An engineering joint rock mass unloading stability evaluation method and system and a storage medium

Abstract

The application provides an engineering joint rock mass unloading stability evaluation method, system and storage medium, and belongs to the technical field of rock mass stability evaluation of underground engineering and slope engineering. The method comprises the following steps: obtaining core parameters of a joint rock mass through drilling core taking, then establishing an uncertainty regression model, performing large sample random sampling, and respectively obtaining probability distributions of rock mechanics parameters; calculating rock mass equivalent mechanics parameters, comparing the rock parameter mean value and the rock mass equivalent parameters to obtain a reduction coefficient, and correcting the rock parameter probability distribution to a rock mass parameter probability distribution; selecting representative parameters of the rock mass to carry out unloading simulation, extracting displacement and plastic zone depth data of key parts, fitting the probability distribution, and completing the stability evaluation. The method realizes full transmission of parameter uncertainty from rock to rock mass, avoids the limitation of single deterministic parameters, and provides an efficient and reliable technical framework for rock mass unloading stability evaluation under complex geological conditions.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of rock mass stability evaluation of underground engineering and slope engineering, and particularly relates to an engineering joint rock mass unloading stability evaluation method, system and storage medium. BACKGROUND

[0002] In the field of geotechnical engineering, the determination of rock mass mechanical parameters is influenced by multiple factors such as geological activity, occurrence state and human engineering activities, and the physical uncertainty, measurement uncertainty and rock mass-rock parameter conversion model uncertainty significantly restrict the accuracy of engineering stability evaluation.

[0003] In underground space excavation engineering such as tunnels, underground powerhouses and mine roadways, the influence of deformation and plastic zone development of surrounding rock mass on engineering safety needs to be evaluated, such as tunnel vault settlement, underground powerhouse shoulder deformation and other problems.

[0004] In excavation slope engineering such as road cutting slope, water conservancy slope and mine slope, after the excavation unloading of the slope rock mass of such engineering, the stress state of the natural joint surface changes, and the risk of landslide and collapse caused by the fluctuation of rock mass mechanical properties needs to be prevented, such as the development depth of slope sliding surface and the displacement of surface rock mass. Such settlement, deformation and collapse risks seriously affect the safety of personnel life.

[0005] In order to avoid accidents, the traditional method is based on Hoek-Brown criterion, and the probability estimation of mechanical parameters is realized through large-scale field geological exploration data, and most of them assume that the parameters follow normal distribution, which has certain human subjectivity. However, in complex geological conditions or engineering scenes with limited exploration cost, such method has obvious limitations: on the one hand, high-precision rock mass parameters depend on intensive drilling, in-situ testing and laboratory testing, resulting in a significant increase in economic and time costs; on the other hand, under the condition of small sample, the statistical characteristics of parameters are distorted, and the empirical rock mass-rock parameter reduction coefficient easily introduces subjective bias, affecting the reliability of stability analysis.

[0006] Traditional methods rely on large-scale field geological exploration (such as intensive drilling, multiple in-situ tests) to obtain rock mass mechanical parameters to achieve probability estimation. However, in the scene of high cost of exploration, complex terrain of engineering area (such as remote mountainous hydropower underground powerhouse, high and steep slope), it is impossible to obtain enough sample size, which leads to distortion of parameter statistical characteristics, and further affects the reliability of stability analysis. Rock mass mechanical parameters are influenced by three factors: geological activity (such as joint development degree), occurrence state (such as groundwater influence), measurement error, and there is significant uncertainty. But the traditional method assumes that the parameters obey the normal distribution, which has subjective human nature, and does not fully couple the parameter uncertainty and stability evaluation, and cannot quantify the influence of parameter fluctuation on engineering risk. Accurate acquisition of rock mass mechanical parameters is the premise of stability evaluation, but in actual engineering, rock parameters are often obtained through indoor test of rock sample, and then converted into rock mass parameters through empirical reduction factor. The empirical reduction factor of the traditional method is highly subjective, and depends on the experience of engineers, and does not establish a scientific conversion relationship combined with geological conditions and test data, which leads to large deviation of the converted rock mass parameters from the actual rock mass mechanical properties.

[0007] Accurate acquisition of rock mass mechanical parameters is the premise of stability analysis of engineering rock mass, but there is no reliable rock mass stability analysis scheme in the processing scheme of existing rock mass analysis technology, which seriously affects the disaster prediction and risk assessment of related workers on rock mass engineering. SUMMARY

[0008] In order to solve the problem of difficult to accurately carry out rock mass stability evaluation caused by parameter statistical distortion due to small sample survey, rock mass parameter uncertainty not quantified, and subjective deviation of rock-rock mass parameter conversion, the present application provides an engineering jointed rock mass unloading stability evaluation method, system and storage medium.

[0009] In order to achieve the above purpose, the present application provides an engineering jointed rock mass unloading stability evaluation method, comprising:

[0010] The geological strength of the jointed rock mass at the target engineering site and the compression wave velocity value, elastic modulus, shear strength, tensile strength and uniaxial compressive strength of the drill core rock block of different depth sections are obtained respectively.

[0011] establishing an uncertainty regression equation of the compressional wave velocity value and the elastic modulus of the rock mass; randomly generating a simulated elastic modulus based on the uncertainty regression equation, and statistically calculating a mean value of the simulated elastic modulus; randomly generating a simulated shear strength and a simulated tensile strength based on the shear strength and the tensile strength of the rock mass, and respectively statistically calculating mean values of the simulated shear strength and the simulated tensile strength; calculating equivalent elastic modulus, equivalent shear strength and equivalent tensile strength of the rock mass by using the geological strength of the rock mass and the uniaxial compressive strength of the rock mass; and determining rock-mass mechanical parameter reduction coefficients by using the mean values of the simulated elastic modulus, the simulated shear strength and the simulated tensile strength of the rock mass and the equivalent elastic modulus, the equivalent shear strength and the equivalent tensile strength of the rock mass.

[0012] updating the simulated elastic modulus, the simulated shear strength and the simulated tensile strength of the rock mass by using the rock-mass mechanical parameter reduction coefficients to obtain the elastic modulus, the shear strength and the tensile strength of the rock mass; selecting mean values and standard deviations of the elastic modulus, the shear strength and the tensile strength of the rock mass as representative value points; and performing permutation and combination on all the representative value points.

[0013] performing simulation calculation based on different combinations of the representative value points, and evaluating the jointed rock mass unloading stability by using the calculation results.

[0014] Preferably, the compressional wave velocity of the rock core of the rock mass at different depth sections of the target engineering site jointed rock mass is tested by using the acoustic wave instrument to obtain the compressional wave velocity value of the rock core of the target engineering site jointed rock mass at different depth sections; the elastic modulus and the uniaxial compressive strength of the rock core of the target engineering site jointed rock mass at different depth sections are obtained by using the indoor uniaxial compression test; and the shear strength and the tensile strength of the rock core of the target engineering site jointed rock mass at different depth sections are obtained by using the indoor triaxial compression test and the Brazilian splitting test, wherein the shear strength includes cohesion and internal friction angle.

[0015] Preferably, the random generation of the simulated elastic modulus and the random generation of the simulated shear strength and the simulated tensile strength are random sampling calculation methods, and specifically, the random sampling calculation method is a Markov chain Monte Carlo sampling method.

[0016] Preferably, the equivalent elastic modulus, the equivalent shear strength and the equivalent tensile strength of the rock mass are calculated by using the geological strength of the rock mass and the uniaxial compressive strength of the rock mass according to the Hoek-Brown criterion.

[0017] Preferably, the mean values and the standard deviations of the elastic modulus, the shear strength and the tensile strength of the rock mass are selected as the representative value points by using the Rosenblueth point estimation method; and permutation and combination are performed on all the representative value points to obtain 16 combination schemes.

[0018] Preferably, the simulation calculation is carried out based on different combinations of representative points, and the calculation results are used to evaluate the stability of the jointed rock mass under unloading.

[0019] A strain softening model capable of describing the deterioration characteristics of the rock mass during the excavation construction process of the engineering rock mass is established.

[0020] The 16 combinations are sequentially brought into the strain softening model, the key positions affecting the stability of the engineering jointed rock mass are identified, the displacement and plastic zone depth data of the key positions in the 16 simulations are extracted, and the probability distribution of the displacement and plastic zone depth data is fitted.

[0021] The probability distribution is used to evaluate the response behavior and stability of the engineering jointed rock mass under unloading.

[0022] Preferably, before the compression wave velocity, elastic modulus, shear strength, tensile strength and uniaxial compressive strength of the drill core rock mass of the different depth sections of the target engineering site jointed rock mass are obtained, the target engineering site jointed rock mass at different depth sections is drilled for core sampling; the number of drill holes is determined according to the site rock mass quality zoning, the number of drill holes in the area with consistent rock mass quality classification results is not less than 2, and the drill hole depth is not less than 10 meters; the corresponding coding labels are marked according to the different depth positions of the drill core.

[0023] The application also provides an engineering jointed rock mass unloading stability evaluation system, comprising:

[0024] The data acquisition module is used to acquire the geological strength of the target engineering site jointed rock mass and the compression wave velocity, elastic modulus, shear strength, tensile strength and uniaxial compressive strength of the drill core rock mass of different depth sections.

[0025] The reduction factor calculation module is used to establish an uncertainty regression equation of the compression wave velocity and elastic modulus of the rock mass; simulate the elastic modulus based on the uncertainty regression equation, and statistically analyze the mean value of the simulated elastic modulus; simulate the shear strength and tensile strength based on the shear strength and tensile strength of the rock mass, and statistically analyze the mean values of the simulated shear strength and tensile strength; calculate the equivalent elastic modulus, equivalent shear strength and equivalent tensile strength of the rock mass by using the geological strength of the rock mass and the uniaxial compressive strength of the rock mass; and determine the rock-mass mechanical parameter reduction factor by using the mean values of the simulated elastic modulus, simulated shear strength and simulated tensile strength of the rock mass and the equivalent elastic modulus, equivalent shear strength and equivalent tensile strength of the rock mass.

[0026] The unloading stability evaluation module is used to update the simulated elastic modulus, simulated shear strength, and simulated tensile strength of the rock block using the reduction coefficient of the rock-rock mass mechanical parameters, thereby obtaining the elastic modulus, shear strength, and tensile strength of the rock mass; the mean and standard deviation of the elastic modulus, shear strength, and tensile strength of the rock mass are selected as representative value points; all representative value points are arranged and combined; simulation calculations are performed based on different combinations of representative value points, and the calculation results are used to evaluate the unloading stability of the jointed rock mass.

[0027] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement any of the steps in the method for evaluating the unloading stability of engineering jointed rock masses.

[0028] The present invention also provides a computer-readable storage medium storing a computer program that, when loaded by a processor, can execute any of the steps in the method for evaluating the unloading stability of engineering jointed rock masses.

[0029] The method for evaluating the unloading stability of jointed rock masses provided by this invention has the following beneficial effects:

[0030] Core parameters of borehole cores from different depth sections of the jointed rock mass at the target project site were obtained, and the compression wave velocity and elastic modulus were compared. E s This system links on-site rapid detection data with precise indoor mechanical parameters; it randomly generates simulated elastic modulus, shear strength, and tensile strength based on an uncertainty regression equation; it updates rock block parameters by determining the reduction coefficient through the ratio of the mean rock parameter to the equivalent rock mass parameter, obtaining the most realistic rock mass data; it performs simulation calculations based on representative value points of different rock mass combinations, and uses the calculation results to evaluate the unloading stability of jointed rock mass, realizing the full transfer of parameter uncertainty from rock to rock mass, avoiding the one-sided evaluation caused by missing parameters and the limitations of single deterministic parameters. The simulation data ensures a sufficiently large sample size, and the statistical results are closer to the real distribution, reducing the risk of statistical distortion in small samples, providing high-quality parameter basis for subsequent quantitative analysis of rock mass parameters. The reduction coefficient is entirely based on measured and calculated data, avoiding the bias of subjective experience assignment in traditional methods, ensuring the objectivity of the coefficient, and providing an efficient and reliable technical framework for evaluating the unloading stability of rock mass under complex geological conditions. Attached Figure Description

[0031] In order to more clearly illustrate the embodiments of the present application and the design scheme thereof, the following will briefly introduce the drawings required by the present embodiment. The drawings in the following description are only part of the embodiments of the present application, and other drawings can also be obtained according to these drawings without creative labor for those skilled in the art.

[0032] Figure 1 The flow chart of the engineering joint rock mass unloading stability evaluation method of the present embodiment.

[0033] Figure 2 The rock mass elastic modulus parameter probability density graph of the present embodiment;

[0034] Figure 3 The rock mass cohesion parameter probability density graph of the present embodiment;

[0035] Figure 4 The rock mass internal friction angle parameter probability density graph of the present embodiment;

[0036] Figure 5 The rock mass tensile strength parameter probability density graph of the present embodiment;

[0037] Figure 6 The comparison graph of the rock mass displacement measured and calculated after the vault excavation of the present embodiment;

[0038] Figure 7 The surrounding rock plastic zone probability distribution graph after the vault excavation of the present embodiment;

[0039] Figure 8 The comparison graph of the surrounding rock plastic zone depth measured and calculated after the vault excavation of the present embodiment. DETAILED DESCRIPTION

[0040] In order to make those skilled in the art better understand the technical scheme of the present application and can be implemented, the present application will be described in detail below in combination with the drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical scheme of the present application, and cannot limit the protection scope of the present application.

[0041] The present application provides an engineering joint rock mass unloading stability evaluation method, specifically as shown in Figure 1 , comprising:

[0042] To mitigate the impact of uncertainties in mechanical parameters during rock mass stability assessment, this invention proposes a method for evaluating the unloading stability of jointed rock masses considering parameter uncertainties. This method, based on random sampling, constructs a joint probability distribution model for elastic modulus, cohesion, and internal friction angle. Following the Hoek-Brown criterion, it determines the rock-rock mass mechanical parameter reduction coefficient by comparing the statistical means of small-sample rock mass parameters with those of large-sample rock mass parameters. This reduces the rock mechanical parameters, yielding a probability distribution of the rock mass mechanical parameters. The reduced rock mass mechanical parameters are then input into a numerical calculation model, and the probability distribution of displacement values ​​and plastic zone depths at key rock mass locations is calculated using point estimation methods, thus achieving a probabilistic evaluation of rock mass stability.

[0043] To achieve the above objectives, the present invention provides the following solution:

[0044] (1) Core samples were taken from the jointed rock mass at the engineering site, and the compression wave velocity of the jointed rock mass was tested using a sonic logging instrument to obtain the compression wave velocity value of the jointed rock mass in the borehole section. The dataset was used. Simultaneously, rock samples were processed from cores at different borehole depths, and the elastic modulus of the corresponding rock sections was obtained through indoor uniaxial compression tests. ) dataset, build - Uncertainty regression model (uncertainty regression equation).

[0045] (2) Based on the established - The regression model randomly selects the rock mass compression wave velocity value as the input parameter for random sampling calculation, and combines it with determined prior information as the input parameter. Tens of thousands of sampling calculations are performed, and the results are statistically analyzed to obtain the rock elastic modulus under large sample conditions. The probability distribution of ).

[0046] (3) The rock samples obtained from the core drilling in step 1 are processed and subjected to no less than 30 indoor triaxial compression tests and Brazilian splitting tests to obtain the shear strength (cohesion) of the rock. c and internal friction angle φ And tensile strength samples.

[0047] (4) Based on the rock shear strength samples obtained in step 3, and combined with the determined prior information as input parameters, thousands of random sampling calculations were performed. The statistical calculation results yielded the rock shear strength under large sample conditions. c and φ ) and the probability distribution of tensile strength.

[0048] (5) Conduct a geological condition survey of the study area to determine parameters such as the number of joint groups, rock mass structure type, and weathering degree. Based on the results of laboratory tests, determine the input parameters of the Hoek-Brown criterion, including the geological strength index GSI, uniaxial compressive strength UCS, and Hoek-Brown material constant. Based on the empirical formula of the Hoek-Brown strength criterion, the equivalent elastic modulus of jointed rock mass is calculated. ), equivalent cohesion ( c ), equivalent internal friction angle ( φ and equivalent tensile strength ( ).

[0049] (6) Perform statistical calculations on the probability distributions of the rock mechanical parameters obtained in steps 2 and 4 to obtain the mean values ​​of rock elastic modulus, cohesion, internal friction angle, and tensile strength. Compare these mean values ​​with the equivalent rock mass mechanical parameters obtained in step 5 to determine the rock-rock mass mechanical parameter reduction coefficients respectively.

[0050] (7) Using the rock-rock mass mechanical parameter reduction coefficient obtained in step 6, the calculation results of rock mechanical parameters under the large sample conditions obtained in steps 2 and 4 are reduced to obtain the probability distribution of rock mass mechanical parameters (elastic modulus, cohesion, internal friction angle and tensile strength) under the large sample conditions.

[0051] (8) Based on the rock mass mechanical parameters (elastic modulus) obtained in step 7 Cohesion c internal friction angle φ ,tensile strength The probability distribution results are obtained by using the point estimation method, selecting the mean ± standard deviation of the probability distribution of each parameter. μ±σ Using these as representative value points, 16 sets of rock mass mechanics parameter combination calculation schemes are formed. In the point estimation method, the first moment of the random variable... The point estimate is:

[0052] (1)

[0053] in M 1 is a random variable Z The first moment, E ( Z ) represents a random variable Z Expectations μ Z It is also a random variable Z The expectation is expressed as μ Z =E ( Z ), P j It is the firstj The probability of one of the possible values, here equal to , represents a uniform distribution, Z j is the random variable Z . j

[0054] The second central moment M2, the second central moment of the random variable Z is the variance of Z, whose point estimate is:

[0055] (2)

[0056] where M 2 is the second central moment of the random variable Z , i.e. the variance represents Z the variance of is Z the variance of is Z the square of the expected value of P j and Z j have the same meaning as in equation (1).

[0057] (9) A 3D numerical model of the simulation site excavation step sequence is established, numerical simulation and rock mass stability analysis are carried out, wherein the strain softening model capable of describing the deterioration characteristics of rock mass is selected as the mechanical model, and the parameter combination scheme obtained from step 7 is selected in sequence. The key parts (such as parts with larger deformation or more significant plastic zone) affecting the stability of the engineering jointed rock mass are identified, the displacement and plastic zone depth data of the parts in 16 simulations are extracted, the probability distribution thereof is fitted, and then the response behavior and stability of the engineering jointed rock mass under excavation unloading are evaluated.

[0058] The present application proposes a method of fusing geological exploration (field wave velocity test) and indoor test (triaxial test) prior information and H-B criterion rock mass reduction coefficient, solves the problem of probability representation of rock mass mechanical parameters (elastic modulus , c、 cohesion φ internal friction angle) under small sample condition, and significantly improves the distribution precision thereof; based on random sampling to generate parameter groups, combined with three-dimensional numerical simulation and point estimation method, the key parameters (E, , c , φ , Coupling effect on rock mass displacement and plastic zone depth, realize the complex jointed rock mass excavation unloading response behavior and stability of the probability evaluation. The method has both theoretical rigor and engineering practicability, and provides an efficient and reliable quantitative analysis framework for the stability evaluation of engineering jointed rock mass considering parameter uncertainty.

[0059] The specific implementation of the present application is described by taking a hydropower station underground powerhouse in the upper reaches of the Jinsha River in southwest China as an example. The following steps are included:

[0060] Step 1: Drilling and coring the jointed rock mass at the construction site, using a sonic instrument to test the compressional wave velocity of the jointed rock mass, obtaining a dataset of compressional wave velocity values of the jointed rock mass at the drilling section. Step 2: Selecting rock cores at different depths of the drilling section to process rock samples, and obtaining a dataset of elastic moduli of the corresponding section rock through indoor uniaxial compression test. Step 3: Establishing an uncertainty regression model. -

[0061] Specifically, by drilling and coring the basalt of the right bank underground powerhouse of the hydropower station, the compressional wave velocity of the jointed rock mass is tested on site, and the compressional wave velocity values of 36 rocks are obtained. The samples are processed to obtain the elastic modulus through indoor uniaxial compression test. Nine data are randomly selected from the 36 sample data for regression analysis, and a power function model is established based on these randomly selected data, i.e. =0.28 V p 3.11 .

[0062] Step 2: Based on the established - regression model, randomly select the compressional wave velocity of the rock mass as the input parameter of the random sampling calculation, and combine the determined prior information as the input parameter to perform more than 10,000 times of sampling calculation. The sampling calculation results are statistically analyzed to obtain the probability distribution of the elastic modulus of the rock under the condition of large sample.

[0063] Specifically, according to the engineering experience, the appropriate prior information is selected, and it is assumed that the mean and standard deviation of the rock are subject to uniform distribution, the average value range is between 10GPa~100GPa, and the standard deviation range is between 5GPa~20GPa. The randomly selected 9 rock compressional wave velocities are used as the input parameters of the Markov chain Monte Carlo (MCMC) sampling calculation, and the input parameters also include the determined prior information and the sampling times. Through 30000 times of sampling calculation, the sampling sample distribution of the rock elastic modulus is obtained, and the mean value of the normal distribution is 59.17GPa and the standard deviation is 26.85GPa. ​​

[0064] Step 3: Process the rock obtained by drilling and coring in Step 1 to prepare samples, conduct not less than 30 indoor triaxial compression tests and Brazilian splitting tests, and obtain the shear strength (cohesion c and internal friction angle φ ) and tensile strength samples of the rock.

[0065] Specifically, the shear strength values of basalt rock samples determined by indoor triaxial compression tests are shown in Table 1.

[0066] Table 1 Shear strength of basalt rock

[0067]

[0068] Step 4: Based on the shear strength samples of the rock obtained in Step 3, combine the determined prior information as input parameters, and perform thousands of random sampling calculations. Calculate the statistical results to obtain the probability distribution of the shear strength (c c and φ ) and tensile strength (T) of the rock under large sample conditions.

[0069] Specifically, the shear strength of basalt rock samples is determined by indoor triaxial compression tests, and 40 sample parameters are obtained, which are used as initial samples in the MCMC sampling process for calculation. The prior information is determined based on engineering experience, the mean value of cohesion c is 5-50 MPa, and the standard deviation is 1 MPa-15 MPa; the mean value of internal friction angle φ is 5°-85°, and the standard deviation is 1°-10°; the correlation coefficient of mutual correlation ρ ranges from -0.5 to -1. Through 3000 sampling calculations, the distribution of MCMC sampling samples of the shear strength of basalt rock is obtained, the samples of cohesion c and internal friction angle φ present a normal distribution, wherein the mean value of cohesion c is 29.04 MPa, and the standard deviation is 5.41 MPa; the mean value of internal friction angle φ is 39.43°, and the standard deviation is 10°. Through Brazilian splitting tests, the mean value of rock tensile strength is 3.35 MPa, and the standard deviation is 1.83 MPa.

[0070] Step 5: Conduct a geological condition survey of the research area to determine parameters such as the number of joint sets, rock mass structure type, and weathering degree. Combine the indoor test results to determine the Hoek-Brown criterion input parameters (GSI, UCS, ). Based on the empirical formula of the Hoek-Brown strength criterion, calculate the equivalent elastic modulus (E ), equivalent cohesion (c c ​), equivalent internal friction angle ( φ and equivalent tensile strength ( ).

[0071] Specifically, for the underground powerhouse on the right bank of this hydropower station, the input parameters for the HB criterion were determined based on the researchers' preliminary geological survey. It should be noted that the joints in the underground powerhouse on the right bank of this hydropower station are relatively intact, and the rock mass quality should be evaluated based on the RMR grading system. Based on the field geological survey and laboratory test results, the HB input parameters are RMR=67 and UCS=137MPa. m i =25, and based on this, the equivalent elastic modulus of the rock mass is calculated. E m Equivalent cohesion c , equivalent internal friction angle φ The equivalent tensile strengths are 26.6 GPa, 13.05 MPa, 42.23° and 0.50 MPa, respectively.

[0072] Step 6: Perform statistical calculations on the probability distributions of the rock mechanical parameters obtained in Steps 2 and 4 to calculate the mean values ​​of the rock's elastic modulus, cohesion, internal friction angle, and tensile strength. Compare these mean values ​​with the equivalent rock mass mechanical parameters obtained in Step 5 to determine the rock-rock mass mechanical parameter reduction coefficients.

[0073] Specifically, the calculated rock-rock mechanical parameters and their reduction coefficients are shown in Table 2.

[0074] Table 2 Rock-rock mechanical parameters and their reduction factors

[0075]

[0076] Step 7: Using the rock-rock mass mechanics parameter reduction coefficient obtained in Step 6, reduce the rock mechanics parameter calculation results obtained in Step 2 and Step 4 under the large sample condition to obtain the rock mass mechanics parameters (elastic modulus) under the large sample condition. Cohesion c internal friction angle φ ,tensile strength The probability distribution of ).

[0077] Specifically, by substituting the calculated reduction factor into the MCMC sample of the rock for overall reduction, we can obtain the following: Figure 2 , Figure 3 , Figure 4 and Figure 5 The diagram shows the probability density distribution of rock mass mechanical parameters. Among them, the elastic modulus of the rock mass... E m The mean value is 26.6 GPa, and the standard deviation is 12.1 GPa; rock mass cohesionc and internal friction angle φ The mean values of the uniaxial compressive strength, the uniaxial tensile strength and the internal friction angle are 13.05 MPa, 42.23° and 0.50 MPa, respectively, and the standard deviations are 2.43 MPa, 10.71° and 0.22 MPa, respectively. The mean values and the standard deviations are shown in Table 3.

[0078] Table 3 Mean values and standard deviations of the mechanical parameters of the rock mass

[0079]

[0080] Step 8: Based on the probability distribution results of the mechanical parameters of the rock mass obtained in Step 7, the point estimation method is used to select the mean value ± standard deviation (± μ ) of each parameter probability distribution as the representative value point, and then combined to form 16 sets of rock mass mechanical parameter combination calculation schemes. σ Specifically, each joint rock mass mechanical parameter random variable usually takes two value points, which are generally one positive and negative standard deviation above and below the mean value. Since there are 4 random variables, there will be 2 4 = 16 calculation combinations, and the parameter permutation and combination schemes are shown in Table 4.

[0081] Table 4 Engineering joint rock mass mechanical parameter cases for numerical simulation

[0082]

[0083] Step 9: A 3D numerical model simulating the on-site excavation steps is established, and numerical simulation and rock mass stability analysis are carried out. The strain softening model is selected for the mechanical model, and the parameter values are selected from the parameter combination schemes obtained in Step 7. The key parts affecting the stability of the engineering joint rock mass (such as parts with large deformation or significant plastic zone) are identified, and the displacement and plastic zone depth data of the parts in 16 simulations are extracted. The probability distribution is fitted, and then the response behavior and stability of the engineering joint rock mass under excavation unloading are evaluated.

[0084]

[0085] ​​Specifically, according to the determined joint rock mass mechanical parameters, corresponding numerical simulation analysis is carried out to predict the stability behavior of the surrounding rock during the whole process of the powerhouse excavation. The FLAC3D is used to sequentially carry out the numerical simulation of the excavation unloading of the cavern rock mass, and the simulation obtains that the region with large deformation of the surrounding rock after the first layer excavation is concentrated in the middle and lower reaches of the arch shoulder and the arch bottom, and the deformation thereof exceeds 1 cm. In order to real-time master the excavation deformation of the surrounding rock, the multi-point displacement meters can be arranged by the pre-buried mode in the auxiliary caverns (such as the anchoring observation cavern, the drainage corridor and the like) around the powerhouse. The K0+050~080 tunnel section and the K0+120~150 tunnel section are selected, and the displacement data of the multi-point displacement meters in the tunnel section are recorded, as shown in Figure 6 The measured results and the simulation calculation results of the displacement values of the surrounding rock of the powerhouse after the first layer excavation are given, and it can be seen that the measured displacement monitoring results are within the numerical simulation calculation range. The average values of the displacements at D1, D2 and D3 after the first layer excavation are 3.08 mm, 8.73 mm and 10.79 mm respectively.

[0086] Further, considering the parameter uncertainty, a series of simulation is carried out according to the rock mass mechanical parameter combination scheme to obtain the cavern stability results under a series of different parameter value combinations, and then the average value and the standard deviation of the plastic zone depth can be obtained by using formula (1)~(2), so as to probabilistically evaluate the unloading response behavior and the stability caused by the cavern excavation. Figure 7 The probability distribution of the plastic zone depth of the surrounding rock at the specified position (corresponding to the monitoring hole position) of the first layer excavation of the cavern is given. It can be known from Figure 7 that the average values of the plastic zones of Z1, Z2, Z3 and Z4 after the first layer excavation of the cavern are 1.44 m, 0.30 m, 0.37 m and 0.27 m respectively, and the standard deviations thereof are 0.71 m, 0.14 m, 0.17 m and 0.11 m respectively.

[0087] Finally, the drilling camera and the acoustic wave test results of the plastic zone of the rock mass of the typical monitoring section are statistically processed, and the results are compared with the above numerical calculation results, as shown in Figure 8 It can be seen that the measured plastic zone depths of the drilling holes after the completion of the first layer excavation are within the simulation calculation range, and the average value deviation of the point estimation simulation results is small, and the calculation results are basically consistent with the actual situation.

[0088] Thus, the engineering joint rock mass stability probabilistic evaluation method considering the parameter value uncertainty in the embodiment of the present application is realized.

[0089] Based on the same inventive concept, the present application further provides an engineering joint rock mass unloading stability evaluation system, comprising:

[0090] The data acquisition module is configured to acquire the geological strength of the jointed rock mass at the target engineering site and the compression wave velocity value, the elastic modulus, the shear strength, the tensile strength and the uniaxial compressive strength of the rock block of the drill core at different depth sections.

[0091] The reduction factor calculation module is configured to establish an uncertainty regression equation of the compression wave velocity value and the elastic modulus of the rock block, randomly generate simulated elastic modulus based on the uncertainty regression equation, and statistically acquire the mean value of the simulated elastic modulus; randomly generate simulated shear strength and simulated tensile strength based on the shear strength and the tensile strength of the rock block, and respectively statistically acquire the mean values of the simulated shear strength and the simulated tensile strength; calculate the equivalent elastic modulus, the equivalent shear strength and the equivalent tensile strength of the rock mass by using the geological strength of the rock mass and the uniaxial compressive strength of the rock block; and determine the rock-mass mechanical parameter reduction factor by using the mean values of the simulated elastic modulus, the simulated shear strength and the simulated tensile strength of the rock block and the equivalent elastic modulus, the equivalent shear strength and the equivalent tensile strength of the rock mass.

[0092] The unloading stability evaluation module is configured to update the simulated elastic modulus, the simulated shear strength and the simulated tensile strength of the rock block by using the rock-mass mechanical parameter reduction factor to obtain the elastic modulus, the shear strength and the tensile strength of the rock mass; select the mean values and the standard deviations of the elastic modulus, the shear strength and the tensile strength of the rock mass as representative value points; arrange and combine all the representative value points; perform simulation calculation based on the representative value points of different combinations, and evaluate the unloading stability of the jointed rock mass by using the calculation results.

[0093] The computer device comprises a processor, an internal bus, a network interface, a memory and a non-volatile memory at the hardware level, and of course, other hardware required by business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs to realize the above-provided engineering jointed rock mass unloading stability evaluation method.

[0094] The computer device comprises a processor, an internal bus, a network interface, a memory and a non-volatile memory at the hardware level, and of course, other hardware required by business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs to realize the above-provided engineering jointed rock mass unloading stability evaluation method.

[0095] The specific limitations of the engineering jointed rock mass unloading stability evaluation method calculation system can be referred to the limitations of the engineering jointed rock mass unloading stability evaluation method provided above, which will not be described here. The various modules in the above engineering jointed rock mass unloading stability evaluation system can be realized by software, hardware and combinations thereof in whole or in part. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above various modules.

[0096] Any combination of the technical features in the above embodiments can be made, for the sake of brevity, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combination of the technical features does not exist contradictory, it should be considered within the scope of the present disclosure. In addition, the above described embodiments only express several embodiments of the present application, the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be noted that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of the present application. Therefore, the scope of protection of the patent of the present application should be subject to the appended claims.

Claims

1. A method for evaluating the unloading stability of jointed rock masses in engineering, characterized in that, include: The geological strength of the jointed rock mass at the target project site and the compressive wave velocity, elastic modulus, shear strength, tensile strength and uniaxial compressive strength of the drill core blocks at different depths were obtained respectively. An uncertainty regression equation is established for the compressive wave velocity and elastic modulus of the rock block. Based on the uncertainty regression equation, a simulated elastic modulus is randomly generated, and the mean and probability distribution of the simulated elastic modulus are statistically analyzed. Based on the shear strength and tensile strength of the rock block, simulated shear strength and tensile strength are randomly generated, and the mean and probability distribution of the simulated shear strength and tensile strength are statistically analyzed respectively. Using the geological strength of the rock mass and the uniaxial compressive strength of the rock block, the equivalent elastic modulus, equivalent shear strength, and equivalent tensile strength of the rock mass are calculated. Using the mean and probability distribution of the simulated elastic modulus, simulated shear strength, and simulated tensile strength of the rock block and the equivalent elastic modulus, equivalent shear strength, and equivalent tensile strength of the rock mass, the reduction coefficient of the rock-rock mass mechanical parameters is determined. The simulated elastic modulus, simulated shear strength, and simulated tensile strength of the rock block are updated using the rock-rock mass mechanical parameter reduction coefficients to obtain the elastic modulus, shear strength, and tensile strength of the rock mass; the mean and standard deviation of the elastic modulus, shear strength, and tensile strength of the rock mass are selected as representative value points respectively; Arrange and combine all representative value points; Simulation calculations were performed based on representative value points with different combinations, and the unloading stability of jointed rock masses was evaluated using the calculation results.

2. The method for evaluating the unloading stability of jointed rock mass according to claim 1, characterized in that, Compression wave velocities were measured segment by segment from borehole core blocks at different depths of the jointed rock mass at the target engineering site using a sonic instrument. The elastic modulus and uniaxial compressive strength of the borehole core blocks at different depths of the jointed rock mass at the target engineering site were obtained through indoor uniaxial compression tests. The shear strength and tensile strength of the borehole core blocks at different depths of the jointed rock mass at the target engineering site were obtained through indoor triaxial compression tests and Brazilian splitting tests. The shear strength includes cohesion and internal friction angle.

3. The method for evaluating the unloading stability of jointed rock mass according to claim 1, characterized in that, The random generation of the simulated elastic modulus, simulated shear strength, and simulated tensile strength refers to the random sampling calculation method, specifically the Markov chain Monte Carlo sampling method.

4. The method for evaluating the unloading stability of jointed rock mass according to claim 1, characterized in that, Based on the Hoek-Brown criterion, the equivalent elastic modulus, equivalent shear strength, and equivalent tensile strength of the rock mass are calculated using the geological strength of the rock mass and the uniaxial compressive strength of the rock block.

5. The method for evaluating the unloading stability of jointed rock mass according to claim 1, characterized in that, Using the Rosenblueth point estimation method, the mean and standard deviation of the elastic modulus, shear strength and tensile strength of the rock mass were selected as representative points. All the representative points were arranged and combined to obtain 16 combination schemes.

6. The method for evaluating the unloading stability of jointed rock mass according to claim 1, characterized in that, The simulation calculations based on representative value points with different combinations, and the evaluation of the unloading stability of jointed rock masses using the calculation results, include: Establish a strain softening model that can describe the deterioration characteristics of rock mass during the engineering rock mass excavation process; Sixteen combination schemes were sequentially introduced into the strain softening model to identify key components affecting the stability of the jointed rock mass. Displacement and plastic zone depth data of the key components were extracted in the 16 simulations, and the probability distribution of displacement and plastic zone depth data was fitted. The probability distribution is used to evaluate the response behavior and stability of jointed rock masses under excavation unloading.

7. The method for evaluating the unloading stability of jointed rock mass according to claim 1, characterized in that, Before obtaining the compression wave velocity, elastic modulus, shear strength, tensile strength and uniaxial compressive strength of the drill core blocks of different depth sections of the jointed rock mass at the target engineering site, the method further includes drilling and core sampling of the jointed rock mass at different depth sections of the target engineering site. The number of boreholes is determined based on the on-site rock mass quality zoning. In areas where the rock mass quality grading results are consistent, there should be no fewer than two boreholes, and the borehole depth should be no less than 10 meters. The borehole cores should be coded and marked accordingly based on their depth.

8. A system for evaluating the unloading stability of jointed rock masses in engineering applications, characterized in that, include: The data acquisition module is used to obtain the geological strength of the jointed rock mass at the target project site and the compression wave velocity, elastic modulus, shear strength, tensile strength and uniaxial compressive strength of the drill core blocks at different depths. The reduction factor calculation module is used to establish an uncertainty regression equation for the compressive wave velocity and elastic modulus of the rock block; based on the uncertainty regression equation, a simulated elastic modulus is randomly generated, and the mean and probability distribution of the simulated elastic modulus are statistically analyzed; based on the shear strength and tensile strength of the rock block, simulated shear strength and simulated tensile strength are randomly generated, and the mean and probability distribution of the simulated shear strength and simulated tensile strength are statistically analyzed respectively; using the geological strength of the rock mass and the uniaxial compressive strength of the rock block, the equivalent elastic modulus, equivalent shear strength, and equivalent tensile strength of the rock mass are calculated; using the mean and probability distribution of the simulated elastic modulus, simulated shear strength, and simulated tensile strength of the rock block and the equivalent elastic modulus, equivalent shear strength, and equivalent tensile strength of the rock mass, the reduction factor of the rock-rock mass mechanical parameters is determined; The unloading stability evaluation module is used to update the simulated elastic modulus, simulated shear strength, and simulated tensile strength of the rock block using the reduction coefficient of the rock-rock mass mechanical parameters, so as to obtain the elastic modulus, shear strength, and tensile strength of the rock mass; the mean and standard deviation of the elastic modulus, shear strength, and tensile strength of the rock mass are selected as representative value points respectively; All representative value points are arranged and combined; simulation calculations are performed based on different combinations of representative value points, and the unloading stability of jointed rock mass is evaluated using the calculation results.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is loaded by the processor, it is able to perform the steps of the method according to any one of claims 1 to 7.

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