A tunnel convergence value prediction method and system based on probability distribution of measurement data

Abstract

The present invention discloses a method and system for predicting tunnel convergence value based on probability distribution of measurement data. The method of the present invention includes determining the optimal convergence intervals of each level of steps and the ith convergence rate subinterval where the convergence rate of the first step of any section is located based on the probability distribution of measurement data in advance. For any m-th step at the target section: according to the convergence rate of the first step at the target section and the ith convergence rate subinterval where it is located, and the optimal convergence interval of the m-th step corresponding to the ith convergence rate subinterval, interpolate and calculate the tunnel convergence value prediction result of the m-th step at the target section. The present invention can realize convergence value prediction based on the association between convergence rate subinterval and convergence value prediction, which not only avoids the complex variable analysis process at the micro level, but also considers the effect results of various variables at the macro level, predicts convergence behavior in the form of probability, has strong practicality, and is simple and convenient to operate.

Description

Technical Field

[0001] The present invention relates to tunnel engineering measurement technology, and in particular to a tunnel convergence value prediction method and system based on probability distribution of measurement data. Background Art

[0002] After the tunnel is excavated, the tunnel body will deform due to the rock and soil pressure. The horizontal or vertical deformation of the points on the tunnel body is called tunnel convergence. At present, the analysis and prediction of surrounding rock measurement are mainly carried out by establishing a constitutive model of the surrounding rock and analyzing and predicting its convergence behavior through an idealized mechanical model. However, since there are many factors that affect the convergence of the surrounding rock and there is a certain degree of randomness, the tunnel convergence prediction based on the surrounding rock constitutive model has theoretical accuracy. However, in the actual construction process, due to the randomness of the lithology distribution of the surrounding rock, it is difficult to dynamically and accurately obtain the parameters of various surrounding rocks. There is often a large difference between the model and the actual situation of the surrounding rock, and its prediction accuracy also varies depending on the state of the surrounding rock. Summary of the invention

[0003] Technical problem to be solved by the present invention: In view of the above-mentioned problems in the prior art, considering that the surrounding rock measurement data is the most macroscopic and objective manifestation of the surrounding rock convergence behavior under the action of various variables, and its convergence factors and results satisfy certain probability distribution laws, the present invention provides a tunnel convergence value prediction method and system based on the probability distribution of measurement data. The present invention can realize convergence value prediction based on the correlation between convergence rate subintervals and convergence value predictions, which avoids the complex variable analysis process at the microscopic level and considers the results of the action of various variables at the macroscopic level, and predicts the convergence behavior in the form of probability, which has strong practicality and is simple and convenient to operate.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] A method for predicting tunnel convergence value based on probability distribution of measurement data, comprising:

[0006] 1) Determine the measurement data of each section during the tunnel construction process, including the convergence rate of the first step of each section V And the convergence value of each level of steps S 1 ～S N ,in N The number of steps for tunnel construction;

[0007] 2) The convergence rate of the first step of each section V The sample interval is divided into n Convergence rate subintervals;

[0008] 3) The convergence value of the first step of each section SThe sample interval of 1 is based on the initial interval Δ S 1 divided into n The convergence value sub-intervals are adjusted according to the corresponding relationship between the convergence rate of the first step and the probability distribution of the convergence value. S 1Re-divided to get n Optimal convergence value subintervals;

[0009] 4) Start from the second step of each section to the N At the end of the first step, j +1 step convergence value S j+1 The sample interval is based on the initial interval Δ S j+1 Divide into n The convergence value subinterval, according to the j Level j +1 level step between the probability distribution of convergence value of the corresponding adjustment interval Δ S j+1 Re-divided n Optimal convergence value subintervals;

[0010] 5) For the target section A to be predicted, first determine the first step convergence rate at the target section A V a The convergence rate subinterval where the target section A is located is then interpolated and calculated based on the corresponding relationship between the convergence rate subinterval and the optimal convergence value subinterval of each level of steps. m Prediction results of tunnel convergence value for step-by-step tunnels S m .

[0011] Optionally, step 2) includes: counting the convergence rate of the first step of each section V , and obtain the minimum convergence rate of the first step V min and the maximum convergence rate V max , the convergence rate of the first step of each section V The sample interval of V min , V max ]Equally spaced n Convergence rate subintervals, statistics of any i The convergence rate subinterval [ V i , V i+1 ) appears in the convergence rate V Number of f Vi.

[0012] Optionally, step 3) includes:

[0013] 3.1) Count the convergence values ​​of the first step of each section S 1. Get the minimum convergence value of the first step S 1min and the maximum convergence value S 1max , the convergence value of the first step of each section S The sample interval of 1[ S 1min , S 1max ] According to the initial group distance Δ S 1Equally spaced n The convergence value sub-intervals are counted to calculate the first step of any i The convergence value subinterval [ S 1,i , S 1,i+1 ) contains the number of converged values ​​as the sample frequency f S(1,i) , and calculate the first step n The sum of the squares of the differences between the sample frequencies of the convergence rate subintervals and the sample frequencies of the convergence value subintervals B The initial value is 1;

[0014] 3.2) Adjust the group distance Δ S 1 and the convergence value of the first step of each section S The sample interval of 1[ S 1min , S 1max ]According to the new group distance Δ S 1 point n Convergence value subintervals are updated and counted for any of the first steps. i The convergence value subinterval [ S 1,i , S 1,i+1 ) contains the number of converged values ​​as the sample frequency f S(1,i) , and update the calculation of the first step n The sum of the squares of the differences between the sample frequencies of the convergence rate subintervals and the sample frequencies of the convergence value subintervals B The value of 1;

[0015] 3.3) Determine the new group distance Δ S 1 is equal to the set value. If not, jump to step 3.2); otherwise, find all the first steps.n The sum of the squares of the differences between the sample frequencies of the convergence rate subintervals and the sample frequencies of the convergence value subintervals B The minimum value among 1 B 1min , the minimum value B 1min Corresponding n The convergence value subinterval is used as the first step. n The optimal convergence value subinterval is determined n Any of the convergence rate subintervals i The convergence rate subinterval [ V i , V i+1 ) corresponds to the optimal convergence value subinterval of the first step [ S 1,i , S 1,i+1 ).

[0016] Optionally, the first step n The sum of the squares of the differences between the sample frequencies of the convergence rate subintervals and the sample frequencies of the convergence value subintervals B The calculation function expression of 1 is:

[0017]

[0018] In the above formula, n is the number of convergence rate subintervals and convergence value subintervals, f S(1,i) Any number of steps on the first step i The convergence value subinterval [ S 1,i , S 1,i+1 ) contains the number of converged values, f Vi for n Any of the convergence rate subintervals i The convergence rate subinterval [ V i , V i+1 ) contains the number of convergence rates.

[0019] Optionally, step 4) includes:

[0020] 4.1) Initialize the loop variable used to represent the number of stages j The value of is 1;

[0021] 4.2) Count the number of j +1 step convergence value Sj+1 , get the j +1 step minimum convergence value S j+1min and the maximum convergence value S j+1max , each section j +1 step convergence value S j+1 The sample interval of S j+1min , S j+1max ] According to the initial group distance Δ S j+1 Divide into equal parts n The convergence value sub-intervals are counted respectively. j +1 step to any i The convergence value subinterval [ S j+1,i , S j+1,i+1 ) contains the number of converged values ​​as the sample frequency f S(j+1,i) , and calculate the j +1 level, j Step n The sum of the squares of the differences in the sample frequencies of the convergence value subintervals is B j+1 The initial value of

[0022] 4.3) Adjust the group distance Δ S j+1 The size of each section j +1 step convergence value S j+1 The sample interval of S j+1min , S j+1max ]According to the adjusted group interval Δ S j+1 Divide n Convergence value subintervals are updated and counted respectively. j +1 step to any i The convergence value subinterval [ S j+1,i , S j+1,i+1 ) contains the number of converged values ​​as the sample frequency f S(j+1,i) , and update the calculation j +1 level, j Step n The sum of the squares of the differences in the sample frequencies of the convergence value subintervals is B j+1 The value of

[0023] 4.4) Determine the adjusted group distance Δ S j+1 Is the number of times equal to the set value? If not, jump to step 4.3); otherwise, find all the j +1 level, j Step n The sum of the squares of the differences in the sample frequencies of the convergence value subintervals is B j+1 The minimum value in B j+1min , the minimum value B j+1min Corresponding n The convergence value subinterval is taken as the j +1 step n The optimal convergence value subinterval is determined n Any of the convergence rate subintervals i The convergence rate subinterval [ V i , V i+1 ) corresponds to j The optimal convergence value subinterval of +1 step [ S j+1,i , S j+1,i+1 );

[0024] 4.5) Judgment j +1 greater than the total number of steps N Is it true? If not, jump to step 4.2); otherwise, determine all N Once the steps have been processed, go to step 5).

[0025] Optionally, the j +1 step n The sum of the squares of the differences between the sample frequencies of the convergence rate subintervals and the sample frequencies of the convergence value subintervals B j+1 The calculation function expression is:

[0026]

[0027] In the above formula, n is the number of convergence rate subintervals and convergence value subintervals, f S(j+1,i) For the j +1 step to any i The convergence value subinterval [ S j+1,i , S j+1,i+1 ) contains the number of converged values, fS(j,i) For the j Any number of steps i The convergence value subinterval [ S j,i , S j,i+1 ) contains the number of converged values.

[0028] Optionally, in step 5), the interpolation calculation at any position of the target section A m Prediction results of tunnel convergence value for step-by-step tunnels S m The function expression is:

[0029]

[0030] In the above formula, V a is the convergence rate of the first step of the target section A, [ V i , V i+1 ) is the convergence rate V a exist n The first i Convergence rate subintervals, [ S m,i , S m,i+1 ] is the first m Step n The optimal convergence value subinterval i The optimal convergence value subinterval.

[0031] Optionally, before step 1), there is also a step of collecting measurement data of each section during the tunnel construction process, and when collecting the measurement data of each section during the tunnel construction process, the step of collecting the convergence value of any step in any section includes: firstly measuring the initial spatial position of each preset measurement point corresponding to the step in the section at the initial time, and then T The spatial position of each measuring point is measured until the spatial position of each measuring point is stable, and then the convergence value of the step in the section is determined according to the spatial position of each measuring point, and the convergence rate of the first step at each section is V It is obtained by dividing the convergence value of the first step at the section by the time constant.

[0032] In addition, the present invention also provides a tunnel convergence value prediction system based on probability distribution of measurement data, comprising a microprocessor and a memory connected to each other, wherein the microprocessor is programmed or configured to execute the steps of the tunnel convergence value prediction method based on probability distribution of measurement data.

[0033] In addition, the present invention also provides a computer-readable storage medium, in which a computer program is stored. The computer program is used to be executed by a microprocessor to implement the steps of the tunnel convergence value prediction method based on the probability distribution of measurement data.

[0034] Compared with the prior art, the present invention has the following advantages: research has found that the measurement data of the surrounding rock is the most macroscopic and objective expression of the convergence behavior of the surrounding rock under the action of various variables, and its convergence factors and results satisfy a certain probability distribution law. The stable convergence value of the first step is related to the convergence rate, and the stable convergence values ​​of the subsequent steps are related to the stable convergence value of the previous step. The tunnel deformation prediction method of the present invention utilizes the convergence behavior characteristics of the measurement data of the surrounding rock from a probabilistic perspective, and can realize convergence value prediction based on the correlation between the convergence rate sub-interval and the convergence value prediction. It avoids the complex variable analysis process at the micro level, and considers the results of the action of various variables at the macro level, and predicts the convergence behavior in the form of probability, which is highly practical and simple and convenient to operate. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a graphical representation of the lemma in an embodiment of the present invention.

[0036] Figure 2 Schematic diagram of the basic flow of the method of the embodiment of the present invention.

[0037] Figure 3 It is the correspondence between the convergence rate sub-interval and the optimal convergence value sub-interval of each level of steps in the embodiment of the present invention.

[0038] Figure 4 This is a frequency distribution diagram of the first-level step convergence rate in an embodiment of the present invention.

[0039] Figure 5 This is a frequency distribution diagram of the initial grouping of the first-level step stable convergence in an embodiment of the present invention.

[0040] Figure 6 This is a frequency distribution diagram of the final grouping in the first-level step stable convergence in an embodiment of the present invention.

[0041] Figure 7 This is a frequency distribution diagram of the initial grouping of the mid-step stable convergence in the embodiment of the present invention.

[0042] Figure 8 This is a frequency distribution diagram of the final grouping after stable convergence at the middle step in an embodiment of the present invention.

[0043] Fig. 9 It is a schematic diagram of the structure of the tunnel before excavation in an embodiment of the present invention.

[0044] Fig.10It is a schematic diagram of the first state structure of a tunnel being excavated in an embodiment of the present invention.

[0045] Fig.11 Schematic diagram of the second state structure of a tunnel being excavated in an embodiment of the present invention.

[0046] Fig.12 It is a schematic diagram of the third state structure of the tunnel being excavated in an embodiment of the present invention.

[0047] Fig.13 It is a schematic diagram of the fourth state structure of a tunnel being excavated in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The present invention is further described below in conjunction with the accompanying drawings and specific preferred embodiments, but the protection scope of the present invention is not limited thereby.

[0049] The basic principle of the present invention is to use the measured data of surrounding rock as the sample of macroscopic characterization of surrounding rock convergence during construction, analyze its statistical distribution with probability analysis theory, and predict the surrounding rock convergence from the probability distribution based on the observed data. In order to solve the analysis and prediction problems of surrounding rock convergence by using probability analysis tools, the following lemma is proposed and proved:

[0050] If there is a certain functional relationship between random variables and the independent variables obey a certain probability density distribution law, then the probability of the independent variable and the corresponding dependent variable occurring in the domain of the function is equal.

[0051] See also Figure 1 , the above lemma can be described as follows: for a random variable x Its continuous function y = A ( x ), satisfying the domain [ a , b ]∈ D ( A ) and range[ c , d ]∈ R ( A ),in D ( A ) is the containing function A The set of domains, R ( A ) is the containing function A A collection of value domains, A To characterize the independent variable x With dependent variable y The function of the relationship between ∀[ a , b ]∈ D ( A) functions all satisfy y ∈[ c , d ] and the random variable x exist D ( A ) obeys the probability density distribution f ( x ); y exist R ( A ) obeys the probability density distribution g ( x ). And we have:

[0052]

[0053] According to the lemma, the random variable x The area enclosed by the probability density curve of Sx ,random variable x A continuous function y The area enclosed by the probability density curve of Sy Equal, that is Sx=Sy ,like Figure 1 shown.

[0054] The proof of the above lemma is as follows:

[0055] set up , ,but ,in A -1 For the function A The corresponding inverse function; is the inverse function A -1 Range of values. From the definition of the inverse function, we can see that The inclusion of the domain and range and their corresponding subintervals can be divided into the following two cases:

[0056] Case (1): If , , then:

[0057] , It must be established, so there is .

[0058] in, for The probability of for probability.

[0059] Case (2): If , , then:

[0060]

[0061] in, for The probability of for probability.

[0062] From the above we know: , so if and only if When , the requirement is met, so:

[0063]

[0064] in, for The probability of for The probability of for The probability of . That is:

[0065] ,

[0066] The proof of the lemma is complete.

[0067] The condition for analyzing the surrounding rock convergence by using the probability analysis method in this invention is that the lithology of the surrounding rock sections to be analyzed is similar. The following rules summarized from engineering practice are obtained: (1) The convergence value of the first step is related to the convergence rate; (2) The convergence value of each step is related to the convergence value of the previous step.

[0068] Based on the above lemma, the following analysis model is established:

[0069] Analytical model (1): The convergence rate of the first step is V , and its convergence value is S 1, the two have some unknown relationship. The convergence rate is obtained during the observation convergence process V and convergence value S The total number of samples of 1 is N and M 1, and divide it into n The first step convergence rate V Any i intervals ( i is the interval number, which is a positive integer ranging from 1 to n -1)[ Vi , Vi +1) The number of observation samples is Ni ;[ S 1, i , S 1, i +1) is the same as [ Vi , Vi +1) corresponds to a convergence interval, and the number of samples is M 1, i According to the lemma, the interval sample satisfies the mapping F :

[0070]

[0071] Then we have:

[0072]

[0073] Analytical model (2): j class( j is the number of steps, which is a positive integer) The convergence value of the step is Sj , its convergence value with the next step Sj+ 1 has some unknown relationship. The total number of samples that obtain stable convergence of each level of steps during the observation process is Mj , among which i interval[ Sj , i , Sj , i +1) The number of samples is Mj , i , and divide them into n interval.

[0074] According to the lemma, its interval samples satisfy the mapping G :

[0075]

[0076] Then we have:

[0077]

[0078] in,[ S j+1,i , S j+1,i+1 ) is the j+ Step 1 i intervals, M j+1 For the j+ The total number of samples for which each step of the 1-step stably converges.

[0079] Analysis model (3): In the actual observation process, i Step convergence rate V and the convergence value is S i The sample is discrete data, so the sample interval [ Vi , V i+1 )and[ S j,i , S j,i+1 ), so that the sample mapping at each level achieves the optimal match. According to the least squares method, the optimal match satisfies the following relationship:

[0080] Relation ①: Determine the optimal first-level step convergence interval based on the convergence rate. Under the given convergence rate sub-interval grouping, adjust the convergence value S 1, so that the following B 1 is the smallest value:

[0081]

[0082] at this time,[ S 1,i , S 1,i+1 ) is given in [ V i , V i+1 ) corresponds to the optimal convergence interval. In particular, when N = M 1 (i.e. the upper step convergence rate is equal to the number of samples that converge stably), B The minimum value that can be obtained is 0.

[0083] Relation ②: Based on the first step [ S 1,i , S 1,i+1 ) Determine the optimal convergence interval of the remaining steps.

[0084] According to the relationship ① B 1 is the minimum convergence value S 1, and adjust the convergence values ​​of other steps respectively. S j The convergence intervals of make the following formula B j The value is minimum:

[0085]

[0086] at this time,[ S j+1,i , S j+1,i+1 ) is the known [ S 1,i , S 1,i+1 ) corresponds to the convergence response interval under . In particular, when Mj = M j+1 (that is, the number of convergent and stable samples at each step is equal), B j The minimum value can be a fixed value of 0.

[0087] Analytical model (4): Convergence rate of variables V and convergence value S j When dividing the interval, the number of sub-intervals of each variable (i.e. i Finally, based on the first-level step convergence rate subinterval [ V i , V i+1 ) and the optimal convergence interval of the first step [ S 1,i , S 1,i+1 ), and the corresponding relationship of j The optimal convergence interval of the step S j,i , S j,i+1 ) and j+ 1 The optimal convergence interval of the step S j+1,i , S j+1,i+1 ), the first-level step convergence rate subinterval [ V i , V i+1 ) and the correspondence between the optimal convergence intervals of each level of steps.

[0088] Based on the above lemma and model analysis, Figure 2 As shown, this embodiment provides a method for predicting a tunnel convergence value based on probability distribution of measurement data, including:

[0089] 1) Determine the measurement data of each section during the tunnel construction process, including the convergence rate of the first step of each section V And the convergence value of each level of steps S 1 ～S N ,in N The number of steps for tunnel construction;

[0090] 2) The convergence rate of the first step of each section V The sample interval is divided into n convergence rate subintervals; n The general value is 4 to 6.

[0091] 3) The convergence value of the first step of each section S The sample interval of 1 is based on the initial interval Δ S 1 divided into n The convergence value sub-intervals are adjusted according to the corresponding relationship between the convergence rate of the first step and the probability distribution of the convergence value. S 1Re-divided to get n Optimal convergence value subintervals;

[0092] 4) Start from the second step of each section to the N At the end of the first step, j +1 step convergence value S j+1 The sample interval is based on the initial interval Δ S j+1 Divide into n The convergence value subinterval, according to the j Level j +1 level step between the probability distribution of convergence value of the corresponding adjustment interval Δ S j+1 Re-divided n Optimal convergence value subintervals;

[0093] 5) For the target section A to be predicted, first determine the first step convergence rate at the target section A V a The convergence rate subinterval where the target section A is located is then interpolated and calculated based on the corresponding relationship between the convergence rate subinterval and the optimal convergence value subinterval of each level of steps. m Prediction results of tunnel convergence value for step-by-step tunnels S m , which can be used to guide the setting of engineering parameters during tunnel excavation.

[0094] In this embodiment, for the sake of simplicity, the tunnel construction process is simplified to a two-step construction. During the construction of a railway tunnel, the measurement data of each section during the tunnel construction process are determined through measurement and calculation as shown in Table 1.

[0095] Table 1: Measurement data of each section.

[0096]

[0097] In this embodiment, step 2) includes: counting the convergence rate of the first step of each section V , and obtain the minimum convergence rate of the first step V min and the maximum convergence rate Vmax , the convergence rate of the first step of each section V The sample interval of V min , V max ]Equally spaced n Convergence rate subintervals, statistics of any i The convergence rate subinterval [ V i , V i+1 ) appears in the convergence rate V The number of (sample frequency) f Vi .like Figure 3 As shown, n The sample frequencies of the convergence rate subintervals are f Vi ~ f Vn , Δ V for n The group distance (subinterval size) of the convergence rate subintervals, and:

[0098]

[0099] According to the corresponding relationship between the convergence rate interval and the optimal convergence interval of the first step, and the j The optimal convergence interval of the first step and the j +1 level step optimal convergence interval, establish the corresponding relationship between the convergence rate sub-interval and the optimal convergence value sub-interval of each level of steps, that is, the mapping of the convergence rate interval and the optimal convergence interval of each level of steps, so that the convergence rate sub-interval, the optimal convergence value sub-interval of the first level step, any j The optimal convergence value subintervals of the level steps are in a one-to-one correspondence, for example Figure 3 The second convergence rate subinterval in f V2 ), the second optimal convergence value subinterval of the first step (sample frequency is f S(1,2) ), No. j The second optimal convergence value subinterval of the step (sample frequency is f S(j,2) ) one-to-one correspondence, here j The value range is 2~ N .

[0100] In this embodiment, the convergence data of 25 sections in the DK88+266-417 interval were collected during the construction of the railway tunnel. The convergence rate of the first step was 0.27-47.6 mm / d, which was divided into 4 groups at equal intervals to form 4 convergence rate sub-intervals. The group interval = (47.6-0.27) / 4 = 11.83 mm / d. V Plot the convergence rate of the first step of each section against the frequency of occurrence of each group of samples V The frequency histogram of Figure 4 shown.

[0101] In this embodiment, step 3) includes:

[0102] 3.1) Count the convergence values ​​of the first step of each section S 1. Get the minimum convergence value of the first step S 1min and the maximum convergence value S 1max , the convergence value of the first step of each section S The sample interval of 1[ S 1min , S 1max ] According to the initial group distance Δ S 1Equally spaced n The convergence value sub-intervals are counted to calculate the first step of any i The convergence value subinterval [ S 1,i , S 1,i+1 ) contains the number of converged values ​​as the sample frequency f S(1,i) , and calculate the first step n The sum of the squares of the differences between the sample frequencies of the convergence rate subintervals and the sample frequencies of the convergence value subintervals B The initial value is 1;

[0103] 3.2) Adjust the group distance Δ S 1 and the convergence value of the first step of each section S The sample interval of 1[ S 1min , S 1max ]According to the new group distance Δ S 1 point n Convergence value subintervals are updated and counted for any of the first steps. i The convergence value subinterval [ S 1,i , S 1,i+1) contains the number of converged values ​​as the sample frequency f S(1,i) , and update the calculation of the first step n The sum of the squares of the differences between the sample frequencies of the convergence rate subintervals and the sample frequencies of the convergence value subintervals B The value of 1;

[0104] 3.3) Determine the new group distance Δ S 1 is equal to the set value. If not, jump to step 3.2); otherwise, find all the first steps. n The sum of the squares of the differences between the sample frequencies of the convergence rate subintervals and the sample frequencies of the convergence value subintervals B The minimum value among 1 B 1min , the minimum value B 1min Corresponding n The convergence value subinterval is used as the first step. n The optimal convergence value subinterval is determined n Any of the convergence rate subintervals i The convergence rate subinterval [ V i , V i+1 ) corresponds to the optimal convergence value subinterval of the first step [ S 1,i , S 1,i+1 ).

[0105] In this embodiment, the first step n The sum of the squares of the differences between the sample frequencies of the convergence rate subintervals and the sample frequencies of the convergence value subintervals B The calculation function expression of 1 is:

[0106]

[0107] In the above formula, n is the number of convergence rate subintervals and convergence value subintervals, f S(1,i) Any number of steps on the first step i The convergence value subinterval [ S 1,i , S 1,i+1 ) contains the number of converged values, f Vi for n Any of the convergence rate subintervals i The convergence rate subinterval [ V i ,V i+1 ) contains the number of convergence rates.

[0108] In this embodiment, the maximum stable convergence value of the first step of a total of 25 sections in the DK88+266~417 interval is 15~247.2 mm The number of groups is the same as the number of convergence rate groups, both of which are 4 groups. First, the initial grouping is performed into 4 groups with equal intervals, and the group interval Δ S 1 is 58.05 mm , the grouping results and corresponding frequencies are as follows Figure 5 As shown. Figure 5 It can be seen that the stable convergence value of the first step is 58.05 mm The equidistant grouping is not the optimal mapping interval with the convergence rate frequency distribution. n The sum of the squares of the differences between the sample frequencies of the convergence rate subintervals and the sample frequencies of the convergence value subintervals B 1 value is:

[0109]

[0110] After multiple partition adjustments and calculations, we get B Minimum value of 1 B 1min is 0, which is the optimal convergence value subinterval of the corresponding convergence rate subinterval. At this time, the corresponding first-level step stable convergence subintervals of each group convergence value are as follows: Figure 6 shown.

[0111] In this embodiment, step 4) includes:

[0112] 4.1) Initialize the loop variable used to represent the number of stages j The value of is 1;

[0113] 4.2) Count the number of j +1 step convergence value S j+1 , get the j +1 step minimum convergence value S j+1min and the maximum convergence value S j+1max , each section j +1 step convergence value S j+1 The sample interval of S j+1min , S j+1max ] According to the initial group distance Δ S j+1 Divide into equal parts nThe convergence value sub-intervals are counted respectively. j +1 step to any i The convergence value subinterval [ S j+1,i , S j+1,i+1 ) contains the number of converged values ​​as the sample frequency f S(j+1,i) , and calculate the j +1 level, j Step n The sum of the squares of the differences in the sample frequencies of the convergence value subintervals is B j+1 The initial value of

[0114] 4.3) Adjust the group distance Δ S j+1 The size of each section j +1 step convergence value S j+1 The sample interval of S j+1min , S j+1max ]According to the adjusted group interval Δ S j+1 Divide n Convergence value subintervals are updated and counted respectively. j +1 step to any i The convergence value subinterval [ S j+1,i , S j+1,i+1 ) contains the number of converged values ​​as the sample frequency f S(j+1,i) , and update the calculation j +1 level, j Step n The sum of the squares of the differences in the sample frequencies of the convergence value subintervals is B j+1 The value of

[0115] 4.4) Determine the adjusted group distance Δ S j+1 Is the number of times equal to the set value? If not, jump to step 4.3); otherwise, find all the j +1 level, j Step n The sum of the squares of the differences in the sample frequencies of the convergence value subintervals is B j+1 The minimum value in B j+1min , the minimum value B j+1min Corresponding n The convergence value subinterval is taken as the j+1 step n The optimal convergence value subinterval is determined n Any of the convergence rate subintervals i The convergence rate subinterval [ V i , V i+1 ) corresponds to j The optimal convergence value subinterval of +1 step [ S j+1,i , S j+1,i+1 );

[0116] 4.5) Judgment j +1 greater than the total number of steps N Is it true? If not, jump to step 4.2); otherwise, determine all N Once the steps have been processed, go to step 5).

[0117] In this embodiment, j +1 step n The sum of the squares of the differences between the sample frequencies of the convergence rate subintervals and the sample frequencies of the convergence value subintervals B j+1 The calculation function expression is:

[0118]

[0119] In the above formula, n is the number of convergence rate subintervals and convergence value subintervals, f S(j+1,i) For the j +1 step to any i The convergence value subinterval [ S j+1,i , S j+1,i+1 ) contains the number of converged values, f S(j,i) For the j Any number of steps i The convergence value subinterval [ S j,i , S j,i+1 ) contains the number of converged values.

[0120] In this embodiment, the maximum stable convergence value of the second step of 25 sections in the DK88+266~417 interval is 25~331.4 mm The number of groups should be the same as the number of convergence rate groups, divided into 4 groups, firstly, the initial grouping is performed according to the equidistant interval Δ S j+1=(331.4-25) / 4=76.6 mm , the grouping results and corresponding frequencies are as follows Figure 7 As shown. Figure 7 It can be seen that the stable convergence value of the middle step (second step) is 76.6 mm The equidistant grouping is not the optimal convergence value subinterval of the convergence rate frequency distribution. j +1 step (middle step) n The sum of the squares of the differences between the sample frequencies of the convergence rate subintervals and the sample frequencies of the convergence value subintervals B 2 values ​​are:

[0121]

[0122] Since the initial grouping is not the grouping of the optimal convergence value sub-interval corresponding to the stable convergence of the first step, it is necessary to adjust the group distance Δ of the stable convergence interval of the middle step multiple times according to the predicted interval of the stable convergence value of the first step. S j+1 , and calculate the j +1 step (middle step) n The sum of the squares of the differences between the sample frequencies of the convergence rate subintervals and the sample frequencies of the convergence value subintervals B 2. Calculate the minimum value B 2min is 0. At this time, the stable convergence interval of the middle step corresponding to the stable convergence value of the second step and the first step is determined. At this time, the corresponding convergence value sub-interval of each group of the stable convergence of the first step is as follows: Figure 8 shown.

[0123] Finally, based on the above calculation results, the convergence rate of the first step can be obtained V , the correspondence between the optimal convergence value subinterval of the first step and the optimal convergence value subinterval of the middle step, constitutes a mapping set, which is listed in Table 2:

[0124] Table 2: Convergence parameter comparison table of each step.

[0125]

[0126] In actual construction, for the observed convergence rate of the first step, the reserve amount of each step can be predicted within a certain range by referring to Table 2. When the measured data falls within the interval, the predicted data can be approximately linearly interpolated according to the interval position of the measured point, and the predicted value of the tunnel deformation can be calculated.

[0127] In this embodiment, in step 5), the interpolation calculation at any position of the target section A is m Prediction results of tunnel convergence value for step-by-step tunnels Sm The function expression is:

[0128]

[0129] In the above formula, V a is the convergence rate of the first step of the target section A, [ V i , V i+1 ) is the convergence rate V a exist n The first i Convergence rate subintervals, [ S m,i , S m,i+1 ] is the first m Step n The optimal convergence value subinterval i For example, in this embodiment, the convergence value of each step corresponding to a certain section is predicted, and the convergence rate of the first step corresponding to a certain section is observed. V a is 20, and the optimal convergence value subinterval corresponding to the first step is , the corresponding optimal convergence value subinterval of the second step is , calculated by linear interpolation, the predicted convergence value of the first step is:

[0130] mm.

[0131] The convergence value of the second-level step prediction is:

[0132] mm.

[0133] The predicted convergence value can be used to guide the reserved deformation and steel arch size adjustment during tunnel excavation. In this embodiment, by using the directly observable convergence value to predict the convergence of the excavation working surface of each part of the tunnel within an interval range, it is verified that it can effectively guide the reserved deformation and steel arch size adjustment during tunnel excavation, and has strong practicality and construction guidance.

[0134] In this embodiment, before step 1), the step of collecting the measurement data of each section during the tunnel construction process is also included. When collecting the measurement data of each section during the tunnel construction process, the step of collecting the convergence value of any step in any section includes: firstly measuring the initial spatial position of each preset measurement point corresponding to the step in the section at the initial time, and thenT The spatial position of each measuring point is measured until the spatial position of each measuring point is stable, and then the convergence value of the step in the section is determined according to the spatial position of each measuring point, and the convergence rate of the first step at each section is V It is obtained by dividing the convergence value of the first step at the section by the time constant.

[0135] Fig. 9 The figure shows a schematic diagram of the tunnel structure before excavation. The spacing between sections is selected according to national or industry specifications; the number of points on each section is arranged according to national or industry specifications. Within the measurement interval, the geometric shapes of any two sections and the layout positions of the measurement points on the sections are exactly the same. During excavation, for the convenience of illustration, the following only describes the situation when the number of steps is 2, and other situations are similar. Therefore, the measurement points in the upper half are laid out during the construction of the upper step, and the measurement points in the lower half are laid out during the construction of the lower step, and the rest are the same. The construction process is as follows: first excavate the first step and lay out the points on the first step of the first section. The initial spatial position of the aforementioned points is measured at time T1, and their spatial positions are measured at time intervals of ΔT until their spatial positions are stable, such as Fig.10 As shown. The 1st and 2nd steps are excavated simultaneously, and the points of the 2nd step of the 1st section and the 1st step of the 2nd section are arranged. The initial spatial position of the above points is measured at time T2, and its spatial position is measured at a time interval of ΔT until its spatial position is stable, as shown in FIG. Fig.11 As shown. The 1st and 2nd steps are excavated simultaneously, and the points of the 2nd step of the 1st section and the 1st step of the 2nd section are arranged. The initial spatial position of the above points is measured at time T2, and its spatial position is measured at a time interval of ΔT until its spatial position is stable, as shown in FIG. Fig.12 As shown. The 1st and 2nd steps are excavated simultaneously, and the points of the 2nd step of the i-th section and the 1st step of the i+1-th section are arranged. i The initial spatial position of the aforementioned point is measured at the same time, and its spatial position is measured at a time interval of ΔT until its spatial position is stable, such as Fig.13 As shown. Repeat the above steps to arrange and measure points until the tunnel excavation is completed. It should be noted that the measurement of the points of the first and second steps of the same section is not completed at the same time, but there is a time difference T i+1 -T i However, the spatial position changes of each point on the same section will eventually tend to be stable, and the measurement time difference does not affect the final stability.

[0136] In summary, the method of this embodiment includes determining in advance based on the probability distribution of the measured data the first step convergence rate of any section. i The optimal convergence intervals of the convergence rate subintervals and their corresponding steps at each level are calculated for any m Step: According to the convergence rate of the first step at the target section and thei The convergence rate subintervals, and i The convergence rate subinterval corresponds to the m The optimal convergence interval of the first step is calculated by interpolation. m The present invention can realize the prediction of convergence value based on the association between convergence rate subinterval and convergence value prediction, which not only avoids the complex variable analysis process from a microscopic perspective, but also considers the effects of various variables from a macroscopic perspective, predicts the convergence behavior in the form of probability, has strong practicality, and is simple and convenient to operate.

[0137] In addition, this embodiment also provides a tunnel convergence value prediction system based on probability distribution of measurement data, including a microprocessor and a memory connected to each other, and the microprocessor is programmed or configured to execute the steps of the aforementioned tunnel convergence value prediction method based on probability distribution of measurement data.

[0138] In addition, this embodiment further provides a computer-readable storage medium, in which a computer program is stored. The computer program is used to be executed by a microprocessor to implement the steps of the aforementioned tunnel convergence value prediction method based on probability distribution of measurement data.

[0139] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application can take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the process Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0140] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A tunnel convergence value prediction method based on probability distribution of measurement data, characterized in that: include: 1) Determine the measurement data of each section during the tunnel construction process, including the convergence rate of the first step of each section V And the convergence value of each level of steps S 1 ～S N ,in N The number of steps for tunnel construction; 2) The convergence rate of the first step of each section V The sample interval is divided into n Convergence rate subintervals; 3) The convergence value of the first step of each section S The sample interval of 1 is based on the initial interval Δ S 1 divided into n The convergence value sub-intervals are adjusted according to the corresponding relationship between the convergence rate of the first step and the probability distribution of the convergence value. S 1Re-divided to get n Optimal convergence value subintervals; 4) Start from the second step of each section to the N At the end of the first step, j +1 step convergence value S j+1 The sample interval is based on the initial interval Δ S j+1 Divide into n The convergence value subinterval, according to the j Level j +1 level step between the probability distribution of convergence value of the corresponding adjustment interval Δ S j+1 Re-divided n Optimal convergence value subintervals; 5) For the target section A to be predicted, first determine the first step convergence rate at the target section A V a The convergence rate subinterval where the target section A is located is then interpolated and calculated based on the corresponding relationship between the convergence rate subinterval and the optimal convergence value subinterval of each level of steps. m Prediction results of tunnel convergence value for step-by-step tunnels S m ; Step 3) includes: 3.1) Count the convergence values ​​of the first step of each section S 1. Get the minimum convergence value of the first step S 1min and the maximum convergence value S 1max , the convergence value of the first step of each section S The sample interval of 1[ S 1min , S 1max ] According to the initial group distance Δ S 1Equally spaced n The convergence value sub-intervals are counted to calculate the first step of any i The convergence value subinterval [ S 1,i , S 1,i+1 ) contains the number of converged values ​​as the sample frequency f S(1,i) , and calculate the first step n The sum of the squares of the differences between the sample frequencies of the convergence rate subintervals and the sample frequencies of the convergence value subintervals B The initial value is 1; 3.2) Adjust the group distance Δ S 1 and the convergence value of the first step of each section S The sample interval of 1[ S 1min , S 1max ]According to the new group distance Δ S 1 point n Convergence value subintervals are updated and counted for any of the first steps. i The convergence value subinterval [ S 1,i , S 1,i+1 ) contains the number of converged values ​​as the sample frequency f S(1,i) , and update the calculation of the first step n The sum of the squares of the differences between the sample frequencies of the convergence rate subintervals and the sample frequencies of the convergence value subintervals B The value of 1; 3.3) Determine the new group distance Δ S 1 is equal to the set value. If not, jump to step 3.2); otherwise, find all the first steps. n The sum of the squares of the differences between the sample frequencies of the convergence rate subintervals and the sample frequencies of the convergence value subintervals B The minimum value among 1 B 1min , the minimum value B 1min Corresponding n The convergence value subinterval is used as the first step. n The optimal convergence value subinterval is determined n Any of the convergence rate subintervals i The convergence rate subinterval [ V i , V i+1 ) corresponds to the optimal convergence value subinterval of the first step [ S 1,i , S 1,i+1 ); Step 4) includes: 4.1) Initialize the loop variable used to represent the number of stages j The value of is 1; 4.2) Count the number of j +1 step convergence value S j+1 , get the j +1 step minimum convergence value S j+1min and the maximum convergence value S j+1max , each section j +1 step convergence value S j+1 The sample interval of S j+1min , S j+1max ] According to the initial group distance Δ S j+1 Divide into equal parts n The convergence value sub-intervals are counted respectively. j +1 step to any i The convergence value subinterval [ S j+1,i , S j+1,i+1 ) contains the number of converged values ​​as the sample frequency f S(j+1,i) , and calculate the j +1 level, j Step n The sum of the squares of the differences in the sample frequencies of the convergence value subintervals is B j+1 The initial value of 4.3) Adjust the group distance Δ S j+1 The size of each section j +1 step convergence value S j+1 The sample interval of S j+1min , S j+1max ]According to the adjusted group interval Δ S j+1 Divide n Convergence value subintervals are updated and counted respectively. j +1 step to any i The convergence value subinterval [ S j+1,i , S j+1,i+1 ) contains the number of converged values ​​as the sample frequency f S(j+1,i) , and update the calculation j +1 level, j Step n The sum of the squares of the differences in the sample frequencies of the convergence value subintervals is B j+1 The value of 4.4) Determine the adjusted group distance Δ S j+1 Is the number of times equal to the set value? If not, jump to step 4.3); otherwise, find all the j +1 level, j Step n The sum of the squares of the differences in the sample frequencies of the convergence value subintervals is B j+1 The minimum value in B j+1min , the minimum value B j+1min Corresponding n The convergence value subinterval is taken as the j +1 step n The optimal convergence value subinterval is determined n Any of the convergence rate subintervals i The convergence rate subinterval [ V i , V i+1 ) corresponds to j The optimal convergence value subinterval of +1 step [ S j+1,i , S j+1,i+1 ); 4.5) Judgment j +1 greater than the total number of steps N Is it true? If not, jump to step 4.2); otherwise, determine all N Steps are processed, jump to step 5); Step 5) Interpolate and calculate the target section A at any m Prediction results of tunnel convergence value for step-by-step tunnels S m The function expression is: In the above formula, V a is the convergence rate of the first step of the target section A, [ V i , V i+1 ) is the convergence rate V a exist n The first i Convergence rate subintervals, [ S m,i , S m,i+1 ] is the first m Step n The optimal convergence value subinterval i The optimal convergence value subinterval.

2. The method for predicting tunnel convergence value based on probability distribution of measurement data according to claim 1, characterized in that: Step 2) includes: counting the convergence rate of the first step of each section V , and obtain the minimum convergence rate of the first step V min and the maximum convergence rate V max , the convergence rate of the first step of each section V The sample interval of V min , V max ]Equally spaced n Convergence rate subintervals, statistics of any i The convergence rate subinterval [ V i , V i+1 ) appears in the convergence rate V Number of f Vi .

3. The method for predicting tunnel convergence value based on probability distribution of measurement data according to claim 1, characterized in that: The first step n The sum of the squares of the differences between the sample frequencies of the convergence rate subintervals and the sample frequencies of the convergence value subintervals B The calculation function expression of 1 is: In the above formula, n is the number of convergence rate subintervals and convergence value subintervals, f S(1,i) Any number of steps on the first step i The convergence value subinterval [ S 1,i , S 1,i+1 ) contains the number of converged values, f Vi for n Any of the convergence rate subintervals i The convergence rate subinterval [ V i , V i+1 ) contains the number of convergence rates.

4. The method for predicting tunnel convergence value based on probability distribution of measurement data according to claim 1, characterized in that: The said j +1 step n The sum of the squares of the differences between the sample frequencies of the convergence rate subintervals and the sample frequencies of the convergence value subintervals B j+1 The calculation function expression is: In the above formula, n is the number of convergence rate subintervals and convergence value subintervals, f S(j+1,i) For the j +1 step to any i The convergence value subinterval [ S j+1,i , S j+1,i+1 ) contains the number of converged values, f S(j,i) For the j Any number of steps i The convergence value subinterval [ S j,i , S j,i+1 ) contains the number of converged values.

5. The method for predicting tunnel convergence value based on probability distribution of measurement data according to claim 1, characterized in that: Step 1) also includes the step of collecting the measurement data of each section during the tunnel construction process, and when collecting the measurement data of each section during the tunnel construction process, the step of collecting the convergence value of any step in any section includes: firstly measuring the initial spatial position of each preset measurement point corresponding to the step in the section at the initial time, and then T The spatial position of each measuring point is measured until the spatial position of each measuring point is stable, and then the convergence value of the step in the section is determined according to the spatial position of each measuring point, and the convergence rate of the first step at each section is V It is obtained by dividing the convergence value of the first step at the section by the time constant.

6. A tunnel convergence value prediction system based on probability distribution of measurement data, comprising a microprocessor and a memory connected to each other, characterized in that: The microprocessor is programmed or configured to execute the steps of the tunnel convergence value prediction method based on probability distribution of measurement data as described in any one of claims 1 to 5.

7. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, characterized in that: The computer program is used to be executed by a microprocessor to implement the steps of the tunnel convergence value prediction method based on the probability distribution of measurement data as described in any one of claims 1 to 5.

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