CN112818439A - Soft rock tunnel surrounding rock sub-grade grading method - Google Patents
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Abstract
A soft rock tunnel surrounding rock sub-grade grading method comprises the following steps: determining grading indexes and grading; calculating the numerical characteristic value of each grading index; MATLAB generates a corresponding cloud model diagram, and the test value certainty is read; acquiring four test parameter values of the sub-level section to be divided through an indoor test; determining each grading index weight; calculating comprehensive certainty and judging the grade; according to the invention, four parameters which do not pass through a rock sample are obtained through an indoor test, the soft rock tunnel surrounding rock sub-level classification is carried out by combining a targeted classification method, the IV-level surrounding rock and the V-level surrounding rock are divided into five sub-levels of IV-a, IV-b, IV-c, V-a and V-b, and corresponding support measures are formulated according to different sub-levels, so that the subdivided surrounding rock brings great convenience for subsequent tunnel construction, plays a role in saving a construction period and avoiding construction safety accidents, and can be popularized and applied to the fields of soft rock tunnel surrounding rock classification and tunnel support design.
Description
Technical Field
The invention belongs to the technical field of supporting equipment or devices for graded design of tunnel surrounding rocks, and particularly relates to a soft rock tunnel surrounding rock sub-grade grading method.
Background
The surrounding rock grading refers to selecting some indexes related to the tunnel surrounding rock, such as rock hardness degree, rock integrity degree, rock structure surface state and the like, and dividing the tunnel surrounding rock into a plurality of grades according to specific standards or methods according to the requirements of tunnel engineering design and construction. In the current design process of the highway tunnel, after the surrounding rock grade is divided, the excavation method, the supporting measure and the supporting parameter corresponding to the surrounding rock grade can be further determined according to the specification and the standard.
For IV-grade and V-grade soft surrounding rocks, the grading system has large span and is very easy to cause adverse effects on the aspects of construction progress, support measures, support parameters and the like of the soft rock tunnel, and the tunnel needs more detailed surrounding rock grade division because the soft rock has strong complexity and uncertainty.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide the soft rock tunnel surrounding rock sub-grade classification method which is reasonable in design, brings great convenience to subsequent tunnel construction, saves the construction period and avoids construction safety accidents.
The technical scheme for solving the technical problems comprises the following steps:
A. determining grading indexes and grading;
(A1) determining the value range of the uniaxial compressive strength Rc in IV and V-grade surrounding rock to be 0-60 MPa;
(A2) determining the value range of the rock integrity coefficient Kv at IV and V level surrounding rocks to be 0-0.75, and determining the value range of the cohesive force c at IV and V level surrounding rocks to be 0-0.7 MPa;
(A3) softening coefficient K of surrounding rockfThe value range of the IV-grade and V-grade surrounding rocks is determined to be 0-0.75;
B. calculating the numerical characteristic value of each grading index;
determining expected E of normal cloud with surrounding rock sub-grade grading index corresponding to certain grade standardxEntropy of normal cloud EnNormal cloud super entropy He，
He＝K
In the formula, CmaxAnd CminThe variable fuzzy threshold value is used for adjusting the variable fuzzy threshold value according to the constant K;
C. MATLAB generates a corresponding cloud model diagram, and the test value certainty is read;
(C1) producing the expected value is ExEntropy is EnThe normal random number x of (a); calculating the expected value is ExEntropy is EnIs normal membership cloud, the degree of membership at the desired curve x
Respectively calculating the entropy E of the changes along the expected curves MA and MB according to a reduced semi-normal rulenl、Enr：
(c2) Calculating points
Variance σ of (c)x：
(C3) Generating the expected value is
Variance is σxNormal random number μ of (a):
(C4) finally obtaining the cloud droplet xi (x, mu);
(C5) after the cloud model digital characteristic values of all indexes under different sub-level grades are obtained, the cloud model graph corresponding to the tunnel surrounding rock sub-level grading indexes is obtained after the cloud model digital characteristic values of all surrounding rock sub-level grading indexes are input by using a forward cloud generator;
D. acquiring four test parameter values of the sub-level section to be divided through an indoor test;
(D1) adopting an indoor uniaxial compression test to obtain uniaxial compression strength and softening coefficient of the surrounding rock, and dividing the uniaxial compression test into a uniaxial compression test in a drying state, a uniaxial compression test in a water saturation state and a uniaxial compression test in a natural state according to different water containing states of the sample, wherein the ratio of the uniaxial compression strength in a rock saturation state to the uniaxial compression strength in a drying state is the softening coefficient of the rock;
(D2) after the longitudinal wave velocity of the rock mass and the elastic longitudinal wave velocity of the rock are respectively obtained through tests, the ratio of the longitudinal wave velocity to the elastic longitudinal wave velocity of the rock is the rock integrity coefficient;
(D3) obtaining corresponding shear strength indexes, namely cohesive force c and internal friction angle through direct shear test of rock
E. Determining each grading index weight; d, after obtaining a grading index test value of the required sub-grade grading section through an indoor test, determining the index weight through an entropy weight method by MATLAB software;
(E1) carrying out standardized processing on the data, and selecting j tunnel surrounding rock grading indexes, namely X1,X2,X3,···,XjIf i evaluation objects are set, the evaluation index value of the ith object is xi1,xi2,xi3,···,xijFormula y of normalization processijThe following were used:
(E2) according to the information entropy definition, calculating the information entropy corresponding to each index value of the n evaluation objects:
(E3) obtaining weight values omega of all indexes of i groups of surrounding rock grading objectsj：
F. Calculating comprehensive certainty and judging the grade;
(F1) firstly, calculating the test value certainty, obtaining the grading index test value of the grading section of the soft rock tunnel according to an indoor test, reading the test value certainty mu (x) of each grading index test value corresponding to each grade according to a generated table by using MATLAB software, and marking the test value certainty mu (x) as 0 if the test value does not belong to a certain grade;
(F2) calculating comprehensive certainty factor, determining degree mu (x) by the calculated test value, and calculating each grading index weight omega by combining entropy weight methodjAnd calculating the comprehensive certainty U of each section:
(F3) and judging the sub-grade level of the surrounding rock of each section, and judging the final sub-grade level attribution of the surrounding rock of each section according to the obtained numerical value of the comprehensive certainty factor U of each section.
In step B of the present invention, K is 0.01[95 ].
According to the invention, four parameters including uniaxial compressive strength, rock integrity coefficient, softening coefficient and cohesive force which do not pass through a rock sample are obtained through an indoor test, the soft rock tunnel surrounding rock sub-grade classification is carried out by combining a targeted classification method, the IV-grade surrounding rock and the V-grade surrounding rock are divided into five sub-grades of IV-a, IV-b, IV-c, V-a and V-b, and corresponding support measures are formulated according to different sub-grades, so that the subdivided surrounding rock brings great convenience for subsequent tunnel construction, plays a role in saving construction period and avoiding construction safety accidents, and can be popularized and applied to the field of soft rock tunnel surrounding rock classification and tunnel support design.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a schematic diagram of a cloud model of the present invention, wherein the origin is defined as point A.
FIG. 3 is a diagram of a uniaxial compressive strength cloud model of the present invention.
FIG. 4 is a diagram of an integrity coefficient cloud model of the present invention.
FIG. 5 is a cloud model of cohesion according to the present invention.
FIG. 6 is a diagram of a softening coefficient cloud model of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following drawings and examples, but the present invention is not limited to these examples.
Example 1
In fig. 1, the soft rock tunnel surrounding rock sub-grade grading method related by the invention comprises the following steps:
A. determining grading indexes and grading; through statistical arrangement of rock mass grading and tunnel surrounding rock grading index systems at home and abroad and combing of engineering grading and grade evaluation research results aiming at soft rocks or weak surrounding rocks, four parameters including uniaxial compressive strength, rock integrity coefficient, softening coefficient and cohesive force of the rocks are used as the grading index system for soft rock tunnel surrounding rock sub-grade grading, and the IV-grade surrounding rocks and the V-grade surrounding rocks are divided into five sub-grades of IV-a, IV-b, IV-c, V-a and V-b.
(A1) When surrounding rock stability analysis is carried out, the hardness degrees of IV-level surrounding rocks and V-level surrounding rocks mainly comprise three types of harder rocks, softer rocks and soft rocks, and in addition, extreme conditions, namely the conditions of completely weathered soft rocks and completely disintegrated soaked water are considered, so when index numerical level classification of uniaxial compressive strength is carried out, the value ranges of the uniaxial compressive strength Rc in the IV-level and V-level surrounding rocks are determined to be 0-60 MPa according to the relationship of qualitative classification of the uniaxial compressive strength and the hardness degrees of the rocks in the Highway tunnel design Specification (JTG 3370.1-2018);
(A2) determining the value range of the rock integrity coefficient Kv at IV and V level surrounding rocks to be 0-0.75, and determining the value range of the cohesive force c at IV and V level surrounding rocks to be 0-0.7 MPa;
(A3) the rock softening tendency can be defined as that the value is less than 0.75 by the specification of geotechnical engineering investigation Specification (GB 50021-2001), so that the surrounding rock softening coefficient K is definedfThe value range of the IV-grade and V-grade surrounding rocks is determined to be 0-0.75;
for the value ranges of the indexes among different surrounding rock grades, the related specifications and documents are mainly referred, and finally determined surrounding rock sub-grade grading index grades are classified and shown in table 1.
TABLE 1 carbonaceous mudstone tunnel surrounding rock grading index grading
B. Calculating the numerical characteristic value of each grading index;
determining expected E of normal cloud with surrounding rock sub-grade grading index corresponding to certain grade standardxEntropy of normal cloud EnNormal cloud super entropy He，
He＝K
In the formula, CmaxAnd CminThe variable fuzzy threshold value is used for adjusting the variable fuzzy threshold value according to the constant K; in the invention, K is uniformly 0.01[95]]. The numerical characteristic value calculation results of each grade index are shown in table 2.
TABLE 2 numerical characteristics of the respective grading indices
In the above steps, the cloud definition and the cloud model digital features are as follows:
and x, Y and C are set, wherein x belongs to Y, Y is a common quantitative set and is called a domain of discourse, and C represents a qualitative concept on Y. If any object x in Y has a random number mu (x) epsilon [0,1 ] with a stable tendency]It is called the membership of x to C, also calledAnd (5) determining the degree. The distribution of the certainty factor in Y is called a cloud model. If x satisfies: x to N (E)x,E'n) Wherein, E'n～N(En,H2 e) μ (x) and satisfies the following formula:
the distribution of the certainty μ on the domain of discourse Y is called a normal cloud, as shown in FIG. 1, in which a cubic normal distribution rule is implied, i.e. three numerical characteristics of a normal cloud model, denoted as N3 (E)x,E'n,H2 e) Wherein E isx、En、HeExpectation, entropy and hyper-entropy, respectively, referred to as normal clouds.
(1) Expectation of normal cloud Ex: representing the expectation of the spatial distribution of cloud droplets in the universe of discourse, i.e. the centroid of the area under the coverage of the cloud model
Is the information center value which can reflect the fuzzy concept most;
(2) entropy E of Normal cloudn: the uncertainty measure represents a qualitative concept and represents the value range size which can be accepted by the qualitative concept in the discourse space, namely the ambiguity;
(3) hyper-entropy of normal cloud He: i.e. points on the expected curve of the cloud model
The corresponding variance of the deterministic random distribution, which describes a measure of uncertainty in entropy, reflects the randomness of the appearance of the samples representing qualitative conceptual values, revealing a correlation of ambiguity and randomness.
C. MATLAB generates a corresponding cloud model diagram, and the test value certainty is read;
(C1) producing the expected value is ExEntropy is EnThe normal random number x of (a); calculating the expected value is ExEntropy is EnIs normal membership cloud, the degree of membership at the desired curve x
Respectively calculating the entropy E of the changes along the expected curves MA and MB according to a reduced semi-normal rulenl、Enr：
(c2) Calculating points
Variance σ of (c)x：
(C3) Generating the expected value is
Variance is σxNormal random number μ of (a):
(C4) finally obtaining the cloud droplet xi (x, mu);
(C5) after the cloud model digital characteristic values of all indexes under different sub-level grades are obtained, the cloud model graph corresponding to the tunnel surrounding rock sub-level grading indexes is obtained after the cloud model digital characteristic values of all surrounding rock sub-level grading indexes are input by using a forward cloud generator;
after all the index numerical characteristic values calculated in table 2 are imported into MATLAB for calculation, cloud model diagrams of all five sub-level certainty degrees of iv-av-b corresponding to uniaxial compressive strength, integrity coefficient, cohesive force and softening coefficient in the sub-level hierarchical index system can be obtained, as shown in fig. 3 to fig. 6.
In the above steps, a normal cloud Generator (MCG) is an important implementation tool for cloud model theory, and the formula is as follows:
forward cloud generator is an expectation of a known cloud model ExEntropy EnAnd entropy HeAnd generating a two-dimensional point xi (x, mu) meeting the normal cloud distribution rule by using the three digital characteristic values, namely realizing the conversion from a qualitative concept to a quantitative value, wherein the two-dimensional point xi (x, mu) is also called cloud drop.
The normal cloud model is formed by thousands of cloud droplets generated by the forward cloud generator, and the mathematical model of the normal cloud generator is as follows without loss of generality:
(1) the expected curve of the normal cloud satisfies N (E)x,En 2) The normal distribution of (1) is shown in the above formula.
(2) Points on the desired curve
The random number is a normal random number with the variance of the point as the convergence center in the direction of certainty degree, and the normal distribution N (E) isx,En 2) Is generated from a normal distribution
And (4) generating.
(3) Varying along the expected curve of a normal cloud, reaching a maximum value σ at point Mx＝HeAt points A and B σA＝σ B0. On both sides of point M, the curve is changed according to two decreasing semi-normal rules, and conforms to the "3 b rule".
D. Acquiring four test parameter values of the sub-level section to be divided through an indoor test;
(D1) adopting an indoor uniaxial compression test to obtain uniaxial compression strength and softening coefficient of the surrounding rock, and dividing the uniaxial compression test into a uniaxial compression test in a drying state, a uniaxial compression test in a water saturation state and a uniaxial compression test in a natural state according to different water containing states of the sample, wherein the ratio of the uniaxial compression strength in a rock saturation state to the uniaxial compression strength in a drying state is the softening coefficient of the rock;
(D2) after the longitudinal wave velocity of the rock mass and the elastic longitudinal wave velocity of the rock are respectively obtained through tests, the ratio of the longitudinal wave velocity to the elastic longitudinal wave velocity of the rock is the rock integrity coefficient;
(D3) obtaining corresponding shear strength indexes, namely cohesive force c and internal friction angle through direct shear test of rock
E. Determining each grading index weight; d, after obtaining a grading index test value of the required sub-grade grading section through an indoor test, determining the index weight through an entropy weight method by MATLAB software;
(E1) carrying out standardized processing on the data, and selecting j tunnel surrounding rock grading indexes, namely X1,X2,X3,···,XjIf i evaluation objects are set, the evaluation index value of the ith object is xi1,xi2,xi3,···,xijFormula y of normalization processijThe following were used:
(E2) according to the information entropy definition, calculating the information entropy corresponding to each index value of the n evaluation objects:
(E3) obtaining i groups of surrounding rocksWeight value omega of each index of grading objectj：
F. Calculating comprehensive certainty and judging the grade;
(F1) firstly, calculating the test value certainty, obtaining the grading index test value of the grading section of the soft rock tunnel according to an indoor test, reading the test value certainty mu (x) of each grading index test value corresponding to each grade according to a generated table by using MATLAB software, and marking the test value certainty mu (x) as 0 if the test value does not belong to a certain grade;
(F2) calculating comprehensive certainty factor, determining degree mu (x) by the calculated test value, and calculating each grading index weight omega by combining entropy weight methodjAnd calculating the comprehensive certainty U of each section:
(F3) and judging the sub-grade level of the surrounding rock of each section, and judging the final sub-grade level attribution of the surrounding rock of each section according to the obtained numerical value of the comprehensive certainty factor U of each section.
The classification method of the invention is adopted to perform the sub-grade classification application of the surrounding rock on five sections selected from K9+525, K9+660, K9+910, K10+405 and K11+968 of the ancient top tunnel as follows:
and step A to step C are as described above, and in the step D, the rock samples of five sections are subjected to indoor tests, and the obtained grading index test values of the sections are arranged.
And E, according to the efficient operation of the entropy weight method realized by using MATLAB software in the step E, calculating the weights of the uniaxial compressive strength, the rock integrity coefficient, the cohesion and the softening coefficient, and finally obtaining the result shown in Table 3.
TABLE 3 test values and weights of ranking indices
And D, calculating the certainty factor according to the step F, judging the grade, and acquiring the certainty factor of the test value of each section corresponding to each grading index according to the research content in the step c, wherein the certainty factor is shown in the table 4, and the test value of each section corresponding to each grading index corresponds to each grading index.
TABLE 4 Each index certainty of five sections
From the calculated certainty μ (x), the comprehensive certainty U of each section was calculated in combination with the classification index weight calculated by the entropy weight method, and the result is shown in table 5.
TABLE 5 comprehensive determination of each section and quality grading results of surrounding rocks
From the results in Table 5, it was found that in the K9+525 section, U (V-a) > U (IV-c) > U (V-b) > U (IV-a) ═ U (IV-b), and therefore the grade of the surrounding rock was determined to be V-a in the K9+525 section. Similarly, the grade of the surrounding rock of the K9+660 section is V-b, the grade of the surrounding rock of the K9+910 section is V-a, the grade of the surrounding rock of the K10+405 section is IV-b, and the grade of the surrounding rock of the K11+968 section is IV-c.
Finally, according to the tunnel geological survey report, in the five sections selected at this time, the sections K9+525, K9+660 and K9+910 all belong to V-level surrounding rock, and the sections K10+405 and K11+968 belong to IV-level surrounding rock, so that the results of the five sections after a new grading index system and a cloud model grading method are adopted are consistent with the results of BQ-method surrounding rock grading in the geological survey report on the main level, and on the basis, the five sections are subjected to sub-level division by combining with the soft rock tunnel surrounding rock sub-level grading standard, so that the soft rock tunnel surrounding rock sub-level grading system has scientificity and practicability.
Claims (2)
1. A soft rock tunnel surrounding rock sub-grade grading method is characterized by comprising the following steps:
A. determining grading indexes and grading;
(A1) determining the value range of the uniaxial compressive strength Rc in IV and V-grade surrounding rock to be 0-60 MPa;
(A2) determining the value range of the rock integrity coefficient Kv at IV and V level surrounding rocks to be 0-0.75, and determining the value range of the cohesive force c at IV and V level surrounding rocks to be 0-0.7 MPa;
(A3) softening coefficient K of surrounding rockfThe value range of the IV-grade and V-grade surrounding rocks is determined to be 0-0.75;
B. calculating the numerical characteristic value of each grading index;
determining expected E of normal cloud with surrounding rock sub-grade grading index corresponding to certain grade standardxEntropy of normal cloud EnNormal cloud super entropy He，
He＝K
In the formula, CmaxAnd CminThe variable fuzzy threshold value is used for adjusting the variable fuzzy threshold value according to the constant K;
C. MATLAB generates a corresponding cloud model diagram, and the test value certainty is read;
(C1) producing the expected value is ExEntropy is EnThe normal random number x of (a); calculating the expected value is ExEntropy is EnNormal affiliation ofMembership at the desired curve x of the genus cloud
Respectively calculating the entropy E of the changes along the expected curves MA and MB according to a reduced semi-normal rulenl、Enr：
(c2) Calculating points
Variance σ of (c)x：
(C3) Generating the expected value is
Variance is σxNormal random number μ of (a):
(C4) finally obtaining the cloud droplet xi (x, mu);
(C5) after the cloud model digital characteristic values of all indexes under different sub-level grades are obtained, the cloud model graph corresponding to the tunnel surrounding rock sub-level grading indexes is obtained after the cloud model digital characteristic values of all surrounding rock sub-level grading indexes are input by using a forward cloud generator;
D. acquiring four test parameter values of the sub-level section to be divided through an indoor test;
(D1) adopting an indoor uniaxial compression test to obtain uniaxial compression strength and softening coefficient of the surrounding rock, and dividing the uniaxial compression test into a uniaxial compression test in a drying state, a uniaxial compression test in a water saturation state and a uniaxial compression test in a natural state according to different water containing states of the sample, wherein the ratio of the uniaxial compression strength in a rock saturation state to the uniaxial compression strength in a drying state is the softening coefficient of the rock;
(D2) after the longitudinal wave velocity of the rock mass and the elastic longitudinal wave velocity of the rock are respectively obtained through tests, the ratio of the longitudinal wave velocity to the elastic longitudinal wave velocity of the rock is the rock integrity coefficient;
(D3) obtaining corresponding shear strength indexes, namely cohesive force c and internal friction angle through direct shear test of rock
E. Determining each grading index weight; d, after obtaining a grading index test value of the required sub-grade grading section through an indoor test, determining the index weight through an entropy weight method by MATLAB software;
(E1) carrying out standardized processing on the data, and selecting j tunnel surrounding rock grading indexes, namely X1,X2,X3,···,XjIf i evaluation objects are set, the evaluation index value of the ith object is xi1,xi2,xi3,···,xijFormula y of normalization processijThe following were used:
(E2) according to the information entropy definition, calculating the information entropy corresponding to each index value of the n evaluation objects:
(E3) obtaining weight values omega of all indexes of i groups of surrounding rock grading objectsj：
F. Calculating comprehensive certainty and judging the grade;
(F1) firstly, calculating the test value certainty, obtaining the grading index test value of the grading section of the soft rock tunnel according to an indoor test, reading the test value certainty mu (x) of each grading index test value corresponding to each grade according to a generated table by using MATLAB software, and marking the test value certainty mu (x) as 0 if the test value does not belong to a certain grade;
(F2) calculating comprehensive certainty factor, determining degree mu (x) by the calculated test value, and calculating each grading index weight omega by combining entropy weight methodjAnd calculating the comprehensive certainty U of each section:
(F3) and judging the sub-grade level of the surrounding rock of each section, and judging the final sub-grade level attribution of the surrounding rock of each section according to the obtained numerical value of the comprehensive certainty factor U of each section.
2. The soft rock tunnel surrounding rock sub-grade grading method according to claim 1, characterized in that: and in the step B, K is uniformly 0.01[95 ].
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| CN117077027B (en) * | 2023-07-24 | 2024-03-15 | 西南交通大学 | Surrounding rock sub-level grading method and device based on intelligent grading model grading probability |
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