AU2019312330A1 - Method for predicting mine strata pressure behavior data of stoping tunnel - Google Patents
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Abstract
The present invention relates to the technical field of mine tunnel surrounding rock control, and provides a method for predicting mine strata pressure behavior data of a stoping tunnel. The method comprises performing nonlinear regression on an actual difference value of a series of mine strata pressure behavior data by using a prediction variation model of the mine strata pressure behavior data to obtain a determined function expression of a change speed and a cumulative variation of the mine strata pressure behavior data of the stoping tunnel, so as to obtain mine strata pressure behavior features of the stoping tunnel. By means of the method for predicting the mine strata pressure behavior data of the stoping tunnel, the mine strata pressure behavior features of the stoping tunnel can be quickly and efficiently predicted, and the application range is wide.
Description
Method for Predicting Mine Strata Pressure Behavior Data of Stoping Tunnel
Technical Field
[0001] The present invention relates to a method for predicting mine strata pressure behavior data of stoping tunnel and belongs to the technical field of mine tunnel surrounding rock control. Background Art
[0002] Among the three types of coal mine tunnels including development tunnels, preparatory tunnels and stoping tunnels, stoping tunnels have a shorter service period, so their support design only need to satisfy the normal use in the stoping period. Therefore, the mine strata pressure behavior of a stoping tunnel during driving and stoping is stronger than that of the other two types of tunnels. Measuring the deformation convergence of the surface of a stoping tunnel is one of the compulsory measuring items during driving and stoping of the stoping tunnel, and is realized by the following basic method: immediately after the excavation of the coal-rock mass, some anchor points are buried on the two walls and roof of the tunnel as measurement base points; a monitoring instrument is used to measure the small changes in distance between any base points within a certain period of time, so as to calculate the convergence deformation and deformation rate of the roof and floor, and the two walls of the tunnel, which are used to achieve the purpose of evaluating engineering stability and guiding support design.
[0003] The observation of stoping tunnel surrounding rock convergence is the most commonly used and most widely applied tunnel strata pressure observation method in mines at home and abroad, the content of which includes tunnel roof subsidence, floor heave, tunnel wall convergence, deep surrounding rock convergence, and remaining sectional area of the tunnel, etc. Among them, the most widely applied and most fundamental method for arrangement of measuring points for observing convergence is the "cross-shaped" measuring point arrangement method, that is, the change and development of the convergence of the roof, the floor and the two walls with time are observed after the stoping tunnel is excavated. When the section of the tunnel is large and the complex stress-bearing process of deformation needs to be analyzed and studied, the deformation observation method with multiple periphery measuring points is also used. The stoping tunnel is on a side of the working face. In a considerable period of time during its service period, the stoping tunnel is influenced by mining on the working face and its stress shows obvious asymmetry, so its deformation development process also has obvious asymmetry. In order to study the deformation and control features thereof,"* -shaped" and "well-shaped" measuring point arrangement methods are often adopted.
[0004] With regard to mine strata pressure observation of a stoping tunnel, in domestic and foreign mines, the method adopting fixed observation stations for long-term monitoring is mainly used, which takes a longer time, and has a poor effect in predicting mine strata pressure disasters of the stoping tunnels.
[0005] At present, there are mainly three types of commonly used methods for studying the mine strata pressure behavior features of stoping tunnels: in the first method, long-term field observation is conducted during the mine strata pressure change process of the stoping tunnel, for example, the convergence of roof and floor, the load of the support and the shrinkage of the column (movable column), generally referred to as "three amounts", are observed; the second method is a numerical analysis method based on finite difference method (FDM), finite element method (FEM), boundary element method (BEM), discrete element method (DEM), Lagrange element method, discontinued deformation analysis method (DDA), manifold element method (MEM), element-free method and mixed applications thereof, and other numerical simulation techniques. For example, FLAC3D, 3DEC or ANSYS software is used to simulate the stress distribution law and displacement distribution features of the tunnel; the third method is the similar material simulation experiment research method, which is to make a model similar to the stoping tunnel prototype in the laboratory according to the similarity principle, use the test instruments to observe the mechanical parameters in the model and their distribution laws, and use the model research results to infer the mechanical phenomena that may occur in the stoping tunnel prototype and the law of rock mass pressure distribution, so as to solve the actual problems in rock mass engineering production.
[0006] Although the mine strata pressure law obtained from long-term observation of a stoping tunnel is the most authentic and accurate, the biggest disadvantage is that a long time is required.
[0007] When the numerical analysis method is used to study the mine strata pressure behavior features of a stoping tunnel, the physical and mechanical parameters of the surrounding rock of the tunnel are used, whereas the actual conditions of the stoping tunnel are often very complex, it is difficult for an established model to accurately reflect the actual condition of the stoping tunnel, and the results obtained are also quite different from the actual condition and are generally used as a reference only.
[0008] The similar material simulation experiment is more suitable for studying the mine strata pressure behavior features of a tunnel under special conditions. This method is rarely used for the mine strata pressure behavior features and change laws of an ordinary stoping tunnel. Contents of the Invention
[0009] Object of the invention: to address the defects of the existing methods for studying the mine strata pressure behavior features of a stoping tunnel in the prior art, such as time consuming, low efficiency, low accuracy, poor prediction effect and limited scope of application, the present invention provides a method for predicting mine strata pressure behavior data of stoping tunnel, to reliably predict the mine strata pressure behavior features of the stoping tunnel in a short time.
[0010] Technical solution: a method for predicting mine strata pressure behavior data of stoping tunnel according to the present invention comprises establishing a prediction model for change speed of mine strata pressure behavior data on any section of the stoping tunnel, and integrating it to get a predicted variation model of mine strata pressure behavior data in a certain interval; obtaining a series of actual differences of mine strata pressure behavior data by collecting mine strata pressure behavior data from different observation stations in different positions relative to the working face for multiple times; and using the predicted variation model of mine strata pressure behavior data in a certain interval to perform nonlinear regression on a series of actual differences of mine strata pressure behavior data, to determine a functional expression of a change speed and a cumulative variation of the mine strata pressure behavior data of the stoping tunnel.
[0011] A method for predicting mine strata pressure behavior data of stoping tunnel according to the present invention, comprising the following steps: 1) Establishing a predicted variation model of mine strata pressure behavior data on the same tunnel section In the stoping tunnel, the prediction model of a change speed of mine strata pressure behavior data on any tunnel section is v(x), the prediction model of a cumulative variation of mine strata u(x)=Jv(x)dx pressure behavior data is f , and the predicted variation model of mine strata AU(x, X')=f v(x)dx pressure behavior data is E; the deformation time experienced on any
tunnel section is L ;the mine strata pressure behavior data is mechanical and displacement data related to the mine strata pressure behavior of the stoping tunnel, including roof separation, roof subsidence, floor heave, convergence of two walls, displacement of deep rock formation, anchor rod load and anchor cable load of the stoping tunnel; where, v is a change speed of mine strata pressure behavior data, u is a cumulative variation of mine strata pressure behavior data; x is a distance of the tunnel section relative to the working face in the advancement direction of the working face, wherein, when the tunnel section is in front of the working face, x<O, and when the tunnel section is behind the working face, x>O; the working face at the driving influence stage refers to the driving working face, and the working face at the mining influence stage refers to the stoping working face; xo is a distance of any tunnel section relative to the working face when it begins deforming; x and x, represent different distances of the tunnel section relative to the working face, and x,<xn; L is a daily drilling footage of the working face; 2) Obtaining a series of actual differences of mine strata pressure behavior data through mine strata pressure observation A plurality of observation stations are arranged in the same stoping tunnel at the same time to collect mine strata pressure behavior data; with the advancement of the working face, the actual difference of mine strata pressure behavior data collected by the same observation station from different positions relative to the working face in two times is Aux x j=U -U) IJ+ 1 I , and after a plurality of observation stations collect mine strata pressure behavior data for multiple times, a series of actual differences of mine strata pressure behavior data . . ....... , are obtained; where, Uxi is a mine strata pressure behavior data value collected by an observation station when the distance relative to the working face is x;; when the distance of the tunnel section relative to the working face is xi, the deformation time experienced by the tunnel section is
3) Predicting the mine strata pressure behavior data of the stoping tunnel by means of nonlinear regression
A predicted variation model of mine strata pressure behavior data is used to perform nonlinear regression on a series of actual differences of mine strata pressure
behavior data A X,,+ 1 ) x 2 2,X ,, ) ,Au ...... ,Au(X +') to obtain parameters of prediction models v(x) and u(x), i.e., determine a functional expression of a change speed and a cumulative variation of the mine strata pressure behavior data of the stoping tunnel, and meanwhile obtain the change speed and cumulative variation of mine strata pressure behavior data on the tunnel section at any distance relative to the working face, as well as the influencing range and duration of the mine strata pressure behavior.
[0012] An advantage is that, as the present invention adopts an observation station arrangement method of concentrated arrangement of observation stations at the collection stage of mine strata pressure behavior data, mine strata pressure behavior data collection can be completed in a short time (2-7 days), the established predicted variation model of mine strata pressure behavior data is used to perform nonlinear regression on a series of actual differences of mine strata pressure behavior data obtained through mine strata pressure observation, and mine strata pressure change features of a specific stoping tunnel can be predicted in a short time with high accuracy. Alternatively, previous mine strata pressure behavior data can be used to predict the final results and whole process of mine strata pressure behavior.
[0013] As a further definition, the curves in the curve family contained in the prediction model v(x) are all concave curves at the mining influence stage, and the corresponding functions are decreasing functions; the curves in the curve family contained in the prediction model u(x) are all convex curves that are steep first and then gentle at the driving influence stage, and the corresponding functions are all increasing functions.
[0014] As a further definition, the curves in the curve family contained in the prediction model v(x) at the mining influence stage are all bell-shaped curves, which are convex in the middle and concave at two sides, and the corresponding functions all increase first and then decrease; the curves in the curve family contained in the prediction model u(x) at the mining influence stage are all S-shaped increasing curves.
[0015] An advantage is that basic features of prediction models v(x) and u(x) are obtained by summarizing the general features of changes in mine strata pressure behavior data at the driving influence stage and mining influence stage, so as to provide guidance for the establishment of specific mathematical models.
[0016] There are many mathematical models that meet the above requirements and cannot be exhaustive, but the following three mathematical models are preferred by the present invention: As a further definition, the prediction model v(x) is expressed as following at the driving influence stage
-V(X) = Ue,(1
where a is the maximum change speed of mine strata pressure behavior data; a and b are parameters to be determined; e is a natural constant; and, a>0,0<b<1. As a further definition, the prediction model v(x) is expressed as following at the driving influence stage
)(X) ac" (2) where a is the maximum change speed of mine strata pressure behavior data; a and c are parameters to be determined, and a>0, 0<c<l. As a further definition, the prediction model v(x) is expressed as following at the driving influence stage and the mining influence stage
v (x)= k 11 + (3) where k, d and p are all parameters to be determined, k>0, <d<1, -1000<p<1000.
[0017] As a further improvement, observation stations are arranged within 100m behind the working face; at the mining influence stage, observation stations are also arranged within 50m in front of the stoping working face.
[0018] The advantage is that, for different stoping tunnels, the ranges of mine strata pressure behavior in the stoping tunnels are different, so arranging observation stations in the tunnel range of mine strata pressure behavior will raise the collection efficiency of mine strata pressure behavior data, and definition to the observation station arrangement range can provide a reference for mine strata pressure observation.
[0019] As a further definition, with more mine strata pressure behavior data being collected, the mine strata pressure behavior features of the stoping tunnel can be predicted more accurately; with the observation stations being arranged more densely, the time required for collecting the mine strata pressure behavior data will be shorter.
[0020] The advantage is that, a large distance between adjacent observation stations is not conducive to rapid collection of mine strata pressure behavior data, and denser arrangement of observation stations is a great help to prediction accuracy.
[0021] The nonlinear regression is completed using numerical analysis software with the function of nonlinear regression, including ORIGIN, MATLAB, EXCEL and SPSS.
[0022] There are many types of numerical analysis software with the function of nonlinear regression, which are not exhaustive. The above listed are just some popular types.
[0023] As a further definition, the stoping tunnel has the same surrounding rock properties, consistent mine strata pressure control method and unchanged daily drilling footage of the working face.
[0024] The advantage is that, the factors influencing mine strata pressure behavior features may vary at different zones of the same stoping tunnel, and if one kind of mine strata pressure behavior feature is obtained for tunnel surrounding rock under different conditions, this feature is only between their actual conditions, rather than the expression of mine strata pressure behavior feature of the stoping tunnel under the same condition, so the mine strata pressure behavior feature of the tunnel under this condition can be obtained only when no observation station is arranged in the range where the factors influencing the mine strata pressure behavior of the stoping tunnel substantially remain unchanged.
[0025] The mine strata pressure behavior feature is an analysis result to which those skilled in the art pay key attention when studying mine strata pressure behavior feature of a stoping tunnel.
[0026] Beneficial effect of the present invention: a method for predicting mine strata pressure behavior feature of stoping tunnel in the present invention can be used to rapidly and efficiently obtain mine strata pressure behavior features of the stoping tunnel, and has a wide scope of application. The specific advantages are as follows: 1) Generally it takes two to three days to complete the mine strata pressure observation stage in the prediction of mine strata pressure behavior features of a stoping tunnel. Compared with long-term observation, the accuracy is low to some extent, but the efficiency is raised remarkably; 2) The method for predicting mine strata pressure behavior features of stoping tunnel uses numerical analysis software such as SPSS, MATLAB and ORINGIN, but the using data is actually observed, so the mine strata pressure behavior features of stoping tunnel reflected by it is closer to the actual condition than numerical simulation; 3) Using partial mine strata pressure behavior data to predict complete mine strata pressure behavior features of a stoping tunnel plays a role of early warning to mine strata pressure disasters and is conducive to the safe and efficient production of the coal mine. Description of Drawings
[0027] FIG. 1 is a schematic view for arrangement of observation stations in 110102 gate road of Ji'anda Mine;
[0028] FIG. 2 is a schematic view of "cross-shaped measuring point arrangement" for measuring surface convergence of a tunnel;
[0029] FIG. 3 is a chart of tunnel deformation of 110102 gate road of Ji'anda Mine in any 2 days;
[0030] FIG. 4 shows the convergence velocity of two walls of 110102 gate road of Ji'anda Mine;
[0031] FIG. 5 shows the convergence amount of two walls of110102 gate road of Ji'anda Mine;
[0032] FIG. 6 is an arrangement diagram of surface displacement observation stations in 30102 gate road of Youzhong Mine;
[0033] FIG. 7 shows prediction results of deformation of two walls of 30102 gate road of Youzhong Mine.
[0034] FIG. 8 is a curve chart of change speed v and cumulative variation u of predicted mine strata pressure behavior data, which change with time t at the driving influence stage;
[0035] FIG. 9 is a curve chart of change speed v and cumulative variation u of predicted mine strata pressure behavior data, which change with coordinate x at the mining influence stage. Embodiments
[0036] The technical solution of the present invention will be further detailed through specific examples with reference to the accompanying drawings. Two examples are used to describe the present invention. Firstly, the prediction of mine strata pressure behavior features of tunnel of 110102 gate road of Ji'anda Mine in Shouyang, Shanxi at the driving influence stage is taken as an example; secondly, the prediction of mine strata pressure behavior features of tunnel of 30102 gate road of Youzhong Mine of Shanxi Duanwang Group at the mining influence stage is taken as an example.
[0037] Example 1: 1) Arrangement of observation stations In this example, the whole process of mine strata pressure behavior of the two walls of the tunnel is predicted by collecting the displacement data of the two walls of the tunnel. The adopted measuring instruments can be laser range finder, steel tape and other distance measuring instruments. During mine strata pressure observation of 110102 working face gate road of Shanxi Shouyang Ji'anda Mine, the working face had been advanced by 450m. As there were driving machines, support materials and uncleared coal near the driving working face, observation stations could not be arranged on the newly excavated tunnel section to carry out observation. For the above reason, the first observation station was arranged nearest the working face. Typically, the mine strata pressure of the tunnel changes more violently within 50m behind the working face, and changes relatively gently beyond 50m. Therefore, six observation stations (0-5) were arranged within 50m from the working face, and beyond this range, four observation stations were arranged at an interval of 25m, and then one observation station was arranged at an interval of 50m. Within 200m behind the working face, 11 observation stations were arranged, as shown in FIG. 1. Arrangement of measuring points and observation stations: The monitoring of tunnel surface convergence includes roof subsidence, floor heave, and convergence of two walls. Measuring instruments: instruments were selected according to tunnel section dimensions and the requirements for accuracy of displacement testing results. The measuring points were arranged by the "cross-shaped measuring point arrangement method", and convergence amount of roof and floor as well as two walls of the tunnel were observed every day. The arrangement manner of measuring points is shown in FIG. 2.
2) Recording results of the displacement of the two walls of the tunnel Arrange observation stations and record the dimensions of the tunnel section on the first day, and record the dimensions of the tunnel section on the third day. The dimensional record of tunnel width and the deformation amount are shown in Table 1. A histogram showing variation of tunnel width of 110102 gate road of Ji'anda Mine in different time periods is shown in FIG. 3.
Table 1: Tunnel width record of 110102 gate road of Ji'anda Mine Day 1 The first Tunnel width Day 3 Distance The second Tunnel The Serial Distance equivalent AB / from the equivalent width AB deformation N from the deformation mm working face deformation time /mm amount in 2 working face time /m /d days UA /M /d /mm 0 0 4638 20 2 4534 104 1 10 1 4557 30 3 4492 65 2 20 2 4500 40 4 4440 60 3 30 3 4380 50 5 4323 57 4 40 4 4573 60 6 4539 34 50 5 4520 70 7 4500 20 6 75 7.5 4680 95 9.5 4661 19 7 100 10 4627 120 12 4610 17 8 125 12.5 4435 145 14.5 4420 15 9 150 15 4500 170 17 4488 12 10 200 20 4523 220 22 4520 3
3) Establish mathematical models and predict using different mathematical models Based on the tunnel deformation amount uA in any time period expressed by the established four mathematical models, and two groups of the above data including the first equivalent deformation time and tunnel width deformation amount in 2 days, SPSS software is used to conduct nonlinear regression analysis. The analysis results are as follows:
(1) Types of exponential function Its deformation velocity model is:
The expression of the input model in SPSS is: a/b*(exp(-b*t)-exp(-b*(t+2))). The initial value is set to be a(l), b(0.999), and the value range is: a>=1, a<=104,b>=0.001, b<=0.999. Parameter estimation: a=59.193, b=0.227. The analysis of results is as follows: the standard error of a is 5.441, which is very large, suggesting that the confidence level of this estimation is not high; the standard error of b is 0.031, which is very low, suggesting that the confidence level of this estimation is very high; the correlation between a and b is 0.806, which is relatively high; certainty coefficient R 2 =1-(residual sum of squares)/(corrected sum of squares)=0.934, indicating very high degree of fitting. The expression of deformation velocity is:
v = 59.193e- 227t The expression of deformation amount is:
u= 260.762(1--227,) In this model, when t-0, the maximum convergence velocity of the two walls of the tunnel is 59.193mm/d; let the convergence velocity of the two walls of the tunnel v=lmm/d, then t=17.977, in other words, from the 18th days on, the tunnel begins to enter a stable deformation stage, and at this moment the convergence amount of the two walls of the tunnel has reached 256.357mm; on the 22nd day, the convergence amount of the two walls of the tunnel reaches 258.994; let t-o, the maximum convergence amount of the two walls of the tunnel influenced by driving is 260.762mm.
(2) Type of composite function Its deformation velocity model is: v=ab' The deformation amount of the tunnel in 2 days is: u=a -1) b' Imb Regression is conducted through curve estimation in SPSS. A composite function is selected to obtain a/lnb*(b2-1)=75.159, its standard error is 10.897, which is very large, suggesting that the confidence level of this estimation is not high; b=0.861, its standard error is 0.13, which is very low, suggesting that the confidence level of this estimation is very high; by calculation, a=43.484 is determined, certainty coefficient R2=l-(residual sum of squares) / (corrected sum of squares)=0.914, indicating very high degree of fitting. The expression of deformation velocity is: n = 43,484x0.861' The deformation amount expression is obtained by integrating the above expression and using the data of "when t-0, u=O":
=290.550 x(1-0-861')
In this model, when t-0, the maximum convergence velocity of the two walls of the tunnel is 43.484mm/d; let the convergence velocity of the two walls of the tunnel v=lmm/d, then t-25.206, in other words, from the 25th day on, the tunnel begins to enter a stable deformation stage, at this moment the convergence amount of the two walls of the tunnel has reached 312.500mm; let t-oo, the maximum convergence amount of the two walls of the tunnel influenced by driving is 319.391mm.
(3) Type of Logistic function Its deformation velocity model is:
v=k i
[I+e"-y j2
The expression of the input model in SPSS is: k(1/(+exp(a(t+2))), the initial value is set to be a(1), k(1), p(20). Value range: a>=0.0001,a<=1, k>=1, k<=1,000,000, p>=-20, p<=20. Parameter estimation: a=0.233, k-9887.972, p=-15.519. The analysis of results is as follows: the standard error of a is 0.115, which is very low, suggesting that the confidence level of this estimation is very high; the standard error of k is 192,001.355, which is very high, suggesting that the confidence level of this estimation is very low; the standard error of p is 92.620, which is very high, suggesting that the confidence level of this estimation is not high; certainty coefficient R2=l-(residual sum of squares)/(corrected sum of squares)=0.932, indicating very high degree of fitting. The expression of deformation velocity is:
v = 2303-897 [ .3e J2
The expression of deformation amount is: u= 9887.972[ - J 1+ e- +e In this model, when t-O, the maximum convergence velocity of the two walls of the tunnel is 58.754mm/d; let the convergence velocity of the two walls of the tunnel v=lmm/d, then t-17.706, in other words, from the 18th day on, the tunnel begins to enter a stable deformation stage, at this moment the convergence amount of the two walls of the tunnel has reached 255.110mm; let t-oo, the maximum convergence amount of the two walls of the tunnel influenced by driving is 259.956mm.
(4) Type of normal distribution function Its deformation velocity model is:
v= e
From the fitting results of the foregoing exponential function model and Logistic function model, it can be known that their certainty degrees R are both very high and their analyses on important issues such as tunnel deformation velocity and deformation amount are substantially the same. As the parameter values of the normal distribution function model all have special meanings, they are crucial to the analysis of mine strata pressure change law of the tunnel. As the fitting results of the exponential function model and Logistic function model are reliable, here the parameter values of the normal distribution model are solved by using the data of tunnel deformation velocity expressed with the Logistic function as the data needed for regression of the normal distribution function model. k SPSS is used for nonlinear regression, let A= I , B=2a2
The expression of the input model in SPSS is: A*2.7183**(-(t-pt)**2/B), the initial value is set to be A(50), B(100), p(0). Value range: A>=1, A<=10,000, B>=1, k<=1,000,000, p>=-500, p<=O. Parameter estimation: A=766.365, B=265.203, p=-26.214. The analysis of results is as follows: the standard error of a is 539.806, which is very high, suggesting that the confidence level of this estimation is very low; the standard error of B is 56.015, which is relatively high, suggesting that the confidence level of this estimation is relatively low; the standard error of p is 6.299, which is relatively high, suggesting that the confidence level of this estimation is relatively low; certainty coefficient R2=1-(residual sum of squares)/(corrected sum of squares)=0.931, indicating very high degree of fitting. Obtain o=11.515, k-22,120.2249, p=-26.214 The expression of deformation velocity is: (t+26.214)2 v = 766.365e 265.203
Transform the above expression for standard normal distribution: t+26.214 11.515
By using the table of integrals of standard normal distribution, deformation amount of tunnel at different time at the driving influence stage can be calculated. -(_"' )2 t 1 v = 22120.2249 f e 2, dt
Under this expression, when t=, the convergence velocity of the two walls of the tunnel is 57.428mm/d. According to the 3a principle, p+3a=8.31, in other words, from the 13th day on, the tunnel deformation at the driving influence stage is substantially completed; let the convergence velocity of the two walls of the tunnel v=lmm/d, then t=15.755, in other words, from the 16th day on, the tunnel begins to enter a stable deformation stage, at this moment the deformation amount is 245.53mm, and the maximum convergence amount of the two walls of the tunnel under the influence of driving is 249.96mm. The foregoing formula is converted into a standard normal distribution formula. The convergence amount of tunnel at different time can be obtained by using the table of integrals of normal distribution.
4) Analysis of prediction results Table 2 shows the convergence velocity of two walls of 110102 gate road of Ji'anda Mine, FIG. 4 shows the convergence velocity of two walls of 110102 gate road of Ji'anda Mine, Table 3 shows the convergence amount of two walls of 110102 gate road of Ji'anda Mine, and FIG. 5 shows the convergence amount of two walls of 110102 gate road of Ji'anda Mine.
Table 2 Convergence velocity of two walls of 110102 gate road of Ji'anda Mine formation time Exponential function model Composite model Logistic function model Normal distribution function model T-d mm-d 1 mm-d 1 mm-d 1 mm-d 1 0 59.19 43.48 58.75 57.43 1 47.17 37.44 47.05 46.95 2 37.59 32.24 37.60 38.09 3 29.96 27.75 29.99 30.68 4 23.87 23.90 23.89 24.52 5 19.03 20.58 19.01 19.45 6 15.16 17.72 15.11 15.31 7 12.08 15.25 12.00 11.96 8 9.63 13.13 9.53 9.28 9 7.67 11.31 7.56 7.14 10 6.12 9.74 6.00 5.46 11 4.87 8.38 4.76 4.14 12 3.88 7.22 3.77 3.11 13 3.10 6.21 2.99 2.32 14 2.47 5.35 2.37 1.72 15 1.97 4.61 1.88 1.27 16 1.57 3.97 1.49 0.93 17 1.25 3.41 1.18 0.67 18 0.99 2.94 0.93 0.48 19 0.79 2.53 0.74 0.34 20 0.63 2.18 0.59 0.24 21 0.50 1.88 0.46 0.17 22 0.40 1.62 0.37 0.12 23 0.32 1.39 0.29 0.08
Table 3 Convergence amount of two walls of 110102 gate road of Ji'anda Mine Normal Tunnel deformation time t/d Exponential function model Composite model Logistic function model futtion model 0 0.00 0.00 0.00 0.00 1 52.96 40.39 52.68 48.66 2 95.16 75.16 94.84 92.90 3 128.79 105.10 128.49 128.30 4 155.59 130.88 155.32 152.63 5 176.95 153.07 176.67 174.75
6 193.97 172.18 193.66 192.45 7 207.53 188.63 207.15 205.72 8 218.34 202.80 217.87 216.78 9 226.96 215.00 226.37 225.63 10 233.82 225.50 233.12 232.26 11 239.29 234.54 238.47 236.69 12 243.65 242.33 242.72 238.90 13 247.13 249.03 246.08 243.32 14 249.90 254.80 248.75 245.53 15 252.10 259.77 250.86 245.53 16 253.86 264.05 252.54 17 255.26 267.73 253.86 18 256.38 270.90 254.92 19 257.27 273.63 255.75 20 257.98 275.99 256.41 21 258.54 278.01 256.93 22 258.99 279.75 257.35 23 259.35 281.25 257.67
The analysis on the deformation law of tunnel width of 110102 gate road of Ji'anda Mine with time further describes the effectiveness of mathematical models in tunnel mine strata pressure observation, and the comparison of various models indicates that the exponential function model and Logistic function model have higher confidence levels, and their certainty coefficients reach 0.934 and 0.932 respectively, while the composite function has the lowest confidence level, which is only 0.914. The normal distribution function does not have an obvious effect during mine strata pressure observation of a driving tunnel. From the analysis of tunnel mine strata pressure observation data in a short term through a mathematical model, a law for change of tunnel deformation velocity and deformation amount with time is obtained. It not only shows an active effect in modifying the tunnel mine strata pressure observation method but also provides a more scientific method for studying the change law of tunnel mine strata pressure. The analysis result of the deformation of the two walls of the tunnel in 110102 gate road of Ji'anda Mine is shown in Table 4. It can be known from the observation results that it takes 18 days from the start of deformation to final stabilization of 110102 gate road of Ji'anda Mine at the driving influence stage, whereas in actual observation, observation was conducted for only 3 days in a row and the efficiency in obtaining the mine strata pressure observation results was raised by 83.3% compared with conventional observation, significantly shortening the time needed to obtain the mine strata pressure change law.
Table 4 Analysis result of deformation of two walls of the tunnel of 110102 gate road of Ji'anda Mine
Model type Exponential Composite Logistic dis tion Item function function function function
Certainty coefficient R2 0.934 0.914 0.932 0.931 Maximum convergence velocity of two walls nmax 59.193 43.484 58.754 57.428 /mm*d 1 Time for entering the stable deformation stage 18 25 18 16 /d Convergence amount of two walls when entering the stable deformation stage 256.357 312.500 255.110 245.53 /mm Final convergence amount of two walls at the driving influence stage 260.762 319.391 259.956 249.96 /mm
[0038] Example 2:
1) Selection of measuring instruments In this example, the whole process of mine strata pressure behavior of the two walls of the tunnel is predicted by collecting the displacement data of the two walls of the tunnel. The measuring instruments adopted can be laser range finder, steel tape and other distance measuring instruments.
2) Arrangement of observation stations During tunnel mine strata pressure observation, the daily drilling footage of 30104 stoping working face of Youzhong Mine was 5m/d. The monitoring length is 120m along the working face advancement direction from the 30102 open-off cut as the starting point of the remained tunnel, and there are 7 groups in total. The specific arrangement of surface displacement observation stations is shown in FIG. 6; Table 5 shows the positions of 1-7# observation stations in 30102 gate road of Youzhong Mine;
Table 5 Positions of 1-7# observation stations in 30102 gate road of Youzhong Mine Observation Distance from 30102 open-off cut Initial distance from 30104 working face station No. /m /m 1# 10 25 2# 30 5 3# 50 -15 4# 70 -35 5# 90 -55 6# 110 -75 7# 130 -95
3) Recording results of the displacement of the two walls of the tunnel Observation was recorded for 5 times in 7 days. The recording results of mine strata pressure observation are shown in Table 6.
Table 6 Mine strata pressure observation record of 30102 gate road of Youzhong Mine 1# observation station 2# observation station Observation Distance from Overall Deformation Distance from Overall Deformation 30104 working width AB amount 30104 working width AB amount timed face/m /mm /mm face/m /mm /mm 1 25 4155 5 4350 2 30 4055 100 10 4300 50 3 34 3865 190 14 4200 100 5 44.6 3718 147 24.6 4000 200 7 55.30 3620 98 35.3 3820 180 3# observation station 4# observation station Observation Distance from Overall Deformation Distance from Overall Deformation time 30104 working width AB amount 30104 working width AB amount /d face/m /mm /mm face/m /mm /mm 1 -15 4625 -35 4485 2 -10 4615 10 -30 4485 0 3 -6 4583 32 -26 4485 0 5 4.6 4515 68 -15.4 4480 5 7 15.3 4410 105 -4.7 4460 20 5# observation station 6# observation station Observation Distance from Overall Deformation Distance from Overall Deformation time 30104 working width AB- amount 30104 working width AB amount
/d face/m /mm /mm face/m /mm /mm 1 -55 4520 -75 4420 2 -50 4510 10 -70 4420 0 3 -46 4500 10 -66 4420 0 5 -35.4 4500 0 -55.4 4420 0 7 -24.7 4500 0 -44.7 4420 0 7# observation station Observation Distance from Overall Deformation time 30104 working width AB amount /d face/m /mm /mm 1 -95 4420 2 -90 4420 0 3 -86 4420 0 5 -75.4 4420 0 7 -64.7 4420 0
4) Establish mathematical models and predict using different mathematical models Based on the model for tunnel deformation amount uA between any distances from the stoping working face expressed in the two established mining influence stage models, SPSS software is used to conduct nonlinear regression analysis.
(1) Type of Logistic function Its deformation velocity model is:
vk ae*- ")
[ 1+e'- )]2
It can be known that in the advancement process of the stoping working face, the deformation amount between any tunnel sections from distance Xi to distance X 2 from the working face is: 1 1 ^ 61+e- - ") 1a+e-- ")
The expression of the input model in SPSS is: k*(1/(1+exp(-a*(X2-t)))- 1/(+exp(-a*(X1-t)))), the initial value is set to be k(100), a(0.1), p(), value range: k>=0.0001, k<=4,000, a>=0.0001, a<=0.9999, p>=-200, p<=200; parameter estimation: k--739.304, a=0.087, p=27.503. The analysis results are as follows: the standard error of k is 65.829, which is very large, suggesting that the confidence level of this estimation is not high; the standard error of a is 0.010, which is very low, suggesting that the confidence level of this estimation is very high; the standard error of p is 1.086, which is relatively low, suggesting that the confidence level of this estimation is relatively high; certainty coefficient R2=1-(residual sum of squares)/(corrected sum of squares)=0.718, indicating relatively high degree of fitting. It is assumed that the functional relation between tunnel deformation amount and distance from the working face in the initial mining period is:
u = 739.304 1 1+e-0.087(x-27.503) Functional relation between tunnel deformation velocity and distance from the working face in the initial mining period 0. 087(x-27. 503)
087 2
[1+eoo (x-27.503)
In this model, when x=27.503, i.e., a position at a distance of 27.503m behind the stoping working face, the maximum convergence velocity of the two walls of the tunnel is 16.080mm/d; the deformation of the tunnel mainly appears in section (-20, 75), i.e., 20m in front of the working face to 75m behind the working face; at 100m behind the stoping working face, tunnel deformation is substantially completed, the ultimate convergence amount of the two walls of the tunnel can be up to 739mm.
(2) Type of normal distribution function As a normal distribution function per se is a transcendental function, and cannot be directly integrated to obtain a definite expression, it cannot be fit through tunnel deformation amount in a specific period of time as other models do. Here the parameter values of the normal distribution model are solved by using the data of tunnel deformation velocity expressed with the Logistic function as the data needed for regression of the normal distribution function model.
SPSS is used for nonlinear regression, to obtain k/(2T Y)=15.611, its standard error is 0.149, which is very low, suggesting that the confidence level of this estimation is very high; 2a2 =670.618, its standard error is 14.743, which is relatively low, suggesting that the confidence level of this estimation is relatively high; p=27.503, its standard error is 0.201, which is very low, suggesting that the confidence level of this estimation is very high; a--18.311; k--716.527. Certainty coefficient R2=1-(residual sum of squares)/(corrected sum of squares)=0.716, indicating relatively high degree of fitting.
The expression of deformation velocity is: (x-27. 503)2 v = 15. 611e 670.618
By using the table of integrals of standard normal distribution, deformation amount of tunnel at different time at the driving influence stage can be calculated. 2 (X1-) u =716. 527t -e 2 dt
The influenced range of tunnel at the mining influence stage is (p-3a, p+3a), i.e., (-27.43, 82.436). The tunnel begins to deforme at 27.43m in front of the stoping working face, and the tunnel influenced by mining begins to become stable at 82.436m behind the working face. The two walls of the tunnel reaches maximum convergence amount of 716.527mm. When x=0, i.e., near the stoping working face, the convergence velocity of the two walls of the stoping tunnel is about 4.93mm/d; when x=p=27.503, the maximum convergence velocity of the two walls of the stoping tunnel is 15.611mm.
5) Analysis of prediction results The prediction results of the deformation of the two walls of 30102 gate road of Youzhong Mine are shown in Table 7 and FIG. 7.
Table 7 Prediction results of the deformation of the two walls of 30102 gate road of Youzhong Mine: osition relative to Logistic model Normal distribution model stoping working face Convergence velocity of Convergence amount Convergence velocity of Convergence amount of /m two walls of two walls two walls two walls mm-d' mm mm-d' mm -35 0.28 3.20 0.05 0.00 -30 0.43 4.93 0.11 0.00 -25 0.65 7.60 0.26 1.50 -20 1.00 11.67 0.54 3.44 -15 1.52 17.88 1.06 7.31 -10 2.28 27.26 1.92 14.47 -5 3.39 41.28 3.23 26.87 0 4.93 61.90 5.05 47.86 5 6.97 91.46 7.34 78.32 10 9.45 132.38 9.89 117.87 15 12.13 186.33 12.36 178.06 20 14.48 253.11 14.35 244.26 25 15.89 329.56 15.47 318.35 30 15.89 409.65 15.47 398.17 35 14.48 486.10 14.36 472.26 40 12.13 552.90 12.37 538.61 45 9.46 606.87 9.89 598.66 50 6.98 647.80 7.34 638.21 55 4.94 677.38 5.06 668.66 60 3.39 698.00 3.23 689.01 65 2.29 712.03 1.92 702.05 70 1.52 721.42 1.06 709.22 75 1.00 727.63 0.54 713.09 80 0.65 731.70 0.26 715.02 85 0.43 734.37 0.11 716.53 90 0.28 736.10 0.05 716.53
[0039] It can be calculated according to the observation results that the mining influence of 30102 gate road of Youzhong Mine lasts for 22 days, but the observation takes only 7 days when utilizing mine strata pressure observation data for prediction, and the efficiency is increased significantly.
[0040] Although the present invention take the displacement of two walls of the tunnel as examples, the technical solution of the present invention is also applicable to other types of observation data of the mine strata pressure behavior of the stoping tunnel, such as roof separation, roof subsidence, displacement of deep rock formation, floor heave, anchor rod load and anchor cable load. This is because the features of mathematical models summarized in the technical solution of the present invention are all applicable to them. In actual observation, the observed data does not fully conform to the mathematical models defined by this technical solution and the corresponding images, but the actual mine strata pressure behavior data points fluctuates around the regression images; in other words, at least one of the functional expressions covered by the mathematical models defined by the present invention can accurately reflect the actual mine strata pressure behavior features.
[0041] As shown in FIG. 8, at the driving influence stage, the curves in the curve family contained in the prediction model v(x) are all concave curves at the driving influence stage, and the corresponding functions are decreasing functions; the curves in the curve family contained in the prediction model u(x) are all convex curves that are steep first and then gentle at the driving influence stage, and the corresponding functions are all increasing functions.
[0042] As shown in FIG. 9, at the mining influence stage, the curves in the curve family contained in the prediction model v(x) are all bell-shaped curves, which are convex in the middle and concave at two sides, and the corresponding functions all increase first and then decrease; the curves in the curve family contained in the prediction model u(x) are all S-shaped increasing curves at the mining influence stage.
[0043] The two examples in the present invention are only simple application of the technical solution of the present invention. The setting of observation station arrangement manner and observation frequency is not optimum design given according to the technical solution of the present invention. Under the condition of increasing the quantity of observation stations and observation frequency and using more accurate measuring instruments, the technical solution of the present invention can be used to more rapidly and more accurately predict the mine strata pressure behavior features of a stoping tunnel.
Claims (10)
1. A method for predicting mine strata pressure behavior data of stoping tunnel, characterized in that, said method comprises: establishing a prediction model for change speed of mine strata pressure behavior data on any section of the stoping tunnel, and integrating it to get a predicted variation model of mine strata pressure behavior data in a certain interval; obtaining a series of actual differences of mine strata pressure behavior data by collecting mine strata pressure behavior data from different observation stations in different positions relative to the working face for multiple times; and using the predicted variation model of mine strata pressure behavior data in a certain interval to perform nonlinear regression on a series of actual differences of mine strata pressure behavior data, so as to determine a functional expression of a change speed and a cumulative variation of the mine strata pressure behavior data of the stoping tunnel.
2. The method for predicting mine strata pressure behavior data of stoping tunnel according to claim 1, wherein the prediction method comprises the following steps: 1) establishing a predicted variation model of mine strata pressure behavior data on the same tunnel section in the stoping tunnel, the prediction model of change speed of mine strata pressure behavior data on any tunnel section is v(x), the prediction model of cumulative variation of mine strata pressure u(x) v(x)dx behavior data is , and the predicted variation model of mine strata pressure Au(x ,~x)=f (x)dx behavior data is ;the deformation time experienced on any tunnel section is t = °O L ;the mine strata pressure behavior data is mechanical and displacement data related to the mine strata pressure behavior of the stoping tunnel, including roof separation, roof subsidence, floor heave, convergence of two walls, displacement of deep rock formation, anchor rod load and anchor cable load of the stoping tunnel; where, v is a change speed of mine strata pressure behavior data, u is a cumulative variation of mine strata pressure behavior data; x is a distance of the tunnel section relative to the working face in the advancement direction of the working face, when the tunnel section is in front of the working face, x<, and when the tunnel section is behind the working face, x>O; the working face at the driving influence stage refers to the driving working face, and the working face at the mining influence stage refers to the stoping working face; xo is a distance of any tunnel section relative to the working face when any tunnel section begins deforming; xm and x, represent different distances of the tunnel section relative to the working face, and X' <X; L is a daily drilling footage of the working face; 2) obtaining a series of actual differences of mine strata pressure behavior data through mine strata pressure observation a plurality of observation stations are arranged in the same stoping tunnel at the same time to collect mine strata pressure behavior data; with the advancement of the working face, the actual difference of mine strata pressure behavior data collected by the same observation station from
different positions relative to the working face in two times is Au(x, ,x, U - Uijij , and after a plurality of observation stations collect mine strata pressure behavior data for multiple
times, a series of actual differences of mine strata pressure behavior data Au(x 1 , x,]) Au(x 2 ,jx-,) Au(x , x +')are obtained; where, is a distance of the observation station relative to the working face when thej-th mine strata pressure observation is conducted at the i-th observation station; Uti is mine strata pressure behavior data value collected when the distance of the i-th observation station relative to the working face is j ; 3) predicting the mine strata pressure behavior data of the stoping tunnel by means of nonlinear regression
A$"x, x")= v(x)dx. a predicted variation model of mine strata pressure behavior data is used to perform nonlinear regression on a series of actual differences of mine strata pressure behavior
data Au(x, x,+) Ju(x2 , x21 , ) ... IAu(x , x ,,) to obtain parameters of prediction models v(x) and u(x), i.e., determine a functional expression of a change speed and a cumulative variation of the mine strata pressure behavior data of the stoping tunnel, and meanwhile obtain the change speed and cumulative variation of mine strata pressure behavior data on the tunnel section at any distance relative to the working face, as well as the influencing range and duration of the mine strata pressure behavior.
3. The method for predicting mine strata pressure behavior data of stoping tunnel according to claim 2, wherein the curves in the curve family contained in the prediction model v(x) are all concave curves at the driving influence stage, and the corresponding functions are decreasing functions; the curves in the curve family contained in the prediction model u(x) are all convex curves that are steep first and then gentle at the driving influence stage, and the corresponding functions are all increasing functions; the curves in the curve family contained in the prediction model v(x) are all bell-shaped curves at the mining influence stage, which are convex in the middle and concave at two sides, and the corresponding functions all increase first and then decrease; the curves in the curve family contained in the prediction model u(x) are all S-shaped increasing curves at the mining influence stage.
4. The method for predicting mine strata pressure behavior data of stoping tunnel according to claim 2, wherein the prediction model v(x) at the driving influence stage is expressed as follows v(x)= ae- (1)
where a is the maximum change speed of mine strata pressure behavior data; a and b are parameters to be determined; e is a natural constant; and, a>,O<b<1.
5. The method for predicting mine strata pressure behavior data of stoping tunnel according to claim 2, wherein the prediction model v(x) at the driving influence stage is expressed as follows
v(x)= acx (2) where a is the maximum change speed of mine strata pressure behavior data; a and c are parameters to be determined, and a>O,O<c<1.
6. The method for predicting mine strata pressure behavior data of stoping tunnel according to claim 2, wherein the prediction model v(x) at the driving influence stage and the mining influence stage is expressed as follows ded(x-p) v(x)= k 2 2ed(x-)]
11+ed(X-0 1(3) where k, d and p are all parameters to be determined, k>0, <d<1, -1,000<p<1,000.
7. The method for predicting mine strata pressure behavior data of stoping tunnel according to claim 1, wherein the observation stations are arranged within 100m behind the working face; at the mining influence stage, the observation stations are also arranged within 50m in front of the stoping working face.
8. The method for predicting mine strata pressure behavior data of stoping tunnel according to claim 1, wherein with more mine strata pressure behavior data being collected, the mine strata pressure behavior features of the stoping tunnel can be predicted more accurately; with the observation stations being arranged more densely, the time required for collecting the mine strata pressure behavior data will be shorter.
9. The method for predicting mine strata pressure behavior data of stoping tunnel according to claim 1, wherein the nonlinear regression is completed using numerical analysis software with the function of nonlinear regression, and the numerical analysis software includes ORIGIN, MATLAB, EXCEL and SPSS.
10. The method for predicting mine strata pressure behavior data of stoping tunnel according to claim 1, wherein the stoping tunnel has the same surrounding rock properties, consistent mine strata pressure control method and unchanged daily drilling footage of the working face.
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