Tunnel Boring Machine Penetration Research Articles

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.

A study of rotary cutting machine (RCM) performance on Korean granite

PurposeDespite the many advantages this type of equipment offers, there are still some major drawbacks. Linear cutting machine (LCM) cannot accurately simulate the true rock-cutting process as 1. it does not account for the circular path along which tunnel boring machine (TBM) disk cutters cut the tunnel face, 2. it does not accurately model the position of a disk cutter on the cutterhead, 3. it cannot perfectly replicate the rotational speed of a TBM. To enhance the knowledge of these issues and in order to mimic the real rock-cutting process, a new lab testing equipment was developed by Hyundai Engineering and Construction.Design/methodology/approachA new testing machine called rotary cutting machine (RCM) is designed to simulate the excavation process of hard-rock TBMs and includes features such as TBM cutterhead, RPM simulation, constant normal force mode and constant penetration rate mode. Two sets of tests were conducted on Hwandeung granite using different disk cutter sizes to analyze the cutting forces in various excavation modes. The results are analyzed using statistical analysis and dimensional analysis. A new model is generated using dimensional analysis, and its results are compared against the results of actual cases.FindingsThe effectiveness of the new RCM test was demonstrated in its ability to apply various modes of excavation. Initial analysis of chip size revealed that the thickness of the chips is largely dependent on the cutter spacing. Tests with varying RPM showed that an increase in RPM results in an increase in the normal force and rolling force. The cutting coefficient (CC) demonstrated a linear correlation with penetration. The optimal specific energy is achieved at an S/p ratio of around 15. However, a slightly lower S/p ratio can also be used in the design if the cutter specifications permit. A dimensional analysis was utilized to develop a new RCM model based on the results from approximately 1200 tests. The model's applicability was demonstrated through a comparison of TBM penetration data from 26 tunnel projects globally. Results indicated that the predicted penetration rates by the RCM test model were in good agreement with actual rates for the majority of cases. However, further investigation is necessary for softer rock types, which will be conducted in the future using concrete blocks.Originality/valueThe originality of the research lies in the development of Hyundai Engineering and Construction’s advanced full-scale laboratory rotary cutting machine (RCM), which accurately replicates the excavation process of hard-rock tunnel boring machines (TBMs). The study provides valuable insights into cutting forces, chip size, specific energy, RPM and excavation modes, enhancing understanding and decision-making in hard-rock excavation processes. The research also presents a new RCM model validated against TBM penetration data, demonstrating its practical applicability and predictive accuracy.

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.

Artificial intelligence for tunnel boring machine penetration rate prediction

Penetration rate prediction of Tunnel Boring Machines (TBM) is critical for understanding excavation performances. In this paper, we investigate the possibility of developing machine learning models that accurately predict the Penetration Rate of a TBM using only machine parameters. We leveraged two datasets collected from the Exploratory and Main excavation of the Lot Mules 2–3 of the Brenner Base Tunnel Project. We compared the performance of two different Artificial Neural Network architectures, one based on feedforward architecture and the other on long short-term memory (LSTM). We also studied which features lead to a good estimation of the penetration rate using SHAP, an explainable AI tool, discovering that the Specific Energy (SE) and the Cutterhead Power (CP) are the most impactful features. We also explore the possibility of performing cross-tunnel prediction by training the model on the Exploratory tunnel and testing it on the Main Tunnel, obtaining promising results.

Performance Evaluation of TBM Using an Improved Load Prediction Model

Excavation load prediction is of great importance for the prior design and latter performance evaluation of tunnel boring machines (TBMs). In this paper, an improved load prediction model is developed based on classical Colorado school of mines model for TBMs equipped with constant cross-sectional disc cutters. The typical structure and principle are introduced to predict the single cutter force, and the total cutter group load is calculated by defining the equivalent diameter and cutter spacing. Subsequently, the improved model of a more brief and acceptable type is established via summation. Some novel performance indexes, including the reformed field penetration index, torque/thrust penetration index, and specific energy are, respectively, derived in formulaic form. By field data verification in the borehole zones of two cases, the proposed model is proven to be more accurate in the total load prediction. The single-factor regression results show that the reformed field penetration index reveals the nonlinear relationship between TBM load and penetration rate, and the torque/thrust penetration index is a new TBM inherent index to evaluate the working conditions. Specific energy, used to evaluate the excavation efficiency, is positive with rock strength and proved negative with penetration rate via a normalization analysis. Finally, suggestions on the cutter group configuration against abominable stratum are discussed.

Correlation Analysis of Machine Performance Parameters and Rock Mass Properties during Tunnel Boring Machine Excavation Process: A Case Study in Yin-song Headrace Tunnel, China

Abstract A rock breaking process during the tunnel boring machine (TBM) excavation sequence involves a complicated interaction between the disc cutters and the tunnel face, and the excavation process is influenced both by the rock mass properties and the TBM performance. Hence, in achieving a highly efficient TBM excavation, a mutual feedback analysis of rock mass properties and machine parameters is important. In this study, a correlation analysis of rock mass properties and TBM parameters is conducted based on the field data obtained from the Yin-song headrace tunnel in Jilin Province, China. Firstly, the correlation between the rock mass properties (i.e., uniaxial compression strength and rock quality designation index) and the machine parameters (i.e., thrust force, cutterhead torque, rotational speed, penetration depth, and penetration rate) obtained during the stable operation stage of TBM is extensively investigated. The variation in the TBM penetration rate as a function of rock mass properties and machine operation parameters is studied. A statistical prediction model that defines the effects of rock mass properties on the TBM penetration rate is established by multivariate linear regression analysis. The validity of the prediction model is verified by using the in situ monitoring data obtained from two different chainages of the project. This study can help rationalize the TBM machine parameters in order to obtain a highly efficient TBM excavation and a low life-cycle cost for the machine.

Reply to discussion on ‘Predicting tunnel-boring machine penetration rate utilizing geomechanical properties’ by Karrari et al. 2022 ( QJEGH , 55, qjegh2021-126)

First of all, the authors would like to thank Nick Shirlaw for his interest in the paper and for providing us with the opportunity to present additional comments and clarify certain aspects of our paper that may not have been clear.

Success and challenges in predicting TBM penetration rate using recurrent neural networks

Tunnel Boring Machines (TBMs) have been increasingly used in tunnelling projects. Forecasting future TBM performance would be desirable for project time management and cost control. We aim to use recurrent neural networks to predict the near future TBM penetration rate from historical data. Our datasets are composed of Changsha and Zhengzhou metro lines, with totally different geological conditions. In the experiments, the one-step forecast of TBM penetration rate by the traditional recurrent neural network (RNN) or long short-term memory (LSTM) is relatively accurate, irrespective of the different geological conditions used in training and evaluation. Predicting the next Nth step penetration rate proves to be more challenging and depends on the time to the future or the distance ahead of the TBM cutterhead. There are generally time lags between measured and predicted results. The recursive RNN is then developed to address the lag problems, but to no avail. Alternative methods for predicting future penetration rates are studied, including the penetration rate at the Nth step in the future and the average penetration rate of the next N steps, with the latter being trained by long-input or short-input methods. The average N-step forecast using short inputs provides the best results, and its performance over other alternatives becomes more distinct as the number N increases. We also discuss the possibility of the forecast problem as a quasi-random walk, which means that forecasting penetration rate cannot easily be achieved using low-frequency data with RNNs, and that the accuracy depends on the correlation between the last and predicted steps in the data.

Probabilistic Evaluation of Tunnel Boring Machine Penetration Rate Based on Case Analysis

Probabilistic Evaluation of Tunnel Boring Machine Penetration Rate Based on Case Analysis

Forecasting tunnel boring machine penetration rate using LSTM deep neural network optimized by grey wolf optimization algorithm

Achieving an accurate and reliable estimation of tunnel boring machine (TBM) performance can diminish the hazards related to extreme capital costs and planning tunnel construction. Here, a hybrid long short-term memory (LSTM) model enhanced by grey wolf optimization (GWO) is developed for predicting TBM-penetration rate (TBM-PR). 1125 datasets were considered including six input parameters. To vanish overfitting, the dropout technique was used. The effect of input time series length on the model performance was studied. The TBM-PR results of the LSTM-GWO model were compared to some other machine learning (ML) models such as LSTM. The results were evaluated using root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (R2). Finally, the LSTM-GWO model produced the most accurate results (test: R2 = 0.9795; RMSE = 0.004; MAPE = 0.009 %). The mutual information test revealed that input parameters of rock fracture class and uniaxial compressive strength have the most and least impact on the TBM-PR, respectively.

Research on Prediction of TBM Performance of Deep-Buried Tunnel Based on Machine Learning

Based on the relevant data in the construction process of the south of the Qinling tunnel of the Hanjiang-to-Weihe River Diversion Project, this article obtains the main influencing factors of the tunnel boring machine (TBM) performance of the deep-buried tunnel. According to the characteristics of deep-buried tunnel excavation, the random forest algorithm is used to select the features of the factors affecting the TBM penetration rate, and the four factors with large influence weights including total thrust, revolutions per minute, uniaxial compressive strength and volumetric joint count, are used as TBM penetration rate prediction models input parameters, which can improve the prediction accuracy and convergence speed of the model, and enhance the engineering practicality of the prediction model. Three types of TBM penetration rate prediction models are established: multiple regression model (MR), back propagation neural network model (BPNN) and support vector regression model (SVR). The prediction accuracy of the three models is compared and analyzed. The BPNN prediction model exhibits better prediction performance and generalization ability than the multiple regression model and SVR model, which manifest higher prediction accuracy and prediction stability.

Introducing Tree-Based-Regression Models for Prediction of Hard Rock TBM Performance with Consideration of Rock Type

Prediction of machine performance is a fundamental step for planning, cost estimation/control and selection of the machine type when using a tunnel boring machine (TBM). Penetration rate (PR) and machine utilization (U) are the two principal measures of TBM performance for evaluating the feasibility of using a machine in a given ground condition. However, despite the widespread use of TBMs and established track records, accurate estimation of machine performance could still be a challenge, particularly in complex geological conditions. Since different types of rocks have varied texture (cementation and grain size), and respond differently to cutting forces in the TBM tunnelling, incorporating the effects of rock type in performance prediction models can improve the accuracy of the estimates. The aim of this study was to develop models for predicting penetration rate of hard rock TBMs in different types of rock based on field penetration index (FPI), using multivariable regression analysis and machine learning algorithm, including classification and regression tree (CART). The proposed models offer estimated FPIs in different rock types, rock strength, and rock mass properties in the form of graphs (diagrams), which can be used to estimate TBM penetration rate. The proposed models have been developed based on the analysis of a comprehensive database of TBM performance in various rock types and offers more accurate estimates of machine performance by incorporating many of the key parameters available in typical geotechnical reports and contract documents. The models also exhibit sensitivity to rock mass parameters for predicting the penetration rate.

Predicting tunnel-boring machine penetration rate utilizing geomechanical properties

Predicting the penetration rate plays a key role in tunnel projects using a tunnel-boring machine (TBM). Developing accurate prediction models can improve project management, and save budget and time in tunnel projects. In this research, the Gelas water-tunnel project data were used to obtain new statistical models for predicting the TBM penetration rate per revolution (PRev) utilizing the toughness index ( T i ), modulus ratio ( E /UCS) and joint parameters ( J P ). The relationships between various geomechanical properties and rock classification systems, including uniaxial compressive strength, Brazilian tensile strength, Young's modulus, joint parameter, toughness index, rock quality designation, rock mass rating, geological strength index, rock mass quality and rock mass index, were analysed and considered on the TBM performance in sedimentary, igneous and metamorphic rocks. The statistical analysis clearly showed that T i revealed a significant correlation with the actual PRev ( R 2 = 0.75). In addition, the PRev was computed using T i , and J P showed good agreement with the coefficient of determination ( R 2 ): i.e. 0.79. The results indicated that the T i decreased by increasing the modulus ratio, so the PRev increased. This model can be used easily as it provides a straightforward predictive model using a multi-parameter model.

Analysis of the Performance of Cutting Tools of Tunnel Boring Machine (TBM) in Silty-Sand Soils Using Artificial Neural Network (ANN) – Case Study: Tabriz Metro Line 2 Project

The choice of cutting tools for soil excavation has a significant impact on the performance of tunnel boring machines. In this study, the data of two excavated sections of the Tabriz metro line 2 project have been used, and the crossed soils were sandy silts. Two sections have investigated the performance of two kinds of cutting tools (disk cutters and rippers). The cutting tools used in the first section of the drilling route are disc cutters, while in the second section, rippers have been used. To predict and evaluate the performance of the two types of cutting tools in silty-clay soils, artificial neuron networks were used, and the Levenberg-Marquardt algorithm was chosen for training the ANNs. Three important TBM parameters (torque, thrust force, and speed) were considered as input parameters, and the TBM penetration rate is chosen as the output variable in the developed artificial neuron network models. The obtained results showed that the best-predicted value obtained from the ANN was obtained with one hidden layer that contains four neurons. Finally, considering the performance parameter observed in silty soils, the ripper’s performance is better compared to the disc cutter one.

A Preliminary Study: Influence of Sub-Surface Ground Condition on the Tunnel Boring Machine Performance

Tunnelling through various ground condition affects the performance of Tunnel Boring Machine (TBM). Previous researches have conducted studies to explain on this relationship. However, a more localised influence of Malaysian Kenny Hill Formation subsurface condition towards TBM drive are yet to be carried out. This study will assess this relationship at areas nearby Museum Station during the Klang Valley Mass Rapid Transit (KVMRT) Line 1 with a length span of 540m. After the data collection from TBM and borehole report, data analysis was carried out namely using cutter head, thrust cylinder and subsurface profiling. These data were plotted based on the chainage and tunnel ring number. It is observed that subsurface profile at Ring 9 and Ring 11 required a higher thrust force due to its cohesive soil condition and inclusion of sandstone rock. However, Ring 10 is vice versa as there were more non-recovered soil samples at the specified area. The reduction of thrust force eventually increases the speed of TBM penetration. Thus, a clear relationship showing that subsurface condition highly influences the TBM drive.

Comprehensive evaluation of machine learning algorithms applied to TBM performance prediction

To date, the accurate prediction of tunnel boring machine (TBM) performance remains a considerable challenge owing to the complex interactions between the TBM and ground. Using evolutionary polynomial regression (EPR) and random forest (RF), this study develops two novel prediction models for TBM performance. Both models can predict the TBM penetration rate and field penetration index as outputs with four input parameters: the uniaxial compressive strength, intact rock brittleness index, distance between planes of weakness, and angle between the tunnel axis and planes of weakness (α). First, the performances of both EPR- and RF-based models are examined by comparison with the conventional numerical regression method (i.e., multivariate linear regression). Subsequently, the performances of the RF- and EPR-based models are further investigated and compared, including the model robustness for unknown datasets, interior relationships between input and output parameters, and variable importance. The results indicate that the RF-based model has greater prediction accuracy, particularly in identifying outliers, whereas the EPR-based model is more convenient to use by field engineers owing to its explicit expression. Both EPR- and RF-based models can accurately identify the relationships between the input and output parameters. This ensures their excellent generalization ability and high prediction accuracy on unknown datasets.

Prediction of TBM penetration rate using the imperialist competitive algorithm (ICA) and quantum fuzzy logic

Quantum computer brings many advantages to the world of computations. Also, the tunnel boring machine (TBM) has been industrialized over the prior decades to make the tunneling process safer and more affordable. Several factors such as economic considerations, rock properties, and schedule deadlines play an important role in the use of TBMs mining projects. Hence, future projects are interested in enhanced means for predicting the TBM performance. The TBM penetration is usually an essential factor for the prosperous implementation of a plan for tunneling in a rock situation. In this research, the statistical analyses of rock features and the measured penetration rate of TBMs are presented using the imperialist competitive algorithm (ICA) and quantum fuzzy logic. Also, a database has been used that includes the parameters of rock properties of the Queens Water Tunnel. The proposed hybrid method is applied to provide a new predictive model for improving TBM performance. Results demonstrated that this method has a high capability to predict the performance of TBM with R2 = 0.93 and RMSE = 0.09. This shows that the application of the proposed approach is preferable compared to the prior models in terms of penetration rate.

Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP) approaches for predicting TBM penetration rate

The history of tunnel boring machine (TBM) tunnelling dates back to nearly 50 years ago. Due to high construction cost, the investigation on TBM performance is regarded as one of the crucial issues which should be considered from different aspects. The prediction of TBM penetration rate is one of the most important part of every mechanized tunnelling project which plays a key role in selecting the machine as well. One of the major difficulties and challenges in TBM performance prediction is to apply novel approaches to predict the TBMs penetration rate. Considering the importance of this issue, the objective of this research work is to attain more realistic models for predicting TBM penetration rate in Iranian water conveyance tunneling. With this respect, a database comprises field data and machine parameters in Chamshir water conveyance tunneling project were established. The data were then analyzed through artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP). Results demonstrated that obtained values of the coefficient of determination (R2) and the root mean square error (RMSE) found to be 0.99 and 0.15 for ANN, 0.95 and 0.37 for SVM, 0.99 and 0.11 for GEP, respectively. These models can be applied to predict TBM penetration rate in the Chamshir water conveyance tunnel. Moreover, it can be concluded that the GEP method has the higher accuracy (maximum R2 and minimum RMSE) among all predictive models.

Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques

A reliable and accurate prediction of the tunnel boring machine (TBM) performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects. This research aims to develop six hybrid models of extreme gradient boosting (XGB) which are optimized by gray wolf optimization (GWO), particle swarm optimization (PSO), social spider optimization (SSO), sine cosine algorithm (SCA), multi verse optimization (MVO) and moth flame optimization (MFO), for estimation of the TBM penetration rate (PR). To do this, a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation, the rock mass rating, Brazilian tensile strength (BTS), rock mass weathering, the uniaxial compressive strength (UCS), revolution per minute and trust force per cutter (TFC), were set as inputs and TBM PR was selected as model output. Together with the mentioned six hybrid models, four single models i.e., artificial neural network, random forest regression, XGB and support vector regression were also built to estimate TBM PR for comparison purposes. These models were designed conducting several parametric studies on their most important parameters and then, their performance capacities were assessed through the use of root mean square error, coefficient of determination, mean absolute percentage error, and a10-index. Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of (0.1453, and 0.1325), R2 of (0.951, and 0.951), mean absolute percentage error (4.0689, and 3.8115), and a10-index of (0.9348, and 0.9496) in training and testing phases, respectively. The developed hybrid PSO-XGB can be introduced as an accurate, powerful and applicable technique in the field of TBM performance prediction. By conducting sensitivity analysis, it was found that UCS, BTS and TFC have the deepest impacts on the TBM PR.

A TBM advance rate prediction method considering the effects of operating factors

Due to the widespread use of Tunnel Boring Machines (TBMs) in China, all affected parties involved in related construction of projects have proposed higher requirements for the controllability of TBM construction periods. Consequently, this report proposes a new TBM advance rate prediction method. By mining TBM construction data obtained from a project diverting water from the Songhua River to cities in the middle region of the Jilin Province in China (hereinafter referred to as the “Songhua River Diversion Water Supply Project”) and analysing the varying rules of the tunnelling parameters under different surrounding rock conditions, this report proposes a corresponding relationship relating the surrounding rock conditions, reduction coefficient of the disc cutter thrust (Fn) and cutterhead rotation speed (N), and establishes a TBM penetration rate (PR) prediction model, which is affected by factors associated with the operator’s control. Furthermore, after investigating and summarizing the TBM construction processes of several projects, TBM downtime is classified and evaluated to obtain the TBM utilization factor (U). Based on the PR and U, a TBM advance rate (AR) prediction model is established. The established AR model was applied to a water diversion project in the Xinjiang Uygur Autonomous Region of China. This engineering practice has demonstrated that the operator adjusts the PR according to the surrounding rock conditions, and the prediction of the PR affected by the operating factors has been proven accurate. Furthermore, the prediction method of U obtained by evaluating the downtime is simple and feasible. Overall, the results indicate that the proposed AR model possesses satisfactory predictive performance.