Big Data-Based Boring Indexes and Their Application during TBM Tunneling

State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, Jiangsu, China Department of Geotechnical Engineering, China Institute of Water Resources and Hydropower Research, Beijing 100038, China Key Laboratory of Urban Underground Engineering of Ministry of Education, Beijing Jiaotong University, Beijing 100044, China Sinohydro Bureau 6 Co., Ltd., Shenyang 110179, Liaoning, China


Introduction
Tunnel boring machines (TBMs), a type of engineering machinery, have been widely applied in the construction of tunnels, such as water conveyance, highway, and railway tunnels [1]. With the development of information technology [2] and construction of big data management platforms [1], the Internet of ings technology for real-time TBM data acquisition has been used widely. e method for data acquisition has been used in the Yinsong TBM project [3,4] for effectively recording extensive data, including cutterhead thrust, cutterhead torque, cutterhead rotation speed, advance rate, and penetration, which are closely related to rock mass fragmentation [5,6]. erefore, the analyzing and mining of TBM tunneling data can provide valuable information regarding the rock mass [7].
TBM tunneling can be regarded as a large-scale field linear cutting test [8]. e main characteristics of rock mass fragmentation are the interaction between the TBM cutters and rock mass, as well as loading the thrust and torque of the cutterhead acting on the rock mass, which exceeds the ultimate strength of the rock mass [9]. e performance parameters are closely related to both the physical and operational parameters of the rock mass; some of the related studies in F-p and T-p relationships are [10][11][12][13][14][15][16][17][18]. Based on the studies, many indexes have been derived from TBM performance parameters to reflect the characteristics of rock mass. Some of the proposed indexes are the field penetration index (FPI) [19], specific penetration (SP) [20], boreability index (BI) [5], and drilling efficiency index (TPI) [4], which can be used for rock mass parameter prediction [21], rock mass classification [22], and adverse geological diagnosis [4]. In addition, they can achieve better results based on the fine relationships of thrust-penetration (F-p) and torque-penetration (T-p). Regarding the F-p and T-p relationships, a linear relationship between the normal force, rolling force, and penetration was proposed in [6] using a stepwise test on a linear cutting machine [23]; they concluded a comparable linear relationship under different confining stresses. In field tunneling tests, Liujie et al. [24] demonstrated the effectiveness of the linear F-p relationship using field-stepped tunneling tests in limestone strata. Existing studies considered the F-p and T-p relationships, providing simple, but convenient, methods to describe the TBM parameters. However, determining a linear relationship coefficient for different rock masses has many disadvantages. First, the laboratory tests make many simplifications; thus, the obtained results might not be appropriate for field tunneling. Second, the F-p and T-p relationships are acquired using repeated stepped tunneling tests by adjusting different penetrations that are not appropriate for continuous field tunneling. erefore, field tunneling data to determine the F-p and T-p relationships for rock mass evaluation should be easy to acquire and have robust features, which are crucial for improving the precision of relationships. TBM big data had been collected during tunneling, from which we could obtain valuable information about the rock mass through data mining. However, an excessive amount of TBM tunneling data would increase the complexity of the problems, relationship analysis, and comprehensive index extraction from big data, which could be helpful in decreasing the complexity. Conventional methods for the determination of the F-p and T-p relationships have the abovementioned disadvantages; therefore, comprehensive index extraction may not yield a high accuracy. Big data could provide sufficient proof about the F-p and T-p relationships and obtain acceptable results for comprehensive index extraction.
e key contribution of this study is proposing a novel model and data hybrid-driven boring indexes in cyclic tunneling processes, followed by a preliminary application for support measure suggestions.
is study introduces novel indexes for reflecting the process of rock mass fragmentation, which differ from conventional indexes, such as FPI and SP. Subsequently, a preliminary application is adopted for support measure suggestions. e remainder of this paper is organized as follows. Section 2 describes the project and TBM and the flowchart of this study. Section 3 describes the parameter characteristics during TBM tunneling. Section 4 introduces novel boring indexes and corresponding calculation methods. Section 5 presents a preliminary application of boring indexes for support measure suggestions. Section 6 discusses the application of the presented boring indexes. Finally, Section 7 presents the conclusion of the study.  Figure 1.

Description of the Project and
During the tunneling process, some adverse geology conditions, such as fault fracture zone and weak rock mass, exist. Rock reinforcement measures, such as rock bolt supports [25] (Figure 2) and steel rib supports [26] (Figure 3), are widely used to improve the safety against adverse geology conditions. A total of 4 steel rib and 25 rock bolt support measures were recorded completely in the studied sections (2.42 km), whereas all support measures were several meters long. Based on the statistical features of actual support measures and proposed boring indexes in this study, quantitative advice could be provided for support measure suggestions. Figure 4 shows that the main TBM specifications include a diameter of 5.2 m, 34 cutters, a data acquisition frequency of 1 Hz, and other specifications, as listed in Table 1. e cutterhead thrust refers to the loading of a cylinder, which includes the valid rock mass fragmentation thrust on the rock mass and the friction thrust to overcome the friction of pulling the auxiliary equipment. So, a field tunneling test was conducted to determine the friction torque and thrust during the shield friction tests [5], and the valid rock mass fragmentation thrust (torque) could be calculated through total thrust (torque) subtracting to friction thrust (torque).

Study Flowchart.
To obtain novel boring indexes based on TBM big data, a flowchart is proposed, as shown in Figure 5. e three main steps are (1) data processing (used for data production), (2) proposed boring indexes (demonstrating the methods and including the verification of the proposed boring indexes), and (3) application of boring indexes in support measure suggestions.

Parameter Characteristics during the TBM Cyclic Tunneling Process
In this study, the analysis of parameter characteristics during a cyclic tunneling process is conducted, linear F-p and T-p relationships in the ascending section are proposed, and a physical mechanism is introduced to verify the relationships. Subsequently, a data processing method is proposed based on the parameter characteristic analysis for achieving a better cyclic segmentation.  Advances in Civil Engineering 3

Main Rock Mass Fragmentation
[mm/r] and expressed as p � v/n), cutterhead thrust, and cutterhead torque. e rock mass is effectively broken under a preset cutterhead rotation speed (denoted as n_set [r/min]) and advance rate (denoted as v_set [%]) by the TBM operators and action of F and T by the TBM driver systems. erefore, operational and performance parameters are acquired. e realization process is as follows.
Operational parameters (active parameters), including cutterhead rotation speed and advanced rate, are the main controlling parameters. By adjusting performance parameters (passive parameters), such as n_set and v_set, the cutters penetrate the rock mass under the action of F and T. e values of performance parameters reflect the resistance of rock mass fragmentation at a certain p, which can be expressed as F and T �f(p) [27] for the same rock mass.

Parameter Characteristics and Section Segmentation for
Typical TBM Cyclic Tunneling. Figure 6 shows the parameter characteristics of a typical TBM cyclic tunneling process, which includes processes among n, n_set, v, v_set, F, and T, where the x-axis and y-axis represent the time and the parameter values. Four sections were acquired in one cyclic tunneling process according to changes in parameter characteristics [4,28], including idling, ascending, stable, and descending sections. e parameter characteristics in each section are described as follows: (a) Idling section: the startup process of the cutterhead with an increasing n and v is accompanied by no effective rock mass fragmentation. is process is an invalid rock mass fragmentation process. (b) Ascending section: n and n_set remain constant, whereas v, p, F, and T gradually increase with an increasing v_set. Interaction in the TBM rock is gradually accompanied by an effective rock mass fragmentation, whereas this section lasts 2-5 min with a propel stroke of 100-200 mm, during which the rock mass rarely changes. Furthermore, this section has two features according to the fluctuation of different parameters at approximately 220 s. Prior to that, F and T increase with few fluctuations, but they also increase with severe fluctuations. e corresponding cutterhead thrust is denoted by F b . e first and second stages in the ascending section are introduced to describe the features. is process is a valid rock mass fragmentation process. (c) Stable section: n_set and v_set remain constant, the same as other parameters; however, they exhibit some fluctuations until the maximum propel stroke. is is the main rock mass fragmentation process during cyclic tunneling [3]. is process is a valid rock mass fragmentation process. (d) Descending section: at the end of a cyclic tunneling, each parameter gradually decreases, and the cutterhead continues to rotate for a short period to clean the muck in the cutterhead until the maximum propel stroke. Subsequently, the TBM operators stop    Advances in Civil Engineering the machine, retract the propel pumps, and prepare for the next cyclic tunneling. is process is an invalid rock mass fragmentation process. Figure 7 shows the F-p and T-p relationships from the ascending and stable sections in the valid tunneling process shown in Figure 6.

Relationship between Different Parameters.
Two linear F-p relationships exist according to the fluctuations in the ascending section [20]: one has a higher F-p slope in the first stage, but F fluctuates rarely, and the majority of p is less than 2 mm/r. e other has a lower F-p slope [24] in the second stage, whereas F fluctuates severely and the slope is less than the former (here, p exceeds 2 mm/r and F exceeds 6,900 kN). In the stable section, the F-p relationship has a spindle shape, whereas p fluctuates more than F does. e linear F-p relationship of the second stage in the ascending section could reflect the F-p relationship in the stable section, whereas the F-p relationship of the first stage in the ascending section could not. erefore, the second linear F-p relationship reflects the valid rock mass fragmentation process and the stable section is a special process of the second stage in the ascending section. e T-p relationship is linear in all ascending sections [4], and the intercept can be ignored. In summary, the linear F-p and T-p relationships of the second stage in the ascending section can reflect the general trend of the entire section during the valid rock mass fragmentation process based on the statistical features.

Physical Mechanism Analysis for Parameter
Characteristics. Two processes related to the two stages in the ascending section are defined from the perspective of the physical mechanism during rock mass fragmentation (referred to as the initial TBM-rock interaction process and valid rock mass fragmentation process, respectively) to illustrate the parameter fluctuation characteristics of the two stages in the ascending section, as shown in Figure 6.

Initial TBM-Rock Interaction Process.
Two principles can be used to explain the initial TBM-rock interaction process. e first principle proposed a crushed zone [9] in the interaction of TBM rock in the early stages; the rock mass would not break unless the loading was greater than the ultimate strength [29]. e second principle proposed the footing bearing capacity [30], which regarded the rock mass as a footing. e rock mass was broken when the rock penetration pressure was greater than the bearing capacity, which is a function of physical parameters, such as cohesion (c) and internal friction angle (φ), as shown in Figures 8(a) and 8(c).

Valid Rock Mass Fragmentation Process.
In the valid rock mass fragmentation process, the rock mass is broken under the interaction of the normal force (denoted by F n , provided by F) and tangential force (denoted by F r , provided by T) of the cutters, as shown in Figures 8(b) and 8(d) and expressed in the following equation: where T f is the friction torque, F f is the friction thrust, N is the number of cutters in the cutter head, and R i is the radius of cutters installed in the cutter head.
For the disc cutter, Rostami and Ozdemir [31] defined the rock mass fragmentation force as the product of the uniaxial compressive strength and interaction area in the normal and tangential directions, as expressed in (2) and shown in Figure 9.
As shown in Figure 9, when p is greater than 2 mm/r and less than 10 mm/r, an acceptable linear relationship exists between F n and F r with an increasing p, indicating a linear relationship among F r , F n , F, and T.

Data Processing.
After performing the parameter characteristic analysis in cyclic tunneling processes, a data processing method is proposed, which includes data extraction, section segmentation, and adjustment of ascending sections.

Data Extraction.
Data extraction was aimed to extract cyclic tunneling data and downtime processes from time series TBM tunneling data, as shown in Figure 10. e cyclic tunneling processes are the main rock mass fragmentation processes, and the downtime processes are invalid fragmentation processes. For the extraction of cyclic tunneling processes, a comprehensive judgment method is adopted, which combines the TBM operational features when F, T, n, and v are greater than zero, as expressed in (3). erefore, cyclic tunneling processes are obtained using (4) when C is greater than 0, and the downtime processes are obtained when C is less than or equal to 0.
C > 0, cyclic tunneling processes,  tunneling processes because of the maximum electric current of motors [28]. erefore, the outlier detection was focused on v, and the method is expressed in the following equation:

Section Segmentation.
e ascending and stable sections of the cyclic tunneling process were obtained using the data extraction method, as expressed in equation (4). In many cases, the values of v, F, and T in the ascending section are lower than those in the stable section, as shown in Figure 6. In this study, the mean value method was proposed for section segmentation in the ascending and stable sections. e above three parameters typically change together; therefore, a single value of T is adopted to achieve section segmentation. When the time series T is less than the mean value T m , it should be regarded as an ascending section; otherwise, it is regarded as a stable section. and where Q is the duration of the ascending and stable sections, T m is the mean value of T in the ascending and stable sections, and T i represents the real-time T in the ascending and stable sections.

Adjustment of the Ascending Section Data.
From the characteristic analysis of the cyclic tunneling process in Figures 6 and 7, a p value of less than 2 mm/r implies an invalid rock mass fragmentation process. erefore, the adjustment of ascending section data could adopt a method that can be expressed as follows:

Feature
Expression. e analysis presented in Section 3 showed that linear F-p and T-p relationships in the ascending sections are clear according to the field tunneling data and physical mechanism analysis. is concise and practical method avoids complex forms. e stable section is a special stage of the second stage of the ascending section. erefore, three coefficients of the F-p and T-p relationships (i.e., a, b, and k) are introduced to express the characteristics of the rock mass fragmentation process as where a and b are the slope and intercept coefficients of the linear F-p relationship, with the units of (kN/cutter)/(mm/r) and kN/cutter, respectively; k is the slope coefficient of the linear T-p relationship with a unit of (kNm/cutter)/(mm/r); and the intercept of the linear T-p relationship can be ignored owing to the minor effect on the linear relationship [4]. Equation (9) is a simplified expression because it calculates the coefficients by directly averaging the total thrust and torque over the number of cutters, which may differ from equation (1). For example, the definition of single cutter torque and tangential force in the average torque of cutter head shows that the coefficient between the single cutter torque and tangential force is approximately 1.3 for this case. However, equation (9) can still be considered a secure and simple feature expression for the cyclic tunneling process owing to the calculation convenience compared with   (2)).  Advances in Civil Engineering the expressions presented in [33,34] without considering rock mass parameters.

Definition of Boring Indexes.
Boring indexes are introduced to reflect the physical characteristics of the coefficients in the linear F-p and T-p relationships, which could reveal the law from the mechanism perspective of rock mass fragmentation and provide comprehensive information concerning the rock mass: (1) Normal boring difficulty index, a: the coefficient a in the F-p slope reflects the required increment of cutterhead thrust for a unit increment of penetration. A larger value corresponds to a harder rock mass to overcome the resistance for a unit increment of penetration, as reported in [24]. (2) Initial rock mass fragmentation difficulty index, b: the coefficient b in the F-p intercept reflects the required cutterhead thrust for initial penetration, which supposing p � 0 is related to the rock mass physical parameters [24]. Consequently, the rock mass is broken unless p > p b and F > F b . However, p b is uncertain within the range of 1-2 mm/r in this project, and b is an intercept derived from the linear F-p relationship, which can be conveniently determined. Meanwhile, the relationship between F b and b can be expressed as Tangential boring difficulty index, k: the coefficient k in the T-p slope reflects the required increment of cutterhead torque for a unit increment of penetration [4]. A larger value indicates more energy consumed for fragmentation of a unit volume of rock mass.
Some of the many advantages of the three indexes are (a) higher accurate reflection of the rock mass characteristics, such as the physical parameters in a user-friendly manner [24]; (b) faster recognition of characteristics for the use of data in ascending section during the cyclic tunneling process; and (c) obtaining F and T at different p values, which is crucial for adjusting and optimizing different operational parameters for safe and efficient tunneling.

Determination of Boring Indexes.
e ascending section is the start of the valid cyclic tunneling process, the parameters of which have a large range from zero to a stable value, as shown in Figure 6. Section 3.5 presents the data processing method, where the ascending section data could be obtained readily. us, the boring indexes could be acquired through linear regression using the F-p and T-p data in ascending sections, which can quickly reflect the characteristics of the rock mass during tunneling. erefore, the boring indexes were obtained. In general, the coefficient of determination R 2 of linear regression is introduced to select valid ascending section data, whereas R 2 is set to be greater than or equal to 0.6 [4].

Influence of Boring Indexes in Different Rock Masses.
Two sets of tunneling processes were selected, distributed between chainages K64 + 310 to K64 + 304 (Yanshanian granite) and K65 + 483 to K65 + 478 (Jurassic tuff), to analyze the influence of boring indexes in different rock masses. e length of each set of tunneling processes is approximately 5.3 m, comprising three adjacent cyclic tunneling processes, within which the rock mass conditions of each set could be considered constant ( Figure 11). Based on the flowchart presented in Figure 5, boring indexes are obtained in cyclic tunneling processes, as shown in Table 2.
Boring indexes a, b, and k in the same set of cyclic tunneling sections can be regarded identical. For example, boring indexes in the three cyclic tunneling processes of each set have minor deviations, indicating that boring indexes are unique under the same rock mass conditions. Boring indexes differ for various rock masses, and each of their mean values exhibits a large deviation ( Table 2). For example, the mean of index a for each set is 9.5 and 8.1 (kN/cutter)/(mm/r), respectively, with a low absolute error of 1.4 (kN/cutter)/(mm/ r) and relative error of 15%. e mean of index b is 192.6 and 115.3 kN/cutter for each set, respectively, with a moderate absolute error of 77.3 kN/cutter and relative error of 40%. e mean of index k for each set is 4.7 and 2.4 (kNm/cutter)/ (mm/r), respectively, with a high absolute error of 2.3 (kNm/ cutter)/(mm/r) and relative error of approximately 100%. ese results confirm that boring indexes vary for different rock masses.

4.5.1.
Significance. e proposed boring indexes were verified using performance prediction to illustrate the rationality of the proposal. e calibration of model was performed using the data of F, T, and p in the ascending section (linear relationships are proposed in (9)), and verification of the model was carried out using the data in the stable section of the same cyclic tunneling processes. e rock mass could be assumed identical for a cyclic tunneling stroke of approximately 1,800 mm, as described in Section 4.4. However, it is difficult to determine the physical parameters of the rock mass in a timely manner, thus conventional methods [10][11][12][13][14][15] are difficult to be used for performance prediction. Based on the fine linear F-p and T-p relationships expressed by boring indexes in the ascending sections, as shown in Figure 7, accurate performance prediction models can be determined for the cyclic tunneling process.
us, boring indexes could be used for the following. (1) Adjusting the presetting operational parameters in stable sections in advance: different performance parameters can be obtained with various operational parameters, and TBM operators can select the proper operation parameters in stable sections based on the performance prediction models.
(2) Optimizing operational parameters subjected to different constraint conditions: the performance prediction models provided relationships of different parameters, and TBM Advances in Civil Engineering operators could optimize operational parameters using the maximum values of performance parameters. (3) Adverse geology conditions diagnosis: when the predicted and actual performance parameters have large prediction deviations in different operational parameters, there might be adverse geology conditions.

Main Characteristics of Boring Indexes for Performance
Prediction. For the cyclic tunneling process in this project, boring indexes a, b, and k were acquired for 291 cycles using the proposed method. e main characteristics are listed in Table 3, in the range of 1.1-20.8 (kN/cutter)/(mm/r) for index a, 79-224 kN/cutter for index b, and 1.8-5.4 (kNm/ cutter)/(mm/r) for index k, demonstrating some randomness and deviation, indicating some differences in rock mass during the TBM tunneling.

Model Evaluation Indicators.
e evaluation indicators were introduced to assess the accuracy of performance prediction, as expressed in equation (10). Here, the mean absolute error (MAE) indicates the absolute error and mean absolute percentage error (MAPE) indicates the absolute percentage error of the actual and predicted performance parameters as follows: where y i is the actual performance parameter, y i is the predicted performance parameter, and N is the number of cyclic tunneling processes.

Performance Prediction.
e mean values of p, F, and T in stable sections of the cyclic tunneling process and the predicted values of F and T using boring indexes and p, as expressed in equation (9), are shown in Figure 12. e actual and predicted results for F have an MAE and MAPE of 14.2 kN and 6.7%, respectively, indicating a close average predicted thrust to the actual values, with an error of 14.2 kN. e actual and predicted results of T have an MAE and MAPE of 3.14 kNm and 10.7%, respectively. e obtained results could be attributed to the appropriateness of the linear of F-p and T-p relationships in ascending sections owing to a small error between actual and predicted performances.

Significance.
e TBM shield shown in this project has a length of 4,200 mm, during which the rock mass cannot be directly observed; however, the quality of the rock mass on the shield surface and tunnel face is important for safety construction [35]. ere are less significant methods for the real-time detection of rock mass on shield surfaces owing to the narrow space [36], and a quick diagnosis of adverse    Advances in Civil Engineering geology could be helpful for suggesting support measures during continuous tunneling. e tunneling data reflecting the rock mass could provide a useful method for adverse geological diagnosis and support measure suggestions.

Main Characteristics of Boring Indexes in Support
Measures Sections. Table 4 presents geological descriptions of the 29 typical support measures sections and corresponding characteristics of boring indexes a, b, and k. Indeed, 133 valid cyclic tunneling processes exist in 29 typical support measures sections. Table 5 presents the statistical results of boring indexes in the 29 typical support measures sections. e value of index a is in the range of 1.1-16.5 (kN/cutter)/(mm/r), and the mean value is 7.89 (kN/cutter)/(mm/r), demonstrating a significant difference in various support measures sections. Meanwhile, the value of index b is in the range of 89-146 kN/cutter, and the mean value is 115 kN/ cutter, which is close to the friction thrust (F f � 100 kN), indicating that the rock mass can be easily broken after the cutterhead thrust exceeds the friction thrust. e value of index k is in the range of 1.6-5.1 (kNm/cutter)/(mm/r), and the mean value is 2.9 (kNm/cutter)/(mm/r), demonstrating a significant difference in various support measures sections. Table 6 presents the statistical results of boring indexes for 133 support measure processes. e total support sections have small randomness and variability in the range of 1.1-20.8 (kN/cutter)/(mm/r) for index a, 85-219 kN/cutter for index b, and 1.8-5.4 (kNm/cutter)/(mm/r) for index k.
e distribution of boring indexes in the 133 support measure processes is similar to that of the 29 typical support measures sections.

Criterion for Support Measure Suggestions.
In general, higher boring indexes mean more difficulty for rock mass fragmentation. Also, a criterion should be adopted to provide quantitative quotas for support measure suggestions; reasonable criterion could yield precise results. e selection of criterion is random and no theory. 3rd quartile, also called the larger quartile, is a widely used method to describe the distribution of data. In this study, a criterion for support measures is proposed based on the values of the 3rd quartile of boring indexes in the 29 typical support measures sections: (1) e criterion for index a adopts a 3 rd quartile value of 10.05 (kN/cutter)/(mm/r). It is advisable to conduct support measures when index a is less than 10.05 kN/ cutter/mm/r. (2) e criterion for index b adopts a 3 rd quartile value of 127 kN/cutter. It is advisable to conduct support measures when index b is less than 127 kN/cutter. (3) e criterion for index k adopts a 3 rd quartile value of 3.3 (kNm/cutter)/(mm/r). It is advisable to conduct support measures when the index k is less than 3.3 (kNm/cutter)/(mm/r).
Moreover, a parallel relationship exists between the three indexes, confirming that support measures should be implemented if one of the three indexes meets the requirements.

Model Evaluation Indicators.
e criterion for support measure suggestions based on boring indexes is a classifier, and the adaptability of criterion can be assessed using indicators such as precision (P), recall (R), and F 1 -score (F 1 ), which are derived from indicators P and R, where higher values of F 1 indicate higher accuracy of the classifier [37].
where TP represents the number of actual and suggested truly support measures (suggested truly), FP is the number of actual and not suggested truly support measures (suggested incorrectly), and FN is the number of actual but suggested falsely support measures (suggested falsely).  Table 7, and the accuracies for different indicators are presented in Table 8. According to Table 7, the total number of support measures in tuff lithology was 13; that is, TP-a was 9, FP-a was 1, and FN-a was 4, indicating 9 suggested truly, 1 suggested incorrectly, and 4 suggested falsely using index a, for a P-a of 90%, R-a of 69%, and F 1 of 78%. Specifically, F 1 is 35 and 74% using index b and k, respectively, indicating that index a has a higher accuracy based on F 1 in tuff lithology. In lava lithology, the total number of support measures was 57; that is, TP-a was 50, FP-a was 13, and FN-a was 7 using index a, for a P-a of 79%, R-a of 88%, and F 1 of 83%. In addition, F 1 is 78% using index b and k; that is, index a has a higher accuracy based on F 1 in lava lithology. In granite lithology, the total number of support measures was 67, TP-a was 37, FP-a was 7, and FN-a was 26 using index a, for a P-a of 84%, R-a of 59%, and F 1 of 69%. In addition, F 1 is 54 and 63% using index b and k, indicating that index a has a higher accuracy based on F 1 in granite lithology. Furthermore, in lava lithology, evaluation indicators are higher compared to tuff and granite lithologies, confirming a higher suggested accuracy.       Tuff  28  13  9  1  4  4  6  9  10  4  3  Lava  135  57  50  13  7  45  13  12  54  28  3  Granite  128  63  37  7  26  24  10  39  16  11  47 "-a" indicates the result judged by index a, "-b" indicates the result judged by index b, and "-k" indicates the result judged by index k. and fragmented rock masses exist, which contain the number of joints accompanied by increased J v . us, index a in the support measures sections has lower values, which achieved better results for support measure suggestions using index a.

Suggestions Results Using ree Indexes in the Entire
Process. One single index may adversely influence other indexes; a combination of three indexes, where all indexes should meet the criterion for support measures, is conducted to examine the results compared to that of a single index. Figure 14 shows the results of a combination of the three indexes, whereas the accuracies are presented in Table 9.
Compared with results listed in Tables 7 and 8, Table 9 shows better suggestion results in granite lithology, with F 1 of 74% using a combination of three indexes and 69, 49, and 36% using a single index, where accuracies were highly enhanced. However, in tuff and lava lithologies, TP and FP exhibited a slight enhancement, resulting in a higher R, lower P, and lower F 1 compared with that of a single index. All the achieved results indicate that a combination of the three indexes could improve the accuracy of support measure suggestions in granite lithology. However, slight changes in tuff and lava lithologies occur, and index a could still acquire better accuracy suggestions in tuff and lava lithologies.

Discussion
Boring indexes reflect three characteristics of rock mass fragmentation precisely. erefore, we proposed a Table 8: Evaluation indicators using one single index.

Lithology Evaluation indicators P-a (%)
R-a (%) F 1 -a (%) Tuff  90  69  78  40  31  35  71  77  74  Lava  79  88  83  78  79  78  66  95  78  Granite  84  59  69  71  38  49  59  25  36  Average  84  72  77  63  49  54  65  66  preliminary application for support measure suggestions based on the statistical features of boring indexes. e recommended operational parameters for AIaided tunneling are shown in Figure 15. e similarity methods of boring indexes, such as cosine and Euclidean distance similarities, were used to select the maximum similarity samples from the set of samples and actual operating values, such as cutterhead rotation speed n and advance rate v, whereas the controlling parameters could be set as the corresponding values of the similarity samples.
v max � n · p max � n · min F max − b a , T max k , v max n . (12) Moreover, the operational parameters for manual operation tunneling (expressed in equation (12)) were optimized.
e optimum control parameters are effective for improving the advance rate, and the use of boring indexes to predict cutterhead thrust and torque provides a reasonable choice for selecting the advance rate v, which could achieve promising optimization results during manual operation tunneling.
In addition, rock masses can be classified using different project data (Figure 15). Determining the classification of rock mass is based on the actual situation of rock mass and experience of construction workers. Furthermore, many uncertain factors exist because of the complexity of the rock mass and subjectivity of humanity, and quantitative indexes are required to accurately assess the classification. e described similarity methods of using boring indexes to identify the similarity samples from different projects could determine the best matching rock mass and improve the accuracy and operability in rock mass classification, as shown in Figure 15

Conclusions
(1) e second stages of ascending sections (p ≥ 2 mm/r) are valid fragmentation processes, and the first stages of ascending sections (p < 2 mm/r) are invalid fragmentation processes in the Yinchao TBM project. Linear F-p and T-p relationships in the second stages of ascending sections are more appropriate for the expression of valid fragmentation processes. (2) Boring indexes derived from the coefficient of linear F-p and T-p relationships could illustrate the features during TBM-rock mass fragmentation. For instance, index a reflects the initial rock mass fragmentation difficulty, index b reflects the normal boring difficulty, and index k reflects the tangential boring difficulty. (3) Boring indexes could be used for support measure suggestions, with criterion values of 10.05 (kN/ cutter)/(mm/r), 127 kN/cutter, and 3.3 (kNm/cutter)/(mm/r) for indexes a, b, and k, respectively, whereas support measures should be adopted when boring indexes are lower than the criterion.

Data Availability
e data used to support the findings of this study are available from the corresponding author upon request. One-to-one correspondence Recommendation Figure 15: TBM recommendation systems for different goals based on boring indexes. 16 Advances in Civil Engineering

Conflicts of Interest
e authors declare that there are no conflicts of interest in this publication.