Rock mass stability quantitative evaluation method based on disturbance stress and fracture evolution

Abstract

The invention relates to the technical field of rock mass mechanical property detection and stability evaluation, and discloses a rock mass stability quantitative evaluation method based on disturbance stress and fracture evolution, and the method comprises the steps: collecting the amplitude spectrum of a target rock mass; monitoring and separating internal fracture sound of the target rock mass to obtain fracture frequency; performing amplitude interference analysis on the target rock mass based on the amplitude spectrum and the fracture frequency to obtain a vibration interference factor of the target rock mass; and performing humidity compensation on the vibration interference factor based on the humidity degree in the target rock mass to obtain an environment compensation factor of the target rock mass, predicting the fracture state of the target rock mass based on the environment compensation factor and the inherent strength parameter of the target rock mass, and generating a stability evaluation report of the target rock mass based on the fracture state. According to the method, the problems that in existing rock mass stability evaluation, disturbance stress and fracture evolution dynamic association is not effectively combined, environmental interference is neglected, strength verification is lacked, and evaluation accuracy and reliability are insufficient can be solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of rock mass mechanical property detection and stability evaluation, and particularly relates to a rock mass stability quantitative evaluation method based on disturbance stress and fracture evolution. BACKGROUND

[0002] In the field of rock mass mechanical property detection and stability evaluation, the existing technology is often limited to single analysis of the static physical parameters of the rock mass, and cannot effectively combine dynamic disturbance factors from the outside world for evaluation. When obtaining external vibration data, the traditional method directly uses the original signal without processing, lacks a targeted noise reduction mechanism, and causes a large amount of environmental interference noise to be mixed in the signal, so that the amplitude characteristics reflecting the true strength of the external disturbance cannot be accurately extracted, and a reliable correlation between the disturbance and the rock mass response cannot be established.

[0003] In addition, due to the natural heterogeneous pore structure of the rock mass, the water content in different regions will significantly change the propagation speed and attenuation characteristics of the vibration wave, but the traditional method neither couples the moisture to the vibration-related evaluation factor through the porosity parameter, nor introduces a spatial weight adjustment mechanism for the unevenness of the pore distribution, so that the evaluation error caused by moisture is amplified, further reducing the credibility of the evaluation result. SUMMARY

[0004] The present application provides a rock mass stability quantitative evaluation method based on disturbance stress and fracture evolution, which mainly aims to solve the problem that the existing rock mass stability evaluation does not effectively combine the dynamic correlation of disturbance stress and fracture evolution, ignores environmental interference and lacks strength verification, resulting in insufficient accuracy and reliability of the evaluation.

[0005] To achieve the above-mentioned purpose, the present application provides a rock mass stability quantitative evaluation method based on disturbance stress and fracture evolution, which comprises:

[0006] S1: Collecting the amplitude spectrum of the external disturbance signal corresponding to the target rock mass;

[0007] S2: Listening to the internal fracture sound of the target rock mass, and synchronously separating the fracture frequency of the internal fracture sound;

[0008] S3: Based on the amplitude spectrum and the fracture frequency, performing amplitude interference analysis on the target rock mass to obtain a vibration interference factor of the target rock mass;

[0009] S4: Based on the moisture level inside the target rock mass, performing humidity compensation on the vibration interference factor to obtain an environmental compensation factor of the target rock mass;

[0010] S5: predicting a rupture state of the target rock mass based on the environmental compensation factor and the inherent strength parameter of the target rock mass, and generating a stability evaluation report of the target rock mass based on the rupture state.

[0011] Preferably, the amplitude spectrum of the external disturbance signal corresponding to the target rock mass is collected, including:

[0012] Seismic detectors are arranged around the target rock mass, and original vibration data detected by the seismic detectors are collected in real time;

[0013] The original vibration data are subjected to noise reduction processing to obtain the amplitude spectrum of the target rock mass.

[0014] Preferably, the internal rupture sound of the target rock mass is monitored, including:

[0015] The internal rock mass rupture signal inside the target rock mass is collected in real time based on a preset sound sensor;

[0016] The internal rock mass rupture signal is subjected to waveform feature analysis to obtain the internal rupture sound of the target rock mass.

[0017] Preferably, the rupture frequency of the internal rupture sound is separated synchronously, including:

[0018] The original rupture frequency band of the target rock mass is extracted from the internal rupture sound;

[0019] The original rupture frequency band is subjected to frequency domain screening based on the inherent elastic wave of the target rock mass to obtain the rupture frequency of the target rock mass.

[0020] Preferably, the amplitude interference analysis of the target rock mass based on the amplitude spectrum and the rupture frequency is performed to obtain a vibration interference factor of the target rock mass, including:

[0021] The rupture frequency is subjected to frequency band matching based on the amplitude spectrum to obtain a resonance coupling frequency of the target rock mass;

[0022] The resonance coupling frequency is subjected to phase interference correction based on the transient fluctuation of the rupture frequency to obtain a dynamic interference coefficient of the target rock mass;

[0023] The dynamic interference coefficient and the high-risk frequency of the amplitude spectrum are integrated to obtain the vibration interference factor of the target rock mass.

[0024] Preferably, the resonance coupling frequency is subjected to phase interference correction based on the transient fluctuation of the rupture frequency to obtain a dynamic interference coefficient of the target rock mass, including:

[0025] detecting phase shift of the breakage frequency based on the resonance coupling frequency, to obtain a phase delay parameter of the breakage frequency;

[0026] correcting phase delay of the resonance coupling frequency based on the phase delay parameter, to obtain a phase correction factor of the target rock mass;

[0027] correcting the resonance coupling frequency based on the phase correction factor, to obtain a dynamic interference coefficient of the resonance coupling frequency.

[0028] Preferably, the humidity compensation of the vibration interference factor based on the moisture level inside the target rock mass, to obtain an environmental compensation factor of the target rock mass, comprises:

[0029] obtaining a porosity of the target rock mass;

[0030] correcting the vibration interference factor based on the porosity, to obtain a humidity-vibration coupling factor of the target rock mass;

[0031] intervening spatial weight of the humidity-vibration coupling compensation based on the heterogeneous pore distribution of the target rock mass, to obtain the environmental compensation factor of the target rock mass.

[0032] Preferably, the humidity-vibration coupling factor of the target rock mass obtained by correcting the vibration interference factor based on the porosity, comprises:

[0033] decomposing the vibration interference factor into elastic wave component and viscous wave component based on the porosity;

[0034] calculating an exponential correction amount of the viscous wave component based on the porosity, to obtain the exponential correction amount of the viscous wave component;

[0035] calculating a linear reduction amount of the elastic wave component based on the porosity, to obtain the linear reduction amount of the elastic wave component;

[0036] calculating the humidity-vibration coupling compensation of the vibration interference factor based on the exponential correction amount and the linear reduction amount, to obtain the humidity-vibration coupling compensation of the vibration interference factor.

[0037] Preferably, the environmental compensation factor of the target rock mass is obtained by intervening spatial weight of the humidity-vibration coupling compensation based on the heterogeneous pore distribution of the target rock mass, wherein the calculation formula of the environmental compensation factor is:

[0038]

[0039] wherein, an environmental compensation factor, a humidity-vibration coupling factor, is a pore sensitivity coefficient of the heterogeneous pore distribution, is an index of the total number of regions, is a pore space weight of the region, is a local porosity of the region, is an average porosity of the target rock mass, is a moisture attenuation coefficient, is a moisture concentration of the region, is the total number of regions of the rock mass discretization.

[0040] Preferably, the prediction of the fracture state of the target rock mass based on the environmental compensation factor and the inherent strength parameter of the target rock mass comprises:

[0041] verifying the effectiveness of the environmental compensation factor based on the inherent strength of the target rock mass, and taking the result of the verification as a prediction benchmark of the target rock mass;

[0042] deducing the fracture state of the target rock mass based on the prediction benchmark.

[0043] Advantages

[0044] By collecting the amplitude spectrum of external disturbance signals, listening to the internal fracture frequency of the separated rock mass, and then going through steps such as frequency band matching and phase interference correction to obtain the vibration disturbance factor, the correlation mechanism between disturbance and rock mass response is accurately established, solving the evaluation deviation problem caused by the traditional method of relying only on static parameters and ignoring dynamic coupling relationship. At the same time, considering the influence of environmental factors, carrying out moisture coupling correction based on rock mass porosity, combining with the spatial weight intervention of heterogeneous pore distribution, and calculating the environmental compensation factor through a clear formula, the disturbance of the moisture degree of the rock mass to the evaluation result is effectively eliminated, filling the blank of the traditional method of ignoring environmental variables and the evaluation result being easily affected by moisture.

[0045] At the same time, based on the inherent strength parameter of the rock mass, the effectiveness of the environmental compensation factor is verified, and the result of the verification is taken as a prediction benchmark to deduce the fracture state, ensuring the credibility of the evaluation data and the accuracy of the prediction result, avoiding the risk of evaluation failure caused by no strength verification, and then generating a stability evaluation report that can directly provide precise quantitative basis for engineering decision-making, greatly improving the engineering guidance value of rock mass stability evaluation.

[0046] Therefore, this invention proposes a quantitative assessment method for rock mass stability based on disturbance stress and fracture evolution, which can solve the problems in existing rock mass stability assessments, such as the failure to effectively combine the dynamic correlation between disturbance stress and fracture evolution, the neglect of environmental interference, and the lack of strength verification, resulting in insufficient accuracy and reliability of the assessment. Attached Figure Description

[0047] Figure 1 A flowchart illustrating a quantitative assessment method for rock mass stability based on disturbance stress and fracture evolution, provided in an embodiment of the present invention;

[0048] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0049] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0050] This application provides a method for quantitatively assessing rock mass stability based on disturbance stress and fracture evolution. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data and artificial intelligence platforms.

[0051] Reference Figure 1 The diagram shown is a flowchart illustrating a quantitative assessment method for rock mass stability based on disturbance stress and fracture evolution, according to an embodiment of the present invention. In this embodiment, the method includes:

[0052] S1: Collect the amplitude spectrum of the external disturbance signal corresponding to the target rock mass.

[0053] In this embodiment, the amplitude spectrum of the external disturbance signal corresponding to the target rock mass is collected, including:

[0054] Seismic detectors are deployed around the target rock mass, and the raw vibration data detected by the seismic detectors are collected in real time.

[0055] The original vibration data is denoised to obtain the amplitude spectrum of the target rock mass.

[0056] Specifically, the target rock mass refers to a specific rock mass to be quantitatively evaluated for stability.

[0057] The external disturbance signal originates from dynamic interference factors in the physical environment in which the target rock mass is located.

[0058] The amplitude spectrum is a form of external disturbance signal in the frequency domain, reflecting the amplitude of the disturbance signal at different frequencies.

[0059] First, determine the layout scheme according to the engineering scene of the target rock mass, then drill holes or fix supports at the preset points, tightly attach the sensing end of the geophone to the rock mass surface, and finally connect the data transmission line, check the sensitivity of the geophone and the stability of data transmission, and ensure that each geophone can effectively capture the vibration signal. Start the real-time acquisition function of the geophone, set the acquisition frequency and acquisition time length, and real-time receive and store the original vibration data transmitted by the geophone. During this period, the data acquisition state needs to be continuously monitored to avoid data loss due to loose lines or equipment failure.

[0060] Import the collected original vibration data, observe the data waveform through data visualization tools, and identify noise characteristics; then select the corresponding denoising algorithm, such as high-pass filtering for high-frequency mechanical noise and wavelet threshold denoising for low-frequency environmental noise; then set the denoising parameters and perform the denoising operation; finally, compare the data waveform and spectrum graph before and after denoising to verify the denoising effect, and adjust the parameters and re-denoise if the effect is not good.

[0061] Perform Fourier transform on the denoised vibration data to convert time-domain signals to frequency-domain signals, calculate the amplitude values corresponding to different frequencies, generate an amplitude spectrum graph with frequency as the horizontal axis and amplitude as the vertical axis, label the main peak frequency and amplitude peak value in the spectrum, and complete the collection of the amplitude spectrum of the external disturbance signal.

[0062] S2: Listen to the internal cracking sound of the target rock mass and simultaneously separate the cracking frequency of the internal cracking sound.

[0063] In this embodiment, the listening to the internal cracking sound of the target rock mass comprises:

[0064] Based on the preset sound sensor, real-time acquisition of the internal rock mass cracking signal inside the target rock mass;

[0065] Perform waveform feature analysis on the internal rock mass cracking signal to obtain the internal cracking sound of the target rock mass.

[0066] Specifically, the internal rupture sound is a sound signal generated when rupture occurs inside the target rock mass, can reflect the occurrence and development of the internal rupture of the rock mass, and is an important basis for judging the rupture evolution state of the rock mass.

[0067] The preset sound sensor is a device prearranged at a specific position of the target rock mass and used for collecting the internal rupture signal of the rock mass.

[0068] The internal rock mass rupture signal is an original signal collected by the preset sound sensor in real time and directly reflecting the internal rupture of the target rock mass, and the signal contains various information of the rupture of the rock mass.

[0069] The waveform feature analysis is a technical means for processing the internal rock mass rupture signal, which extracts effective information accurately reflecting the internal rupture sound of the target rock mass by analyzing the waveform shape, amplitude, period, phase and other features of the signal, and excludes irrelevant interference such as noise.

[0070] First, according to the distribution range, structural characteristics and spatial resolution required for evaluation of the target rock mass, the preset sound sensor is arranged at the key position of the target rock mass. During the arrangement process, the sensor needs to be closely attached to the surface of the rock mass to reduce the loss in the signal transmission process and avoid additional interference of the sensor from the external environment.

[0071] Further, the preset sound sensor is started to enter the real-time collection state. The sensor will continuously capture the physical vibration generated by the internal rupture of the target rock mass, and convert these vibrations into continuous internal rock mass rupture electrical signals, and transmit the signals to the data processing device for storage according to the set sampling frequency.

[0072] After the data processing device receives the internal rock mass rupture signal, the waveform feature analysis technology is used to process the signal. First, the signal is preprocessed, such as removing the direct current component in the signal and filtering high-frequency noise; then the waveform features of the preprocessed signal are extracted, such as analyzing the amplitude variation law, waveform duration and frequency component of the waveform; finally, according to the extracted waveform features, the effective signal part accurately representing the internal rupture sound of the target rock mass is selected, and the listening of the internal rupture sound of the target rock mass is completed.

[0073] In the embodiment, the rupture frequency of the internal rupture sound is separated and synchronized, including:

[0074] Extracting the original rupture frequency band of the target rock mass from the internal rupture sound;

[0075] Performing frequency domain screening on the original rupture frequency band based on the inherent elastic wave of the target rock mass to obtain the rupture frequency of the target rock mass.

[0076] Specifically, the original breaking frequency band is a frequency range directly related to the rock mass breaking, which is preliminarily screened from the collected internal breaking sound, and does not include the frequency band corresponding to the environmental noise and external irrelevant disturbance.

[0077] The inherent elastic wave of the target rock mass refers to the elastic vibration wave inherent to the target rock mass due to material properties and structural characteristics when there is no external disturbance, and the frequency, wave speed and other parameters thereof have stability and can be used as a physical reference for distinguishing the rock mass breaking signal from the external interference signal.

[0078] The breaking frequency refers to a characteristic frequency corresponding to the internal breaking behavior of the target rock mass after frequency domain screening, and is a core quantitative index reflecting the breaking evolution state.

[0079] From the spectrum of the collected internal breaking sound, a frequency interval with signal intensity significantly higher than the noise background is identified, and this interval is the original breaking frequency band. Meanwhile, the inherent elastic wave frequency range of the target rock mass is called from the previous detection database, and the original breaking frequency band extracted in the first step is analyzed for overlap to determine the frequency sub-interval to be screened. This operation can preliminarily exclude the interference frequency in the original breaking frequency band that is irrelevant to the inherent properties of the rock mass.

[0080] A band-pass filter is used to further filter the signal in the frequency sub-interval to be screened, and the signal component consistent with the inherent elastic wave propagation characteristics is retained. Then, a peak detection algorithm is used to identify the signal intensity peak in the frequency domain spectrum after filtering, and the frequency corresponding to the peak is the candidate breaking frequency. The rationality of the candidate breaking frequency is verified in combination with the field working conditions of the rock mass.

[0081] If the candidate frequency has low signal intensity when the rock mass has no obvious breaking, and the signal intensity significantly increases after excavation disturbance, and the frequency data collected by the adjacent sensors have consistency, then the frequency is confirmed as the final breaking frequency. If there are multiple candidate frequencies, the harmonic characteristics of the inherent elastic wave of the rock mass need to be combined to exclude harmonic interference and ensure the uniqueness and accuracy of the breaking frequency.

[0082] S3: performing amplitude interference analysis on the target rock mass based on the amplitude spectrum and the breaking frequency to obtain a vibration interference factor of the target rock mass.

[0083] In this embodiment, the amplitude interference analysis on the target rock mass based on the amplitude spectrum and the breaking frequency to obtain the vibration interference factor of the target rock mass includes:

[0084] Performing frequency band matching on the breaking frequency based on the amplitude spectrum to obtain a resonance coupling frequency of the target rock mass;

[0085] Performing phase interference correction on the resonance coupling frequency based on the transient fluctuation of the breaking frequency to obtain a dynamic interference coefficient of the target rock mass.

[0086] The high-risk frequency is obtained by integrating the dynamic interference coefficient and the amplitude spectrum of the target rock mass.

[0087] Specifically, the resonance coupling frequency refers to the frequency value at which the amplitude spectrum of external disturbance and the internal rupture frequency of the rock mass overlap or couple in the frequency domain. This parameter reflects the degree of resonance correlation between external disturbance and internal rupture activity of the rock mass.

[0088] The transient fluctuation of the rupture frequency refers to the non-stability of the internal rupture activity of the rock mass, which causes short-term and rapid changes in the rupture frequency over time.

[0089] The dynamic interference coefficient is a parameter that can comprehensively reflect the interference degree of external disturbance and internal rupture activity of the rock mass in the phase level after correcting the phase of the resonance coupling frequency by substituting the phase correction factor into the calculation model of the resonance coupling frequency. This parameter not only contains the frequency information of the resonance coupling frequency, but also incorporates the dynamic characteristics after phase correction, which can more accurately describe the dynamic interaction between disturbance and rupture.

[0090] The high-risk frequency refers to the frequency in the amplitude spectrum whose amplitude value exceeds the preset safety threshold.

[0091] The vibration interference factor is a parameter that can quantitatively evaluate the overall vibration interference degree of external disturbance on the target rock mass by weighted calculation of the dynamic interference coefficient and the high-risk frequency. The larger the value, the greater the threat of external disturbance to the stability of the rock mass.

[0092] In the process of obtaining the vibration interference factor based on the amplitude spectrum and the rupture frequency, first, data preparation and frequency band matching operations are performed: confirm that the layout of the seismic detector and the sound sensor meets the physical environmental requirements of the tunnel excavation or slope support engineering scene, check the effectiveness of the pre- noise reduction and frequency domain screening data and eliminate outliers;

[0093] Then, the amplitude spectrum and the rupture frequency are matched and analyzed in the frequency domain, and the resonance coupling frequency is located by interpolation calculation and peak value identification combined with environmental interference correction. Next, the transient fluctuation capture, phase detection and correction operations are carried out: use a high sampling rate device that meets the Nyquist sampling theorem to monitor the transient fluctuation of the rupture frequency in real time, extract the fluctuation characteristics, calculate the phase delay parameter based on the resonance coupling frequency through cross-correlation analysis, exclude sudden disturbances, establish a model combined with physical parameters such as rock mass viscosity and porosity to obtain the phase correction factor, and calculate the dynamic interference coefficient after correcting the phase deviation of the resonance coupling frequency combined with the amplitude change rate.

[0094] Finally, the amplitude safety threshold is determined according to the rock mass engineering design standard and the inherent strength parameter, the amplitude spectrum peak value is analyzed to mark the high-risk frequency and record the characteristics, the dynamic interference coefficient and the weight of the high-risk frequency are determined through engineering case inversion and expert evaluation, the weighted calculation and normalization processing are performed to obtain the vibration disturbance factor, and the weight is adjusted to ensure accuracy in combination with the actual stability of the rock mass.

[0095] In the embodiment, the resonance coupling frequency is phase interference corrected based on the transient fluctuation of the rupture frequency, and a dynamic interference coefficient of the target rock mass is obtained, including:

[0096] The phase delay parameter of the rupture frequency is obtained by detecting the phase shift of the resonance coupling frequency on the rupture frequency.

[0097] The phase delay parameter of the rupture frequency is obtained by detecting the phase shift of the resonance coupling frequency on the rupture frequency.

[0098] The resonance coupling frequency is corrected based on the phase correction factor, and a dynamic interference coefficient of the resonance coupling frequency is obtained.

[0099] Specifically, the phase delay parameter is a quantitative index describing the degree of phase shift of the rupture frequency relative to the resonance coupling frequency in the time dimension.

[0100] The phase correction factor is a coefficient for correcting the phase deviation of the resonance coupling frequency.

[0101] In the target rock mass monitoring area, the seismic detector and the acoustic sensor are started synchronously, the resonance coupling frequency signal of external disturbance and the rupture frequency signal of internal rupture are collected, after noise reduction preprocessing, the phase sequence of the two signals is extracted and the difference is calculated point by point, and the phase delay parameter is obtained by statistical analysis combined with the influence of the heterogeneous structure of the rock mass; then, a mapping model is constructed according to the physical properties of the rock mass and the monitoring environment, and the phase correction factor is calculated by substituting the phase delay parameter into the model;

[0102] In this process, the resonance coupling frequency phase deviation interval needs to be located and corrected, and the effectiveness of the factor needs to be verified by error to ensure the effectiveness of the factor; finally, in the signal processing platform, the original resonance coupling frequency phase is corrected point by point by calling the phase correction factor, the correlation between the corrected frequency and the actual rupture state of the rock mass is verified combined with the data of the borehole viewer and the stress sensor, and the dynamic interference coefficient is calculated through the Fourier transform type interference coefficient algorithm by integrating the environmental parameters of the rock mass, and the coefficient value and the trend graph are output, which provides support for subsequent vibration disturbance factor calculation.

[0103] S4: Based on the moisture degree of the target rock mass, the vibration disturbance factor is humidity compensated to obtain an environmental compensation factor of the target rock mass.

[0104] In the embodiment, the humidity compensation of the vibration interference factor based on the moisture degree inside the target rock mass obtains the environmental compensation factor of the target rock mass, comprising:

[0105] Obtaining the porosity of the target rock mass;

[0106] Based on the porosity, the moisture-coupling correction of the vibration interference factor is obtained, and the humidity-vibration coupling factor of the target rock mass is obtained;

[0107] Based on the heterogeneous pore distribution of the target rock mass, the spatial weight intervention of the humidity-vibration coupling compensation is obtained, and the environmental compensation factor of the target rock mass is obtained.

[0108] Specifically, the porosity is the ratio of the pore volume inside the target rock mass to the total volume of the rock mass.

[0109] The humidity-vibration coupling factor is a parameter obtained by correcting the vibration interference factor considering the influence of water in the rock mass.

[0110] The heterogeneous pore distribution refers to the difference in porosity in different regions of the target rock mass, which is the core basis for spatial weight intervention.

[0111] The environmental compensation factor refers to the final environmental correction parameter obtained by further correcting the humidity-vibration coupling factor considering the non-uniformity of the pore distribution of the rock mass.

[0112] According to the engineering range of the target rock mass, the target rock mass is sampled and sent to the laboratory, and the porosity of the target rock mass is obtained through experimental calculation. At the same time, the vibration interference factor of the target rock mass is called from the previous data processing system, and the abnormal value is excluded combined with the field monitoring record to ensure that the basic data can truly reflect the influence of external disturbance on the rock mass. The vibration interference factor input signal processing software is used, and the wavelet packet decomposition algorithm is used to decompose the vibration interference factor into elastic wave component and viscous wave component with porosity as the decomposition threshold. The time domain waveform and frequency spectrum of the two components are output, and the component characteristics are observed directly.

[0113] According to the lithology of the target rock mass, the viscous attenuation coefficient is determined, the porosity and the viscous wave component are substituted into the exponential correction amount calculation logic to obtain the exponential correction amount; and the viscous wave component is subtracted by the exponential correction amount to obtain the corrected viscous wave component. According to the lithology, the capillary influence coefficient is determined, the porosity and the elastic wave component are substituted into the linear reduction amount calculation logic to obtain the linear reduction amount.

[0114] Then, the linear reduction amount of the elastic wave component is subtracted from the elastic wave component to obtain a corrected elastic wave component. The corrected elastic wave component and the viscous wave component are vector superimposed to obtain a humidity-vibration coupling factor; if the factor is decreased by more than 20% compared with the vibration interference factor, the values of the viscous attenuation coefficient and the capillary influence coefficient need to be checked for rationality to ensure that the correction logic conforms to the actual moisture influence law of the rock mass.

[0115] Further, in the step of performing spatial weight intervention on the humidity-vibration coupling factor based on the heterogeneous pore distribution of the target rock mass to obtain an environmental compensation factor, first, according to the engineering range of the target rock mass, the grid method is used to discretize it into several equal-area regions and mark the region position and number, and the engineering risk grade of each region is determined;

[0116] At the same time, the partition parameter measurement is carried out, the local porosity is obtained by drilling sampling at the center of each region and laboratory method, the moisture concentration of each region is measured on site by the time domain reflectometer, and then the pore space weight is assigned to each region according to the risk grade; then the average porosity is calculated by substituting the pore space weight and the local porosity of each region, and if the deviation of the average porosity from the overall porosity is more than 3%, the sampling data needs to be rechecked;

[0117] Finally, the spatial weight term of each region is calculated combined with the determined porosity sensitivity coefficient and moisture attenuation coefficient, and the sum of all region results is obtained; finally, the humidity-vibration coupling factor and the sum result are substituted into the environmental compensation factor calculation formula to obtain the environmental compensation factor, and a spatial distribution cloud map is drawn, which provides an environmental correction basis for subsequent rock mass fracture state prediction.

[0118] In the embodiment, the moisture coupling correction of the vibration interference factor based on the porosity to obtain the humidity-vibration coupling factor of the target rock mass comprises:

[0119] Wave group decomposition of the vibration interference factor based on the porosity to obtain the elastic wave component and the viscous wave component of the vibration interference factor;

[0120] Water film attenuation calculation of the viscous wave component based on the porosity to obtain an exponential correction amount of the viscous wave component;

[0121] Capillary tension influence calculation of the elastic wave component based on the porosity to obtain a linear reduction amount of the elastic wave component;

[0122] Coupling compensation calculation of the vibration interference factor based on the exponential correction amount and the linear reduction amount to obtain a humidity-vibration coupling compensation of the vibration interference factor.

[0123] Specifically, the elastic wave component is a vibration component in the vibration interference factor transmitted by the elastic deformation of the rock mass.

[0124] Viscous wave component is the vibration component of the vibration interference factor transmitted by the viscous resistance of water in the pores of the rock mass.

[0125] In the operation of wave component decomposition of the vibration interference factor based on porosity to obtain the elastic wave component and the viscous wave component, the average value of the porosity of the target rock mass verified by multiple experiments is first called from the previous data, and the complete data of the vibration interference factor including the time domain waveform and the frequency domain spectrum are obtained; then a wave component decomposition model with porosity as the core input parameter is constructed;

[0126] The model determines the propagation velocity difference and energy distribution ratio of the elastic wave and the viscous wave according to the porosity, establishes the mathematical mapping relationship between the vibration interference factor and the two wave components; then the time domain signal of the vibration interference factor is substituted into the model, and the Fourier transform is used to convert the frequency domain signal combined with the porosity, and the separation is completed according to the frequency characteristics and attenuation coefficient difference of the two wave components;

[0127] Further, the energy conservation verification is performed on the separation result, if the energy sum of the two wave components and the energy of the original vibration interference factor exist deviation, the porosity related parameters such as the viscosity coefficient of the pore fluid and the elastic parameters of the rock mass skeleton in the model are adjusted and recalculated until the time domain waveform and the frequency domain spectrum of the two wave components meet the accuracy requirements.

[0128] Further, the exponential correction quantity is used to correct the quantitative value of the energy loss of the viscous wave component caused by the water film attenuation.

[0129] Water film thickness is the thickness of the water layer attached to the pore wall inside the rock mass.

[0130] Viscous wave initial amplitude is the initial amplitude value of the viscous wave component before the water film attenuation.

[0131] Water film attenuation coefficient is a parameter describing the degree of energy attenuation of the water film to the viscous wave.

[0132] When the water film attenuation calculation is performed on the viscous wave component based on the porosity to obtain the exponential correction quantity, the time domain waveform peak value of the viscous wave component is first extracted as the viscous wave initial amplitude, then the porosity of the target rock mass is obtained, the water film thickness is calculated by the mercury injection method combined with the porosity, and the water film attenuation coefficient is obtained by substituting the porosity and the water film thickness into the fitting formula calibrated by the indoor experiment;

[0133] Then, based on the energy attenuation theory of the viscous wave in the porous medium, an exponential form water film attenuation model is constructed, and the calculation formula is: the amplitude of the viscous wave after the water film attenuation = the viscous wave initial amplitude × the water film attenuation coefficient × the porosity × the propagation distance of the viscous wave in the rock mass.

[0134] Further, the amplitude loss amount is obtained by calculating the difference between the initial amplitude of the viscous wave and the amplitude after the water film attenuation. Since the energy of the viscous wave is proportional to the square of the amplitude, the exponential correction amount, which is a negative value, is derived and calculated by combining the amplitude loss amount, the water film attenuation coefficient, and the porosity. Finally, a small-size rock core sample of the target rock mass is selected, and a viscous wave propagation experiment under different porosities and water film thicknesses is simulated in the laboratory. The actual attenuation amount and the calculated exponential correction amount are compared. If the deviation exceeds the preset threshold, the water film attenuation coefficient fitting formula parameters are adjusted and recalculated.

[0135] Further, the capillary tension is the pulling force of water in the pores of the rock mass due to surface tension, which is negatively related to the porosity. The lower the porosity, the smaller the pore size, and the greater the capillary tension.

[0136] The linear reduction amount is a correction value for the elastic wave component.

[0137] In the operation of calculating the capillary tension influence on the elastic wave component based on the porosity to obtain the linear reduction amount, the elastic wave component is first retrieved. The initial wave speed of the elastic wave is obtained through the field cross-hole acoustic test or frequency domain analysis combined with the inherent parameters such as the elastic modulus and Poisson's ratio of the rock mass. After obtaining the porosity of the target rock mass, the capillary rise method is used to measure the rising height of water in the core pores. The capillary tension is calculated by combining the surface tension coefficient of water and the core density through the capillary rise formula. Then, the capillary influence coefficient is obtained by substituting the porosity and capillary tension into the relationship formula fitted in the laboratory.

[0138] Further, based on the propagation theory of elastic waves in water-containing porous media, a linear relationship model is constructed, and the calculation formula is: the wave speed change amount of the elastic wave due to capillary tension = capillary influence coefficient * capillary tension * (1-porosity). Then, according to the relationship between the energy of the elastic wave and the wave speed, the linear reduction amount is derived and calculated by combining the wave speed change amount of the elastic wave due to capillary tension, the initial wave speed of the elastic wave, and the capillary influence coefficient. Finally, multiple monitoring points are selected in the field of the target rock mass, and the porosity, capillary tension, and elastic wave propagation speed at different positions are measured. The actual wave speed change amount, the linear reduction amount, and the model calculation results are compared. The capillary influence coefficient is adjusted to optimize the model according to the deviation, ensuring that the linear reduction amount can reflect the actual influence.

[0139] Further, in the coupling compensation calculation of the vibration disturbance factor based on the exponential correction amount and the linear reduction amount to obtain the humidity-vibration coupling compensation, the exponential correction amount and the linear reduction amount are first retrieved and confirmed to be consistent in unit and calculation dimension. Then, the elastic wave component weight coefficient and the viscous wave component weight coefficient are calculated. If the energy proportion difference is large, the reasonableness of the wave component decomposition result is checked. Then, a coupling compensation model is constructed based on the energy superposition principle, and the calculation formula is: humidity-vibration coupling factor = vibration disturbance factor + viscous wave component weight coefficient * exponential correction amount + elastic wave component weight coefficient * linear reduction amount.

[0140] Furthermore, the original vibration interference factor, two correction values ​​and corresponding weighting coefficients are substituted into the model for calculation. During the process, attention is paid to the positive and negative signs of the correction values. After the calculation is completed, the time-domain waveform, frequency-domain spectrum and key statistical parameters such as peak value, mean value and energy value of the humidity-vibration coupling compensation are output.

[0141] Finally, in typical areas of the target rock mass, vibration interference factors and humidity-vibration coupling compensation were calculated under different humidity conditions such as dry, wet, and saturated. The compensation effect was verified by combining actual stability data from the field, such as displacement monitoring and stress monitoring. If the effect was not good, the component weight coefficients were adjusted or the two correction quantities were recalculated to optimize the model and ensure that the compensation factor could support the subsequent rock mass stability assessment.

[0142] In this embodiment, spatial weighting is applied to the humidity-vibration coupling compensation based on the heterogeneous pore distribution of the target rock mass to obtain the environmental compensation factor of the target rock mass, wherein: the calculation formula for the environmental compensation factor is:

[0143]

[0144] in, As an environmental compensation factor, Humidity-vibration coupling factor, It is the pore sensitivity coefficient of the heterogeneous pore distribution. An index for the total number of regions. It is the first Pore ​​space weights for each region It is the first Local porosity of each region It is the average porosity of the target rock mass. It is the moisture decay coefficient. It is the first The humidity concentration in each area It represents the total number of regions where the rock mass is discretized.

[0145] Specifically, environmental compensation factors It is the final environmental correction parameter obtained by comprehensively considering the heterogeneous pore distribution and internal moisture level of the target rock mass after correcting the humidity-vibration coupling factor. It is the core indicator for quantifying the degree of environmental interference on the mechanical response of the rock mass. The value directly reflects the intensity of interference from environmental factors on the rock mass stability assessment.

[0146] like near This indicates weak environmental interference; if and Large deviations indicate that environmental factors have a significant impact on the vibration response and fracture evolution of the rock mass, and should be given priority consideration in stability assessment.

[0147] Moisture-vibration coupling factor is a parameter obtained by modifying the vibration interference factor based on the porosity of the target rock mass. It has preliminarily integrated the influence of moisture on vibration response, but has not considered the spatial heterogeneity of pore distribution.

[0148] is the bridge connecting pure vibration interference and comprehensive environmental interference. Its value reflects the basic interference degree of moisture and vibration coupling on rock mass stability evaluation under the ideal condition of assuming uniform distribution of rock mass pores.

[0149] Pore sensitivity coefficient is a coefficient for quantifying the influence of the heterogeneous pore distribution of the target rock mass on the environmental compensation factor. Its value is calibrated through laboratory experiments and field monitoring data, and is positively correlated with lithology and pore structure complexity. The greater the correction range of the pore heterogeneity distribution to , the more uneven the pore distribution of the rock mass, the more significant the influence of the heterogeneous characteristics on vibration wave propagation and moisture distribution, and the stronger the adjustment effect on ; the smaller, the interference of pore heterogeneity on the environmental compensation factor can be ignored.

[0150] Pore space weight of the th region is the weight value given to the discretized th region according to the engineering risk level and the influence of pore distribution on the overall stability of the target rock mass. It reflects the contribution difference of pore distribution in different regions to the overall environmental interference of the rock mass. For regions with high engineering risk, even if the area is small, the pore development and water accumulation characteristics will have a great influence on the overall stability, so the value is high; while the internal integrity and pore are less, the value is low.

[0151] Local porosity of the th region is the porosity of the th region after the target rock mass is discretized into directly determines the moisture holding capacity and vibration wave propagation characteristics of the local region, regions with high porosity are prone to internal water accumulation, and the energy attenuation of vibration waves is faster due to the viscous effect of the fluid in the pores during propagation; regions with low porosity are difficult to retain moisture, and vibration wave propagation is closer to the rules in ideal elastic media.

[0152] is the ratio of the pore volume of the target rock mass to the total volume of the rock mass, and is calculated by the local porosity of all discrete regions and the corresponding regional volume. reflects the pore development level of the rock mass as a whole, and is a basic index for measuring the macroscopic water permeability and disturbance resistance of the rock mass, High rock mass, strong overall water permeability, poor skeleton stability, easy to be affected by the coupling effect of water and vibration; Low rock mass, the overall structure is dense, and the disturbance of environmental factors on its stability is relatively weak.

[0153] moisture attenuation coefficient The coefficient describes the degree of attenuation of the moisture in the target rock mass to the vibration wave energy and the correction amplitude of the environmental compensation factor. It is positively correlated with lithology and moisture form, and is calibrated through indoor moisture-vibration attenuation experiments. The greater the value, the stronger the weakening ability of moisture to the environmental correction effect. When the moisture concentration of a certain region of the rock mass increases, the greater, the faster the attenuation, the smaller the contribution of the region to the environmental compensation factor, which indirectly reflects the physical law that the influence of further water absorption in the pores of the high-moisture region on the vibration response tends to be stable due to moisture saturation.

[0154] The moisture concentration of the th region refers to the ratio of the moisture mass in the th region to the dry mass of the rock mass in the region, reflecting the actual moisture content of the local region, which is measured by field time domain reflectometry and drying weighing method. is the core index for quantifying the influence of moisture on the local region, High region, sufficient water in the pores, will significantly enhance the attenuation of viscous wave, change the propagation speed of elastic wave, and then affect the authenticity of the vibration disturbance factor; Low region, the disturbance of moisture to vibration response can be ignored.

[0155] Total number of discrete regions of rock mass is the number of small regions obtained by dividing the target rock mass into mutually non-overlapping regions according to the engineering scope and spatial resolution requirements of the target rock mass, The value of is related to the balance between evaluation accuracy and calculation efficiency. determines the degree of detail in describing the heterogeneous pore distribution and moisture distribution of the rock mass, The greater the value, the more accurate the capture of local pore and moisture differences, the calculation accuracy is high, but the calculation amount increases; The smaller, the higher the computational efficiency, but the small-scale pores, moisture anomalies may be ignored.

[0156] is used to quantify the degree of deviation of local area porosity from the overall porosity of the rock mass. If , it means that the area porosity is higher than the overall level, and the water storage capacity is stronger, and the contribution to the environment is greater; if , the area porosity is lower than the overall level, and the contribution to the environment is smaller.

[0157] is used to quantify the attenuation of water contribution to pores, which conforms to the physical law that the more water, the less effective contribution of pores to environmental disturbance. When increases, increases, exponentially attenuates: if , , at this time the water has no attenuation effect, and the contribution of pores to environmental disturbance is the largest; if , , at this time the water attenuation is the strongest, and the contribution of pores to environmental disturbance is greatly weakened, which is consistent with the actual physical phenomenon. In a fully saturated rock mass, the pores are filled with water, and further water absorption or the influence of water on vibration waves tends to be stable, and the interference effect of pore heterogeneity is weakened.

[0158] is used to calculate the single-area environmental contribution. First, determine the deviation of the area porosity from the overall, then correct the attenuation of water contribution to pores, and finally give the area corresponding to the engineering weight to get the effective contribution value of the single area to the overall environmental disturbance.

[0159] is the sum of the effective contribution values of all discrete areas to get the overall environmental disturbance of the rock mass, avoiding the evaluation deviation caused by the neglect of local areas.

[0160] is an adjustment term for pore heterogeneity, where α controls the adjustment amplitude, controls the adjustment direction and size: if , , , it means that the pore heterogeneity further enhances the environmental disturbance, and needs to be further amplified and corrected on the basis of ; if is small or is small, , , it means that the pore heterogeneity has weak influence, and can be approximated by as an environmental compensation factor.

[0161] S5: predicting a rupture state of the target rock mass based on the environmental compensation factor and the inherent strength parameter of the target rock mass, and generating a stability evaluation report of the target rock mass based on the rupture state.

[0162] In the embodiment, the predicting the rupture state of the target rock mass based on the environmental compensation factor and the inherent strength parameter of the target rock mass comprises:

[0163] verifying the effectiveness of the environmental compensation factor based on the inherent strength of the target rock mass, and taking the result of the verification as a prediction benchmark of the target rock mass;

[0164] deducing the rupture state of the target rock mass based on the prediction benchmark.

[0165] Specifically, the inherent strength parameter of the target rock mass is a mechanical index inherent to the rock mass itself and reflecting its resistance to rupture, which includes compressive strength, tensile strength, shear strength, elastic modulus, Poisson's ratio, etc.

[0166] The rupture state of the target rock mass refers to the comprehensive state of whether rupture occurs inside the target rock mass, the position, range, degree and development trend of the rupture, etc., and is the core object for evaluating the stability of the rock mass.

[0167] The prediction benchmark is a standard basis for deducing the rupture state determined after verifying the effectiveness of the environmental compensation factor based on the inherent strength parameter of the target rock mass.

[0168] Firstly, the complete inherent strength parameter of the target rock mass is called from the previous experimental database, and the data in the calculation process of the environmental compensation factor is sorted to ensure that the parameter is traceable and has no abnormality; the inherent strength-environmental compensation factor-rupture risk correlation model is constructed in combination with the rock mass mechanics theory, and the rock mass heterogeneity correction term is introduced to optimize the accuracy;

[0169] In the target rock mass engineering area, typical monitoring points are selected, and actual rupture risk data are obtained by using a borehole viewer and a stress sensor, which are compared with the theoretical rupture risk calculated by the model. If the deviation meets the requirements, the environmental compensation factor is confirmed to be effective, a verification report is formed, and a prediction benchmark is marked.

[0170] Further, based on the prediction benchmark, the rupture state is deduced, the rock mass rupture evolution numerical simulation software is started, the engineering geological model containing the lithology zoning and the pore distribution characteristics is imported, the environmental compensation factor is converted into an equivalent environmental load parameter, and the inherent strength parameter is input as the rock mass mechanical property.

[0171] The simulation boundary conditions are set in combination with the actual engineering physical environment, such as unloading stress path in the tunnel excavation scene, overburden rock self-weight load and groundwater seepage boundary, to ensure that the simulation environment is consistent with the scene; the equivalent environmental load parameters are adjusted for different disturbance scenes, and the rupture related parameters are calculated through deduction under multiple working conditions.

[0172] Finally, the analysis and deduction results are generated to generate an evaluation report, the rupture state simulation results under each working condition are extracted, the rupture state grade is determined in combination with the engineering stability standard, the rupture state trend curve with time is generated to predict the development direction; the stability evaluation report is written according to the engineering technical document specification, the target rock mass basic information, the parameter acquisition and verification process, the rupture state prediction conclusion are sequentially described, and the targeted engineering suggestions are proposed, so that the report logic is clear, the data support is sufficient, and the report can be directly used for engineering decision-making.

[0173] In several embodiments provided by the present application, it should be understood that the disclosed method and system can be implemented by other manners. For example, the system embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and another division manner can be used in actual implementation.

[0174] The modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical units, that is, they can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs.

[0175] In addition, each functional module in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of hardware plus software functional modules.

[0176] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0177] The embodiments of the present application can acquire and process related data based on artificial intelligence technology. Among them, artificial intelligence is to use digital computers or digital computer controlled machines to simulate, extend and expand human intelligence, perceive environment, acquire knowledge and use knowledge to obtain the best results.

[0178] Finally, it should be noted that the above examples are merely intended to illustrate the technical solutions of the present application and not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the present application.

Claims

1. A method for quantitatively evaluating rock mass stability based on disturbance stress and fracture evolution, characterized in that, The method comprises: S1: collecting the amplitude spectrum of the external disturbance signal corresponding to the target rock mass; S2: listening to the internal cracking sound of the target rock mass and synchronously separating the cracking frequency of the internal cracking sound; S3: performing amplitude interference analysis on the target rock mass based on the amplitude spectrum and the cracking frequency, to obtain a vibration interference factor of the target rock mass; S4: performing humidity compensation on the vibration interference factor based on the moisture level inside the target rock mass, to obtain an environmental compensation factor of the target rock mass; S5: predicting the cracking state of the target rock mass based on the environmental compensation factor and the inherent strength parameter of the target rock mass, and generating a stability evaluation report of the target rock mass based on the cracking state.

2. The method for quantitative evaluation of rock mass stability based on disturbance stress and fracture evolution according to claim 1, characterized in that, The collection of the amplitude spectrum of the external disturbance signal corresponding to the target rock mass comprises: arranging a seismic detector around the target rock mass, and collecting original vibration data detected by the seismic detector in real time; performing noise reduction processing on the original vibration data to obtain the amplitude spectrum of the target rock mass.

3. The method for quantitative evaluation of rock mass stability based on disturbance stress and fracture evolution according to claim 1, characterized in that, The listening to the internal cracking sound of the target rock mass comprises: collecting internal rock mass cracking signals inside the target rock mass in real time based on a preset sound sensor; performing waveform feature analysis on the internal rock mass cracking signals to obtain the internal cracking sound of the target rock mass.

4. The method for quantitative evaluation of rock mass stability based on disturbance stress and fracture evolution according to claim 1, characterized in that, The synchronous separation of the cracking frequency of the internal cracking sound comprises: extracting the original cracking frequency band of the target rock mass from the internal cracking sound; performing frequency domain screening on the original cracking frequency band based on the inherent elastic wave of the target rock mass, to obtain the cracking frequency of the target rock mass.

5. The method for quantitative evaluation of rock mass stability based on disturbance stress and fracture evolution according to claim 1, characterized in that, The amplitude interference analysis on the target rock mass based on the amplitude spectrum and the cracking frequency, to obtain the vibration interference factor of the target rock mass, comprises: performing frequency band matching on the cracking frequency based on the amplitude spectrum, to obtain the resonance coupling frequency of the target rock mass; performing phase interference correction on the resonance coupling frequency based on the transient fluctuation of the cracking frequency, to obtain the dynamic interference coefficient of the target rock mass; integrating the dynamic interference coefficient and the high-risk frequency of the amplitude spectrum, to obtain the vibration interference factor of the target rock mass.

6. The method for quantitative evaluation of rock mass stability based on disturbance stress and fracture evolution according to claim 5, characterized in that, The phase interference correction on the resonance coupling frequency based on the transient fluctuation of the cracking frequency, to obtain the dynamic interference coefficient of the target rock mass, comprises: detecting the phase offset of the cracking frequency based on the resonance coupling frequency, to obtain the phase delay parameter of the cracking frequency; performing phase delay correction on the resonance coupling frequency based on the phase delay parameter, to obtain the phase correction factor of the target rock mass; correcting the resonance coupling frequency based on the phase correction factor, to obtain the dynamic interference coefficient of the resonance coupling frequency.

7. The method for quantitative evaluation of rock mass stability based on disturbance stress and fracture evolution according to claim 1, characterized in that, The humidity compensation on the vibration interference factor based on the moisture level inside the target rock mass, to obtain the environmental compensation factor of the target rock mass, comprises: obtaining the porosity of the target rock mass; performing moisture coupling correction on the vibration interference factor based on the porosity, to obtain the humidity-vibration coupling compensation of the target rock mass; The humidity-vibration coupling compensation is intervened by spatial weight based on the heterogeneous pore distribution of the target rock mass, to obtain an environmental compensation factor of the target rock mass.

8. The method for quantitative evaluation of rock mass stability based on disturbance stress and fracture evolution according to claim 7, characterized in that, The moisture coupling correction of the vibration interference factor based on the porosity is performed to obtain a humidity-vibration coupling factor of the target rock mass, including: The vibration interference factor is decomposed into an elastic wave component and a viscous wave component based on the porosity; The viscous wave component is subjected to water film attenuation calculation based on the porosity, to obtain an exponential correction amount of the viscous wave component; The elastic wave component is subjected to capillary tension influence calculation based on the porosity, to obtain a linear reduction amount of the elastic wave component; The vibration interference factor is subjected to coupling compensation calculation based on the exponential correction amount and the linear reduction amount, to obtain a humidity-vibration coupling compensation of the vibration interference factor.

9. The method for quantitative evaluation of rock mass stability based on disturbance stress and fracture evolution according to claim 7, characterized in that, The humidity-vibration coupling compensation is intervened by spatial weight based on the heterogeneous pore distribution of the target rock mass, to obtain an environmental compensation factor of the target rock mass, wherein a calculation formula of the environmental compensation factor is: ； wherein, is an environmental compensation factor, is a humidity-vibration coupling factor, is a pore sensitivity factor of the heterogeneous pore distribution, is an index of the total number of zones, is a pore space weight of the z th zone, is a local porosity of the z th zone, is an average porosity of the target rock mass, is a water attenuation factor, is a moisture concentration of the z th zone, is the total number of zones of the rock mass discretization.

10. The method for quantitative evaluation of rock mass stability based on disturbance stress and fracture evolution according to claim 1, characterized in that, The rupture state of the target rock mass is predicted based on the environmental compensation factor and the inherent strength parameter of the target rock mass, including: The effectiveness of the environmental compensation factor is verified based on the inherent strength of the target rock mass, and a result passed through the verification is taken as a prediction benchmark of the target rock mass; The rupture state of the target rock mass is deduced based on the prediction benchmark.

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