CN109685378B - TBM construction surrounding rock digchability grading method based on data mining - Google Patents
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Abstract
The invention discloses a data mining-based surrounding rock excavation grading method for TBM construction, which comprises the following steps: establishing a penetration prediction model by using a stepwise regression method and considering the operation experience of a main driver on the basis of safety and high efficiency; carrying out regression analysis on the engineering project and establishing a rotating speed prediction model; summarizing engineering cases at home and abroad, and establishing a TBM construction tunneling utilization rate prediction model based on data mining; and finally establishing a surrounding rock excavation classification model based on TBM construction by taking the TBM tunneling speed as an evaluation index. Aiming at the current situation that the traditional tunnel surrounding rock classification method taking surrounding rock stability as an evaluation basis cannot be well adapted to TBM construction, the method provides a data mining method and establishes a tunnel surrounding rock systematic classification method under the TBM construction condition, aims to accurately predict TBM tunneling performance under various surrounding rock levels, provides decision basis for TBM construction cost and construction period prediction, and realizes optimization and adjustment of TBM tunneling parameters.
Description
Technical Field
The invention relates to a surrounding rock excavation grading method for TBM construction, in particular to a TBM surrounding rock excavation grading method considering surrounding rock excavation and equipment adaptability.
Background
Compared with the traditional tunnel construction method, the TBM method is high in construction speed, the tunneling rate is usually 3-10 times that of the traditional drilling and blasting method, however, the TBM is poor in adaptability to geological conditions, and if good tunneling parameters are not adopted to match with the tunneling parameters during tunneling, the TBM can not exert the advantage of high tunneling speed, and further construction period delay is caused.
The traditional tunnel construction method is generally to design tunnel supports and guide construction by a surrounding rock classification method, and carry out cost and construction period budget according to the design and the guidance. However, most of the current tunnel surrounding rock grading methods are provided for the stability evaluation of tunnel surrounding rocks and the design of supporting structures, and it is increasingly difficult to meet the construction requirements of the TBM tunnel. The classification of tunnel surrounding rocks under the TBM construction condition is based on the excavation performance of engineering rocks, and main engineering geological factors influencing the TBM tunneling efficiency are fully considered. At present, no recognized uniform standard for classification of surrounding rocks of TBM construction tunnels exists. The classification methods of the surrounding rocks commonly used at present include Q systematic classification of Norwegian scholars N.Barton, geomechanical classification of Bieniowski (RMR) of south African scholars and RQD classification of Dier. Barton proposed Q in 1999 on the basis of Q phylogenetic classificationTBMAlthough the system considers the interaction between the rock mass and the TBM, if the difference of the surrounding rock lithology of the tunnel is large, the error obtained by utilizing the model prediction is very large, and the related parameter indexes of the system are too many, so that the system is practical and difficult and cannot meet the surrounding rock grading requirement of the TBM construction tunnel.
Therefore, the existing surrounding rock classification system has poor universality and cannot reasonably predict the classification of specific project engineering.
Disclosure of Invention
Aiming at the defects described in the prior art, the invention provides a data mining-based surrounding rock excavation classification method for TBM construction, so that the TBM excavation rate can be accurately predicted, and scientific guidance is provided for TBM construction.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a TBM construction surrounding rock digchability grading method based on data mining comprises the following steps: and S1, establishing a penetration prediction model.
The method comprises the following specific steps: s1.1, establishing a relation model between the equipment operation parameter single-blade thrust and the control parameter penetration of the TBM tunneling parameter ascending section under different rock mass conditions.
Firstly, carrying out TBM tunneling experiments of gradient change of penetration and continuous change of penetration to obtain the change rule of the normal force of a single knife along with the penetration;
then establishing a relation model between the equipment operation parameter single-blade thrust and the control parameter penetration of the TBM tunneling parameter ascending section under different rock mass conditions:
F＝a×P+b；
wherein, a represents the influence coefficient of penetration on the thrust of the single cutter, b is the rock breaking threshold value of the hob, and P represents the penetration.
S1.2, establishing a relation model of rock mass state parameters and penetration.
The method comprises the following specific steps: s1.2.1, establishing a coefficient a of influence of penetration on single-blade thrust and a unit volume rational number JvStepwise regression fit of the function between:
a＝f(Jv)＝p0×Jv 2+p1×Jv+p2，0＜Jv＜35；
in the formula, p0, p1 and p2 are fitting constants, and f is a function.
And a + b with UCS and JvAll have a linear correlation, so the assumed functional relationship is established as follows:
a+b＝g(UCS,Jv)，0＜Jv＜35；0＜UCS＜100。
s1.2.2, establishing compressive strength index UCS and unit volume rational number J when penetration degree is 1vStepwise regression fit of the function between:
a+b＝g(UCS,Jv)＝p3×UCS+p4×Jv+p5；
wherein p3, p4 and p5 are fitting constants, and g is a regression function.
S1.3, calculating a basic quality index BQ of the rock mass:
BQ＝100+3UCS+250Kv；
wherein UCS is an index of compressive strength; kvIs the integrity coefficient of the rock mass; and when UCS is more than or equal to 90Kv+30, UCS ═ 90Kv+ 30; when K isvWhen UCS +0.4 is more than or equal to 0.04, Kv＝0.04UCS+0.4。
And S1.4, determining the traditional surrounding rock grade of the rock mass.
And determining the traditional surrounding rock grade of the rock mass according to the calculated relation between the basic quality index BQ of the rock mass and the traditional surrounding rock grade.
S1.5, calculating the single-cutter thrust value of the hob under the traditional surrounding rock grade of the rock mass:
Fn＝Fmax×Fs；
wherein, FsThe single-cutter thrust coefficient of a hob under the traditional surrounding rock grade is adopted; fmaxThe maximum single-blade thrust is obtained.
And S1.6, calculating to obtain a value P of the penetration degree of the hob according to the step S1.1, the step S1.2 and the single-cutter thrust value of the hob, and thus establishing a starting penetration degree prediction model.
And S2, establishing a rotation speed prediction model.
The method comprises the following specific steps: s2.1, establishing a functional characteristic relation between the maximum rotating speed of the cutter and the diameter of the cutter:
Nmax＝12.44D-0.313；
in the formula, NmaxThe rotating speed of the cutter head is shown, and D is the diameter of the development machine.
S2.2, establishing a cutter head rotating speed N and a rock body integrity index KvFunctional relationship between;
and S3, establishing a TBM construction tunneling working hour utilization rate prediction model.
The working hour utilization rate of TBM construction is the ratio of the net tunneling time of the TBM to the total construction time, and is an important index for measuring the use and management level of the TBM.
And S3.1, performing TBM downtime process classification.
The downtime process is of three types: the method comprises a fixed stopping time process, a stopping process changing along with the tunneling distance, and a stopping process changing along with the rock mass condition and the tunneling distance.
The fixed stopping time process is irrelevant to the rock mass condition and the advancing distance and comprises team handing-over T1, equipment maintenance T2, cutterhead overhaul T3, post-matching processing T4 and other delay time T5.
The shutdown process which changes along with the tunneling distance comprises step changing time T6.
The stopping process which is changed along with the condition of the rock mass and the tunneling distance comprises fault treatment T7, cutter replacement T8, support time T9 and unfavorable geological treatment T10.
And S3.2, establishing a TBM shutdown procedure time statistical table.
S3.3, establishing a TBM tunneling working hour utilization rate prediction function relation:
wherein U is the TBM tunneling working hour utilization rate, and AR is the tunneling speed.
And S4, calculating the tunneling speed AR according to the steps S1-S3, and grading the surrounding rock tunneling performance of the TBM construction by taking the tunneling speed AR of the TBM as an evaluation index.
The method comprises the following specific steps: s4.1, calculating the tunneling speed AR according to the steps S1-S3:
wherein P is penetration degree, N is cutter head rotating speed, and U is TBM tunneling working hour utilization rate.
And S4.2, dividing the TBM construction surrounding rock into 5 grades according to the tunneling speed AR.
When AR is more than or equal to 2.0m/h, the TBM construction surrounding rock conditions are extremely good and are defined as I-grade surrounding rock;
when the AR is more than 1.2m/h and less than or equal to 2.0m/h, the condition of TBM construction surrounding rock is good, and the TBM construction surrounding rock is defined as II-level surrounding rock;
when AR is more than 0.6m/h and less than or equal to 1.2m/h, the TBM construction surrounding rock conditions are better, and the TBM construction surrounding rock is defined as class III surrounding rock;
when AR is more than 0.2m/h and less than or equal to 0.6m/h, TBM construction surrounding rock conditions are general and are defined as VI-level surrounding rock;
when the ratio of 0.2m/h to AR is less than or equal to that of the surrounding rock, the condition of TBM construction surrounding rock is poor, and the surrounding rock is defined as V-level surrounding rock.
The invention adopts a research method of mathematical statistics and mathematical excavation based on engineering cases, clearly defines and divides parameters influencing the TBM tunneling rate, reasonably predicts the cutter penetration degree, the rotating speed and the labor hour utilization rate, and intuitively establishes a surrounding rock tunneling classification model based on TBM construction by taking the daily average tunneling speed of the TBM as an evaluation index, and divides the surrounding rock into four classes by considering the construction surrounding rock tunneling property and geological adaptability of the TBM: the I-grade surrounding rock is TBM surrounding rock construction condition is excellent; the level II surrounding rock is TBM surrounding rock with good construction conditions; the grade III surrounding rock is a TBM construction surrounding rock with better condition; the VI-level surrounding rock is the general condition of TBM construction surrounding rock; the V-level surrounding rock is the surrounding rock with poor TBM construction condition.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of the system of the present invention.
FIG. 2 is a graph of the relationship between the diameter of the cutter head and the rotation speed of the cutter head according to the present invention.
FIG. 3 is a diagram of the classification of the TBM downtime procedure of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
A data mining-based surrounding rock excavation grading method for TBM construction comprises the following steps as shown in figure 1:
and S1, establishing a penetration prediction model.
Early preparation: collecting TBM tunneling parameters and rock mass state parameter data in a TBM construction case, wherein the TBM tunneling parameters comprise running parameters such as thrust and torque of the TBM, penetration and rotating speed control parameters, and the rock mass state parameters comprise rock mass compressive strength UCS and unit volume rational number JvAnd a surrounding rock grade.
The rock uniaxial compressive strength index UCS is obtained by site coring and laboratory rock compressive strength test or by counting the joint spacing and joint number in cavity wall sketch and according to the rock volume joint number JvThe calculation formula of (2):
Jv＝∑(1/dk)+Sk[ 1 ] is obtained, wherein dkSpacing of the kth joint, SkThe number of ungrouped joints of each cubic rock body of the kth group is shown.
The grade of the surrounding rock is obtained through a detailed geological survey report, or the compressive strength UCS of the rock mass and the unit volume regulation number J are utilizedvAnd calculating the basic quality index BQ value of the surrounding rock to grade the surrounding rock, establishing a functional expression of the operation parameters and the control parameters, and providing an empirical coefficient of the single-blade thrust, thereby establishing a penetration prediction model.
The method comprises the following specific steps: s1.1, establishing a relation model between the equipment operation parameter single-blade thrust and the control parameter penetration of the TBM tunneling parameter ascending section under different rock mass conditions.
Firstly, carrying out TBM tunneling experiments of gradient change of penetration and continuous change of penetration to obtain the change rule of the normal force of a single knife along with the penetration;
then establishing a relation model between the equipment operation parameter single-blade thrust and the control parameter penetration of the TBM tunneling parameter ascending section under different rock mass conditions:
F＝a×P+b；
wherein, a represents the influence coefficient of penetration on the thrust of the single cutter, b is the rock breaking threshold value of the hob, and P represents the penetration.
S1.2, establishing a relation model of rock mass state parameters and penetration.
The method comprises the following specific steps: s1.2.1, establishing a penetrationIncidence on single-blade thrust influence coefficient a and unit volume adjustment number JvStepwise regression fit of the function between:
a＝f(Jv)＝p0×Jv 2+p1×Jv+p2，0＜Jv＜35；
in the formula, p0, p1 and p2 are fitting constants, and f is a function.
The coefficient of influence a of penetration on the thrust of a single blade indicates the increment of thrust of a single blade required to increase the unit penetration, and is a function related to the number of joints of the rock mass since the increment of thrust required to increase the unit penetration is smaller as the rock mass is more fractured.
And a + b with UCS and JvAll have a linear correlation, so the assumed functional relationship is established as follows:
a+b＝g(UCS,Jv)，0＜Jv＜35；0＜UCS＜100。
s1.2.2, establishing compressive strength index UCS and unit volume rational number J when penetration degree is 1vStepwise regression fit of the function between:
a+b＝g(UCS,Jv)＝p3×UCS+p4×Jv+p5；
wherein p3, p4 and p5 are fitting constants, and g is a regression function.
The rock breaking threshold value b of the cutter is the minimum threshold value of the hob which invades the rock mass and generates indentation, when P is 1, the single-cutter thrust F is a + b, which explains the thrust required by the hob when the hob generates 1mm effective penetration, so that the a + b can be used for measuring the tunneling performance characteristic of the rock mass.
S1.3, calculating a basic quality index BQ of the rock mass:
BQ＝100+3UCS+250Kv；
wherein UCS is an index of compressive strength; kvIs the integrity coefficient of the rock mass; and when UCS is more than or equal to 90Kv+30, UCS ═ 90Kv+ 30; when K isvWhen UCS +0.4 is more than or equal to 0.04, Kv＝0.04UCS+0.4。
Integrity coefficient of rock mass KvIs calculated by the unit volumevDetermining, the number of units of volume saving JvCoefficient of integrity with rock mass KvThe comparison of (A) and (B) is shown in Table 1.
TABLE 1
And S1.4, determining the traditional surrounding rock grade of the rock mass.
And determining the traditional surrounding rock grade of the rock mass according to the calculated relation between the basic quality index BQ of the rock mass and the traditional surrounding rock grade, as shown in Table 2.
TABLE 2
S1.5, calculating the single-cutter thrust value of the hob under the traditional surrounding rock grade of the rock mass:
Fn＝Fmax×Fs；
wherein, FsThe single-cutter thrust coefficient of a hob under the traditional surrounding rock grade is adopted; fmaxThe maximum single-blade thrust is the corresponding relation between the single-blade thrust and the traditional surrounding rock grade, and is shown in table 3.
TABLE 3
And S1.6, calculating to obtain a value P of the penetration degree of the hob according to the step S1.1, the step S1.2 and the single-cutter thrust value of the hob, and thus establishing a starting penetration degree prediction model.
And S2, establishing a rotation speed prediction model.
The method comprises the following specific steps: s2.1, establishing a functional characteristic relation between the maximum rotating speed of the cutter head and the diameter of the cutter head, as shown in figure 2,
Nmax＝12.44D-0.313；
in the formula, NmaxIs a knifeThe disk rotation speed D is the diameter of the heading machine.
S2.2, establishing a cutter head rotating speed N and a rock body integrity index KvFunctional relationship between;
the details are shown in Table 4.
TABLE 4
| KvValue taking | Integrity of rock mass | Value of cutter head rotation speed |
| 0-0.25 | Crushing | (Kv+0.25)×Nmax |
| 0.25-0.45 | Poor integrity | (Kv+0.25)×Nmax |
| 0.45-0.75 | Is more complete | (Kv+0.25)×Nmax |
| 0.75-1.0 | Complete (complete) | Nmax |
And S3, establishing a TBM construction tunneling working hour utilization rate prediction model.
The working hour utilization rate of TBM construction is the ratio of the net tunneling time of the TBM to the total construction time, and is an important index for measuring the use and management level of the TBM.
S3.1, TBM downtime process classification is carried out, as shown in figure 3.
The downtime process is of three types: the method comprises a fixed stopping time process, a stopping process changing along with the tunneling distance, and a stopping process changing along with the rock mass condition and the tunneling distance.
The fixed stopping time procedures are irrelevant to rock mass conditions and advancing distances and comprise team handing-over T1, equipment maintenance T2, cutter head maintenance T3, post-matching processing T4 and other delay time T5, wherein the other delay time T5 comprises a vulcanized belt, measurement station changing, post-matching processing and the like, and the fixed stopping time procedures are distributed to each procedure according to a large number of case statistics.
The shutdown process which changes along with the tunneling distance comprises step changing time T6.
The stopping process which is changed along with the condition of the rock mass and the tunneling distance comprises fault treatment T7, cutter replacement T8, support time T9 and unfavorable geological treatment T10. The time spent on the procedures of supporting and treating the unfavorable geology by the TBM can be estimated according to the integrity degree of the current surrounding rock and the actual footage. The time consumed by other processes, such as fault processing, cutter replacement and the like is random, but is generally strongly related to the geological condition, statistics shows that when the rock mass strength is higher, the average time consumption of the two processes under the unit tunneling distance is higher, and the averaging processing can be performed according to the statistical result.
And S3.2, establishing a TBM shutdown procedure time statistical table as shown in a table 5.
TABLE 5
S3.3, establishing a TBM tunneling working hour utilization rate prediction function relation:
wherein U is the TBM tunneling working hour utilization rate, and AR is the tunneling speed.
And S4, calculating the tunneling speed AR according to the steps S1-S3, and carrying out TBM construction rock excavation classification by taking the TBM tunneling speed AR as an evaluation index.
The method comprises the following specific steps: s4.1, calculating the tunneling speed AR according to the steps S1-S3:
wherein P is penetration degree, N is cutter head rotating speed, and U is TBM tunneling working hour utilization rate.
And S4.2, dividing the TBM construction surrounding rock into 5 grades according to the tunneling speed AR.
When AR is more than or equal to 2.0m/h, the TBM construction surrounding rock conditions are extremely good and are defined as I-grade surrounding rock;
when the AR is more than 1.2m/h and less than or equal to 2.0m/h, the condition of TBM construction surrounding rock is good, and the TBM construction surrounding rock is defined as II-level surrounding rock;
when AR is more than 0.6m/h and less than or equal to 1.2m/h, the TBM construction surrounding rock conditions are better, and the TBM construction surrounding rock is defined as class III surrounding rock;
when AR is more than 0.2m/h and less than or equal to 0.6m/h, TBM construction surrounding rock conditions are general and are defined as VI-level surrounding rock;
when the ratio of 0.2m/h to AR is less than or equal to that of the surrounding rock, the condition of TBM construction surrounding rock is poor, and the surrounding rock is defined as V-level surrounding rock.
Specifically, the results are shown in Table 6.
TABLE 6
Aiming at the current industrial application situation that the existing TBM construction field lacks an implementation method for unified and effective surrounding rock geological prediction and tunneling rate prediction, the invention provides a tunnel surrounding rock tunneling classification method under the TBM construction condition according to data statistics and data mining of engineering cases, so that the tunnel surrounding rock tunneling classification method can accurately predict the tunneling rate of the TBM under various surrounding rock geological conditions, guide various construction parameters of the TBM, and predict the construction cost and the construction period.
The above embodiments are only for describing the present invention, and not for limiting the present invention. It will be apparent to those skilled in the art that suitable changes and modifications may be made without departing from the scope of the invention, and all equivalents thereof are intended to be encompassed by the invention as defined in the following claims.
Claims (3)
1. A TBM construction surrounding rock excavating classification method based on data mining is characterized by comprising the following steps: s1, establishing a penetration prediction model;
s2, establishing a rotation speed prediction model;
s3, establishing a TBM construction tunneling working hour utilization rate prediction model;
the working hour utilization rate of TBM construction is the ratio of the net tunneling time of the TBM to the total construction time, and is an important index for measuring the use and management level of the TBM;
s4, calculating a TBM tunneling speed AR by combining the steps S1-S3, and grading the surrounding rock tunneling performance of TBM construction by taking the TBM tunneling speed AR as an evaluation index;
in step S2, the specific steps are:
s2.1, establishing a functional characteristic relation between the maximum rotating speed of the cutter and the diameter of the cutter;
Nmax＝12.44D-0.313；
in the formula, NmaxThe rotating speed of the cutter head and the diameter of the development machine are D;
s2.2, establishing a cutter head rotating speed N and a rock body integrity index KvFunctional relationship between;
in step S3, the specific steps are:
s3.1, performing TBM downtime process classification;
the downtime process is of three types: a fixed stopping time process, a stopping process changing along with the tunneling distance, and a stopping process changing along with the rock mass condition and the tunneling distance;
the fixed stopping time process is irrelevant to the rock mass condition and the footage distance and comprises a team handing-over T1, an equipment maintenance T2, a cutterhead overhaul T3, a post-matching treatment T4 and other delay time T5;
the shutdown process changing along with the tunneling distance comprises step changing time T6;
the shutdown process which is changed along with the condition of the rock mass and the tunneling distance comprises fault treatment T7, cutter replacement T8, support time T9 and unfavorable geological treatment T10;
s3.2, establishing a TBM shutdown procedure time statistical table;
s3.3, establishing a TBM tunneling working hour utilization rate prediction function relation:
wherein U is the TBM tunneling working hour utilization rate, and AR is the tunneling speed.
2. The data mining-based surrounding rock excavation grading method for TBM construction based on the claim 1, wherein in the step S1, the specific steps are as follows: s1.1, establishing a relation model between the equipment operation parameter single-blade thrust and the control parameter penetration of the TBM tunneling parameter ascending section under different rock mass conditions:
F＝a×P+b；
wherein a represents the influence coefficient of penetration on the thrust of the single cutter, b is the rock breaking threshold value of the hob, and P represents the penetration;
s1.2, establishing a relation model of rock mass state parameters and penetration;
s1.2.1, establishing a coefficient a of influence of penetration on single-blade thrust and a unit volume rational number JvStepwise regression fit of the function between:
a＝f(Jv)＝p0×Jv 2+p1×Jv+p2，0＜Jv＜35；
in the formula, p0, p1 and p2 are fitting constants, and f is a function;
s1.2.2, establishing compressive strength index UCS and unit volume rational number J when penetration degree is 1vStepwise regression fit of the function between:
a+b＝g(UCS,Jv)＝p3×UCS+p4×Jv+p5；
wherein p3, p4 and p5 are fitting constants, and g is a regression function;
s1.3, calculating a basic quality index BQ of the rock mass:
BQ＝100+3UCS+250Kv；
wherein UCS is an index of compressive strength; kvIs the integrity coefficient of the rock mass; and when UCS is more than or equal to 90Kv+30, UCS ═ 90Kv+ 30; when K isvWhen UCS +0.4 is more than or equal to 0.04, Kv＝0.04UCS+0.4；
S1.4, determining the traditional surrounding rock grade of the rock mass;
determining the traditional surrounding rock grade of the rock mass according to the relation between the basic quality index BQ of the rock mass and the traditional surrounding rock grade;
s1.5, calculating a single-cutter thrust value of the hob under the traditional surrounding rock grade of the rock mass;
Fn＝Fmax×Fs；
wherein, FsThe single-cutter thrust coefficient of a hob under the traditional surrounding rock grade is adopted; fmaxThe maximum single-blade thrust is obtained;
and S1.6, calculating to obtain a value P of the penetration degree of the hob according to the step S1.1, the step S1.2 and the single-cutter thrust value of the hob, and thus establishing a starting penetration degree prediction model.
3. The data mining-based surrounding rock excavation grading method for TBM construction based on the claim 1, wherein in the step S4, the specific steps are as follows:
s4.1, calculating the tunneling speed AR according to the steps S1-S3:
wherein P is penetration degree, N is cutter head rotating speed, and U is TBM tunneling working hour utilization rate;
s4.2, dividing the TBM construction surrounding rock into 5 grades according to the tunneling speed AR;
when AR is more than or equal to 2.0m/h, the TBM construction surrounding rock conditions are extremely good and are defined as I-grade surrounding rock;
when the AR is more than 1.2m/h and less than or equal to 2.0m/h, the condition of TBM construction surrounding rock is good, and the TBM construction surrounding rock is defined as II-level surrounding rock;
when AR is more than 0.6m/h and less than or equal to 1.2m/h, the TBM construction surrounding rock conditions are better, and the TBM construction surrounding rock is defined as class III surrounding rock;
when AR is more than 0.2m/h and less than or equal to 0.6m/h, TBM construction surrounding rock conditions are general and are defined as VI-level surrounding rock; when the ratio of 0.2m/h to AR is less than or equal to that of the surrounding rock, the condition of TBM construction surrounding rock is poor, and the surrounding rock is defined as V-level surrounding rock.
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