Tunnel Boring Machine Penetration Rate Research Articles

Evaluation on the TBM Performance at a Hydropower Project in Ecuador

The aim of this manuscript is to discuss the Tunnel Boring Machine (TBM) performance along the recently constructed headrace tunnel of Minas-San Francisco Hydropower Project in Ecuador. Firstly, the manuscript briefly describes the importance of TBM tunneling and about the Minas-San HPP. Further, discussions are made on the engineering geological conditions along the headrace tunnel. Detailed evaluations are made on the performance of TBM tunneling considering influence of rock mass quality on the TBM penetration rate. The manuscript emphasizes that the knowledge of the rock mass quality parameters and cutter technology available at present are among the key factors that influence the estimation of the net penetration rate of the TBM. It has been demonstrated that the hard to very hard rock masses of high abrasivity that were encountered along the headrace tunnel alignment caused very low penetration giving slow progress, which was not predicted during planning phase design. The authors investigated a fairly good link between TBM penetration and the mechanical strength of the rock mass.

Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance

Prediction of tunnel boring machine (TBM) performance parameters can be caused to reduce the risks associated with tunneling projects. This study is aimed to introduce a new hybrid model namely Firefly algorithm (FA) combined by artificial neural network (ANN) for solving problems in the field of geotechnical engineering particularly for estimation of penetration rate (PR) of TBM. For this purpose, the results obtained from the field observations and laboratory tests were considered as model inputs to estimate PR of TBMs operated in a water transfer tunnel in Malaysia. Five rock mass and material properties (rock strength, tensile strength of rock, rock quality designation, rock mass rating and weathering zone) and two machine factors (trust force and revolution per minute) were used in the new model for predicting PA. FA algorithm was used to optimize weight and bias of ANN to obtain a higher level of accuracy. A series of hybrid FA-ANN models using the most influential parameters on FA were constructed to estimate PR. For comparison, a simple ANN model was built to predict PR of TBM. This ANN model was improved on the basis of new ways. By doing this, the best ANN model was chosen for comparison purposes. After implementing the best models for two methods, the data were divided into five separate categories. This will minimize the chance of randomness. Then the best models were applied for these new categories. The results demonstrated that new hybrid intelligent model is able to provide higher performance capacity for predicting. Based on the coefficient of determination 0.948 and 0.936 and 0.885 and 0.889 for training and testing datasets of FA-ANN and ANN models, respectively, it was found that the new hybrid model can be introduced as a superior model for solving geotechnical engineering problems.

Predicting tunnel boring machine performance through a new model based on the group method of data handling

The tunnel boring machine (TBM), developed within the past few decades, is designed to make the process of tunnel excavation safer and more economical. The use of TBMs in civil and mining construction projects is controlled by several factors including economic considerations and schedule deadlines. Hence, improved methods for estimating TBM performance are important for future projects. This paper presents a new model based on the group method of data handling (GMDH) for predicting the penetration rate (PR) of a TBM. In order to achieve this aim, after investigation of the most effective parameters of PR, rock quality designation, uniaxial compressive strength, rock mass rating, Brazilian tensile strength, weathering zone, thrust force per cutter and revolutions per minute were selected and measured to estimate TBM PR. A database composed of 209 datasets was prepared according to the mentioned model inputs and output. Then, based on the most influential factors of GMDH, a series of parametric investigations were carried out on the established database. In the following, five different datasets with different sets of training and testing were selected and used to construct GMDH models. Aside from that, five multiple regression (MR) models/equations were also proposed to predict TBM PR for comparison purposes. After that, a ranking system was used in order to evaluate the obtained results. As a result, performance prediction results of [i.e. coefficient of determination (R2) = 0.946 and 0.924, root mean square error (RMSE) = 0.141 and 0.169 for training and testing datasets, respectively] demonstrated a high accuracy level of GMDH model in estimating TBM PR. Although both methods are applicable for estimation of PR, GMDH is able to provide a higher degree of accuracy and can be introduced as a new model in this field.

Prediction of TBM penetration rate from brittleness indexes using multiple regression analysis

One of the most important aspects in the excavation of tunnels with a Tunnel Boring Machine (TBM) is the reliable prediction of its penetration rate. This affects the planning and other decision making on the organization of the construction site of the tunneling project, and, therefore, total costs. In this study, raw data obtained from the experimental works of different researchers were used to establish the new statistical models for prediction of rock TBM penetration rate from brittleness indexes, B1, B2, and B3. For this, correlation between the TBM penetration rate with brittleness indexes statistically was investigated using multiple regression analyses. In these analyses, the TBM penetration rate was considered to be the dependent variable, which is dependent on the independent variables of the brittleness indexes. The validity of the predictive models was validated by statistical tests. The results showed that statistical models are in good accuracy for prediction of TBM penetration rate, and thus making a rapid assessment of the TBM performance.

Rock Joint Rate (RJR); a new method for performance prediction of tunnel boring machines (TBMs) in hard rocks

Rock Joint Rate (RJR) is a novel method for predicting the penetration rate (PR) of tunnel boring machines (TBMs) in hard rock environments. This model is devised based on a combination of geological and operational data obtained from a number of tunnels excavated in igneous and sedimentary rocks. The Geological condition, TBM excavation parameters and the tunnel diameter are regarded as the main input parameters in the RJR method. The rock mass uniaxial compressive strength (UCS) and the discontinuities' characteristics along the tunnel excavation axis including the number, frequency, orientation, spacing and aperture of the joints are the principle geological parameters. In addition, the machine thrust force and the cutterhead rotation speed (RPM) have been introduced as the main involved operational parameters of the machine. Easy to use input parameters, the simple investigation procedure, not requiring complicated laboratory tests and the capability to control the overall costs of a project as well as precise estimations of the operation time by dint of accurate determination of penetration rate (PR) are the main advantages of the proposed RJR model. In this method, a number of indices including the machine stress index (MS), strength ratio (SR), raw penetration rate (PRw) and basic penetration rate (PRb), which are derived from the integration of the main input parameters in RJR model are defined for evaluating the penetration rate of the machine. Overall, in this research, around 140,000 performance databases corresponding to 22,300 boring strokes of three different tunnels with a total excavated length of about 19 km were utilized to validate the proposed RJR model. According to the acquired results in this study, the estimated value of PR by the suggested method of RJR (PRRJR) shows a good agreement with the field penetration rates (PRField). Therefore, this model can be employed as a new method for predicting the penetration rate of TBMs in hard rocks.

Estimation of the TBM penetration rate using the post-failure behavior of a rock mass and the equivalent thrust per cutter. A case study: the Amirkabir Water Transferring Tunnel of Iran

One of the most important parameters to predict the cost and time of tunnel excavation is the penetration rate of the tunnel boring machine (TBM). This is because the advance rate of the TBM is highly dependent on this parameter and is directly related to the cost and time of tunnel excavation. There are several methods to estimate advance rate, including experimental, empirical and analytical methods. In this study, based on the actual collected data from the Amirkabir Water Conveyance Tunnel in Iran, a new empirical method to predict the TBM penetration rate using the drop modulus and the equivalent thrust per cutter is proposed. The drop modulus is defined as the slope of the post-failure stage of the stress–strain curve of the rock mass, and the equivalent thrust per cutter is an important operational parameter of the TBM. The results show that there is a direct relation between the TBM penetration rate and the equivalent thrust per cutter, and an inverse relation between the TBM penetration rate and the drop modulus, and, finally, a power equation is proposed to predict the TBM penetration rate using both parameters. It is emphasized that the TBM penetration rate is a function of both parameters.

Performance prediction of tunnel boring machine through developing a gene expression programming equation

The prediction of tunnel boring machine (TBM) performance in a specified rock mass condition is crucial for any mechanical tunneling project. TBM performance prediction in accurate may reduce the risks related to high capital costs and scheduling for tunneling. This paper presents a new model/equation based on gene expression programming (GEP) to estimate performance of TBM by means of the penetration rate (PR). To achieve the aim of the study, the Pahang–Selangor Raw Water Transfer tunnel in Malaysia was investigated and the data related to field observations and laboratory tests were used in modelling of PR of TBM. A database (1286 datasets in total) comprising 7 model inputs related to rock (mass and material) properties and machine characteristics and 1 output (PR) was prepared to use in GEP modelling. To evaluate capability of the developed GEP equation, a multiple regression (MR) model was also proposed. A comparison between the obtained results has been done using several performance indices and the best equations of GEP and MR were selected. System results for the developed GEP equation based on coefficient of determination (R2) were obtained as 0.855 and 0.829 for training and testing datasets, respectively, while these values were achieved as 0.795 and 0.789 for the developed MR equation. Concluding remark is that the GEP equation is superior in comparison with the MR equation and it can be introduced as a new equation in the field of TBM performance prediction.

Applying Optimized Support Vector Regression Models for Prediction of Tunnel Boring Machine Performance

One of the main factors in the effective application of a tunnel boring machine (TBM) is the ability to accurately estimate the machine performance in order to determine the project costs and schedule. Predicting the TBM performance is a nonlinear and multivariable complex problem. The aim of this study is to predict the performance of TBM using the hybrid of support vector regression (SVR) and the differential evolution algorithm (DE), artificial bee colony algorithm (ABC), and gravitational search algorithm (GSA). The DE, ABC and GSA are combined with the SVR for determining the optimal value of its user defined parameters. The optimization implementation by the DE, ABC and GSA significantly improves the generalization ability of the SVR. The uniaxial compressive strength (UCS), average distance between planes of weakness (DPW), the angle between tunnel axis and the planes of weakness (α), and intact rock brittleness (BI) were considered as the input parameters, while the rate of penetration was the output parameter. The prediction models were applied to the available data given in the literature, and their performance was assessed based on statistical criteria. The results clearly show the superiority of DE when integrated with SVR for optimizing values of its parameters. In addition, the suggested model was compared with the methods previously presented for predicting the TBM penetration rate. The comparative results revealed that the hybrid of DE and SVR yields a robust model which outperforms other models in terms of the higher correlation coefficient and lower mean squared error.

Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate

AbstractThis paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM) based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock brittleness index (BI), the distance between planes of weakness (DPW), and the alpha angle (Alpha) between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP). Four (4) statistical regression models (two linear and two nonlinear) are built to predict the ROP of TBM. Finally a fuzzy logic model is developed as an alternative method and compared to the four statistical regression models. Results show that the fuzzy logic model provides better estimations and can be applied to predict the TBM performance. The R-squared value (R2) of the fuzzy logic model scores the highest value of 0.714 over the second runner-up of 0.667 from the multiple variables nonlinear regression model.

Risk Assessment and Estimation of TBM Penetration Rate Using RES-Based Model

The tunnel boring machine (TBM) penetration rate (PR) estimation is one of the crucial and complex tasks encountered frequently to excavate the mechanical tunnels. Predicting the PR is a nonlinear and multivariable complex problem and establishing relationship between rock properties and PR is not a simple task. To overcome this problem, in this paper, based on the basic concepts of a rock engineering systems (RES) approach, a model for the estimation of the PR and the risk associated is presented. The suggested model involves 6 effective parameters on PR (uniaxial compressive strength (UCS), ratio of the maximum load (PSI), average distance between planes of weakness (DPW), angle between tunnel axis and the planes of weakness (Alpha), Brazilian tensile strength (BTS) and rock fracture class (RFC)), while retaining simplicity as well. The performance of the RES model is compared with multiple regression models. The estimation abilities offered using RES and multiple regression models were presented by using field data given in open source literatures. The results indicate that the RES based model predictor with less mean square error (MSE) and higher coefficient of determination (R2) performs better than the other models (used in this study).

Application of various optimization techniques and comparison of their performances for predicting TBM penetration rate in rock mass

The aim of this study is to develop prediction models for estimating tunnel boring machine (TBM) performance using various optimization techniques including Differential Evolution (DE), Hybrid Harmony Search (HS-BFGS) and Grey Wolf Optimizer (GWO), and then to compare the results obtained from introduced models and also in literature. For this purpose, the Queens Water Tunnel, in New York City, was selected as a case study to test the proposed model. From each modeling technique, seven different models, (M1–M7) were developed using the assortment of datasets having various percentage of rock type. In order to find out the optimal values of the parameters and prevent over-fitting, 80% of the total data were selected randomly for training set and the rest were kept for testing the model. Further, the developed models were compared according to the coefficient of correlations (R2), computer process unit (CPU) and number of function evaluation (NFE) values to choice the best accurate and most efficient model. It is found that there is no salient difference between the models according to the R2 values; however, it is concluded that the M7 generated via HS-BFGS algorithm consistently converges faster than both the DE and GWO. Also, total CPU time required by HS-BFGS for M7 was the shortest one. As a result, Model 7 developed using the HS-BFGS is considered to be better, especially for simulations in which computational time and efficiency is a critical factor.

Utilizing partial least square and support vector machine for TBM penetration rate prediction in hard rock conditions

Rate of penetration (ROP) of a tunnel boring machine (TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accuracy of prediction models employing partial least squares (PLS) regression and support vector machine (SVM) regression technique for modeling the penetration rate of TBM. To develop the proposed models, the database that is composed of intact rock properties including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), and peak slope index (PSI), and also rock mass properties including distance between planes of weakness (DPW) and the alpha angle (α) are input as dependent variables and the measured ROP is chosen as an independent variable. Two hundred sets of data are collected from Queens Water Tunnel and Karaj-Tehran water transfer tunnel TBM project. The accuracy of the prediction models is measured by the coefficient of determination (R 2) and root mean squares error (RMSE) between predicted and observed yield employing 10-fold cross-validation schemes. The R 2 and RMSE of prediction are 0.8183 and 0.1807 for SVMR method, and 0.9999 and 0.0011 for PLS method, respectively. Comparison between the values of statistical parameters reveals the superiority of the PLSR model over SVMR one.

A support vector regression model for predicting tunnel boring machine penetration rates

With widespread increasing applications of mechanized tunneling in almost all ground conditions, prediction of tunnel boring machine (TBM) performance is required for time planning, cost control and choice of excavation method in order to make tunneling economical. Penetration rate is a principal measure of full-face TBM performance and is used to evaluate the feasibility of the machine and predict advance rate of excavation. This research aims at developing a regression model to predict penetration rate of TBM in hard rock conditions based on a new artificial intelligence (AI) algorithm namely support vector regression (SVR). For this purpose, the Queens Water Tunnel, in New York City, was selected as a case study to test the proposed model. In order to find out the optimum values of the parameters and prevent over-fitting, 80% of the total data were selected randomly for training set and the rest were kept for testing the model. According to the results, it can be said that the proposed model is a useful and reliable means to predict TBM penetration rate provided that a suitable dataset exists. From the prediction results of training and testing samples, the squared correlation coefficient (R2) between the observed and predicted values of the proposed model was obtained 0.99 and 0.95, respectively, which shows a high conformity between predicted and actual penetration rate.

Influence of rock mass fracturing on the net penetration rates of hard rock TBMs

Penetration rates during excavation using hard rock tunnel boring machines (TBMs) are significantly influenced by the degree of fracturing of the rock mass. In the NTNU prediction model for hard rock TBM performance and costs, the rock mass fracturing factor (ks) is used to include the influence of rock mass fractures. The rock mass fracturing factor depends on the degree of fracturing, fracture type, fracture spacing, and the angle between fracture systems and the tunnel axis. In order to validate the relationship between the degree of fracturing and the net penetration rate of hard rock TBMs, field work has been carried out, consisting of geological back-mapping and analysis of performance data from a TBM tunnel. The rock mass influence on hard rock TBM performance prediction is taken into account in the NTNU model. Different correlations between net penetration rate and the fracturing factor (ks) have been identified for a variety of ks values.

Application of the strain energy ratio and the equivalent thrust per cutter to predict the penetration rate of TBM, case study: Karaj-Tehran water conveyance tunnel of Iran

The penetration rate of tunnel boring machine (TBM) is an important factor in cost and time estimation of excavation operations for project planning and choice of economical tunneling methods. Several methods are used to predict this parameter such as experimental, empirical, and analytical methods. In this study, a new empirical method is proposed to predict the penetration rate of TBM using the actual data of Karaj-Tehran water conveyance tunnel in Iran. It is predicted based on the post-failure residual strain energy to the pre-failure stored strain energy (named strain energy ratio) and the equivalent thrust per cutter. The result shows that there is a direct relation between the penetration rate of TBM and the strain energy ratio, and an inverse relation between the penetration rate of TBM and the equivalent thrust per cutter. In this study, a new polynomial equation is proposed to predict the TBM penetration rate based on the strain energy ratio. Also, a new linear equation is suggested to predict the equivalent thrust per cutter using of the strain energy ratio. Finally, the most useful equation to predict the penetration rate of TBM is achieved using the equivalent thrust per cutter and the strain energy ratio because it is a function of both parameters.

Prediction of TBM penetration rate using intact and mass rock properties (case study: Zagros long tunnel, Iran)

In this paper, the correlations between the different measurements of rock brittleness (i.e., B1, B2, B3, and BI) and the penetration rate (PR) of tunnel boring machine (TBM) through Zagros long tunnel were evaluated. According to the results of simple regression analyses, there was no correlation between the penetration rate of TBM and the brittleness of B1, but stronger log-linear correlations have been observed between the penetration rate of TBM and the B2, B3, and BI brittleness indices. As part of the present study, correlations between some of rock mass classification systems (RQD, RMR, GSI, Q, and QTBM) and penetration rate of TBM have been investigated. The results of simple regression analyses showed that the first four rock mass classification systems did not exhibit a good correlation with the TBM penetration rate, but the QTBM had strong correlation with that. Multiple linear regression (MLR) analyses were applied for estimating the TBM penetration rate based on three properties of the rocks (porosity (n), brittleness of B3, and elastic modulus (E) of intact rock) and rock mass quality system (Q). Also, in this study, artificial neural network (ANN) analyses were applied on the data to develop predictive models for the penetration rate of TBM from porosity, brittleness of B3, elastic modulus, and Q values. The comparison of the models produced from ANN and MLR analyses using the coefficients of determination showed that the ANN models for predicting the TBM penetration rate were more reliable than the MLR models.

Predicting penetration rate of hard rock tunnel boring machine using fuzzy logic

Predicting the penetration rate of a tunnel boring machine (TBM) plays an important role in the economic and time planning of tunneling projects. In the past years, various empirical methods have been developed for the prediction of TBM penetration rates using traditional statistical analysis techniques. Soft computing techniques are now being used as an alternative statistical tool. In this study, a fuzzy logic model was developed to predict the penetration rate based on collected data from one hard rock TBM tunnel (the Queens Water Tunnel # 3, Stage 2) in New York City, USA. The model predicts the penetration rate of the TBM using rock properties such as uniaxial compressive strength, rock brittleness, distance between planes of weakness and the orientation of discontinuities in the rock mass. The results indicated that the fuzzy model can be used as a reliable predictor of TBM penetration rate for the studied tunneling project. The determination coefficient (R 2), the variance account for and the root mean square error indices of the proposed fuzzy model are 0.8930, 89.06 and 0.13, respectively.

The effect of fracture patterns on penetration rate of TBM in fractured rock mass using probabilistic numerical approach

Existence of discontinuity features has a great influence on mechanical properties and cuttability of a rock mass. In the literature, the influence of geometrical properties of joint sets such as joint spacing and orientation on penetration rate (PR) of a tunnel boring machine (TBM) were investigated and reported. However, the actual fracture pattern in a rock mass is more likely a discrete fracture network. The numerical probabilistic distinct element method (DEM) modeling of PR of a TBM is a very useful method that simulates very complex geometrical models of fracture systems, such as stochastic discrete fracture network (DFN) models with different possible fracture patterns close to the tunnel face. The formed chipping area under TBM cutters are investigated for both intact rock (IR) and DEM models and additional chipping area of fractured models in comparison with IR model are calculated. The probability density function of percentage of additional chipping area follows a normal distribution with mean value of 46 %. Results represent that the additional chipping area of 95 and 80 % of DFN-DEM models are 20 and 33 %, respectively, which shows that numerical modeling of PR of a TBM based on an intact rock is underestimated about 20 and 33 %. Fracture pattern also controls the formed chipping area under each cutter.

Prediction of hard rock TBM penetration rate using least square support vector machine

The prediction of tunnel boring machine (TBM) penetration rate is helpful to plan construction time and to control cost. This paper studies on modeling the penetration rate and the rock mass parameters via least square support vector machine (LS-SVM). These rock properties include uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), peak slope index (PSI), distance between planes of weakness (DPW) and the alpha angle. The correlation between the rock parameters and the measured rate of penetration (ROP) is first investigated via LS-SVM. Then the necessity of normalizing the rock parameters is discussed. Finally, comparisons between the LS-SVM and other methods are presented to demonstrate the applicability of LS-SVM for predicting ROP.

Utilising of linear and non-linear prediction tools for evaluation of penetration rate of Tunnel Boring Machine in hard rock condition

Predicting Tunnel Boring Machine (TBM) penetration rate is a crucial issue for the successful fulfilment of a mechanical tunnel project. Penetration rate depends on many factors such as intact rock properties, rock mass conditions and machine specifications. In this paper, linear and non-linear multiple regression as well as Artificial Neural Network (ANN) techniques were applied to predict the penetration rate of TBM. In developing of the proposed models, five parameters, which include Uniaxial Compressive Strength (UCS), Brazilian Tensile Strength (BTS), peak slope index (punch penetration), spacing of discontinuities (of weakness planes) and orientation of discontinuities with respect to the tunnel axis (β angle), were incorporated. For this study, 46 datasets were collected. Performance of these models was assessed through the R2, RMSE and MAPE. As a result, these indices revealed that the prediction performance of the ANN model is higher than that of the non-linear and linear multiple regression models.