CN112818439B - Soft rock tunnel surrounding rock sub-level grading method - Google Patents
Soft rock tunnel surrounding rock sub-level grading method Download PDFInfo
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Abstract
A soft rock tunnel surrounding rock sub-level grading method comprises the following steps: determining a grading index and grading; calculating the digital characteristic value of each grading index; MATLAB generates a corresponding cloud model diagram, and reads the certainty of the test value; acquiring four test parameter values needing to be divided into sub-level sections through an indoor test; determining the weight of each grading index; 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, and a targeted grading method is combined to carry out soft rock tunnel surrounding rock sub-grade grading, IV and V-grade surrounding rock are divided into five sub-grades of IV-a, IV-b, IV-c, V-a and V-b, and corresponding supporting measures are prepared according to different sub-grades, so that more finely-divided 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 grading of soft rock tunnel surrounding rock and tunnel supporting design.
Description
Technical Field
The invention belongs to the technical field of supporting equipment or devices for grading and designing surrounding rocks of a tunnel, and particularly relates to a soft rock tunnel surrounding rock sub-grade grading method.
Background
The classification of surrounding rock refers to selecting some indexes related to the surrounding rock of a tunnel, such as the hardness degree of the rock, the integrity degree of the rock, the structural surface state of the rock and the like, and classifying the surrounding rock of the tunnel into a plurality of grades according to specific standards or methods according to the design and construction requirements of the tunnel engineering. In the current road tunnel design process, 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 by referring to the specifications and standards.
For IV and V-class weak surrounding rocks, the classification system has larger span, and is easy to cause adverse effects on the aspects of construction progress, supporting measures, supporting parameters and the like of a soft rock tunnel, and the classification of surrounding rocks is required to be more refined because the soft rock has such strong complexity and uncertainty.
Disclosure of Invention
The invention aims to solve the technical problems of overcoming the defects of the prior art, and provides the soft rock tunnel surrounding rock sub-level grading method which has reasonable design, brings great convenience for subsequent tunnel construction, saves construction period and avoids construction safety accidents.
The technical scheme adopted for solving the technical problems is that the method comprises the following steps:
A. determining a grading index and grading;
(A1) The value range of the uniaxial compressive strength Rc in IV and V-level surrounding rock is determined to be 0-60 MPa;
(A2) Determining the range of the rock integrity coefficient Kv in the IV and V-level surrounding rock to be 0-0.75, and determining the range of the cohesive force c in the IV and V-level surrounding rock to be 0-0.7 MPa;
(A3) Softening coefficient K of surrounding rock f The value range of the IV and V level surrounding rocks is determined to be 0-0.75;
B. calculating the digital characteristic value of each grading index;
determining expected E of normal cloud of surrounding rock sub-level grading index corresponding to certain level standard x Entropy E of normal cloud n Super entropy H of normal cloud e ，
H e ＝K
Wherein C is max And C min The upper boundary and the lower boundary of different grades of each grading index are respectively represented, K is a constant, and the adjustment can be carried out according to the fuzzy threshold value of the variable;
C. MATLAB generates a corresponding cloud model diagram, and reads the certainty of the test value;
(C1) Generating the desired value to be E x Entropy is E n Normal random number x of (a); calculating the expected value to be E x Entropy is E n Membership at the desired curve x of a normal membership cloud of (a)Respectively calculating entropy E changing along expected curve MA and MB according to decreasing semi-normal rule n l、E n r：
(c2) Calculation pointVariance sigma of the position x ：
(C3) The expected value is generatedVariance is sigma x Normal random number μ of (2):
(C4) Finally obtaining cloud drops xi (x, mu);
(C5) After the cloud model digital characteristic values of all indexes under different sub-level grades are obtained, a forward cloud generator is utilized to input the cloud model digital characteristic values of all surrounding rock sub-level grading indexes, and then a cloud model diagram corresponding to the tunnel surrounding rock sub-level grading indexes is obtained;
D. obtaining four test parameter values needing to divide the sub-level section through an indoor test;
(D1) The uniaxial compressive strength and the softening coefficient of the surrounding rock are obtained by adopting an indoor uniaxial compressive test, and the uniaxial compressive strength and the softening coefficient of the surrounding rock are divided into a uniaxial compressive test in a drying state, a uniaxial compressive test in a water saturation state and a uniaxial compressive test in a natural state according to different water-containing states of a sample, wherein the ratio of the uniaxial compressive strength in the rock saturation state to the uniaxial compressive strength in the drying state is the softening coefficient of the rock;
(D2) After rock mass longitudinal wave speed and rock elastic longitudinal wave speed are respectively obtained through experiments, the ratio of the rock mass longitudinal wave speed to the rock elastic longitudinal wave speed is the rock integrity coefficient;
(D3) The corresponding shear strength index, namely the cohesive force c and the internal friction angle, is obtained through the direct shear test of the rock
E. Determining the weight of each grading index; d, after a grading index test value of a required sub-grade grading section is obtained through a room test, determining index weight through an entropy weight method by MATLAB software;
(E1) The data is standardized, j tunnel surrounding rock grading indexes, namely X, are selected 1 ,X 2 ,X 3 ,···,X j If i evaluation objects are provided, the evaluation index value of the i-th object is x i1 ,x i2 ,x i3 ,···,x ij Normalized treatment formula y ij The following are provided:
(E2) According to the information entropy definition, calculating the information entropy corresponding to each index value of the n evaluation objects:
(E3) Obtaining weight value omega of each index of i groups of surrounding rock grading objects j ：
F. Calculating comprehensive certainty and judging the grade;
(F1) Firstly, calculating the certainty of a test value, acquiring a grading index test value of a sub-grade grading section required by a soft rock tunnel according to an indoor test, and then reading the certainty mu (x) of the test value of each grading index test value corresponding to each sub-grade according to a generated table by utilizing MATLAB software, wherein if the certainty mu (x) does not belong to a certain sub-grade, the certainty mu is marked as 0;
(F2) Calculating the comprehensive certainty factor, determining the certainty factor mu (x) from the calculated test value, and combining the entropyWeight omega of each grading index calculated by weight method j Calculating the comprehensive certainty degree U of each section:
(F3) And judging the level of the surrounding rock sub-level of each section, and judging the final attribution of the level of the surrounding rock sub-level of each section according to the value of the obtained comprehensive certainty U of each section.
In the step B of the present invention, K is uniformly 0.01[95].
According to the invention, four parameters including uniaxial compressive strength, rock integrity coefficient, softening coefficient and cohesive force of a rock sample are obtained through an indoor test, and a targeted grading method is combined to grade the surrounding rock of the soft rock tunnel, so that IV and V-class surrounding rock are divided into five sub-classes of IV-a, IV-b, IV-c, V-a and V-b, corresponding supporting measures are formulated according to different sub-classes, and the more finely-divided surrounding rock brings great convenience for subsequent tunnel construction, plays roles of saving construction period and avoiding construction safety accidents, and can be popularized and applied to the field of grading of surrounding rock of the soft rock tunnel and tunnel supporting design.
Drawings
Fig. 1 is a flow chart of the present invention.
Fig. 2 is a schematic view of a cloud model according to the present invention, in which an origin is defined as a point a.
Fig. 3 is a graph of a uniaxial compressive strength cloud model of the present invention.
Fig. 4 is a graph of an integrity coefficient cloud model of the present invention.
Fig. 5 is a graph of a cohesive cloud model of the present invention.
Fig. 6 is a graph 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 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-level classification method provided by the invention comprises the following steps:
A. determining a grading index and grading; through the statistics and arrangement of rock classification and tunnel surrounding rock classification index systems at home and abroad and the carding of engineering classification and grade evaluation research results of soft rock or weak surrounding rock, four parameters including uniaxial compressive strength, rock integrity coefficient, softening coefficient and cohesive force of the rock are adopted as the classification index systems of soft rock tunnel surrounding rock sub-grade classification, and IV and V surrounding rocks are divided into five sub-grades of IV-a, IV-b, IV-c, V-a and V-b.
(A1) When the stability analysis of the surrounding rock is carried out, the hardness degree of IV-level surrounding rock and V-level surrounding rock mainly comprises three types of harder rock, softer rock and soft rock, and in addition, the extreme conditions, namely the conditions of full weathering and full disintegration of soft rock are considered, so that when the index numerical grade classification of the uniaxial compressive strength is carried out, the relationship between the uniaxial compressive strength and the hardness degree qualitative classification of the rock in the 'highway tunnel design Specification' (JTG 3370.1-2018) is referred to, and the value range of the uniaxial compressive strength Rc in the IV-level and V-level surrounding rock is determined to be 0-60 MPa;
(A2) Determining the range of the rock integrity coefficient Kv in the IV and V-level surrounding rock to be 0-0.75, and determining the range of the cohesive force c in the IV and V-level surrounding rock to be 0-0.7 MPa;
(A3) A value below 0.75 is defined in the geotechnical engineering Specification (GB 50021-2001) as a softening point of rock, and therefore the surrounding rock softening coefficient K f The value range of the IV and V level surrounding rocks is determined to be 0-0.75;
aiming at the value ranges of each index between different surrounding rock grades, the relevant specifications and documents are mainly referred, and finally determined classification of the surrounding rock sub-grade classification indexes is shown in table 1.
TABLE 1 classification of classification indexes of surrounding rocks of carbonaceous mudstone tunnel
B. Calculating the digital characteristic value of each grading index;
determining expected E of normal cloud of surrounding rock sub-level grading index corresponding to certain level standard x Entropy E of normal cloud n Super entropy H of normal cloud e ，
H e ＝K
Wherein C is max And C min The upper boundary and the lower boundary of different grades of each grading index are respectively represented, K is a constant, and the adjustment can be carried out according to the fuzzy threshold value of the variable; in the invention, K is uniformly taken to be 0.01[95]]. The numerical feature value calculation results of the respective grading indexes are shown in table 2.
Table 2 numerical characteristics of each grading index
In the above steps, the definition of the cloud and the digital characteristics of the cloud model are as follows:
there are x, Y, C, where x ε Y, Y is a common quantitative set called the discourse, and C represents the qualitative concept on Y. If any of the study objects x in Y has a random number μ (x) ε [0,1 ] with a tendency to stabilize]The membership of x to C, also called certainty, is called. The distribution of certainty over Y is called the cloud model. If x satisfies: X-N (E) x ,E' n ) Wherein E 'is' n ～N(E n ,H 2 e ) μ (x) and satisfies the following formula:
the distribution of certainty μ over the domain Y is called a normal cloud, as shown in fig. 1, where hiddenContains three normal distribution rules, namely three digital features of a normal cloud model, which are marked as N3 (E x ,E' n ,H 2 e ) Wherein E is x 、E n 、H e The expectation, entropy and superentropy, respectively, of normal clouds.
(1) Expectations of normal cloud E x : representing the expectation of the cloud drop space distribution in the domain, namely the centroid of the area under the coverage of the cloud modelIs the information center value which can most reflect the fuzzy concept;
(2) Entropy E of normal cloud n : an uncertainty measure representing a qualitative term, representing the size of a range of values, i.e., ambiguities, acceptable for the qualitative term in the domain space;
(3) Super entropy H of normal cloud e : i.e. points on the desired curve of the cloud modelThe corresponding variance of the deterministic random distribution, which describes a measure of uncertainty of entropy, reflects the randomness of the appearance of samples representing qualitative conceptual values, revealing the association of ambiguity and randomness.
C. MATLAB generates a corresponding cloud model diagram, and reads the certainty of the test value;
(C1) Generating the desired value to be E x Entropy is E n Normal random number x of (a); calculating the expected value to be E x Entropy is E n Membership at the desired curve x of a normal membership cloud of (a)Respectively calculating entropy E changing along expected curve MA and MB according to decreasing semi-normal rule n l、E n r：
(c2) Calculation pointVariance sigma of the position x ：
(C3) The expected value is generatedVariance is sigma x Normal random number μ of (2):
(C4) Finally obtaining cloud drops xi (x, mu);
(C5) After the cloud model digital characteristic values of all indexes under different sub-level grades are obtained, a forward cloud generator is utilized to input the cloud model digital characteristic values of all surrounding rock sub-level grading indexes, and then a cloud model diagram corresponding to the tunnel surrounding rock sub-level grading indexes is obtained;
all index numerical characteristic values calculated in Table 2 are all led into MATLAB for calculation, and then all five sub-level certainty cloud model diagrams corresponding to the IV-a V-b of single-axis compressive strength, integrity coefficient, cohesion and softening coefficient in the sub-level grading index system can be obtained, as shown in figures 3-6.
In the above steps, a normal cloud generator (Membership Clouds Generator, MCG for short) is an important implementation tool of cloud model theory, and the formula is as follows:
the forward cloud generator is the expected E of the known cloud model x Entropy E n He Chao Entropy H e Three digital eigenvalues generate two-dimensional points xi (x, mu) meeting the normal cloud distribution rule, which is also called cloud drop, namely, the conversion from qualitative concept to quantitative value is realized.
A normal cloud model is formed according to thousands of cloud drops generated by a forward cloud generator, and the mathematical model of the normal cloud generator is as follows:
(1) The desired curve of the normal cloud satisfies N (E x ,E n 2 ) Is shown in the above formula.
(2) Points on the desired curveIn one-to-one correspondence with the cloud droplets xi (x, mu) and is a normal random number with the point as a convergence center and the variance in the definite direction, wherein the random number is formed by a normal distribution N (E x ,E n 2 ) Is produced by normal distribution->And (3) generating.
(3) Along the desired curve of a normal cloud, a maximum value σ is reached at point M x ＝H e At points A and B σ A ＝σ B =0. The desired curve varies according to two decreasing half normal rules at both edges of point M and meets the "3b rule".
D. Obtaining four test parameter values needing to divide the sub-level section through an indoor test;
(D1) The uniaxial compressive strength and the softening coefficient of the surrounding rock are obtained by adopting an indoor uniaxial compressive test, and the uniaxial compressive strength and the softening coefficient of the surrounding rock are divided into a uniaxial compressive test in a drying state, a uniaxial compressive test in a water saturation state and a uniaxial compressive test in a natural state according to different water-containing states of a sample, wherein the ratio of the uniaxial compressive strength in the rock saturation state to the uniaxial compressive strength in the drying state is the softening coefficient of the rock;
(D2) After rock mass longitudinal wave speed and rock elastic longitudinal wave speed are respectively obtained through experiments, the ratio of the rock mass longitudinal wave speed to the rock elastic longitudinal wave speed is the rock integrity coefficient;
(D3) The corresponding shear strength index, namely the cohesive force c and the internal friction angle, is obtained through the direct shear test of the rock
E. Determining the weight of each grading index; d, after a grading index test value of a required sub-grade grading section is obtained through a room test, determining index weight through an entropy weight method by MATLAB software;
(E1) The data is standardized, j tunnel surrounding rock grading indexes, namely X, are selected 1 ,X 2 ,X 3 ,···,X j If i evaluation objects are provided, the evaluation index value of the i-th object is x i1 ,x i2 ,x i3 ,···,x ij Normalized treatment formula y ij The following are provided:
(E2) According to the information entropy definition, calculating the information entropy corresponding to each index value of the n evaluation objects:
(E3) Obtaining weight value omega of each index of i groups of surrounding rock grading objects j ：
F. Calculating comprehensive certainty and judging the grade;
(F1) Firstly, calculating the certainty of a test value, acquiring a grading index test value of a sub-grade grading section required by a soft rock tunnel according to an indoor test, and then reading the certainty mu (x) of the test value of each grading index test value corresponding to each sub-grade according to a generated table by utilizing MATLAB software, wherein if the certainty mu (x) does not belong to a certain sub-grade, the certainty mu is marked as 0;
(F2) Calculating the comprehensive certainty factor, determining the certainty factor mu (x) by the calculated test value, and calculating each grading index weight omega by combining an entropy weight method j Calculating the comprehensive certainty degree U of each section:
(F3) And judging the level of the surrounding rock sub-level of each section, and judging the final attribution of the level of the surrounding rock sub-level of each section according to the value of the obtained comprehensive certainty U of each section.
The surrounding rock sub-level classification method is applied to five sections selected from K9+525, K9+660, K9+910, K10+405 and K11+968 of the ancient top tunnel, and is as follows:
as described above, in the step D, the rock samples of five cross sections are subjected to the indoor test, and the obtained grading index test values of each cross section are collated.
And E, calculating weights of the uniaxial compressive strength, the rock integrity coefficient, the cohesive force and the softening coefficient according to the efficient operation of the entropy weight method by using MATLAB software in the step E, wherein the final result is shown in Table 3.
TABLE 3 test values and weights for grading indicators
And F, calculating the certainty factor and judging the grade according to the step F, and obtaining the certainty factor of the test value of each section corresponding to each grading index according to the research content of the step c from the test value of each grading index of each section in the table 3, wherein the certainty factor is shown in the table 4.
Table 4 degree of certainty of each index of five sections
From the calculated certainty degree μ (x), the comprehensive certainty degree U of each cross section was calculated in combination with the hierarchical index weight calculated by the entropy weight method, and the results are shown in table 5.
TABLE 5 comprehensive certainty of sections and surrounding rock quality grading results
As is clear from the results shown in Table 5, in the section K9+525, U (V-a) > U (IV-c) > U (V-b) > U (IV-a) =U (IV-b), and therefore, the surrounding rock grade of the section K9+525 was determined to be V-a. Similarly, the surrounding rock grade of the K9+660 section is V-b, the surrounding rock grade of the K9+910 section is V-a, the surrounding rock grade of the K10+405 section is IV-b, and the surrounding rock grade of the K11+968 section is IV-c.
Finally, according to the geological survey report of the tunnel, three sections of K9+525, K9+660 and K9+910 belong to V-class surrounding rocks, two sections of K10+405 and K11+968 belong to IV-class surrounding rocks, so that the results of the five sections after a new classification index system and a cloud model classification method are consistent with the classification results of the BQ method surrounding rocks in the geological survey report on the main level, and on the basis, the five sections are classified by combining the classification standards of the soft rock tunnel surrounding rocks to ensure that the classification system of the soft rock tunnel surrounding rocks has scientificity and practicability.
Claims (2)
1. A soft rock tunnel surrounding rock sub-level grading method comprises the following steps:
A. determining a grading index and grading;
(A1) The value range of the uniaxial compressive strength Rc in IV and V-level surrounding rock is determined to be 0-60 MPa;
(A2) Determining the range of the rock integrity coefficient Kv in the IV and V-level surrounding rock to be 0-0.75, and determining the range of the cohesive force c in the IV and V-level surrounding rock to be 0-0.7 MPa;
the method is characterized in that: (A3) Softening coefficient K of surrounding rock f The value range of the IV and V level surrounding rocks is determined to be 0-0.75;
B. calculating the digital characteristic value of each grading index;
determining expected E of normal cloud of surrounding rock sub-level grading index corresponding to certain level standard x Entropy E of normal cloud n Super entropy H of normal cloud e ，
H e ＝K
Wherein C is max And C min The upper boundary and the lower boundary of different grades of each grading index are respectively represented, K is a constant, and the adjustment can be carried out according to the fuzzy threshold value of the variable;
C. MATLAB generates a corresponding cloud model diagram, and reads the certainty of the test value;
(C1) Generating the desired value to be E x Entropy is E n Normal random number x of (a); calculating the expected value to be E x Entropy is E n Membership at the desired curve x of a normal membership cloud of (a)Respectively calculating entropy E changing along expected curve MA and MB according to decreasing semi-normal rule n l、E n r：
(c2) Calculation pointVariance sigma of the position x ：
(C3) The expected value is generatedVariance is sigma x Normal random number μ of (2):
(C4) Finally obtaining cloud drops xi (x, mu);
(C5) After the cloud model digital characteristic values of all indexes under different sub-level grades are obtained, a forward cloud generator is utilized to input the cloud model digital characteristic values of all surrounding rock sub-level grading indexes, and then a cloud model diagram corresponding to the tunnel surrounding rock sub-level grading indexes is obtained;
D. obtaining four test parameter values needing to divide the sub-level section through an indoor test;
(D1) The uniaxial compressive strength and the softening coefficient of the surrounding rock are obtained by adopting an indoor uniaxial compressive test, and the uniaxial compressive strength and the softening coefficient of the surrounding rock are divided into a uniaxial compressive test in a drying state, a uniaxial compressive test in a water saturation state and a uniaxial compressive test in a natural state according to different water-containing states of a sample, wherein the ratio of the uniaxial compressive strength in the rock saturation state to the uniaxial compressive strength in the drying state is the softening coefficient of the rock;
(D2) After rock mass longitudinal wave speed and rock elastic longitudinal wave speed are respectively obtained through experiments, the ratio of the rock mass longitudinal wave speed to the rock elastic longitudinal wave speed is the rock integrity coefficient;
(D3) The corresponding shear strength index, namely the cohesive force c and the internal friction angle, is obtained through the direct shear test of the rock
E. Determining the weight of each grading index; d, after a grading index test value of a required sub-grade grading section is obtained through a room test, determining index weight through an entropy weight method by MATLAB software;
(E1) The data is standardized, j tunnel surrounding rock grading indexes, namely X, are selected 1 ,X 2 ,X 3 ,···,X j If i evaluation objects are provided, the evaluation index value of the i-th object is x i1 ,x i2 ,x i3 ,···,x ij Normalized treatment formula y ij The following are provided:
(E2) According to the information entropy definition, calculating the information entropy corresponding to each index value of the n evaluation objects:
(E3) Obtaining weight value omega of each index of i groups of surrounding rock grading objects j ：
F. Calculating comprehensive certainty and judging the grade;
(F1) Firstly, calculating the certainty of a test value, acquiring a grading index test value of a sub-grade grading section required by a soft rock tunnel according to an indoor test, and then reading the certainty mu (x) of the test value of each grading index test value corresponding to each sub-grade according to a generated table by utilizing MATLAB software, wherein if the certainty mu (x) does not belong to a certain sub-grade, the certainty mu is marked as 0;
(F2) Calculating the comprehensive certainty factor, determining the certainty factor mu (x) by the calculated test value, and calculating each grading index weight omega by combining an entropy weight method j Calculating the comprehensive certainty degree U of each section:
(F3) And judging the level of the surrounding rock sub-level of each section, and judging the final attribution of the level of the surrounding rock sub-level of each section according to the value of the obtained comprehensive certainty U of each section.
2. A soft rock tunnel surrounding rock sub-level classification method according to claim 1, characterized in that: and in the step B, K is uniformly taken to be 0.01[95].
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| WO2020125668A1 (en) * | 2018-12-18 | 2020-06-25 | 中国铁建重工集团股份有限公司 | Method and system for automatically identifying surrounding rock level by applying while-drilling parameters |
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