JP2001032679A - Facing front crack distribution predicting method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a facing front crack distribution predicting method for obtaining a geological state such as crack distribution easily and positively making effective use of simple face information such as a facing sketch without interrupting tunnel excavation work. SOLUTION: This method is executed in the due order of a facing information extraction procedure 1 for extracting crack information on suitable faces including past faces already excavated and passed, a crack tensor computing procedure 2 for computing a two-dimensional crack tensor F0 on the basis of crack information to each face extracted in the facing information extraction procedure 1, a variogram determining procedure 3 for computing a semi-variogram 30 indicating crack density distribution corresponding to a clearance from a specified cardinal point in a tunnel on the basis of the two-dimensional crack tensor F0 obtained on every face and determining a variogram function 35 approximate to the semi-variogram 30, and a crack density estimating procedure 4 for computing and estimating crack density on an unknown rock-bed spaced by a desired clearance from the face corresponding to the known two-dimensional crack tensor F0 by substituting the known two-dimensional crack tensor F0 and a weight value obtained by a maximum method, for the variogram function 30.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for predicting a crack distribution in front of a face used in tunnel excavation.

[0002]

2. Description of the Related Art For example, in tunnel excavation work for a mountain tunnel or the like, a tunnel is formed by excavating the ground of a tunnel using blasting or the like. The support pattern is greatly affected, and an appropriate auxiliary method will be adopted for the bad part of the ground. Since appropriate selection of the auxiliary method and advance planning will affect the excavation cost, in order to achieve efficient tunnel excavation, prior exploration of the unexcavated area in front of the excavation face, that is, unknown rock Work has been performed to predict various geological conditions such as crack distribution.

For example, the preceding boring method is one of the first methods for performing the preceding search and crack distribution prediction in front of the face. The preceding boring method is a method in which a device capable of collecting a geological sample (for example, a boring machine or the like) is arranged near a tunnel face and a geological sample is collected at an appropriate point on the target rock, and the collected geological sample is collected from the rock. Since it is obtained directly, it is possible to grasp the geological condition relatively clearly by reflecting the actual geological condition.

As another method, there is a method called an elastic wave exploration method. In this method, an appropriate number of elastic wave receiving devices such as seismometers are provided inside and outside the exploration area. Is received by the above-mentioned seismometer or the like, and waveform analysis is performed to grasp a discontinuous portion of the rock such as a rock crack or a crush zone.

[0005] Furthermore, there is also a method called a specific resistance search method, in which a current applied from an application electrode provided on the ground surface or in a tunnel is received by a plurality of search electrodes in the tunnel and measured. By obtaining the potential distribution in the rock that exists between the probe and the exploration electrode, the groundwater distribution, the rock crack, etc. can be calculated and grasped as the relative value of the electric resistance. In addition, there is also an old method of simply sketching the face face and checking and confirming cracks generated on the face face on a daily basis.

[0006]

However, the conventional method for predicting the front face crack distribution has the following problems.

That is, the prior boring method has a problem that a relatively large device for collecting a geological sample must be carried in and installed in a tunnel. Further, in carrying out this method, a conventional boring method on a face face is required. There is also a problem that sampling must be performed by temporarily stopping the tunnel excavation work. Therefore, the narrow tunnel, in which other heavy equipment is already arranged, further narrows the inside of the tunnel to reduce various work efficiencies. In addition, the interruption of the tunnel digging work has a serious adverse effect on the reduction of the tunnel digging efficiency. It has.

In addition, since the resistivity exploration method is an electrically performed method, it is basically possible to complete the exploration and prediction work in a short time, but the cracks in the rock obtained as a result of the exploration can be obtained. Various geological conditions, such as distribution and the presence or absence of shatter zones, are all limited to large-scale data, and the accuracy of the results is generally insufficient for detailed crack data.

Furthermore, it is difficult to comprehend a data image including mechanical properties such as the distribution of rock cracks and the elastic strength of the rock from the electric resistance distribution obtained as a result of the exploration. It was difficult to obtain exploration results. For this reason, it is necessary to carry out in parallel with other various exploration methods, and it is inevitable that the operation is complicated and the exploration efficiency is reduced.

[0010] On the other hand, face sketches that are routinely carried out at a tunnel excavation site are rarely used in the above-described conventional method, and are hardly ever looked after. It was rarely used to predict cracks in front of the face.

Accordingly, the present invention has been made in view of such a conventional problem, and effectively utilizes simple face information such as face sketches, without interrupting tunnel excavation work, and determining crack distribution and the like. The present invention provides a method for predicting the distribution of cracks in front of a face, which can easily and surely obtain the geological condition of the face.

[0012]

SUMMARY OF THE INVENTION The present invention has been made to achieve the above object, and is a crack distribution prediction method for predicting various geological conditions of an unknown bedrock in front of a tunnel face. Are performed in order. (1) A face information extraction procedure for appropriately extracting crack information on a face face including a past face face that has already been excavated. (2) A crack tensor calculation procedure for calculating a two-dimensional crack tensor based on the crack information for each face face extracted in the face information extraction procedure. 3 Based on the two-dimensional crack tensor obtained for each facet, calculate a semivariogram showing a crack density distribution according to a distance from a predetermined base point in the tunnel,
A variogram determination procedure for determining a variogram function approximating the semivariogram. 4. An unknown bedrock at a desired distance from a face corresponding to the known two-dimensional crack tensor by substituting a known two-dimensional crack tensor and a weight value obtained by the maximum likelihood method into the variogram function. The crack density estimation procedure for calculating and estimating the crack density for.

[0013]

DESCRIPTION OF THE PREFERRED EMBODIMENTS Preferred embodiments of the present invention will be described below in detail with reference to the accompanying drawings. FIG. 1 is a flowchart showing a method for predicting a crack distribution in front of a face according to the present invention.

The stability of the tunnel face mainly depends on the location and frequency of cracks, groundwater veins, etc. existing in the rock. In particular, the ground survey conducted during tunnel construction focuses on how to reliably grasp the crack state in front of the face. The face front crack distribution prediction method of the present invention is implemented from the viewpoint of predicting a crack distribution state of a face front part that is not yet visible from crack information such as a face face sketch that has already been obtained. Required in the face front crack distribution prediction method of the present invention,
The process of extracting face information such as cracks (process 1 in the figure) will be described below.

In extracting face information, in this embodiment, a face face sketch (not particularly shown) for sketching cracks on a daily basis is employed. Of course, instead of the sketch, an image photographed using an imaging means such as a digital camera may be used. The face face sketch is drawn by a worker or the like at a tunnel excavation site, for example, on a field note or a dedicated sheet before the tunnel excavation operation starts in earnest (or after the end of a day's excavation operation). In this embodiment, the face sketch is captured as a digital image in a storage device or the like attached to a computer by, for example, a digitizing device (not shown). FIG. 2 shows the face image 20 after the capture.
(A). The face image 20 captured by the digitizing device has a crack C drawn in the original sketch, and k cracks each having a tilt angle θ (k) on the face face and a crack length L (k). C is recognized and displayed.

The face information digitized in this way is extracted from the face sketch, and the extracted face information is then applied to various crack evaluation methods devised from various viewpoints. Will be offered. Various evaluation methods include, for example, a rock classification method proposed in Japan for dam foundation rock, and RMR (Ro) as a quantitative rock evaluation method.
ck Mass Rating) and a Q system. However, in the present invention, a crack tensor concept is proposed which is comprehensively evaluating many crack information such as crack length and directionality and the number of cracks, and is proposed as a quantity that can be easily linked to engineering characteristics. A crack is to be evaluated (process 2 in FIG. 1).

The crack tensor is a quantity that expresses only the geometric distribution characteristics of the crack C, focusing on the length, density and directionality of the crack C. If the distribution characteristics of each crack C are obtained, the rock mass It has the characteristic that the deformation characteristic as is estimated. Therefore, in the present embodiment, a two-dimensional crack tensor is determined from the results of crack observation at the face (face sketch etc.), and the crack distribution of the unknown rock in front of the face is predicted using the determined tensor as known information.

FIG. 2 (b) (and FIG. 1) shows the process of calculating the crack tensor based on the face image 20 of FIG. 2 (a). As shown in Equation 1, the crack tensor Fij takes the sum of the square of the length L (k) of the k-th crack C appearing on the face and the tensor product of the direction cosine for the total number of cracks, and calculates the face area A It is obtained by dividing by.
The two-dimensional second-order tensor has a characteristic quantity that is invariant with respect to the coordinate axis, which is F0 shown in Expression 2. This symbolizes the crack density in the rock,
0 itself has an eigenvalue and an eigenvector, which also indicate, respectively, how much the crack C is arranged in a specific direction on the facet face and in which direction.

Next, a description will be given of the structural analysis of the already-excavated portion shown in step 3 in FIG. In this process 3,
In the present invention, for example, a ground statistical method is used as a technique for statistically predicting the state of crack distribution in an unknown rock in front of a face, and the structural characteristics of the ground are analyzed by applying the F0 to this method. First, a semivariogram 30 is obtained according to Equation 3. Here, the semivariogram 30 is 1 of the expected value of the covariance of the variable z at a point separated by the distance h.
/ 2 is calculated for various distances h, and the calculation results are plotted on the vertical axis and the separation distance h on the horizontal axis (see FIG. 3).

Next, the semivariogram 30 is approximated by some function. Several approximation functions can be considered depending on how the shape of the semivariogram curve is considered. In this embodiment, a case where the approximation function is approximated by a spherical model is shown. The function parameters are c and a in Equation 4, and can be easily determined by appropriately using various function approximation software or the like.
Through the above processes 1 to 3, characteristics of how the crack density F0 of the unknown rock is distributed along the traveling axis of the tunnel with a certain correlation can be grasped.

As described above, since the variogram function 35 indicating the structural characteristics of the unknown rock is obtained, the crack density of the unknown rock in front of the current face is determined based on the face information already obtained using the variogram function 35. Predict (step 4). The value z (u) predicted as the crack density is given by the sum of the product of the weight λα (u) and the known data z (uα) (see Equation 5). Therefore, the method of determining this weight is the maximum likelihood method (Kriging).
In this proposal, the calculation is performed using the Lagrange's undetermined constant method under the constraint condition that the sum of the weights λβ is 1 (see Equations 6 and 7). The feature of the maximum likelihood method is that the error distribution of the predicted value is estimated, and the analysis can be performed up to the prediction accuracy. The result obtained by applying the maximum likelihood method is the forward crack density estimation result 40 shown in FIG. The horizontal axis indicates the station in the tunnel, and the vertical axis indicates the crack density.

Hereinafter, a situation in which the present invention is applied to an actual tunnel to predict a crack density distribution in front of a face will be described. FIG.
First, face sketches that were routinely drawn and saved in a tunnel that had already been excavated were adopted as crack information. In this tunnel, a face sketch that describes the crack situation and flooding situation on the face face, and a face daily report that contains various comments and measurement results etc. are left for all working days in the working pit and main pit. In order to digitize all the information, it is inevitable that the operation becomes complicated. Therefore, the face information is selectively extracted and used at intervals of 10 m for each face. The extracted face information was input to a personal computer as face face crack information using a digitizer (see FIG. 2).

Next, the two-dimensional crack tensors Fij of the working pit and the main pit were calculated according to the process 2, and the results are shown in Tables 1 and 2, respectively.

[0024]

[Table 1]

[0025]

[Table 2]

Here, Tables 1 and 2 show the station from the left,
11 components, 12 components, 22 components of crack tensor, crack density (sum of F11 and F22), maximum principal value of tensor,
The main direction and the degree of anisotropy are shown. In the rightmost column, the support level required for the ground section is shown as a reference corresponding to the size of the support.

The calculation results in Tables 1 and 2
Figures 5 and 6 are plotted against on, respectively. It is a graph showing changes 50 and 60 of the support pattern, changes 51 and 61 of the crack density F0, changes 52 and 62 of the main direction, and changes 53 and 63 of the degree of anisotropy from the top,
In each figure, they are shown in FIGS. In each graph, the horizontal axis is station.

When the crack tensor Fij is considered as two-dimensional, the change in its anisotropy is represented by a value from 0 to 1. The closer to 1, the parallel running of the existing cracks is closer to each other. Means that Considering this and the results of the anisotropy shown in FIG. 5D and FIG. 6D, in the case of this rock, the following points are pointed out in both the working pit and the main pit.

1. Comparing the support pattern change and the graphs of the crack density and the graphs (a) and (b), the face with a higher crack density has a heavier support pattern, and the two tendencies are consistent. 2. The degree of the anisotropy of the cracks is close to 1 in many cases from the figure (d), indicating that many cracks are parallel to each other. 3. When the crack density is low, there are many cracks that are nearly horizontal on the face, and when the crack density is slightly high, the cracks are almost vertically oriented.

Based on the above data, the results of the analysis of the structural characteristics of the cracks in the working pit and the main pit by the geostatistics method with respect to the geometric characteristics are shown in FIGS.
0 and 80 are shown. Comparing the two figures, it can be seen that the variation of the semivariogram is smaller in the case of the main shaft of FIG. 8 than in the case of the working shaft of FIG.

Subsequent to the determination of the variogram function 35, the crack density in front of the face was actually estimated using the face information of the main pit. For the estimation operation, a computer program for estimating the crack density at an arbitrary point in front of the face using the known face information and the variogram function 35 was used. In order to implement the maximum likelihood method with this program, it is necessary to select some parameters.

That is, 1. Variogram function 2. Minimum and maximum number of data used for kriging This is the maximum radius of influence.

Here, the variogram function 35 employs a spherical model as in the case of the above-described semivariograms 70 and 80, and has a minimum influence range of 10 m and a maximum influence range of 200 m, for example. The minimum and maximum number of data used for kriging is determined by referring to a minimum of 5 data and a maximum of 100 data. The maximum radius of influence is
The position of the data to be considered for kriging restricts how many meters from the point to be estimated to search for data. In the present embodiment, from the relationship between the maximum and minimum data numbers used for kriging, A search was made for a range of 100 m so that if the number of data was 5 or less, estimation was impossible. First, the influence of the number of known data that can be referred to for kriging on the crack density estimation result obtained as a prediction result is determined, and the range from the current face, which can refer to the known data, is set as a reference distance, and this reference distance is set as the reference distance. 50m, 10
Crack density estimation result 9 when 0 m and 150 m were set
0 is shown in FIG.

This reference distance corresponds to the number of known data that can be referred to at the time of kriging. According to the results of the present embodiment, when the distance is 50 m, the distances until the estimation becomes impossible are 13.5 m, and the distances of 100 m and 150 m. The cases were 64 m and 115 m, respectively.

Therefore, 1. To estimate a point relatively far from the current face, a certain reference distance is required. 2. When the reference distance is long, the estimated distance is also long. For example, as shown in FIG. 9, a difference appears between the estimation result of the crack density and the actual measurement value from around station 298.00. 3. Considering these facts, if there is a sufficient number of known data on the cracks of the face, it is possible to carry out a relatively reliable estimation of the crack density for the bedrock about 30 to 40 m ahead. The point is clear.

The station 298.5 in FIG.
A sudden change in geological conditions as seen in the vicinity,
In the case where large geological structures such as shatter zones exist in the bedrock, it is possible to follow the general trend of change if predictions are made assuming that information can be obtained in the preliminary survey to some extent.

At the actual tunnel excavation site, the estimation of the crack density for all the face faces is not completed by a single estimation operation. (Step 5). Since face information increases as excavation progresses, the variogram function 35 can be determined from more face information and kriging can be performed.
The accuracy of the obtained estimation result will also be improved.

[0038]

As described in detail above, the method for predicting the crack distribution in front of a face according to the present invention is a method for predicting various geological conditions of an unknown bedrock in front of a tunnel face. Are performed in order. (1) A face information extraction procedure for appropriately extracting crack information on a face face including a past face face that has already been excavated. (2) A crack tensor calculation procedure for calculating a two-dimensional crack tensor based on the crack information for each face face extracted in the face information extraction procedure. 3 Based on the two-dimensional crack tensor obtained for each facet, calculate a semivariogram showing a crack density distribution according to a distance from a predetermined base point in the tunnel,
A variogram determination procedure for determining a variogram function approximating the semivariogram. 4. An unknown bedrock at a desired distance from a face corresponding to the known two-dimensional crack tensor by substituting a known two-dimensional crack tensor and a weight value obtained by the maximum likelihood method into the variogram function. The crack density estimation procedure for calculating and estimating the crack density for.

Therefore, according to the present invention, the sketch of the face face, which is routinely performed at the tunnel excavation site, is not only treated as a storage record, but also effectively used for predicting a crack in front of the face. This means that the face face information used for crack prediction in front of the face is usually sufficient for face sketches obtained before (or after) the start of tunnel excavation work, and thus, as in the past, narrow It also leads to eliminating the possibility of lowering tunnel digging efficiency such as carrying measuring devices etc. into a simple tunnel and narrowing it further to reduce various work efficiency or interrupting tunnel digging work. It is.

In addition, various geological conditions such as the distribution of cracks in the bedrock and the presence or absence of shatter zones obtained as a result of the exploration are not limited to large-scale data of the degree of deformation, but rather the form of crack distribution in front of the face. Therefore, it is possible to obtain even if the distribution frequency is small, and it is possible to easily conduct a detailed examination of the ground condition.

That is, in order to make effective use of face face information, such as face sketches, which can be easily obtained without requiring special equipment, tunnel excavation work for obtaining face face information is not interrupted, and cracks are not generated. Geological conditions such as distribution can be obtained easily and reliably.

[Brief description of the drawings]

FIG. 1 is a flowchart showing a method for predicting a crack front crack distribution according to the present invention.

FIG. 2A shows a face image after a face face sketch has been captured as a digital image, and FIG. 2B shows a process of calculating a crack tensor based on the face image in FIG.

FIG. 3 is a graph showing a semivariogram used in the method for predicting the distribution of cracks in front of a face according to the present invention.

FIG. 4 is a graph showing a forward crack density estimation result obtained by using the maximum likelihood method in the method for predicting a front face crack distribution according to the present invention.

5 (a) is a change in a support pattern, FIG. 5 (b) is a change in a crack density F0, and FIG. 5 (c). () Shows a change in the main direction, and (d) shows a change in the degree of anisotropy.

6 (a) is a change in support pattern, FIG. 6 (b) is a change in crack density F0, and FIG. 6 (c), obtained by applying the method for predicting the distribution of cracks in front of a face of the present invention to an actual tunnel (main shaft). () Shows a change in the main direction, and (d) shows a change in the degree of anisotropy.

FIG. 7 is a graph showing a semivariogram obtained by applying the method of predicting the distribution of cracks in front of a face of the present invention to an actual tunnel (working pit).

FIG. 8 is a graph showing a semivariogram obtained by applying the method for predicting the distribution of cracks in front of a face according to the present invention to an actual tunnel (main shaft).

FIG. 9 shows a method of predicting the distribution of cracks in front of a face according to the present invention applied to an actual tunnel (main shaft), and sets a range (reference distance) of known data that can be referred to for kriging to 50 m from the current face, 100 meters;
It is the graph which showed the crack density estimation result at the time of setting m and 150m.

[Explanation of symbols]

 F0 2D crack tensor 1 Face information extraction procedure 2 Crack tensor calculation procedure 3 Variogram determination procedure 4 Crack density estimation procedure 30 Semivariogram 35 Variogram function

Claims (1)

[Claims] 1. A crack distribution prediction method for predicting various geological conditions of an unknown bedrock in front of a tunnel face, which is performed by sequentially performing the following steps 1 to 4. 1
A face information extracting procedure for appropriately extracting crack information on the face face including the past face face that has already been excavated. (2) A crack tensor calculation procedure for calculating a two-dimensional crack tensor based on the crack information for each face face extracted in the face information extraction procedure. 3 Based on the two-dimensional crack tensor obtained for each facet, calculate a semivariogram showing a crack density distribution according to a distance from a predetermined base point in the tunnel, and calculate a variogram function approximating the semivariogram. The variogram determination procedure to be determined. 4. An unknown bedrock at a desired distance from a face corresponding to the known two-dimensional crack tensor by substituting a known two-dimensional crack tensor and a weight value obtained by the maximum likelihood method into the variogram function. The crack density estimation procedure for calculating and estimating the crack density for.