Tunnel Boring Machine Performance Research Articles

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.

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.

Estimating the penetration rate of tunnel boring machines via gradient boosting algorithms

The prediction of tunnel boring machine (TBM) performance from the rate of penetration (ROP) point of view has yet to draw a lot of attention since it is one of the main challenges for excavation with TBMs. This study examined six tunnels excavated with TBM to develop predictive models of the ROP estimation using five algorithms: Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, Adaptive Boosting (AdaBoost), and CatBoost (categorical-features-include-GB). A dataset has been developed, including uniaxial compressive strength (UCS), Rock Type, the distance between planes of weakness (DPW), and thrust force (TF), and contains more than 575 data points for each parameter. The developed models showed that the XGBoost model outperformed the other models, followed by the CatBoost, according to seven different evaluation metrics used to rank the models when the models were modeled with the default values of the algorithm parameters. After tuning the hyperparameters, the GB model outperformed the others, while the other models remained relatively unchanged. By using the overall ranking according to the metrics and considering the parameter tuning time, XGBoost and CatBoost were presented as the two best models. SHAP (Shapley additive explanations: an explainable artificial intelligence tool) values and dependency plots showed that the TF has the highest impact on the ROP, followed by UCS, Rock Type, and DPW. It is concluded that the XGBoost and CatBoost models, with coefficients of determination of 0.9878 and 0.9682, respectively, may be used for estimating the TBM penetration rate for similar rock types.

MatDEM-based study of disc cutter force model in open TBM tunnels: Incorporating installation radius and synergistic effects

The prediction of rock cutting force is critical for tunnel boring machine performance and cutterhead design. This paper presents a novel model for rock cutting force prediction based on the Colorado School of Mines (CSM) model, which incorporates the installation position of disc cutters by introducing installation radius and synergistic effect factors. Linear cutting tests in the laboratory and large-scale rotary cutting simulations in MatDEM software were conducted to examine the impact of these factors. Results indicate that the normal and rolling forces increase and stabilize as the installation radius increases. The synergistic effect produces three force modes in a cutting circle, with mode α having the largest cutting force, mode β having a smaller force, and mode γ having the smallest force. The impact of installation radius and synergistic effect varies with rock-cutter parameters. Multiple regression analysis was used to determine the introduced factors. The proposed model was validated with rock strength and operation data from the Irtysh River conveyance project. The results demonstrate that the proposed model outperforms the CSM model in predicting cutting force in field conditions.

Multi-step real-time prediction of hard-rock TBM penetration rate combining temporal convolutional network and squeeze-and-excitation block

Accurate penetration rate prediction enhances rock-breaking efficiency and reduces disc cutter damage in tunnel boring machine (TBM) construction. However, this process faces significant challenges such as the high uncertainty of ground conditions and the complexity of maintaining optimal TBM operation in long and large tunnels. To address these challenges, we propose TCN-SENet++, a novel hybrid multistep real-time penetration rate prediction model that combines a temporal convolutional network (TCN) and a squeeze-and-excitation (SENet) block for aided tunneling. This study aims to demonstrate the application of TCN-SENet++, as well as other models such as RNN, LSTM, GRU, and TCN, for TBM penetration rate prediction. The model was developed using actual datasets collected from the Yin-Song diversion project. We employ a 30-s time step to predict the future time steps of the penetration rate (1st, 3rd, 5th, 7th, and 9th). The features that influence the penetration rate, such as the cutterhead torque, thrust, and cutterhead power, were considered. A comparative analysis using the mean absolute error and mean squared error revealed that the TCN-SENet++ model outperformed the other models, including RNN, LSTM, GRU, TCN, and TCN-SENet+. In comparison, TCN-SENet++ achieved average MSE reductions of 18%, 6%, 3%, 1%, and 2%, respectively. The TCN-SENet++ model demonstrated fewer errors in the new project, validating its effectiveness and suitability for real-time penetration rate prediction in TBM construction.

A Visual Survey of Tunnel Boring Machine (TBM) Performance in Tunneling Excavation: Mainstream Direction, Brief Review and Future Prospects

This study employs scientometric analysis to investigate the current trajectory of research on tunnel boring machine (TBM) performance and collaborative efforts. Utilizing software tools like Pajek 5.16 and VOSviewer 1.6.18, it scrutinizes literature from 2000 to 2021 sourced from the Web of Science (WOS). The findings illuminate TBM research as an interdisciplinary and intersectoral field attracting increasing national and institutional attention. Notable contributions from China, Iran, the United States, Turkey, and Australia underscore the global significance of TBM research. The recent upsurge in annual publications, primarily driven by Chinese research initiatives, reflects a renewed vigor in TBM exploration. Additionally, the paper presents a succinct evaluation of TBM advantages and drawbacks compared to conventional drill and blast methods, discussing key considerations in excavation methodology selection. Moreover, the study comprehensively reviews TBM performance prediction models, categorizing them into theoretical, empirical, and artificial intelligence-driven approaches. Finally, rooted in metaverse theory, the discourse delves into the immersive learning model and the architecture of a TBM metaverse. In the future, the immersive training and learning model diagram can be employed in scenarios such as employee training and the promotion of safety knowledge. Additionally, the TBM metaverse architecture can simulate, monitor, diagnose, predict, and control the organization, management, and service processes and behaviors of TBMs. This will enhance efficient collaboration across various aspects of the project production cycle. This forward-looking perspective anticipates future trends in TBM technology, emphasizing societal impact and enhancement of economic benefits.

Peridynamics simulation of TBM cutting process in soft and hard composite strata with a simplified loading scheme

In this study, we address the complex geological conditions encountered during tunnel boring machine (TBM) excavation, particularly the challenges posed by soft and hard composite strata. To overcome limitations of conventional numerical methods for analyzing rock fracture in such strata, we employ Peridynamics to simulate rock fragmentation induced by disc cutters. We propose a virtual disc cutter loading scheme that enhances computational efficiency by replacing traditional contact models. Through numerical examples, we demonstrate the efficacy of this loading scheme in simulating rock fragmentation within soft and hard composite strata. With this method, we conduct numerical studies on kerfs-assisted rock fragmentation in such geological conditions, finding that kerfs can help mitigate differences in cutter forces between soft and hard rocks, thus showing potential for improving TBM performance. Our findings offer insights into optimizing TBM operations in challenging geological environments.

Assessment for shallow and large tunnel construction in weak ground conditions: Application of tunnel boring machines

AbstractWith recent technological advancements, tunnel boring machines (TBM) have developed and exhibited high performance in large diameters and weak ground conditions. Tunnels are crucial structures that significantly influence the timelines of highway and railway projects. Therefore, the construction of tunnels with TBMs becomes a preferred option. In this study, a comparative analysis between TBM and the New Austrian Tunneling Method (NATM) for tunnel construction is performed in the construction of the T1 tunnel with a diameter of 13 m, which is the longest tunnel in the Eşme‐Salihli section of Ankara‐İzmir High‐Speed Railway Project (Türkiye). The selection of TBM type, measures taken in problematic sections, and application issues of TBM are discussed. The impact of correct description of geological and geotechnical conditions on both selection and performance of TBM is presented. An earth pressure balanced type TBM is chosen for the construction of the T1 tunnel. Because of the additional engineering measures taken before excavation in problematic areas, the tunnel was completed with great success within the initially planned timeframe. From this point of view, this study is an important case and may contribute to worldwide tunneling literature.

Tunnel boring machine performance assessment and prediction applying hybrid artificial intelligence

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

An empirical method for estimating TBM penetration rate using tunnelling specific energy

Establishing a suitable tunnel boring machine (TBM) performance estimation model is benefit to the machine type selection, project scheduling and budgeting. Although various models have been developed to predict TBM performance, most of these models use a relatively small specific database, which is difficult to be applied to engineering practice. In this study, some equations for estimating TBM penetration rate (PR) applicable to different geological conditions are proposed. Firstly, a TBM construction performance database containing 159 tunnel sections is built according to four different tunnel projects originating from China, which covers rock mass properties, TBM specification parameters and performance information. By eliminating the effects of some TBM specification and operating parameters, a normalized energy index is proposed for assessing the rock mass boreability. On this basis, a series of statistical analysis, i.e. linear multiple regression (LMR) and non-linear multiple regression (NLMR) are used to develop some empirical equations for estimating the PR by employing rock mass properties and TBM specification parameters, and more than 84% of the PR can be accurately predicted. Finally, to further evaluate the prediction performance of these equations, they are compared and discussed with fifteen published typical models, and the results proved that the models developed in this study are more reasonable and reliable. It should be noted that the input parameters of these equations are readily available at the project planning stage, thus they are very simple and practical for designers and constructors.

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.

Tree-Based Solution Frameworks for Predicting Tunnel Boring Machine Performance Using Rock Mass and Material Properties

Tree-Based Solution Frameworks for Predicting Tunnel Boring Machine Performance Using Rock Mass and Material Properties

Physics-informed deep reinforcement learning for enhancement on tunnel boring machine's advance speed and stability

The traditional mode of Tunnel Boring Machine (TBM) operation is limited in their applicability and efficiency to meet the growing demand for underground spaces. Current methods to address this problem often lack physics interpretability and automatic adaptivity. In response to this issue, this paper describes an approach that integrates a physics-informed reinforcement learning (PIRL) algorithm into a TBM operation. The method combines a physics-informed machine learning (PIML) model and physics reward functions considering the working mechanism of earth pressure balance (EPB) TBMs, forming into a physics-informed Twin Delayed Deep Deterministic (pTD3) algorithm. The study reveals that the TBM performance can be improved by 69.3% using the pTD3 algorithm compared to manual operation. Integrating physics knowledge into reinforcement learning proves significantly effective in enhancing the TBM operations. The proposed method has the potential to revolutionize TBM operation and pave the way for more efficient, reliable, and automatic tunnel construction.

Analysis and prediction of small-diameter TBM performance in hard rock conditions

Analysing and predicting the advance rate of a tunnel boring machine (TBM) in hard rock is integral to tunnelling project planning and execution. It has been applied in the industry for several decades with varying success. Most prediction models are based on or designed for large-diameter TBMs, and much research has been conducted on related tunnelling projects. However, only a few models incorporate information from projects with an outer diameter smaller than 5 m and no penetration prediction model for pipe jacking machines exists to date. In contrast to large TBMs, small-diameter TBMs and their projects have been considered little in research. In general, they are characterised by distinctive features, including insufficient geotechnical information, sometimes rather short drive lengths, special machine designs and partially concurring lining methods like pipe jacking and segment lining. A database which covers most of the parameters mentioned above has been compiled to investigate the performance of small-diameter TBMs in hard rock. In order to provide sufficient geological and technical variance, this database contains 37 projects with 70 geotechnically homogeneous areas. Besides the technical parameters, important geotechnical data like lithological information, unconfined compressive strength, tensile strength and point load index is included and evaluated. The analysis shows that segment lining TBMs have considerably higher penetration rates in similar geological and technical settings mostly due to their design parameters. Different methodologies for predicting TBM penetration, including state-of-the-art models from the literature as well as newly derived regression and machine learning models, are discussed and deployed for backward modelling of the projects contained in the database. New ranges of application for small-diameter tunnelling in several industry-standard penetration models are presented, and new approaches for the penetration prediction of pipe jacking machines in hard rock are proposed.

Application of Several Fuzzy-Based Techniques for Estimating Tunnel Boring Machine Performance in Metamorphic Rocks

Application of Several Fuzzy-Based Techniques for Estimating Tunnel Boring Machine Performance in Metamorphic Rocks

Data-driven joint multi-objective prediction and optimization for advanced control during tunnel construction

This research develops a hybrid approach that integrates light gradient boosting machine (LightGBM) and non-dominated sorting genetic algorithm II (NSGA-II) to optimize the tunnel boring machine (TBM) performance during excavation. The TBM operational data are first extracted and meta-models are established to estimate the key TBM performance, including the penetration rate, over/under excavation ratio, and energy consumption. An optimization process is proposed by adopting NSGA-II and the technique for order preference by similarity to an ideal solution (TOPSIS) analysis to determine ideal operational parameters. The developed approach acts as a useful tool that assists tunnel construction automation and improves TBM performance under different in-situ conditions. Real data from a tunnel project in Singapore is utilized as a case study to examine the applicability and efficiency of the proposed approach. The results indicate that (1) The proposed meta-model provides reliable estimation with an average RMSE and MAE of 2.604mm/min and 3.402mm/min for TBM’s penetration rate(O1), 0.0211 and 0.0324 for over/under excavation ratio (O2), and 15.512kwh and 23.088kwh for energy consumption (O3), respectively. The prediction accuracy is better than the state-of-the-art methods; (2) The TBM’s performance can be optimized by the proposed approach with an average improvement of 33.14 %, 1.32 %, and 17.95 % for O1 to O3, respectively, and an overall improvement of 39.60 %; (3) The overall reliability of TBM operation improved after optimization with a significant reduction in data variance by 91.16 %, 76.92 %, and 97.35 % for O1 to O3, respectively. This paper contributes to proposing a novel method that integrates LightGBM with NSGA-II in resolving the complex TBM operation problem by considering the major performance indexes including excavation efficiency, safety, and energy consumption during TBM operation.

Tunnel Boring Machine Performance Prediction Using Supervised Learning Method and Swarm Intelligence Algorithm

This study employs a supervised learning method to predict the tunnel boring machine (TBM) penetration rate (PR) with high accuracy. To this end, the extreme gradient boosting (XGBoost) model is optimized based on two swarm intelligence algorithms, i.e., the sparrow search algorithm (SSA) and the whale optimization algorithm (WOA). Three other machine learning models, including random forest (RF), support vector machine (SVM), and artificial neural network (ANN) models, are also developed as the drawback. A database created in Shenzhen (China), comprising 503 entries and featuring 10 input variables and 1 output variable, was utilized to train and test the prediction models. The model development results indicate that the use of SSA and WOA has the potential to improve the XGBoost model performance in predicting the TBM performance. The performance evaluation results show that the proposed WOA-XGBoost model has achieved the most satisfactory performance by resulting in the most reliable prediction accuracy of the four performance indices. This research serves as a compelling illustration of how combined approaches, such as supervised learning methods and swarm intelligence algorithms, can enhance TBM prediction performance and can provide a reference when solving other related engineering problems.

Laboratory Investigation on Excavation Performance of Foam-conditioned Weathered Granite Soil for EPB Shield Tunnelling

As weathered granite soils are extensively distributed in the Korean Peninsula, determining the optimal foam conditioning parameters for weathered granite soils can provide a high workability, low permeability, and high compressibility of weathered granite soils for successful operation of an earth pressure balance (EPB) shield tunnel boring machine (TBM). In this study, a laboratory-scale excavation apparatus was developed to conduct various laboratory excavation simulations for measuring the excavation-induced torque and the abrasivity of cutter bits. In addition, slump values, compressibility, and permeability were measured to evaluate the workability of foam-conditioned weathered granite soils. From the experimental results, the optimal foaming conditions for the weathered granite soil was obtained at foam injection ratios (F/Rs) of 60–70%, foam expansion ratios (FERs) of 15–18, and foaming agent concentrations (Cf) of 3–4%. The experimental results presented in this study can provide the rheological, mechanical, and hydraulic behavior of foam-conditioned weathered granite soils for successful EPB TBM operation.

A Geometric Model for a Shield TBM Steering Simulator

This study aimed to simulate curved excavation using a tunnel boring machine (TBM) steering system based on the proposed mathematical methodology applied in a TBM simulator. We introduce the concept and mechanism of the TBM steering system and describe the mathematical formulae used for simulating curved excavation. Curved excavation in the top- right direction was simulated using a Python program to verify the mathematical formulae. In addition, Python simulations were undertaken to determine the effects of horizontal and vertical articulation angles on pitching and yawing angles. Finally, the proposed mathematical formulae were applied in the TBM operation simulator, and tested based on the mechanism of the TBM steering system.

Effects of data smoothing and recurrent neural network (RNN) algorithms for real-time forecasting of tunnel boring machine (TBM) performance

Tunnel boring machines (TBMs) have been widely utilised in tunnel construction due to their high efficiency and reliability. Accurately predicting TBM performance can improve project time management, cost control, and risk management. This study aims to use deep learning to develop real-time models for predicting the penetration rate (PR). The models are built using data from the Changsha metro project, and their performances are evaluated using unseen data from the Zhengzhou Metro project. In one-step forecast, the predicted penetration rate follows the trend of the measured penetration rate in both training and testing. The autoregressive integrated moving average (ARIMA) model is compared with the recurrent neural network (RNN) model. The results show that univariate models, which only consider historical penetration rate itself, perform better than multivariate models that take into account multiple geological and operational parameters (GEO and OP). Next, an RNN variant combining time series of penetration rate with the last-step geological and operational parameters is developed, and it performs better than other models. A sensitivity analysis shows that the penetration rate is the most important parameter, while other parameters have a smaller impact on time series forecasting. It is also found that smoothed data are easier to predict with high accuracy. Nevertheless, over-simplified data can lose real characteristics in time series. In conclusion, the RNN variant can accurately predict the next-step penetration rate, and data smoothing is crucial in time series forecasting. This study provides practical guidance for TBM performance forecasting in practical engineering.