A tunnel surrounding rock reinforcement body stability determination method and system

Abstract

The application provides a tunnel surrounding rock reinforcement body stability determination method and system, relates to the technical field of surrounding rock stability determination method, and comprises the following steps: arranging multiple measuring points, and collecting experimental data of each measuring point; the stability coefficient of each measuring point is constructed through an entropy weight method; the inner side of the surrounding rock is divided into uniform grids, and the stability coefficient of any grid point is calculated through inverse distance weighted interpolation; the overall stability coefficient of the surrounding rock is calculated through volume weighting method, an extreme value statistical method is used to construct a risk assessment model, and the surrounding rock instability probability is output; when the surrounding rock instability probability is greater than the set surrounding rock instability threshold probability, an alarm is given. Based on information entropy, the weight coefficient is automatically calculated, the weight is dynamically updated with monitoring data, and the actual engineering state is more suitable; the inner side of the surrounding rock is divided into uniform grids, full area coverage is realized, the stability of the grid point is calculated based on the stability coefficient of the adjacent monitoring point through distance attenuation weight, and the weak area is accurately identified.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of surrounding rock stability determination method, in particular to a tunnel surrounding rock reinforcement stability determination method and system. BACKGROUND

[0002] Traditional tunnel surrounding rock stability evaluation methods mostly rely on single parameters such as displacement or stress threshold value judgment, which has problems such as insufficient data dimension, strong subjectivity of weight distribution, and low spatial interpolation accuracy. In the prior art, the monitoring point layout is usually concentrated in the local key area such as the vault, which leads to incomplete representation of the surrounding rock state of the non-continuous section; expert experience is often used for parameter fusion, which is easy to introduce human bias; the interpolation method ignores the physical space correlation, and it is difficult to accurately reconstruct the whole field stability distribution of the surrounding rock. In addition, the overall stability evaluation is often simplified as the superposition of basic indicators, without considering the volume weight and extreme value risk accumulation effect of the damaged area, which is easy to cause early warning lag or misjudgment.

[0003] In the prior art, the disclosure number CN112131630A discloses a numerical calculation method for solving the stability problem of engineering scale grouting reinforcement, which uses the topological optimization method to connect the local optimal solution through the sliding surface to represent the global optimal solution, and quickly obtains the overall stability and structure surface fracture and sliding trend. At that time, this method does not have equidistant cross section and multi-point layout of arch top, arch waist and side wall, covering the key weak area of surrounding rock, which has evaluation deviation caused by monitoring blind area; it does not dynamically determine the interpolation influence domain based on the Euclidean distance threshold value, and does not calculate the stability coefficient of the whole surrounding rock through the stability coefficient of the measurement point, and does not set the surrounding rock instability threshold probability for the surrounding rock stability.

[0004] The above information disclosed in the background section is only used to strengthen the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0005] The purpose of the present application is to provide a tunnel surrounding rock reinforcement stability determination method and system to solve the problems raised in the background art.

[0006] To achieve the above purpose, the present application provides the following technical scheme:

[0007] A tunnel surrounding rock reinforcement stability determination method, the specific steps comprising:

[0008] S1: Along the tunnel axis, an monitoring section is arranged at equal intervals, a plurality of measuring points are arranged at the vault, haunch and two side walls of each section, experimental data of each measuring point is collected, the parameters of the experimental data include the elastic modulus, compressive strength, stress and displacement of the surrounding rock, and the collected experimental data is pretreated;

[0009] S2: Based on the pretreated experimental data of each measuring point, the proportion of each parameter in the experimental data of all measuring points is calculated, and based on the proportion, the information entropy and weight coefficient of each parameter are calculated, and the stability coefficient of each measuring point is constructed by the entropy weight method;

[0010] S3: The inner side of the surrounding rock is divided into a plurality of uniform grids, for each grid node, a distance threshold is set, the Euclidean distance with the measuring point is calculated within the distance threshold, and the stability coefficient of any grid point is calculated by inverse distance weighted interpolation based on the stability coefficient of each measuring point;

[0011] S4: Based on the stability coefficient of each grid point, the overall stability coefficient of the surrounding rock is calculated by volume weighting method, and the risk assessment model is constructed by extreme value statistical method, wherein the overall stability coefficient of the surrounding rock is taken as the adjustment factor of the risk assessment model;

[0012] S5: Based on the risk assessment model, the overall stability coefficient of the surrounding rock is input, and the surrounding rock instability probability is output, when the surrounding rock instability probability is greater than the set surrounding rock instability threshold probability, an alarm is issued.

[0013] Further, the pretreated experimental data is pretreated, specifically including the following steps:

[0014] The mean and standard deviation of the elastic modulus, compressive strength, stress and displacement are calculated respectively, the data exceeding the range of 3 times the standard deviation of the elastic modulus, compressive strength, stress and displacement is regarded as abnormal value and is removed; for the treatment of missing values, linear interpolation method is used for filling, the missing values are replaced with the mean value of the elastic modulus, compressive strength, stress and displacement respectively according to the value of adjacent known data points through linear relationship; the elastic modulus, compressive strength, stress and displacement are normalized.

[0015] Further, based on the pretreated experimental data of each measuring point, the stability coefficient of each measuring point is constructed by the entropy weight method, specifically including the following steps:

[0016] Based on the normalized experimental data, a total of N measuring points are set, for each measuring point j, the proportion of each parameter is calculated:

[0017]

[0018] Wherein, x′ jknormalized value of the kth parameter of the jth measurement point, k represents the index of the parameter of the experimental data; p jk proportion of the kth parameter of the jth measurement point; j represents the index number of the measurement point; N represents the number of measurement points; ∈ represents the denominator fine-tuning constant to prevent the denominator from being 0;

[0019] Calculate the information entropy:

[0020]

[0021] wherein H k information entropy of the kth parameter; wherein k = 1, information entropy of the elastic modulus; k = 2, information entropy of the compressive strength; k = 3, information entropy of the stress; k = 4, information entropy of the displacement;

[0022] Calculate the difference coefficient and the weight coefficient:

[0023]

[0024] wherein,

[0025] d k = 1 - H k

[0026] w k weight coefficient of the kth parameter; k = 1, w k weight coefficient of the elastic modulus; k = 2, w k weight coefficient of the compressive strength; k = 3, w k weight coefficient of the stress; k = 4, w k weight coefficient of the displacement; d k difference coefficient of the kth parameter; k = 1, d k difference coefficient of the elastic modulus; k = 2, d k difference coefficient of the compressive strength; k = 3, d k difference coefficient of the stress; k = 4, d k difference coefficient of the displacement;

[0027] For each measurement point, calculate its comprehensive stability coefficient as:

[0028]

[0029] wherein SCj represents the comprehensive stability coefficient of the jth measurement point.

[0030] Further, a distance threshold is set, and within the distance threshold range, the Euclidean distance from the measurement point is calculated, and based on the stability coefficient of each measurement point, the stability coefficient of any grid point is calculated by inverse distance weighted interpolation, which specifically includes the following steps:

[0031] Set coordinate axes: a longitudinal coordinate axis along the tunnel axis, a radial coordinate axis in the radial direction of the monitoring section, and a circumferential coordinate axis in the circumferential direction perpendicular to the tunnel axis plane. Then, for each grid point P... i The coordinates are:

[0032] P i =(x i ,r i ,θ i )

[0033] Among them, P i x represents the i-th grid point; i Represents the vertical coordinate; r i Represents the radial coordinate, i.e., the distance from the monitoring section's radial direction to the tunnel axis; θ i Indicates the circumferential angle;

[0034] Set measurement point Q j Coordinates are (x j ,r j ,θ j If the grid point P is... i With measurement point Q j The three-dimensional Euclidean distance is:

[0035]

[0036] in,

[0037] Δθ ij =min(|θ i -θ j |,360°-|θ i -θ j |)

[0038] Δθ ij Represents grid point P i With measurement point Q j The difference in angle; r j ·Δθ ij Indicates the circumferential arc length;

[0039] Set a distance threshold D, and filter those that meet the criteria d. ij The set of measurement points ≤ D:

[0040]

[0041] in, This indicates that d is satisfied. ij The set of measurement points ≤ D; D represents the distance threshold;

[0042] For each neighboring measurement point The weight coefficient of each grid point is calculated by inverse distance weighting and normalized as follows:

[0043]

[0044] wherein,

[0045]

[0046] w ij represents the weight coefficient of Q j and P i ; w ij represents the normalized weight coefficient of w ij ;

[0047] The stability coefficient of any grid point is:

[0048]

[0049] wherein, SC i represents the stability coefficient of the i-th grid point.

[0050] Further, based on the stability coefficient of each grid point, the overall stability coefficient of the surrounding rock is calculated by volume weighting method, and the specific steps are as follows:

[0051] Each grid point P i is taken as a representative point, and the associated volume is calculated:

[0052] V i = r i · Δr · Δθ · Δx

[0053] wherein, Δr represents the radial coordinate difference; Δθ represents the ring angle difference; Δx represents the longitudinal coordinate difference; V i represents the volume represented by the i-th grid point;

[0054] The overall stability coefficient of the surrounding rock is:

[0055]

[0056] wherein, M is the total number of grid points.

[0057] Further, the risk assessment model is constructed by extreme value statistical method, wherein the overall stability coefficient of the surrounding rock is taken as the adjustment factor of the risk assessment model, and the specific steps are as follows:

[0058] The critical threshold SC critical of surrounding rock instability is defined, and the risk assessment model is constructed by extreme value statistical method:

[0059]

[0060] wherein, SCcritical represents a critical threshold value of surrounding rock instability; μ represents a position parameter, adjusting the center position of the extreme value distribution; σ represents a scale parameter, adjusting the width of the distribution; ξ represents a shape parameter, and is not 0;

[0061] The overall stability coefficient SC of the surrounding rock is calculated according to the following formula: total A linear relationship expression of the position parameter is constructed as an adjustment factor of the risk assessment model:

[0062] μ = α · SC total + β

[0063] Wherein, α represents a linear coefficient; β represents an intercept term;

[0064] The surrounding rock instability probability P is calculated according to the following formula: failure

[0065]

[0066] Wherein, P represents the surrounding rock instability probability. failure

[0067] Further, the surrounding rock instability probability is output, and when the surrounding rock instability probability is greater than a set surrounding rock instability threshold probability, an alarm is issued, and the specific steps are as follows:

[0068] A surrounding rock instability threshold probability is set, and when P failure > P threshold , an alarm is issued; wherein, P represents the surrounding rock instability threshold probability. threshold

[0069] Based on the alarm, an alarm level division rule is set, when P failure ∈ (P threshold , 0.3], a low-level alarm is issued; when P failure ∈ (0.3, 0.6], a medium-level alarm is issued; when P failure > 0.6, a high-level alarm is issued.

[0070] The application further provides a tunnel surrounding rock reinforcement body stability determination system, which is used for executing the determination method and comprises:

[0071] A data acquisition module is used for setting a monitoring section at equal intervals along a tunnel axis, arranging a plurality of measuring points on a vault, a haunch and two side walls of each section, acquiring experimental data of each measuring point, and pre-processing the acquired experimental data, wherein parameters of the experimental data include elastic modulus, compressive strength, stress and displacement of the surrounding rock.

[0072] ​​​The measurement point stability coefficient calculation module is configured to calculate the proportion of each parameter in the experimental data of each measurement point based on the preprocessed experimental data of each measurement point, and calculate the information entropy and weight coefficient of each parameter based on the proportion, so as to construct the stability coefficient of each measurement point by using the entropy weight method.

[0073] The grid interpolation calculation module is configured to divide the inner side of the surrounding rock into a plurality of uniform grids, set a distance threshold for the nodes of each grid, calculate the Euclidean distance from the measurement points within the distance threshold, and calculate the stability coefficient of any grid point by using the inverse distance weighted interpolation based on the stability coefficient of each measurement point.

[0074] The risk assessment model construction module is configured to calculate the overall stability coefficient of the surrounding rock by using the volume weighting method based on the stability coefficient of each grid point, and construct a risk assessment model by using the extreme value statistical method, wherein the overall stability coefficient of the surrounding rock is used as an adjustment factor of the risk assessment model.

[0075] The automatic early warning module is configured to input the overall stability coefficient of the surrounding rock based on the risk assessment model, and output the surrounding rock instability probability, and issue an alarm when the surrounding rock instability probability is greater than a set surrounding rock instability threshold probability.

[0076] Compared with the prior art, the present application has the following advantages:

[0077] By using the equidistant section and the multi-point layout of the vault crown, the haunch and the side wall, the key weak areas of the surrounding rock are covered, the evaluation deviation caused by the monitoring blind area in the traditional method is avoided, the noise and the abnormal value are removed, the robustness of the subsequent entropy weight calculation and the interpolation modeling is ensured, the interpolation influence domain is dynamically determined based on the Euclidean distance threshold, the contribution weight of the adjacent measurement points is strengthened, the physical law of the surrounding rock damage diffusion is met, the accurate mapping from the discrete measurement points to the continuous grid field is realized, the high-resolution input is provided for the volume weighting evaluation, and the contribution of the high-risk area is highlighted by adding the product of the stability coefficient and the grid unit volume, so that the local instability is avoided to be covered by the traditional average value method. BRIEF DESCRIPTION OF DRAWINGS

[0078] Figure 1 The figure is a schematic diagram of the overall method of the present application.

[0079] Figure 2 The figure is a schematic diagram of the relationship between the surrounding rock instability probability and the overall stability coefficient of the present application.

[0080] Figure 3 The figure is a block diagram of the overall system structure of the present application. DETAILED DESCRIPTION

[0081] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below with reference to specific embodiments.

[0082] It should be noted that, unless otherwise defined, technical terms or scientific terms used in the present application shall have the usual meaning as understood by a person with ordinary skill in the art to which the present application pertains. The terms "first", "second", and similar terms used in the present application do not indicate any order, number, or importance, but are only used to distinguish different components. The terms "include", "contain", and similar terms mean that the elements or objects before the terms encompass the elements or objects listed after the terms and their equivalents, without excluding other elements or objects. The terms "connect" or "connected" and similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The terms "upper", "lower", "left", "right", and the like only indicate relative positional relationships, which can change accordingly when the absolute positions of the described objects change.

[0083] Embodiment:

[0084] Please refer to Figures 1-2 The present application provides a technical solution:

[0085] A tunnel surrounding rock reinforcement body stability determination method, the specific steps comprising:

[0086] S1: Along the tunnel axis, a plurality of monitoring sections are arranged at equal intervals, a plurality of measurement points are arranged at the crown, haunch, and two side walls of each section, experimental data of each measurement point are collected, the parameters of the experimental data include the elastic modulus, compressive strength, stress, and displacement of the surrounding rock, and the collected experimental data are pretreated;

[0087] During tunnel construction, a plurality of monitoring sections are arranged at equal intervals along the axis according to design requirements, measurement marks are pre-buried or installed at the crown, haunch, and two side walls of each section, and the elastic modulus, compressive strength, displacement, strain, and other data of each measurement point are collected regularly or in real time by devices such as total stations, laser range finders, and optical fiber sensors.

[0088] The pretreatment of the collected experimental data specifically includes the following steps:

[0089] The mean and standard deviation of the elastic modulus, compressive strength, stress, and displacement are calculated respectively, data exceeding the range of 3 times the standard deviation of the elastic modulus, compressive strength, stress, and displacement are regarded as abnormal values and are removed; for the treatment of missing values, linear interpolation is used for filling, the missing values are calculated by linear relationship according to the values of adjacent known data points, the missing values are replaced by the mean values of the elastic modulus, compressive strength, stress, and displacement respectively; the elastic modulus, compressive strength, stress, and displacement are normalized.

[0090] In the data processing process, the mean and standard deviation of the elastic modulus, compressive strength, stress and displacement are calculated to quantify the data distribution characteristics and identify abnormal values, i.e., more than 3 times the standard deviation. Eliminating abnormal values can eliminate measurement errors and improve data reliability. Linear interpolation and mean filling are combined to preserve the spatial and temporal continuity of the data sequence and avoid information loss caused by directly deleting missing values. Normalization eliminates the dimension difference, making different physical quantities comparable, providing standardized input for subsequent machine learning model training or statistical analysis, thereby improving model convergence speed and generalization ability, and ensuring the scientificity and accuracy of surrounding rock stability evaluation.

[0091] S2: Based on the pre-processed experimental data of each measurement point, the proportion of each parameter in the experimental data of all measurement points is calculated, and based on the proportion, the information entropy and weight coefficient of each parameter are calculated, and the stability coefficient of each measurement point is constructed by entropy weight method;

[0092] The stability coefficient of each measurement point is constructed by entropy weight method based on the pre-processed experimental data of each measurement point, which specifically includes the following steps:

[0093] Based on the normalized experimental data, set N common measurement points, for each measurement point j, calculate the proportion of each parameter:

[0094]

[0095] Where x' jk represents the normalized value of the kth parameter of the jth measurement point, k represents the index of the parameter of the experimental data; p jk represents the proportion of the kth parameter of the jth measurement point; j represents the measurement point index number; N represents the number of measurement points; ∈ represents the denominator fine-tuning constant to prevent the denominator from being 0;

[0096] Calculate the information entropy:

[0097]

[0098] Where H k represents the information entropy of the kth parameter; k=1 represents the information entropy of the elastic modulus; k=2 represents the information entropy of the compressive strength; k=3 represents the information entropy of the stress; k=4 represents the information entropy of the displacement;

[0099] H k reflects the dispersion degree of parameter data, and is used to quantify the uniformity of parameter values. The lower the entropy value, the greater the difference between different measurement points, and the higher the information contribution to stability evaluation. H k The proportion of parameters p jkThe normalized parameter value directly affects the data distribution characteristics, H k The distribution uniformity of p jk presents a negative correlation with p jk The greater the difference, the smaller the H k .

[0100] Calculate the difference coefficient and the weight coefficient:

[0101]

[0102] wherein,

[0103] d k = 1-H k

[0104] w k represents the weight coefficient of the kth parameter; k = 1, w k represents the weight coefficient of the elastic modulus; k = 2, w k represents the weight coefficient of the compressive strength; k = 3, w k represents the weight coefficient of the stress; k = 4, w k represents the weight coefficient of the displacement; d k represents the difference coefficient of the kth parameter; k = 1, d k represents the difference coefficient of the elastic modulus; k = 2, d k represents the difference coefficient of the compressive strength; k = 3, d k represents the difference coefficient of the stress; k = 4, d k represents the difference coefficient of the displacement;

[0105] w k characterizes the importance of the parameter in the stability evaluation, the higher the weight, the greater the influence of the parameter on the comprehensive stability coefficient; w k is obtained by normalizing the difference coefficient d k = 1-H k , H k is lower, the difference coefficient d k is greater, and the weight w k follows the rise; w k presents a negative correlation with H k , and a positive correlation with d k , embodying the direct regulation of the parameter information value on the weight.

[0106] For each measurement point, the comprehensive stability coefficient thereof is calculated as:

[0107]

[0108] wherein, SC j represents the comprehensive stability coefficient of the jth measurement point.

[0109] SC j Quantitative measurement of the overall stability state, the higher the value represents the better stability; SC j By the normalized parameter x′ jk And weight w k Linear combination generation, weight coefficient reflects the different parameters on the stability of the differential impact; SC j And x′ jk And w k Both show a positive correlation, the higher the parameter value or the greater the weight, the higher the comprehensive stability coefficient.

[0110] S3:Divide the inner side of the surrounding rock into multiple uniform grids, for each grid node, set a distance threshold, calculate the Euclidean distance with the measurement point within the distance threshold, and calculate the stability coefficient of any grid point based on the stability coefficient of each measurement point by inverse distance weighted interpolation;

[0111] The setting distance threshold, the Euclidean distance with the measurement point within the distance threshold, the stability coefficient of any grid point based on the stability coefficient of each measurement point by inverse distance weighted interpolation, specifically includes the following steps:

[0112] Set the coordinate axis, the longitudinal coordinate axis along the tunnel axis direction, the radial coordinate in the radial direction of the monitoring section, and the ring coordinate in the ring direction perpendicular to the tunnel axis plane, then the coordinates of each grid point P i :

[0113] P i =(x i ,r i ,θ i )

[0114] Wherein, P i Indicates the i-th grid point; x i Indicates the longitudinal coordinate; r i Indicates the radial coordinate, that is, the distance to the tunnel axis along the radial direction of the monitoring section; θ i Indicates the ring angle;

[0115] Set the measurement point Q j Coordinates (x j ,r i ,θ j ), then the three-dimensional Euclidean distance between the grid point P i And the measurement point Q j :

[0116]

[0117] Wherein,

[0118] Δθij = min(|θ i - θ j |, 360° - |θ i - θ j |)

[0119] Δθ ij represents the angle difference between the grid point P i and the measured point Q j ; r j · Δθ ij represents the circumferential arc length;

[0120] Set a distance threshold D, and screen the measured point set that satisfies d ij ≤ D:

[0121]

[0122] wherein, represents the measured point set that satisfies d ij ≤ D; D represents the distance threshold;

[0123] For each adjacent measured point, its weight coefficient is calculated by inverse distance weighting and normalized:

[0124]

[0125] wherein,

[0126]

[0127] w ij represents the weight coefficient of Q j and P i ; represents the normalized weight coefficient of w ij ;

[0128] Then the stability coefficient of any grid point is:

[0129]

[0130] wherein, SC i represents the stability coefficient of the i-th grid point.

[0131] S4: Based on the stability coefficient of each grid point, the overall stability coefficient of the surrounding rock is calculated by volume weighting method, and the risk assessment model is constructed by extreme value statistical method, wherein the overall stability coefficient of the surrounding rock is taken as the adjustment factor of the risk assessment model;

[0132] The overall stability coefficient of the surrounding rock is calculated based on the stability coefficient of each grid point by volume weighting method, and the specific steps are:

[0133] Each grid point P i As a representative point, the associated volume is calculated:

[0134] V i = r i · Δr · Δθ · Δx

[0135] Where Δr represents the radial coordinate difference; Δθ represents the ring angle difference; Δx represents the longitudinal coordinate difference; V i represents the volume represented by the ith grid point;

[0136] The overall stability coefficient of the surrounding rock is:

[0137]

[0138] Where M is the total number of grid points; SC total represents the overall stability coefficient of the surrounding rock;

[0139] The risk assessment model is constructed by using the extreme value statistical method, wherein the overall stability coefficient of the surrounding rock is taken as the adjustment factor of the risk assessment model, and the specific steps are as follows:

[0140] The critical threshold SC critical of surrounding rock instability is defined, and the risk assessment model is constructed by using the extreme value statistical method:

[0141]

[0142] Where SC critical represents the critical threshold of surrounding rock instability; μ represents the position parameter, adjusting the center position of the extreme value distribution; σ represents the scale parameter, adjusting the width of the distribution; ξ represents the shape parameter, and is not 0;

[0143] The above P failure reflects the probability of instability of the surrounding rock under the current overall stability coefficient SC total , which is dynamically calculated by the extreme value statistical model, SC total The position parameter μ adjusts the center position of the extreme value distribution, directly affecting the value of P failure , the larger the SC total , the higher the overall stability of the surrounding rock, the extreme value distribution center μ shifts to the safe side, reducing the instability probability; SC critical As a critical point of the extreme value distribution, it determines the triggering condition of the instability event, the lower the critical threshold, the worse the geological conditions, the higher the instability probability under the same SC total .

[0144] The overall stability coefficient SC total of the surrounding rock is taken as the adjustment factor of the risk assessment model, and a linear relationship expression of the position parameter is constructed:

[0145] μ = a · SC total + β

[0146] wherein a represents a linear coefficient; and β represents an intercept term;

[0147] then the surrounding rock instability probability P failure is:

[0148]

[0149] wherein P failure represents the surrounding rock instability probability.

[0150] P failure represents the probability of failure such as structural damage or instability of the tunnel monitoring point, and its value range is [0, 1), which is used to quantify the real-time risk level of the measurement point; P failure is determined by the critical value of the comprehensive stability coefficient SC total , the global weighted stability coefficient a · SC total + β, and the scale parameter σ and the shape parameter ξ; the difference between the critical value and the actual stability coefficient reflects the risk deviation degree, and the scale parameter σ and the shape parameter ξ control the tail characteristics of the probability distribution; P failure is positively correlated with SC critical -(a · SC total + β), the greater the difference, the lower the actual stability is below the critical requirement, and the higher the failure probability; P failure is negatively correlated with the scale parameter σ, the greater the scale parameter (the higher the data dispersion degree), the lower the failure probability under the same deviation; ξ controls the sensitivity of extreme events, when ξ > 0, the probability distribution has a heavy tail feature, and the increase of ξ will amplify the probability of high-risk events.

[0151] In this embodiment, 5 SC total points are selected, the overall stability coefficient SC total of the surrounding rock is taken as the adjusting factor of the risk assessment model, and the linear relationship of the position parameter is constructed, then the surrounding rock instability probability P failure is the following 5 groups of data, and the experimental labeled data is shown in Table 1:

[0152] Table 1: Relationship data table of surrounding rock instability probability and overall stability coefficient

[0153]

[0154]

[0155] In the above process, it can be seen that with the increase of the overall stability coefficient of the surrounding rock, the position parameter presents a gradually rising trend, which is the gradual decrease of the surrounding rock instability probability, which reflects the actual situation that the surrounding rock instability probability decreases with the increase of the stability coefficient of the surrounding rock.

[0156] S5: Based on the risk assessment model, input the overall stability coefficient of the surrounding rock and output the probability of surrounding rock instability. When the probability of surrounding rock instability is greater than the set threshold probability of surrounding rock instability, an alarm is issued.

[0157] The risk assessment model takes the overall stability coefficient of the surrounding rock as input and outputs the probability of surrounding rock instability. When the probability of surrounding rock instability exceeds the set threshold probability, an alarm is issued. The specific steps are as follows:

[0158] Set the surrounding rock instability threshold probability when P failure >P threshold At that time, an alarm is issued; among them, P threshold Indicates the probability of surrounding rock instability threshold;

[0159] Based on alarms, set alarm level classification rules, when P failure ∈(P threshold When P reaches 0.3, a low-level alarm is issued; when P... failure When P ∈ (0.3, 0.6], a medium-level alarm is issued; when P failure When the value is greater than 0.6, a high-level alarm is issued.

[0160] Please see Figure 3 The present invention also provides a system for determining the stability of tunnel surrounding rock reinforcement, the system being used to perform the above-described determination method, comprising:

[0161] The data acquisition module is used to set up a monitoring section at equal intervals along the tunnel axis, and to arrange multiple measurement points at the arch crown, arch waist and side walls of each section to collect experimental data at each measurement point. The parameters of the experimental data include the elastic modulus, compressive strength, stress and displacement of the surrounding rock, and to preprocess the collected experimental data.

[0162] The measurement point stability coefficient calculation module is used to calculate the proportion of each parameter in the experimental data to all parameters of the measurement points based on the preprocessed experimental data of each measurement point, and to calculate the information entropy and weight coefficient of each parameter based on the proportion, and to construct the stability coefficient of each measurement point through the entropy weight method.

[0163] The grid interpolation calculation module is used to divide the inner surface of the surrounding rock into multiple uniform grids. For each grid node, a distance threshold is set. Within the distance threshold range, the Euclidean distance to the measurement point is calculated. Based on the stability coefficient of each measurement point, the stability coefficient of any grid point is calculated by inverse distance weighted interpolation.

[0164] The risk assessment model construction module is configured to calculate the overall stability coefficient of the surrounding rock based on the stability coefficient of each grid point by using a volume weighting method, and to construct a risk assessment model by using an extreme value statistical method, wherein the overall stability coefficient of the surrounding rock is taken as an adjustment factor of the risk assessment model.

[0165] The automatic early warning module is configured to input the overall stability coefficient of the surrounding rock based on the risk assessment model, and to output a surrounding rock instability probability, and to issue a warning when the surrounding rock instability probability is greater than a set surrounding rock instability threshold probability.

[0166] The above formulas are all dimensionless values, and the formulas are obtained by collecting a large amount of data to simulate a formula of the most recent real situation, and the preset parameters in the formulas are set by a person skilled in the art according to actual conditions.

[0167] The above embodiments can be realized wholly or partially by software, hardware, firmware or any combination thereof. When realized by software, the above embodiments can be realized in the form of a computer program product wholly or partially. Those skilled in the art can realize that the units and algorithm steps of the examples described in connection with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized by hardware or software methods depends on the specific application and design constraints of the technical solutions.

[0168] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, and can be located in one place or distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0169] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for determining the stability of tunnel surrounding rock reinforcement, characterized in that the steps include: include: S1: Along the tunnel axis, multiple monitoring sections are set at equal intervals. Multiple measurement points are arranged at the arch crown, arch waist, and side walls of each section. Experimental data are collected at each measurement point. The parameters of the experimental data include the elastic modulus, compressive strength, stress, and displacement of the surrounding rock. The collected experimental data are preprocessed. S2: Based on the experimental data of each measurement point after preprocessing, calculate the proportion of each parameter in the experimental data to all parameters of the measurement points, and calculate the information entropy and weight coefficient of each parameter based on the proportion. Construct the stability coefficient of each measurement point through the entropy weight method. S3: Divide the inner surface of the surrounding rock into multiple uniform grids. For each grid node, set a distance threshold. Within the distance threshold range, calculate the Euclidean distance to the measurement point. Based on the stability coefficient of each measurement point, calculate the stability coefficient of any grid point by inverse distance weighted interpolation. S4: Based on the stability coefficient of each grid point, the overall stability coefficient of the surrounding rock is calculated by the volume weighting method, and the extreme value statistics method is used to construct a risk assessment model, wherein the overall stability coefficient of the surrounding rock is used as the adjustment factor of the risk assessment model. The risk assessment model is constructed using the extreme value statistical method, wherein the overall stability coefficient of the surrounding rock is used as an adjustment factor for the risk assessment model. The specific steps are as follows: Define the critical threshold SC for surrounding rock instability critical A risk assessment model is constructed using extreme value statistics. Among them, SC critical σ represents the critical threshold for surrounding rock instability; μ represents the location parameter, which adjusts the center position of the extreme value distribution; σ represents the scale parameter, which adjusts the width of the distribution; ξ represents the shape parameter, and is not 0. The overall stability coefficient of the surrounding rock SC total As a moderating factor in the risk assessment model, a linear relationship expression for the location parameters is constructed: μ=α·SC total +b Where α represents the linear coefficient; β represents the intercept term; Then the probability of surrounding rock instability P failure for: Among them, P failure Indicates the probability of surrounding rock instability; S5: Based on the risk assessment model, input the overall stability coefficient of the surrounding rock and output the probability of surrounding rock instability. When the probability of surrounding rock instability is greater than the set threshold probability of surrounding rock instability, an alarm is issued.

2. The method for determining the stability of tunnel surrounding rock reinforcement according to claim 1, characterized in that, The preprocessing of the collected experimental data specifically includes the following steps: The mean and standard deviation of elastic modulus, compressive strength, stress, and displacement are calculated separately. Data exceeding three times the standard deviation of elastic modulus, compressive strength, stress, and displacement are considered outliers and removed. For missing values, linear interpolation is used to fill in the missing values. Based on the values ​​of adjacent known data points, the missing values ​​are extrapolated through linear relationships and replaced with the mean values ​​of elastic modulus, compressive strength, stress, and displacement, respectively. The elastic modulus, compressive strength, stress, and displacement are then normalized.

3. The method for determining the stability of tunnel surrounding rock reinforcement according to claim 1, characterized in that, Based on the preprocessed experimental data of each measurement point, the stability coefficient of each measurement point is constructed using the entropy weight method, specifically including the following steps: Based on the normalized experimental data, and assuming a total of N measurement points, for each measurement point j, calculate the weight of each parameter: Where, x′ jk p represents the normalized value of the k-th parameter at the j-th measurement point, where k represents the index of the parameter in the experimental data; jk This represents the weight of the k-th parameter at the j-th measurement point; j represents the index of the measurement point; N represents the number of measurement points; ∈ represents the denominator adjustment constant to prevent the denominator from being 0; Calculate information entropy: Among them, H k Let k represent the information entropy of the k-th parameter; where k = 1 represents the information entropy of the elastic modulus; k = 2 represents the information entropy of the compressive strength; k = 3 represents the information entropy of the stress; and k = 4 represents the information entropy of the displacement. Calculate the difference coefficient and weighting coefficient: in, d k =1-H k w k This represents the weight coefficient of the k-th parameter; k = 1, w k The weighting coefficients represent the elastic modulus; k = 2, w k The weighting coefficients representing compressive strength; k = 3, w k The stress weighting coefficient; k = 4, w k The weighting coefficient representing the displacement; d k This represents the difference coefficient of the k-th parameter; k = 1, d k The coefficient representing the difference in elastic modulus; k = 2, d k The coefficient of variation represents the compressive strength; k = 3, d k The coefficient of variation of stress; k = 4, d k Indicates the difference coefficient of displacement; For each measurement point, its overall stability coefficient is calculated as follows: Among them, SC j This represents the overall stability coefficient of the j-th measurement point.

4. The method for determining the stability of tunnel surrounding rock reinforcement according to claim 3, characterized in that, The set distance threshold, within which the Euclidean distance to the measurement point is calculated, and based on the stability coefficient of each measurement point, the stability coefficient of any grid point is calculated by inverse distance weighted interpolation, specifically including the following steps: Set coordinate axes: a longitudinal coordinate axis along the tunnel axis, a radial coordinate axis in the radial direction of the monitoring section, and a circumferential coordinate axis in the circumferential direction perpendicular to the tunnel axis plane. Then, for each grid point P... i The coordinates are: P i =(x i ,r i ,i i ) Among them, P i x represents the i-th grid point; i Represents the vertical coordinate; r i Represents the radial coordinate, i.e., the distance from the monitoring section's radial direction to the tunnel axis; θ i Indicates the circumferential angle; Set measurement point Q j Coordinates are (x j ,r j ,θ j If the grid point P is... i With measurement point Q j The three-dimensional Euclidean distance is: in, Dth ij =min(|θ i -θ j |,360°-|θ i -θ j |) Δθ ij Represents grid point P i With measurement point Q j The difference in angle; r j ·Δθ ij Indicates the circumferential arc length; Set a distance threshold D, and filter those that meet the criteria d. ij The set of measurement points ≤ D: in, This indicates that d is satisfied. ij The set of measurement points ≤ D; D represents the distance threshold; For each neighboring measurement point The weighting coefficients are calculated using inverse distance weighting and then normalized. in, w ij Q represents j With P i Weighting coefficients; Indicates w ij Normalized weighting coefficients; The stability coefficient of any grid point is: Among them, SC i This represents the stability coefficient of the i-th grid point.

5. The method for determining the stability of tunnel surrounding rock reinforcement according to claim 4, characterized in that, The overall stability coefficient of the surrounding rock is calculated using the volume weighted method based on the stability coefficient of each grid point. The specific steps are as follows: Each grid point P i As a representative point, calculate the associated volume: V i =r i ·Δr·Δθ·Δx Where Δr represents the radial coordinate difference; Δθ represents the circumferential angle difference; Δx represents the longitudinal coordinate difference; V i This represents the volume represented by the i-th grid point; The overall stability coefficient of the surrounding rock is: Where M is the total number of grid points.

6. The method for determining the stability of tunnel surrounding rock reinforcement according to claim 1, characterized in that, The risk assessment model takes the overall stability coefficient of the surrounding rock as input and outputs the probability of surrounding rock instability. When the probability of surrounding rock instability exceeds the set threshold probability, an alarm is issued. The specific steps are as follows: Set the surrounding rock instability threshold probability when P failure >P threshold At that time, an alarm is issued; among them, P threshold Indicates the probability of surrounding rock instability threshold; Based on alarms, set alarm level classification rules, when P failure ∈(P threshold When P reaches 0.3, a low-level alarm is issued; when P... failure When P ∈ (0.3, 0.6], a medium-level alarm is issued; when P failure When the value is greater than 0.6, a high-level alarm is issued.

7. A system for determining the stability of tunnel surrounding rock reinforcement, characterized in that: The determining system is used to execute the determining method according to any one of claims 1-6, including: The data acquisition module is used to set up a monitoring section at equal intervals along the tunnel axis, and to arrange multiple measurement points at the arch crown, arch waist and side walls of each section to collect experimental data at each measurement point. The parameters of the experimental data include the elastic modulus, compressive strength, stress and displacement of the surrounding rock, and to preprocess the collected experimental data. The measurement point stability coefficient calculation module is used to calculate the proportion of each parameter in the experimental data to all parameters of the measurement points based on the preprocessed experimental data of each measurement point, and to calculate the information entropy and weight coefficient of each parameter based on the proportion, and to construct the stability coefficient of each measurement point through the entropy weight method. The grid interpolation calculation module is used to divide the inner surface of the surrounding rock into multiple uniform grids. For each grid node, a distance threshold is set. Within the distance threshold range, the Euclidean distance to the measurement point is calculated. Based on the stability coefficient of each measurement point, the stability coefficient of any grid point is calculated by inverse distance weighted interpolation. The risk assessment model construction module is used to calculate the overall stability coefficient of the surrounding rock based on the stability coefficient of each grid point using the volume weighting method, and to construct the risk assessment model using the extreme value statistics method. The overall stability coefficient of the surrounding rock is used as the adjustment factor of the risk assessment model. The automatic early warning module is used to take the overall stability coefficient of the surrounding rock as input based on the risk assessment model and output the probability of surrounding rock instability. When the probability of surrounding rock instability is greater than the set probability threshold for surrounding rock instability, an alarm is issued.

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