A method for evaluating stability of dangerous rock mass protection structure system

Abstract

The present application belongs to the technical field of dangerous rock mass treatment, and provides a dangerous rock mass protection structure system stability evaluation method, S1, real-time collection of multi-source dynamic monitoring data of dangerous rock mass under the action of mechanics and environmental disturbance; S2, based on the multi-source dynamic monitoring data, extracting energy dissipation rate, damage variable, S value, Benioff strain value, Hurst index and cumulative apparent volume CAV as precursor indexes; S3, macroscopic and microscopic crack evolution test is carried out on the dangerous rock mass sample, and the weakening parameter Rc and the brittleness parameter Ri are obtained, and a numerical model containing fissures is established; S4, the time series data of each precursor index is respectively processed by sliding time window smoothing, and the trend slope of each index is calculated by applying Sen's slope trend analysis method; S5, based on the trend slope of each index, a multi-index fusion instability early warning index Q is constructed; S6, the instability early warning index Q is associated with the geographic coordinate information of the dangerous rock mass, and a three-dimensional thermal cloud chart representing the risk space distribution is generated.

Description

Technical Field

[0001] This invention belongs to the field of dangerous rock mass treatment technology, and specifically relates to a method for evaluating the stability of a dangerous rock mass protection structure system. Background Technology

[0002] Dangerous rock masses often exhibit problems such as rock strata fissures, loosening, or weathering, making them susceptible to destructive changes under external factors (such as precipitation, earthquakes, and human disturbance). Patent CN103669319A proposes establishing evaluation elements for environmental slopes with unstable rock masses requiring stability assessment, based on national and industry standards. This method offers advantages such as rapid evaluation, high efficiency, and guaranteed accuracy. However, it requires geological personnel to conduct on-site measurements of each evaluation element using appropriate exploration, measurement, and testing equipment, posing a significant challenge to their professional knowledge and skills.

[0003] Patent CN111950151A proposes a stability evaluation method for slip-type unstable rock masses on steep rock slopes based on amplitude ratio. First, the unstable rock mass to be evaluated is investigated to obtain its characteristic parameters and the bond length of the anchorage section between the unstable rock mass and the bedrock. The stability coefficient of the unstable rock mass is calculated by substituting the self-weight of the unstable rock mass, the cohesion c of the anchorage section, the bond length of the anchorage section, the internal friction angle of the anchorage section, and the dip angle of the unstable rock mass fracture surface into the formula. This enables rapid judgment of the stability of unstable rock masses on slopes based on amplitude ratio.

[0004] Limitations of existing methods: (1) Many existing methods rely on a single type of monitoring data, which limits the accuracy and comprehensiveness of the assessment results; (2) Existing stability evaluation methods often ignore the multi-scale characteristics of cracks and fail to perform effective coupling analysis between macroscopic and microscopic scales.

[0005] (3) Many existing evaluation methods are still based on static assessment and cannot capture the dynamic changes of rock masses in real time, especially the fluctuations under the influence of the external environment. Summary of the Invention

[0006] The purpose of this invention is to provide a stability evaluation method for a protective structure system for unstable rock masses. The system employs multiple methods, including microseismic sensors, deformation monitoring instruments, and remote sensing monitoring equipment, to acquire dynamic data of the rock mass in real time; samples the unstable rock mass in the detection area; obtains the crack evolution law of the rock mass under different stress states through macroscopic experiments; further introduces energy dissipation rate, damage variables, and fracture toughness based on traditional precursor indicators; ensures the stability and reliability of the prediction results by trend analysis of multi-source monitoring data combined with crack evolution law; and comprehensively analyzes information such as crack propagation path, stress distribution, and rock mass deformation to generate visualization results such as three-dimensional cloud maps and heat maps.

[0007] The technical solution of this invention is to provide a method for evaluating the stability of a rock mass protection structure system, the method comprising the following steps: Step S1: Deploy microseismic sensors, deformation monitoring instruments and remote sensing monitoring equipment to collect multi-source dynamic monitoring data of the unstable rock mass under mechanical action and environmental disturbance in real time; Step S2: Based on the multi-source dynamic monitoring data, extract energy dissipation rate, damage variable, S value, Benioff strain value, Hurst index and cumulative apparent volume (CAV) as precursor indicators. Step S3: Conduct macro-micro crack evolution tests on the unstable rock mass samples to obtain the weakening parameter Rc and the brittle parameter Ri, and establish a numerical model containing cracks. Step S4: Perform sliding time window smoothing on the time series data of each precursor indicator, and use Sen's slope trend analysis method to calculate the trend slope of each indicator. Step S5: Construct an instability early warning index Q based on the trend slope of each indicator, and establish a quantitative risk classification standard using Python based on the size of the Q value. Step S6: Associate the instability warning index Q with the geographic coordinates of the unstable rock mass, and use GeoPandas and Plotly in Python to visualize the spatial data and generate a three-dimensional heat map representing the spatial distribution of risk.

[0008] Preferably, the energy dissipation rate is specifically expressed as: ; ; The damage variable is specifically represented as follows: ; The S-value is specifically expressed as follows: ; The Benioff strain value is specifically expressed as follows: ; The Hurst index is specifically expressed as: ; The cumulative apparent volume (CAV) is specifically expressed as a precursor indicator as follows: ; Where N represents the number of microseismic events within the monitoring range, Constants a and b are statistical parameters, where the value of b reflects the stability of the rock mass in the monitoring area; M LThe magnitude of the corresponding microseismic event is represented by E; the energy of the microseismic event is represented by E. Let M represent rock density, c represent wave velocity, and v(f) represent the velocity function in the frequency domain; assuming the monitoring area is represented as Ω, and the spatial dimension D is divided into several statistical partitions with side length a; i M represents the magnitude of the i-th microseismic event. max V represents the maximum magnitude of microseismic events within this interval; a,i E represents the apparent volume of the i-th microseismic event; i This represents the energy released by the i-th microseismic event; Let N(t) represent the characteristic stress corresponding to the i-th microseismic event; N(t) represent the total number of microseismic events up to time t; i represents the event number; CBS(t) represent the cumulative Benioff strain at time t; Ei represents the energy released by the i-th microseismic event; N(t) represent the total number of microseismic events occurring before time t; i represents the event number; R(n) represents the range of the time series of length n; S(n) represents the standard deviation of the time series; n represents the length of the time series; C represents a constant; and H represents the Hurst exponent.

[0009] Preferably, the weakening parameter Rc and the brittleness parameter Ri are as follows: ; in, , and These are the peak stress of the defective specimen, the peak stress of the intact specimen, and the crack initiation stress, respectively; R C R is the ratio of the peak stress of the defective specimen to the peak stress of the intact specimen, representing the effect of the defect on the deterioration of the specimen's load-bearing capacity; i It is the ratio of the initiation stress to the peak stress of a defective specimen with the same tilt angle, indicating the difficulty of cracking in the defective specimen.

[0010] Preferably, step S4 includes the following steps: Based on the above indicators, the data is smoothed using a moving average, and the moving average formula is expressed as: ; Among them, MA N (t) represents the N-day moving average at time t, x(i) represents the original index value on day i, t-N+1 is the starting point of the window, and t is the ending point of the window.

[0011] To set a sliding step size to capture daily trends, after calculating the average of an N-day window, the window is shifted forward by one day to observe the overall trend of the current day and the previous N days, and this process is repeated. The specific formula is: .

[0012] Preferably, the calculation method of the multi-index fusion rock mass instability warning index Q is shown in the following formula: ; where, n represents the number of precursor indexes of rock mass instability, F k represents the F-score value corresponding to the k-th index, and W k(+ / -) represents the trend value of the corresponding index.

[0013] Preferably, the cloud diagram is drawn through the Q value, and different colors are marked according to different Q values, and the colored circles and different color gradients represent the warning risk levels at different positions.

[0014] Preferably, during the grading warning of the warning risk level, 0 < Q < 0.25, lower risk; 0.25 < Q < 0.5, low risk; Q < 0.75, medium risk; 0.75 < Q < 1, high risk.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. By integrating multi-source monitoring data such as microseismic, deformation, and remote sensing, as well as the coupled analysis of macro and meso cracks, this invention overcomes the limitation of relying on a single data source in the prior art. The integration of multi-source data improves the accuracy and comprehensiveness of stability assessment, and the coupled analysis of macro-micro cracks can more comprehensively reveal the impact of cracks on the overall stability of the rock mass, enhancing the depth and precision of the assessment.

[0016] 2. The new indexes such as the energy dissipation rate and damage variable proposed in this invention can more accurately describe the degradation process and instability mechanism of dangerous rock masses. Compared with the single precursor index method in the prior art, the energy-damage-driven multi-index system can more comprehensively evaluate the safety of rock masses, improving the dynamicity and accuracy of risk assessment, especially for risk warning under different environmental factor changes.

[0017] 3. By combining Sen's slope trend analysis and time window smoothing processing, the quantitative risk grading method of this invention can weaken the influence of the fluctuation of a single index and provide more stable and accurate risk prediction and grading. Compared with the traditional risk assessment based on empirical rules, this invention can achieve dynamic adjustment and perform precise warning in combination with real-time data.

[0018] 4. The scheme of this invention visualizes the crack propagation path, stress concentration area, and risk grading results in three dimensions, intuitively showing the instability process of dangerous rock masses. Compared with the static assessment in the prior art, this invention can capture the dynamic changes of the rock mass in real time, providing more intuitive and real-time support for on-site decision-making, enhancing the warning effect and emergency response ability. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the sliding window setting process of the technical solution proposed in this invention; Figure 2 This invention presents a slope trend calculation process based on Sen's. Figure 3 This invention provides a spatial data calculation heat map of the technical solution proposed in this invention. Detailed Implementation

[0020] Example 1: Please refer to the appendix. Figures 1-3 This invention provides a method for evaluating the stability of a rock mass protection structure system. The purpose of this example is to provide a method for evaluating the stability of a rock mass-protection structure system. The system employs multiple methods, including microseismic sensors, deformation monitoring instruments, and remote sensing monitoring equipment, to acquire dynamic data of the rock mass in real time; samples are taken from the rock mass in the detection area; the crack evolution law of the rock mass under different stress states is obtained through macroscopic experiments; based on traditional precursor indicators, energy dissipation rate, damage variables, and fracture toughness are further introduced; trend analysis of multi-source monitoring data, combined with crack evolution laws, ensures the stability and reliability of the prediction results; and information such as crack propagation path, stress distribution, and rock mass deformation are comprehensively analyzed to generate visualization results such as three-dimensional cloud maps and heat maps.

[0021] Step 1: Monitoring and scanning are conducted using microseismic sensors, deformation monitoring instruments, and high-resolution remote sensing technology to collect real-time multi-source dynamic data on the effects of unstable rock masses and environmental disturbances. This system features long and short-range radiation coverage, low-frequency and medium-to-high-frequency data acquisition, and a combination of fixed and mobile sensors. This system can comprehensively capture both small-scale release events and large-scale rock deformation and instability during the disaster incubation process.

[0022] Step 2: Process the monitoring and early warning data collected in Step 1 in six aspects: energy and energy density, b-value, S-value, Benioff strain value, Hurst exponent, energy index, and cumulative apparent volume.

[0023] 2.1 Multi-indicator data fusion: (1) Using the following formula, the energy of the rock mass is evaluated by elastic radiation energy, which aims to evaluate the energy released when micro-fractures occur inside the rock mass. The energy density reflects the spatial distribution characteristics of the disturbance event during the energy release process.

[0024] ; ; ; (2) The moment magnitude is calculated using the following formula to statistically analyze the influence of radiated energy on the rock mass. Since the disturbance energy affecting the unstable rock mass is different, the stress state is significantly different. In this formula, N represents the number of micro-seismic events within the detection range, and constants a and b are statistical values. The smaller the value of b, the higher the probability of a large-magnitude earthquake and the worse the stability of the rock mass.

[0025] ; (3) Benioff Strain is an index that characterizes the energy release of microseismic events in rock masses by the square root of energy. Its cumulative form, CBS, is often used in the prediction of unstable rock masses and earthquakes to reflect the accelerating trend of energy release and is an important tool for identifying precursors of instability.

[0026] ; (4) The S-value is another important indicator for analyzing precursors of rock mass instability. It is usually used to reflect the intensity and spatiotemporal distribution of microseismic activity, thereby providing feedback on the stability of the monitored area.

[0027] (5) The Hurst index is used to characterize the long-term correlation and trend of time series. In the evaluation of the stability of dangerous rock mass, it can reflect the fluctuation characteristics of crack propagation and energy release: H<0.5 indicates violent fluctuation, H=0.5 indicates random process, and H>0.5 indicates that the trend continues and may enter the unstable stage.

[0028] (6) The increase of CAV over time indicates that the cracks are expanding or the damage volume is accumulating. The accelerated growth of CAV usually means that the rock mass is entering the stage of near instability. Combined with Benioff strain and Hurst index, CAV can be used as an important indicator for determining the critical period.

[0029] .

[0030] Where N represents the number of microseismic events within the monitoring range, Constants a and b are statistical parameters, where the value of b reflects the stability of the rock mass in the monitoring area; M L The magnitude of the corresponding microseismic event is represented by E; the energy of the microseismic event is represented by E. Let M represent rock density, c represent wave velocity, and v(f) represent the velocity function in the frequency domain; assuming the monitoring area is represented as Ω, and the spatial dimension D is divided into several statistical partitions with side length a; i M represents the magnitude of the i-th microseismic event. max V represents the maximum magnitude of microseismic events within this interval; a,iE represents the apparent volume of the i-th microseismic event; i This represents the energy released by the i-th microseismic event; Let N(t) represent the characteristic stress corresponding to the i-th microseismic event; N(t) represent the total number of microseismic events up to time t; i represents the event number; CBS(t) represent the cumulative Benioff strain at time t; Ei represents the energy released by the i-th microseismic event; N(t) represent the total number of microseismic events occurring before time t; i represents the event number; R(n) represents the range of the time series of length n; S(n) represents the standard deviation of the time series; n represents the length of the time series; C represents a constant; and H represents the Hurst exponent.

[0031] The new indicators proposed in this invention, such as energy dissipation rate and damage variables, can more accurately describe the degradation process and instability mechanism of unstable rock masses. Compared with the single precursor indicator method of existing technologies, the energy-damage driven multi-indicator system can more comprehensively assess the safety of rock masses, improve the dynamism and accuracy of risk assessment, especially in risk early warning under different environmental factors.

[0032] Step 3: Based on extensive experiments and current research on rock fracture characteristics, the stability of the rock mass is evaluated using two indicators, Rc and Ri. A new classification system is established, dividing rock stability into four categories: safe zone, fracture-sensitive zone, dangerous zone, and brittle zone. Rock samples from the monitoring area are subjected to triaxial experiments and electron microscopy to observe microscopic fractures, and quantitative analysis is performed using the two indicators.

[0033] 3.1 Statistics on Rock Fracture Indicators When the crack tip reaches the critical stress threshold, the wing crack initiates, leading to irreversible fracture, releasing some of the stored energy within the rock, and reducing the rock's brittleness. To address these two main defect effects, a weakening parameter Rc and a brittleness parameter Ri are proposed as methods for quantitatively evaluating the performance of defective rock specimens. The weakening parameter (Rc) and the brittleness parameter (Ri) can be determined as follows: in, , and These are the peak stress of the defective specimen, the peak stress of the intact specimen, and the crack initiation stress, respectively; R C R is the ratio of the peak stress of the defective specimen to the peak stress of the intact specimen, representing the effect of the defect on the deterioration of the specimen's load-bearing capacity; i It is the ratio of the initiation stress to the peak stress of defective specimens with the same tilt angle, indicating the difficulty of cracking in defective specimens. See Table 1 for details.

[0034] Table 1. Specific data on Rc and Ri for various types of rocks with a single defect. Step 4: Based on Sen's slope model, comprehensively analyze the parameters obtained in the first three steps, and set a reasonable time window for quantitative evaluation of landslide stability, and construct a multi-index fusion rock mass instability warning index Q based on the trend change of Sen's slope.

[0035] 4.1 Multi-index fusion rock mass instability warning index Q based on the trend change of Sen's slope <000,0174>After collecting all the indicators in the previous steps, the first step of data processing using Sen's slope model is to apply a moving average to smooth the data. Assume that the disturbance time series data is x(T1, T2,... Tn), where MA represents the precursor index of rock mass instability at time interval tn, and we set N as the window to smooth the data. The moving average formula can be expressed as: ; [[ID=...]] The second step is to set the sliding step to capture the daily trend. Usually, a sliding step of 1 day is selected. This means that after calculating the average value of the N-day window, the window will move forward 1 day to observe the overall trend of the current day and the previous N 1-day periods. Then repeat this process. The formula is: ; Based on the trend changes of different precursor indicators, a multi-index fusion rock mass instability warning index Q based on the trend change of Sen's slope is constructed. The calculation method is as follows: ; Step 5: Draw a cloud map through the Q value in Step 4, mark different colors according to different Q values, and use colored circles and different color gradients to represent the warning risk levels at different positions. Combine the multi-index fusion rock mass instability warning index Q with the spatial distribution map to visually reveal the relationship between the spatial change of the Q value and the geographical coordinate system. Among them, when grading the warning, 0 < Q < 0.25, lower risk; 0.25 < Q < 0.5, low risk; Q < 0.75, medium risk; 0.75 < Q < 1, high risk.

[0036] 5.1 Visualization of spatial data ① Before the formal analysis of the data, eliminate the noise data in the structural plane dataset; ② After calculation and testing, the trend speculation error is small with a 15-day time window, and the time series in the model moves forward with a 1-day time step to collect the trend changes of the Q index and potential warning signals; ③ Generate an image with a color gradient based on the geographical coordinates and index values of the monitoring points, which can visually display the spatial distribution of rock mass stability: such as Figure 3As shown, the left figure is a superposition of discrete events, with high-value areas scattered in multiple points, reflecting the locality and randomness of crack activity; the right figure is a continuous cloud map formed by interpolation, with high-value areas concentrated in the middle to the right, indicating that this area is the main control danger zone for crack propagation and energy release, reflecting the process of damage evolving from discrete to concentrated.

Claims

1. A method for evaluating the stability of a rock mass protection structure system, characterized in that, It includes the following steps: Step S1: Deploy microseismic sensors, deformation monitors and remote sensing monitoring equipment to collect multi-source dynamic monitoring data of dangerous rock masses under mechanical actions and environmental disturbances in real time; Step S2: Based on the multi-source dynamic monitoring data, extract the energy dissipation rate, damage variable, S value, Benioff strain value, Hurst index and cumulative apparent volume CAV as precursor indicators; Step S3: Conduct macro-meso crack evolution tests on dangerous rock mass samples to obtain the weakening parameter Rc and brittleness parameter Ri, and establish a numerical model with fractures; Step S4: Respectively perform sliding time window smoothing processing on the time series data of each precursor indicator, and use Sen's slope trend analysis method to calculate the trend slope of each indicator; Step S5: Construct an instability warning indicator Q for multi-indicator fusion based on the trend slopes of each indicator, and establish a quantitative risk grading standard using Python according to the magnitude of the Q value; Step S6: Associate the instability warning indicator Q with the geographical coordinate information of the dangerous rock mass, and use the combination of GeoPandas and Plotly in Python to visualize spatial data on a map to generate a three-dimensional thermal cloud map representing the spatial distribution of risks.

2. The method according to claim 1, characterized in that, The energy dissipation rate is specifically expressed as: ； ； The damage variable is specifically expressed as: ； The S value is specifically expressed as: ； The Benioff strain value is specifically expressed as: ； The Hurst index is specifically expressed as: ； The cumulative apparent volume CAV as a precursor indicator is specifically expressed as: ； Where N represents the number of microseismic events within the monitoring range, Constants a and b are statistical parameters, where the value of b reflects the stability of the rock mass in the monitoring area; M L The magnitude of the corresponding microseismic event is represented by E; the energy of the microseismic event is represented by E. Let M represent rock density, c represent wave velocity, and v(f) represent the velocity function in the frequency domain; assuming the monitoring area is represented as Ω, and the spatial dimension D is divided into several statistical partitions with side length a; i M represents the magnitude of the i-th microseismic event. max V represents the maximum magnitude of microseismic events within this interval; a,i E represents the apparent volume of the i-th microseismic event; i This represents the energy released by the i-th microseismic event; Let N(t) represent the characteristic stress corresponding to the i-th microseismic event; N(t) represent the total number of microseismic events up to time t; i represents the event number; CBS(t) represent the cumulative Benioff strain at time t; Ei represents the energy released by the i-th microseismic event; N(t) represent the total number of microseismic events occurring before time t; i represents the event number; R(n) represents the range of the time series of length n; S(n) represents the standard deviation of the time series; n represents the length of the time series; C represents a constant; and H represents the Hurst exponent.

3. The method according to claim 1, characterized in that, The weakening parameter Rc and brittleness parameter Ri are shown as follows: ； in, , and These are the peak stress of the defective specimen, the peak stress of the intact specimen, and the crack initiation stress, respectively; R C R is the ratio of the peak stress of the defective specimen to the peak stress of the intact specimen, representing the effect of the defect on the deterioration of the specimen's load-bearing capacity; i It is the ratio of the initiation stress to the peak stress of a defective specimen with the same tilt angle, indicating the difficulty of cracking in the defective specimen.

4. The method according to claim 1, characterized in that, Step S4 includes the following steps: Based on the above indicators, smooth the data through a moving average. The moving average formula is expressed as: ； Among them, MA N (t) represents the N-day moving average at time t, x(i) represents the original index value on day i, t-N+1 is the starting point of the window, and t is the ending point of the window. [[ID=十六]]Set the sliding step length to capture the daily trend. Specifically, after calculating the average value of the N-day window, the window will move forward 1 day to observe the overall trend of the current day and the previous N one-day periods, and repeat this process. The specific formula is: 。 6. The method according to claim 1, characterized in that, The calculation method of the multi-indicator fusion rock mass instability warning indicator Q is shown as follows: ； Where n represents the number of precursor indicators of rock mass instability, F k W represents the F-score value corresponding to the k-th indicator. k(+ / -) This indicates the trend value of the corresponding indicator.

7. The method according to claim 1, characterized in that, Draw the cloud map through the Q value, and perform different color markings according to different Q values. The colored circles and different color gradients represent the warning risk levels at different positions.

8. The method according to claim 6, characterized in that, When conducting graded warning of the warning risk level, 0 < Q < 0.25, lower risk; 0.25 < Q < 0.5, low risk; Q < 0.75, medium risk; 0.75 < Q < 1, high risk.

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