Development of a rock mass characteristics model for TBM penetration rate prediction

The TBM tunneling process in hard rock is actually a rock or rock mass breakage process, which determines the efficiency of tunnel boring machine (TBM). On the basis of the rock breakage process, a rock mass conceptual model that identifies the effect of rock mass properties on TBM penetration rate is proposed. During the construction of T05 and T06 tunnels of DTSS project in Singapore, a comprehensive program was performed to obtain the relevant rock mass properties and TBM performance data. A database, including rock mass properties, TBM specifications and the corresponding TBM performance, was established. Combining the rock mass conceptual model for evaluating rock mass boreability with the established database, a statistical prediction model of TBM penetration rate is set up by performing a nonlinear regression analysis. The parametric studies of the new model showed that the rock uniaxial compressive strength and the volumetric joint count have predominantly effects on the penetration rate. These results showed good agreement with the numerical simulations. The model limitations were also discussed.     

Multifactorial fuzzy approach to the penetrability classification of TBM in hard rock conditions

Rate of penetration of a tunnel boring machine in a hard rock environment is generally a key parameter which expresses the ease or difficulty with which the rock mass can be excavated. In this paper, the penetrability of TBM in hard rock conditions was investigated with the developed fuzzy classification system. TBM penetration rate and rock properties (such as Uniaxial Compressive Strength (UCS), Brazilian Tensile Strength (BTS), rock brittleness/toughness, Average Distance between Planes of Weakness (DPW) and orientation of discontinuities in rock mass) were evaluated by using the multifactorial fuzzy approach which is a special case of multiple objective multifactorial decision making for the penetrability classification of TBM in hard rock conditions. Using the decision function, the penetrating performance of TBM was classified into three categories; Good, Medium and Poor. Eventually, it is possible to evaluate the penetrability and determine the advance rate for new conditions by carrying out the proposed rock properties tests and using the developed fuzzy classification system.     

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 (k_s) 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 (k_s) have been identified for a variety of k_s values.     

Case studies of TBM tunneling performance in rock–soil interface mixed ground

This paper investigates the performance of tunnel boring machines (TBMs) in rock–soil mixed-face ground based on TBM tunneling projects in Singapore. Currently several methods are available to estimate TBM tunneling performance in homogenous rock or soil. However, the existing models cannot be effectively applied to predict TBM penetration rate in mixed ground. The tunnels in this study were excavated in adverse mixed-face ground conditions. The geological profiles and the TBM operational parameters are compiled and analyzed. The influence of different geological face compositions on the performance of the TBMs is studied. The statistical analysis shows that there is a possible correlation between the mixed-face ground characteristics and the TBM advancement. Different approaches are used to find a reliable model. Finally, a method is proposed to predict the TBM performance in mixed-face ground for project planning and optimization.     

Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs

Prediction of machine performance is an essential step for planning, cost estimation and selection of excavation method to assure success of tunneling operation by hard rock TBMs. Penetration rate is a principal measure of TBM performance and is used to evaluate the feasibility of using a machine in a given ground condition and to predict TBM advance rate. In this study, a database of TBM field performance from two hard rock tunneling projects in Iran including Zagros lot 1B and 2 for a total length of 14.3 km has been used to assess applicability of various analysis methods for developing reliable predictive models. The first method used for this purpose was principal component analysis (PCA) which resulted in development of a set of new empirical equations. Also, two Soft computing techniques including adaptive neuro-fuzzy inference system (ANFIS) and support vector regression (SVR) have been employed for this purpose. As statistical indices, root mean square error (RMSE), correlation coefficient (R²), variance account for (VAF), and mean absolute percentage error (MAPE) were used to evaluate the efficiency of the developed artificial intelligence models for TBM performance prediction. The results of the analysis show that AI based methods can effectively be implemented for prediction of TBM performance. Moreover, it was concluded that performance of the SVR model is better than the ANFIS model. A high correlation was observed between predicted and measured TBM performance for the SVR model. This study shows the feasibility of using these systems and subsequent work is underway to expand the database of TBM field performance and use the aforementioned methods to develop a more comprehensive TBM performance prediction model.     

Analysis and prediction of TBM performance in blocky rock conditions at the Lötschberg Base Tunnel

This paper focuses on the analysis of the TBM performance recorded during the excavation of the Lötschberg Base Tunnel. The southern part of the tunnel was excavated by two gripper TBMs, partly through blocky rock masses at great depth. The jointed nature of the blocky rock mass posed serious problems concerning the stability of the excavation face. A detailed analysis has been carried out to obtain a relationship between the rock mass conditions and the TBM performance, using the Field Penetration Index (FPI). In blocky rock conditions, the FPI is defined as the ratio between the applied thrust force and the actual penetration rate. A database of the TBM parameters and the geological/geotechnical conditions for 160 sections along the tunnel has been established. The analysis reveals a relationship between the FPI and two rock mass parameters: the volumetric joint count (J_v) and the intact rock uniaxial compressive strength (UCS). Through a multivariate regression analysis, a prediction model for FPI in blocky rock conditions (FPI_blocky) is then introduced. Finally, other TBM performance parameters such as the penetration rate, the net advance rate and the total advance rate are evaluated using FPI_blocky.     

Estimation of convergence of a high-speed railway tunnel in weak rocks using an adaptive neuro-fuzzy inference system(ANFIS) approach

Estimation of tunnel diameter convergence is a very important issue for tunneling construction,especially when the new Austrian tunneling method(NATM) is adopted.For this purpose,a systematic convergence measurement is usually implemented to adjust the design during the whole construction,and consequently deadly hazards can be prevented.In this study,a new fuzzy model capable of predicting the diameter convergences of a high-speed railway tunnel was developed on the basis of adaptive neuro-fuzzy inference system(ANFIS) approach.The proposed model used more than 1 000 datasets collected from two different tunnels,i.e.Daguan tunnel No.2 and Yaojia tunnel No.1,which are part of a tunnel located in Hunan Province,China.Six Takagi-Sugeno fuzzy inference systems were constructed by using subtractive clustering method.The data obtained from Daguan tunnel No.2 were used for model training,while the data from Yaojia tunnel No.1 were employed to evaluate the performance of the model.The input parameters include surrounding rock masses(SRM) rating index,ground engineering conditions(GEC) rating index,tunnel overburden(H),rock density(?),distance between monitoring station and working face(D),and elapsed time(T).The model’s performance was assessed by the variance account for(VAF),root mean square error(RMSE),mean absolute percentage error(MAPE) as well as the coefficient of determination(R2) between measured and predicted data as recommended by many researchers.The results showed excellent prediction accuracy and it was suggested that the proposed model can be used to estimate the tunnel convergence and convergence velocity.     

高地应力作用下大理岩岩体的TBM掘进试验研究

滚刀破岩效率的研究主要集中在室内线性试验机破岩试验和数值分析2个方面,在工地开展TBM掘进试验尚不普遍。锦屏二级水电站采用3台TBM开挖隧道群,3台TBM在不同洞深(不同地应力)条件对大理岩岩体进行TBM掘进试验、岩石渣片筛分试验及大渣片统计分析,研究岩体条件、TBM机器参数、TBM运行参数对TBM掘进速度的影响及高地应力作用下岩体可掘性指数的变化。研究结果表明:在高地应力条件下,尽管TBM掘进速度随推力增加而增大,但推力超过一定值后,TBM并不在优化状态下运行,TBM的运行需与岩体条件及地应力条件相匹配。     

Correlation of tunnel convergence with TBM operational parameters and chip size in the Ghomroud tunnel, Iran

Evaluating the impact of rock mass properties on a tunneling operation is crucial, especially when using a tunnel boring machine (TBM). It is an integral part of machine selection and performance prediction in the design and bidding stage. Monitoring and analysis of ground conditions during the construction is also essential to allow the operator to take precautionary measures in adverse geological conditions. This involves adjusting TBM operational parameters such as machine thrust and penetration to avoid potential problems caused by face collapse or excessive convergence and subsequent machine seizure that can cause long delays. Tunnel wall convergence is a function of rock mass characteristics, in situ stresses, size of excavation, and rate of penetration (ROP). It is one of the main factors in determining the use of shielded machines in deep rock tunnel projects. The case study of the Ghomroud water conveyance tunnel project, under construction by a double shield TBM, is used to examine the effect of rock mass parameters on tunnel convergence and hence on the need for over excavation and shield lubrication to avoid problems such as shield seizure. Results of a preliminary analysis of field observations show that the amount of the tunnel convergence can have a direct relationship with the percentage of powder and large rock fragments in the muck. In addition, tunnel convergence has shown a strong relationship with the TBM thrust/torque and rate of penetration (ROP). These relationships have been examined and the results of the analysis as well as the resulting formulas will be explained in this paper.     

TBM performance estimation using rock mass classifications

Three tunnels for hydraulic purposes were excavated by tunnel-boring machines (TBM) in mostly hard metamorphic rocks in Northern Italy. A total of 14 km of tunnel was surveyed almost continually, yielding over 700 sets of data featuring rock mass characteristics and TBM performance. The empirical relations between rock mass rating and penetration rate clearly show that TBM performance reaches a maximum in the rock mass rating (RMR) range 40–70 while slower penetration is experienced in both too bad and too good rock masses. However, as different rocks gives different penetrations for the same RMR, the use of Bieniawski's classification for predictive purpose is only possible provided one uses a normalized RMR index with reference to the basic factors affecting TBM tunneling. Comparison of actual penetrations with those predicted by the Innaurato and Barton models shows poor agreement, thus highlighting the difficulties involved in TBM performance prediction.     

A real-time prediction method for tunnel boring machine cutter-head torque using bidirectional long short-term memory networks optimized by multi-algorithm

Based on data from the Jilin Water Diversion Tunnels from the Songhua River (China), an improved and real-time prediction method optimized by multi-algorithm for tunnel boring machine (TBM) cutter-head torque is presented. Firstly, a function excluding invalid and abnormal data is established to distinguish TBM operating state, and a feature selection method based on the SelectKBest algorithm is proposed. Accordingly, ten features that are most closely related to the cutter-head torque are selected as input variables, which, in descending order of influence, include the sum of motor torque, cutter-head power, sum of motor power, sum of motor current, advance rate, cutter-head pressure, total thrust force, penetration rate, cutter-head rotational velocity, and field penetration index. Secondly, a real-time cutter-head torque prediction model's structure is developed, based on the bidirectional long short-term memory (BLSTM) network integrating the dropout algorithm to prevent overfitting. Then, an algorithm to optimize hyperparameters of model based on Bayesian and cross-validation is proposed. Early stopping and checkpoint algorithms are integrated to optimize the training process. Finally, a BLSTM-based real-time cutter-head torque prediction model is developed, which fully utilizes the previous time-series tunneling information. The mean absolute percentage error (MAPE) of the model in the verification section is 7.3%, implying that the presented model is suitable for real-time cutter-head torque prediction. Furthermore, an incremental learning method based on the above base model is introduced to improve the adaptability of the model during the TBM tunneling. Comparison of the prediction performance between the base and incremental learning models in the same tunneling section shows that: (1) the MAPE of the predicted results of the BLSTM-based real-time cutter-head torque prediction model remains below 10%, and both the coefficient of determination (R²) and correlation coefficient (r) between measured and predicted values exceed 0.95; and (2) the incremental learning method is suitable for real-time cutter-head torque prediction and can effectively improve the prediction accuracy and generalization capacity of the model during the excavation process.     

An evolutionary adaptive neuro-fuzzy inference system for estimating field penetration index of tunnel boring machine in rock mass

Field penetration index (FPI) is one of the representative key parameters to examine the tunnel boring machine (TBM) performance. Lack of accurate FPI prediction can be responsible for numerous disastrous incidents associated with rock mechanics and engineering. This study aims to predict TBM performance (i.e. FPI) by an efficient and improved adaptive neuro-fuzzy inference system (ANFIS) model. This was done using an evolutionary algorithm, i.e. artificial bee colony (ABC) algorithm mixed with the ANFIS model. The role of ABC algorithm in this system is to find the optimum membership functions (MFs) of ANFIS model to achieve a higher degree of accuracy. The procedure and modeling were conducted on a tunnelling database comprising of more than 150 data samples where brittleness index (BI), fracture spacing, α angle between the plane of weakness and the TBM driven direction, and field single cutter load were assigned as model inputs to approximate FPI values. According to the results obtained by performance indices, the proposed ANFIS_ABC model was able to receive the highest accuracy level in predicting FPI values compared with ANFIS model. In terms of coefficient of determination (R²), the values of 0.951 and 0.901 were obtained for training and testing stages of the proposed ANFIS_ABC model, respectively, which confirm its power and capability in solving TBM performance problem. The proposed model can be used in the other areas of rock mechanics and underground space technologies with similar conditions.     

Study on rock mass boreability by TBM penetration test under different in situ stress conditions

Rock mass boreability is a comprehensive parameter reflecting the interaction between rock mass and a tunnel boring machine (TBM). Many factors including rock mass conditions, TBM specifications and operation parameters influence rock mass boreability. In situ stress, as one of the important properties of rock mass conditions, has not been studied specifically for rock mass boreability in TBM tunneling. In this study, three sets of TBM penetration tests are conducted with different in situ stress conditions in three TBM tunnels of the Jinping II Hydropower Station. The correlation between TBM operation parameters collected during the tests and the rock mass boreability index is analyzed to reveal the influence of in situ stress on rock mass boreability and TBM excavation process. The muck produced by each test step is collected and analyzed by the muck sieve test. The results show that in situ stress not only influences the rock mass boreability but also the rock fragmentation process under TBM cutters. If the in situ stress is high enough to cause the stress-induced failure at the tunnel face, it facilitates rock fragmentation by TBM cutters and the corresponding rock boreability index decreases. Otherwise, the in situ stress restrains rock fragmentation by TBM cutters and the rock mass boreablity index increases. Through comparison of the boreability index predicted by the Rock Mass Characteristics (RMC) prediction model with the boreability index calculated from the penetration test results, the influence degree of different in situ stresses for rock mass boreability is obtained.     

A new hard rock TBM performance prediction model for project planning

Among the models used for performance prediction of hard rock tunnel boring machines two stand out and are often used in the industry. They include the semi theoretical model by Colorado School of Mines and the empirical model by Norwegian University of Science and Technology in Trondheim (NTNU). While each have their strong points and area of applications, more accurate prediction has been sought by modifying one of the existing models or introduction of a new model. To achieve this, a database of actual machine performance from different hard rock TBM tunneling projects has been compiled and analyzed to develop a new TBM performance prediction model. To analyze the available data and offer new equations using statistical methods, relationships between different geological and TBM operational parameters were investigated. Results of analyzes show that there are strong relationships between geological parameters (like UCS, joint spacing and RQD) and TBM performance parameters specially Field Penetration Index (FPI). In this study, a boreability classification system and a new empirical chart, for preliminary estimation of rock mass boreability and TBM performance is suggested.     

QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency

In recent years, tunnel boring machines (TBMs) have been widely used in tunnel construction. However, the TBM control parameters set based on operator experience may not necessarily be suitable for certain geological conditions. Hence, a method to optimize TBM control parameters using an improved loss function-based artificial neural network (ILF-ANN) combined with quantum particle swarm optimization (QPSO) is proposed herein. The purpose of this method is to improve the TBM performance by optimizing the penetration and cutterhead rotation speeds. Inspired by the regularization technique, a custom artificial neural network (ANN) loss function based on the penetration rate and rock-breaking specific energy as TBM performance indicators is developed in the form of a penalty function to adjust the output of the network. In addition, to overcome the disadvantage of classical error backpropagation ANNs, i.e., the ease of falling into a local optimum, QPSO is adopted to train the ANN hyperparameters (weight and bias). Rock mass classes and tunneling parameters obtained in real time are used as the input of the QPSO-ILF-ANN, whereas the cutterhead rotation speed and penetration are specified as the output. The proposed method is validated using construction data from the Songhua River water conveyance tunnel project. Results show that, compared with the TBM operator and QPSO-ANN, the QPSO-ILF-ANN effectively increases the TBM penetration rate by 14.85% and 13.71%, respectively, and reduces the rock-breaking specific energy by 9.41% and 9.18%, respectively.     

Application of several optimization techniques for estimating TBM advance rate in granitic rocks

This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang-Selangor raw water transfer tunnel in Malaysia.Rock properties consisting of uniaxial compressive strength(UCS),Brazilian tensile strength(BTS),rock mass rating(RMR),rock quality designation(RQD),quartz content(q)and weathered zone as well as machine specifications including thrust force and revolution per minute(RPM)were measured to establish comprehensive datasets for optimization.Accordingly,to estimate the advance rate of TBM,two new hybrid optimization techniques,i.e.an artificial neural network(ANN)combined with both imperialist competitive algorithm(ICA)and particle swarm optimization(PSO),were developed for mechanical tunneling in granitic rocks.Further,the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice.To evaluate the accuracy of the proposed models for both testing and training datasets,various statistical indices including coefficient of determination(R~2),root mean square error(RMSE)and variance account for(VAF)were utilized herein.The values of R~2,RMSE,and VAF ranged in 0.939-0.961,0.022-0.036,and 93.899-96.145,respectively,with the PSO-ANN hybrid technique demonstrating the best performance.It is concluded that both the optimization techniques,i.e.PSO-ANN and ICA-ANN,could be utilized for predicting the advance rate of TBMs;however,the PSO-ANN technique is superior.     

In situ TBM penetration tests and rock mass boreability analysis in hard rock tunnels

Boreability is popularly adopted to express the ease or difficulty with which a rock mass can be penetrated by a tunnel boring machine. Because the boreability is related to the rock mass properties, TBM specifications and TBM operation parameters, an accurately definable quantity has not been obtained so far. In order to analyze and compare rock mass boreability, a series of TBM shield friction tests were conducted in a TBM tunneling site. Two sets of TBM penetration tests were performed in different rock mass conditions during tunneling in rock. In each step of the penetration test, the rock muck was collected to perform the muck sieve analyses and the shape of large chips was surveyed in order to analyze the TBM chipping efficiency under different cutter thrusts. The results showed that a critical point exists in the penetration curves. The penetration per revolution increases rapidly with increasing thrust per cutter when it is higher than the critical value. The muck sieve analysis results verified that with increasing thrust force, the muck size increases and the rock breakage efficiency also increases. When the thrust is greater than the critical value, the muck becomes well-graded. The muck shape analysis results also showed with the increase of the thrust, the chip shape changes from flat to elongated and flat. The boreability index at the critical point of penetration of 1 mm/rev. defined as the specific rock mass boreability index is proposed to evaluate rock mass boreability.     

Real-time rock mass condition prediction with TBM tunneling big data using a novel rock–machine mutual feedback perception method

Real-time perception of rock mass information is of great importance to efficient tunneling and hazard prevention in tunnel boring machines (TBMs). In this study, a TBM–rock mutual feedback perception method based on data mining (DM) is proposed, which takes 10 tunneling parameters related to surrounding rock conditions as input features. For implementation, first, the database of TBM tunneling parameters was established, in which 10,807 tunneling cycles from the Songhua River water conveyance tunnel were accommodated. Then, the spectral clustering (SC) algorithm based on graph theory was introduced to cluster the TBM tunneling data. According to the clustering results and rock mass boreability index, the rock mass conditions were classified into four classes, and the reasonable distribution intervals of the main tunneling parameters corresponding to each class were presented. Meanwhile, based on the deep neural network (DNN), the real-time prediction model regarding different rock conditions was established. Finally, the rationality and adaptability of the proposed method were validated via analyzing the tunneling specific energy, feature importance, and training dataset size. The proposed TBM–rock mutual feedback perception method enables the automatic identification of rock mass conditions and the dynamic adjustment of tunneling parameters during TBM driving. Furthermore, in terms of the prediction performance, the method can predict the rock mass conditions ahead of the tunnel face in real time more accurately than the traditional machine learning prediction methods.     

基于改进的距离判别分析法的南水北调西线工程TBM施工围岩分级

 对马氏距离判别法和层次分析法存在的不足进行改进,将改进的距离判别分析法应用于南水北调西线工程TBM施工围岩分级中。根据TBM施工特点和相关研究成果,将TBM施工围岩分级标准定为4级。选用岩石强度、岩组特征、结构面间距、结构面与洞轴线夹角以及石英含量5项指标作为判别因子,以南水北调西线工程杜柯河-玛柯河段实例数据作为学习样本进行训练,建立TBM施工围岩分级的改进的距离判别分析模型,利用得到的线性判别函数对待判样本进行分级。最后,将改进的距离判别分析法得到的判定结果与传统马氏距离判别法、RTBM法以及RMR方法得到的判别结果进行对比分析,验证了改进的距离判别分析法的有效性。研究结果表明,改进的距离判别分析法具有预测精度高等优点,为TBM施工围岩分级提供了一种新的有效方法。     

Utilizing rock mass properties for predicting TBM performance in hard rock condition

The key parameters on the estimation of tunnel-boring machine (TBM) performance are rock strength, toughness, discontinuity in rock mass, type of TBM and its specifications. The aim of this study is to both assess the influence of rock mass properties on TBM performance and construct a new empirical equation for estimation of the TBM performance. To achieve this aim, the database composed of actual measured TBM penetration rate and rock properties (i.e., uniaxial compressive strength, Brazilian tensile strength, rock brittleness/toughness, distance between planes of weakness, and orientation of discontinuities in rock mass) were established using the data collected from one hard rock TBM tunnel (the Queens Water Tunnel # 3, Stage 2) about 7.5 km long, New York City, USA. Intact rock properties were obtained from laboratory studies conducted at the Earth Mechanics Institute (EMI) in the Colorado School of Mines, CO, USA. Based on generated database, the statistical analyses were performed between available rock properties and measured TBM data in the field. The result revealed that rock mass properties have strong affect on TBM performance. It is concluded that TBM performance could be estimated as a function of rock properties utilizing new equation (r = 0.82).