and UCS are established. Comparison of the established correlations in this study with previous investigations demonstrates the sensitivity of the indices to the parameters affecting wear of the disc cutters and repeatability of the outputs obtained from abrasivity tests using scaled down discs. Findings of this study can be used to enhance the accuracy of rock abrasivity classifications.

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.

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.