Tunnel Boring Machine Penetration Rate Research Articles

Enhancing Tunnel Boring Machine Penetration Rate Predictions through Particle Swarm Optimization and Elman Neural Networks

Enhancing Tunnel Boring Machine Penetration Rate Predictions through Particle Swarm Optimization and Elman Neural Networks

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.

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.

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.

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.

Development of a PLSR-BRT Model for Predicting the Performance of Tunnel Boring Machines

Scientific prediction of the penetration rate (PR) of tunnel boring machines (TBMs) is of great significance to the selection of tunnel construction methods, the prediction of construction progress, and the evaluation of benefits. In view of the high nonlinearity, fuzziness, and complexity of the TBM excavation process, this paper attempts to present a new algorithm that integrates partial least squares regression (PLSR) with boosted regression trees (BRT) for predicting the PR of TBMs. For this purpose, 150 datasets were obtained from the Lanzhou water conveyance tunnel project in China. Six impact factors were selected as the input layers of the proposed model by single-factor correlation analysis. To develop the PLSR-BRT model, two principal components of the influencing parameters were extracted via the PLSR method, and the BRT algorithm was employed to establish the predictive model. In addition, other models such as PLSR back propagation neural network (BPNN), BRT, PLSR, support vector regression (SVR), and artificial neural network (ANN) were adapted for use in this problem to verify the predictive accuracy of the proposed model. The results demonstrated that the PLSR-BRT model achieved the best predictive performance compared with the other models, with coefficient of determination value (R2) and root mean square error (RMSE) of 0.96 and 1.78, respectively, for the testing set. The PLSR-BRT model can maximally avoid both overfitting and inadequate fitting. In view of engineering applications, the complexity and redundancy of the field dataset can be solved via reducing the dimensionality of input parameters, and the training samples could be expanded by a boosted method so that the discontinuously acquired geological parameters are unable to restrain the model performance. This method serves as an effective approach for TBM PR prediction and has excellent potential for a variety of scientific and engineering applications.

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.

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

Although geological parameters are known to affect the penetration rate (PR) of a tunnel boring machine (TBM), their relation to the probability of TBM PR has been rarely considered. In this article, a probabilistic evaluation model of TBM PR was proposed. Firstly, the marginal distributions of five geological parameters were confirmed by mathematical statistics. Then Copula theory was used to construct a five-dimensional joint probability distribution of the geological parameters in line with the marginal distributions. Next, the collected geological parameters were utilized to train a three-layer backpropagation neural network (BPNN) model for predicting the TBM PR. Finally, A Copula—BPNN coupled model was built for estimating the probability of TBM PR, and a Weibull distribution function of the predicted TBM PR was obtained through Monte Carlo simulation. Considering the uncertainty, correlation, and multi-factor influence, this paper realized the probabilistic evaluation of TBM PR. Discussion on the parameter uncertainty and independence shows that the variability of the geological parameters is necessary in TBM PR prediction. Quantitative probability estimation of the TBM PR can help with optimizing the driving parameters under different geological conditions to improve construction efficiency.

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.

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 Novel Intelligent Method for Predicting the Penetration Rate of the Tunnel Boring Machine in Rocks

In the construction of rock tunnels, the penetration rate of the tunnel boring machine (TBM) is influenced by many factors (e.g., geomechanical parameters), some of which are highly uncertain. It is difficult to establish a precise model for predicting the penetration rate on the basis of the influencing factors. Thus, this work proposed a useful method, based on the relevance vector machine (RVM) and particle swarm optimization (PSO), for the prediction of the TBM penetration rate. In this method, the RVM played a vital role in establishing a nonlinear mapping relationship between the penetration rate and its influencing factors through training-related samples. Then, the penetration rate could be predicted using some collected data of the influencing factors. As for the PSO, it helped to find the optimum value of a key parameter (called the basis function width) that was needed in the RVM model. Subsequently, the validity of the proposed RVM-PSO method was checked with the data monitored from a rock tunnel. The results showed that the RVM-PSO method could estimate the penetration rate of the TBM, and it proved superior to the back-propagation artificial neural network, the least-squares support vector machine, and the conventional RVM methods, in terms of the prediction performance. Moreover, the proposed RVM-PSO method could be applied to identify the difference in the importance of the various factors affecting the TBM penetration rate prediction for a tunnel.

Evaluation of geotechnical parameters affecting the penetration rate of TBM using neural network (case study)

Rate of penetration of a tunnel boring machine (TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. Tunneling time is one of the factors that determine the tunneling method (blast or mechanical work). In traditional method, tunneling time depends on the progression function of blast cycle, while in mechanized tunneling method the required time for boring is determined by advance rate and consequently penetration rate. The advance rate is a function of penetration rate of TBM, which is the ratio of the length of boring part of the tunnel to the excavated time of the same part. Penetration rate, on the other hand, is a function of lithology and geomechanical properties of the rock and also the boring machine. So far, many efforts have been made to develop a method to predict the penetration rate (TBM) of the machine. Among these methods are empirical models, particularly NTH model, including models that can be used to predict the penetration rate. In this study, statistical analysis was used to obtain importance of parameters involved in penetration rate and compare the performance of neural networks with other mathematical models based on the principles of probability. In addition, the artificial neural network (ANN) was compared with models of Innaurato and NTH, which indicates high performance of neural networks in predicting penetration rate compared with the other two models. As a result, neural network was chosen and then proceeded to build the network optimized.