Tunnel surrounding rock parameter inversion method and system under multi-disaster coupling

Abstract

The invention discloses a tunnel surrounding rock parameter inversion method and system under multi-disaster coupling, and relates to the technical field of tunnel engineering. Comprising the steps of obtaining on-site soft rock and preparing a soft rock test piece; grouping the soft rock test pieces and performing indoor test; obtaining basic static mechanical parameters of the on-site soft rock and a test stress-strain curve under the dynamic action according to an indoor test result; determining constitutive parameters to be inverted, and obtaining upper and lower limits of each parameter value according to experience; obtaining a target function based on the test stress-strain curve and the simulated stress-strain curve; and performing inversion through a particle swarm optimization algorithm to obtain tunnel surrounding rock inversion parameters. According to the method, the test cost of RHT constitutive parameter calibration is remarkably reduced, and the inverted constitutive parameters are highly matched with the actual engineering. A simulation result based on the inversion parameters can provide powerful reference basis for blasting construction design.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel engineering, and particularly to a method and system for inverse analysis of tunnel surrounding rock parameters under multi-hazard coupling. Background Art

[0002] During the construction of soft rock tunnels, the surrounding rock exhibits complex mechanical properties and poor stability, specifically manifested as non-linearity, creep, and high sensitivity to stress. Especially when facing the coupling effects of multiple disaster factors (such as bedding bias pressure, goaf, and water-rich karst), these properties will deteriorate further, thus posing a severe challenge to the safe construction of tunnels and the accurate assessment of the stability of the surrounding rock. This complexity not only increases the difficulty of blasting design and construction operations, but also often leads to over-excavation or under-excavation of the heading face profile, as well as the loss of the stability of the surrounding rock, thereby triggering potential safety hazards and construction risks. In order to achieve scientific optimization of the construction design, there is an urgent need for a method that can accurately inverse the dynamic mechanical parameters of the surrounding rock under complex environments to simulate its mechanical behavior.

[0003] The RHT (Riedel-Hiermaier-Thoma) constitutive model, as a validated dynamic mechanical model, can accurately describe the hardening, softening, and failure processes of the surrounding rock under multi-stress states, and thus has been widely used in the simulation of mechanical responses under impact and explosion loads. However, this model involves up to 34 parameters, and its parameter calibration process is extremely complex and highly sensitive to test data, which directly affects the accuracy and reliability of the simulation results. In the traditional parameter calibration process, the determination of RHT parameters mainly relies on laboratory tests, including triaxial tests, shear tests, Brazilian splitting tests, and Hopkinson bar tests, etc. But these test methods not only take a long time and are costly, but also it becomes particularly difficult to obtain a sufficient amount of test data under complex geological conditions, which further restricts the practical application of the RHT model in tunnel engineering under multi-hazard coupling. Summary of the Invention

[0004] The purpose of the present invention is to propose a method and system for inverse analysis of tunnel surrounding rock parameters under multi-hazard coupling, which solves the problems of difficult acquisition of test data and high error sensitivity in the traditional calibration process, and significantly improves the efficiency and accuracy of parameter calibration under complex conditions.

[0005] According to the first aspect of the embodiments of the present disclosure, a method for inverse analysis of tunnel surrounding rock parameters under multi-hazard coupling is provided, including the following steps:

[0006] Obtain in-situ soft rock and prepare soft rock specimens;

[0007] Group the soft rock specimens and conduct laboratory tests;

[0008] Obtain the basic static mechanical parameters of in-situ soft rock and the test stress-strain curve under dynamic action according to the indoor test results;

[0009] Determine the constitutive parameters to be inverted, and obtain the upper and lower limits of the values of each parameter according to experience;

[0010] Obtain the objective function based on the test stress-strain curve and the simulated stress-strain curve;

[0011] Invert through the particle swarm optimization algorithm to obtain the inverted parameters of the tunnel surrounding rock.

[0012] In one embodiment, the soft rock specimens are divided into a uniaxial compression test group, a triaxial compression test group, a Brazilian splitting test group, a shear test group, and an SHPB test group. Among them, the uniaxial compression test group, the triaxial compression test group, the Brazilian splitting test group, and the shear test group are used to obtain the basic static mechanical parameters, and the SHPB test group is used to obtain the test stress-strain curve under dynamic action.

[0013] In one embodiment, the basic static mechanical parameters include material density ρ, uniaxial compressive strength f c , tensile strength f t , shear strength f s , initial porosity α 0 , Poisson's ratio υ, elastic modulus E, transverse wave velocity, and longitudinal wave velocity. After calibrating the basic static mechanical parameters, it is necessary to determine the p-α state equation parameters and the parameters characterizing the failure surface.

[0014] In one embodiment, the test stress-strain curve under dynamic action is obtained by the two-wave method:

[0015]

[0016] where ε t = ε r + ε i ; ε i is the incident strain; A e is the cross-sectional area of the bar; A s is the cross-sectional area of the specimen; σ s is the stress; L s is the length of the rock specimen; C e is the wave velocity of the incident wave in the impact bar; ε t is the transmitted strain; ε s is the strain; is the strain rate; the stress obtained in this step is the engineering stress, and when the numerical simulation result is obtained, the incident stress wave needs to be converted into the true stress σ Ture :

[0017] σ Ture = σs (1 + ε t )。

[0018] In one embodiment, the constitutive parameters to be inverted include A, N, f t * , Q 0 , ξ, D 1 , A f , n f , p comp and n, where g c * is the compression yield surface parameter, f s * , f t * is the shear modulus, ξ is the shear modulus reduction coefficient, D 1 is the damage evolution parameter, is the minimum equivalent plastic strain, A f , n f are the residual stress surface parameters, p comp is the pressure at material pore compaction, and n is the compression index.

[0019] In one embodiment, the objective function is obtained by Method 1 or Method 2:

[0020] Method 1: Take the difference between the experimental stress-strain curve and the simulated stress-strain curve , and then use the root mean square error as the evaluation index:

[0021]

[0022] J M = ω 1 f 1 (p) + ω 2 f 2 (p)

[0023] where J M is the multi-objective optimization function, ω 1 , ω 2 are the optimization weights; p = [p 1 , p 2 ,..., p 13 are the constitutive parameters to be inverted, represents the stress-strain value obtained by simulation under the given inversion parameter p, represents the stress value obtained by experiment, and ε i corresponds to the strain;

[0024] Method 2: Using the experimental stress-strain curve and the simulated stress-strain curve The absolute value of the difference in the integrated area is taken as the objective function:

[0025]

[0026] where J S is the single-objective optimization function, [ε min , ε max is the value range of the stress-strain curve; since the actual data is discrete, the trapezoidal method is used to obtain the area:

[0027]

[0028] The final objective function is expressed as:

[0029] J S = |A exp - A sim |.

[0030] In one of the embodiments, inversion is performed through the particle swarm optimization algorithm to obtain the inversion parameters of the tunnel surrounding rock, including:

[0031] Assign the parameters to be inverted and pass them to the objective function; use the *PARAMETER keyword to define the parameters to be inverted, and use the "&" symbol to reference the parameters to be inverted in the k file to achieve dynamic modification of the parameters;

[0032] Use system(command) in Matlab to call the LS-DYNA solver, where command = is replaced with the installation path of the LS-DYNA solver root directory; according to the specific positions of the strain gauges in the SHPB test, set the output of the element results, and the output point interval is f is the sampling frequency, and the specific keyword is: *DATABASE_HISTORY_SOLID_ID, and the specific output file is the binary binout file;

[0033] After the simulation results are output, use the written m function file to read the binary binout file in LS-DYNA, obtain the strain values of the output points, and obtain the signed equivalent strain ε vM :

[0034]

[0035] where, ε x , ε y , ε z respectively represent the normal strains in the x, y, and z directions, γ xy , γ yz , γzx They respectively represent the shear strains in the xy, yz, and zx planes, and Sign is the sign function; plot ε vM The incident wave, reflected wave, and transmitted wave will be obtained;

[0036] For the three waves obtained, use the max command to find the maximum values corresponding to the incident wave and the reflected wave. The wave speed C of the rod in the simulation is the incident rod length divided by the time difference corresponding to the wave peak and wave trough. 1 :

[0037] C 1 = L / (T R - T I )

[0038] where L is the incident rod length; T R is the time corresponding to the minimum value of the incident wave; T I is the time corresponding to the maximum value of the reflected wave; determine the arrival times t 1 , t 2 , t 3 :

[0039]

[0040] t 2 = t 1 + (2L 1 / C 1 )

[0041] t 3 = t 1 + (L 1 + L 2 ) / C 1 + L s / C s

[0042] where L 1 , L 2 are respectively the distances from the strain gauge to the left and right ends of the specimen; C s is the wave speed of the stress wave in the specimen, and L s is the length of the specimen;

[0043] After determining the arrival times of the three waves ε i , ε r , and ε t , judge whether the three waves are aligned by left and right translation. The evaluation index is:

[0044] min[ε t - (ε i+ΔT + ε r+ΔT )]

[0045] where ΔT is the left - right translation value during the alignment of the wavefronts. After the minimum value of the three waves after alignment meets the conditions, the two - wave method is selected to plot the stress - strain curve.

[0046] According to the second aspect of the embodiments of the present disclosure, a tunnel surrounding rock parameter inversion system under multi - hazard coupling is provided, including:

[0047] A specimen preparation module, which obtains in - situ soft rock and prepares soft rock specimens;

[0048] An experiment module, which groups the soft rock specimens and conducts indoor experiments;

[0049] A parameter and curve acquisition module, which obtains the basic static mechanical parameters of the in - situ soft rock and the experimental stress - strain curve under dynamic action according to the results of the indoor experiments;

[0050] A value - taking module, which determines the constitutive parameters to be inverted and obtains the upper and lower limits of the values of each parameter according to experience;

[0051] A target function determination module, which obtains the target function based on the experimental stress - strain curve and the simulated stress - strain curve;

[0052] An inversion module, which performs inversion through the particle swarm optimization algorithm to obtain the inverted parameters of the tunnel surrounding rock.

[0053] According to the third aspect of the embodiments of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program running on the memory. When the processor executes the program, it implements the method for inverting the parameters of the tunnel surrounding rock under multi - hazard coupling.

[0054] According to the fourth aspect of the embodiments of the present disclosure, a computer - readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, it implements the method for inverting the parameters of the tunnel surrounding rock under multi - hazard coupling.

[0055] The above - mentioned technical solutions adopted by the present invention, compared with the prior art, have the following advantages: (1) By using MATLAB to call the LS - DYNA solver, it effectively avoids the cumbersome and repetitive numerical simulation work in the process of determining the constitutive parameters, and realizes the simplification of the constitutive parameter inversion.

[0056] (2) The present invention significantly reduces the experimental cost of calibrating the RHT constitutive parameters, and the inverted constitutive parameters are highly matched with the engineering practice. The simulation results based on these inverted parameters can provide a strong reference basis for the blasting construction design.

[0057] (3) The present invention is not only applicable to the inversion of RHT constitutive parameters, but also applicable to the constitutive models of multi-parameter ductile-brittle materials that also require a large number of experimental calibrations, such as HJC, JH / JH-2, CSCM, etc. This provides a new solution for the parameter calibration and inversion of such complex models. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The accompanying drawings forming a part of this application are used to provide a further understanding of this application. The schematic embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation of this application.

[0059] Figure 1 It is a flowchart of the method for inverting the parameters of tunnel surrounding rock under multi-disaster coupling;

[0060] Figure 2 It is a schematic diagram of the combined simulation inversion of MATLAB and LS-DYNA;

[0061] Figure 3 It is a schematic diagram of the three-wave simulation results;

[0062] Figure 4 It is a schematic diagram of the three-wave alignment process. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] The present disclosure will be further described below in conjunction with the accompanying drawings and embodiments.

[0064] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanations of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs.

[0065] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to this application. As used herein, unless the context clearly indicates otherwise, the singular forms are also intended to include the plural forms. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0066] Note that the flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of methods and systems according to various embodiments of the present disclosure. It should be noted that each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code may include one or more executable instructions for implementing the logical functions specified in the respective embodiments. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. Similarly, it should be noted that each block in the flowchart and / or block diagram, and the combinations of blocks in the flowchart and / or block diagram, may be implemented using a dedicated hardware-based system that performs the specified functions or operations, or may be implemented using a combination of dedicated hardware and computer instructions.

[0067] Embodiment 1:

[0068] Please refer to Figure 1 , this embodiment provides a method for inverse analysis of tunnel surrounding rock parameters under multi-disaster coupling, and its main steps are as follows:

[0069] Step 1. Obtain in-situ soft rock and prepare soft rock specimens;

[0070] Specifically, take 5 groups of in-situ soft rock specimens, with 5 specimens in each group, for a total of 25 specimens; label them as groups I to V respectively.

[0071] Step 2. Group the soft rock specimens and conduct laboratory tests;

[0072] Specifically, group I is for uniaxial compression test, group II is for triaxial compression test, group III is for Brazilian splitting test, group IV is for shear test, and group V is for SHPB test.

[0073] Step 3. Obtain the basic static mechanical parameters of the in-situ soft rock and the test stress-strain curve under dynamic action according to the laboratory test results;

[0074] Specifically, obtain the basic static mechanical parameters according to the test results of groups I-IV, and group V is used to obtain the test stress-strain curve under dynamic action. The basic static mechanical parameters include material density ρ, uniaxial compressive strength f c , tensile strength f t , shear strength f s , initial porosity α 0 , Poisson's ratio υ, elastic modulus E, shear wave c s wave velocity and longitudinal wave c p wave velocity. After calibrating the basic static mechanical parameters, determine the p-α state equation parameters and the parameters characterizing the failure surface;

[0075] Shear modulus G, f s * , f t * , the shear modulus reduction coefficient ξ is obtained according to the following formula. It should be noted that the parameters with "*" in the formula are dimensionless parameters after normalization:

[0076] G = E / 2(1 + υ)

[0077] f s * = f s / f c

[0078] f t * = f t / f c

[0079] The shear reduction coefficient ξ takes values between 0 and 1, and takes 0.8 in this embodiment.

[0080] The p-α equation of state mainly includes: the initial density ρ of the material 0 , the initial porosity α 0 , the pressure p when the material starts to crush el , the pressure p when the material pores are compacted comp ;

[0081] p = A 1 μ + A 2 μ 2 + A 3 μ 3 +(B 0 + B 1 μ)ρ 0 e, μ > 0

[0082] p = T 1 μ + T 2 μ + B 0 ρ 0 e, μ < 0

[0083]

[0084] In the above formula, A 1 、A 2 、A 3 are Hugoniot polynomial coefficients; B 0 、B 1 、T 1 and T 2 are equation of state parameters. When μ is greater than 0, it means the material is compressed; when μ is less than 0, it means the material volume expands, and e is the initial internal energy of the material.

[0085]

[0086] A 2 = A 1 (2s - 1)

[0087] A 3 = A 1 (3s 2 - 4s + 1)

[0088] B 0 = B 1 = 2s - 1

[0089] T 1 = A 1 、T 2 = 0

[0090] In the formula, c 0 is the sound velocity in the rock mass when the pressure is zero, s is an empirical parameter and can be taken as 0.95, p el = f c / 3; p comp is taken as 6000 MPa;

[0091] The parameters characterizing the failure surface specifically include:

[0092] a. Failure surface parameters: A, N, Q 0 , B, β c , β t ,

[0093] Among them, A and N can be fitted according to the following formula based on the results of triaxial compression tests and the failure surface failure equation:

[0094]

[0095] In the formula, represents the normalized failure value, and is the failure surface equation related to P * and F r . P * is the normalized pressure. Under quasi-static conditions, F r = 1; When given and P * , the values of A and N can be fitted.

[0096] Q 0 is the initial tensile-compressive meridian ratio parameter and can be taken as 0.68. B is the Lode angle correlation coefficient and can be taken as 0.0105; The compressive strain rate exponent β c = 4 / (20 + 3f c ), and the tensile strain rate exponent β t = 2 / (20 + f c ). The initial values are given by the model.

[0097] b. Elastic limit surface parameters: g c * is the compression yield surface parameter; f c.el is the uniaxial compression elastic limit stress; g t * is the tensile yield surface parameter; f t.el is the uniaxial tensile elastic limit stress.

[0098] c. Residual stress surface parameters: A f , n f

[0099]

[0100] where A f is the residual stress intensity, n f is the residual stress intensity index

[0101] d. Damage evolution parameters: D 1 , D 2 ,

[0102]

[0103] In the above formula, when the rock is completely damaged, D 2 = 1, p * = 1 / 6, the minimum equivalent plastic strain

[0104] The constitutive parameters calibrated by the above steps are adjusted with the parameters determined by the experiment as the initial values for subsequent parameter inversion.

[0105] Preferably, based on the test data measured in Test V, the test stress-strain curve under dynamic action is obtained by the two-wave method:

[0106]

[0107] where ε t = ε r + ε i ; ε i is the incident strain; A e is the cross-sectional area of the bar; A s is the cross-sectional area of the specimen; σ s is the stress; L s is the length of the rock specimen; C e is the wave velocity of the incident wave in the impact bar; ε t is the transmitted strain; ε sis strain; is strain rate; the stress obtained in this step is engineering stress. When the numerical simulation results are used, the incident stress wave needs to be converted into the true stress σ Ture :

[0108] σ Ture = σ s (1 + ε t )

[0109] Step 4. Determine the constitutive parameters to be inverted, and obtain the upper and lower limits of the values of each parameter according to experience;

[0110] After simulating with the constitutive parameters determined above, a simulated stress-strain curve will be obtained, but this curve usually has a large error from the experimental stress-strain curve. To avoid increasing experiments or manually correcting parameters. Select 13 relatively sensitive and complex parameters: A, N, f s * , f t * , Q 0 , ξ, D 1 , A f , n f , p comp and n are set as the parameters to be inverted, where n is the compression index.

[0111] Table 1 Upper and lower limits of inversion parameters

[0112]

[0113] Step 5. Obtain the objective function based on the experimental stress-strain curve and the simulated stress-strain curve;

[0114] a. Take the difference between the experimental stress-strain curve and the simulated stress-strain curve , and then use the root mean square error as the evaluation index:

[0115]

[0116] J M = ω 1 f 1 (p) + ω 2 f 2 (p)

[0117] where J M is the multi-objective optimization function, ω 1 、ω 2 are the optimization weights. Since stress and strain are in one-to-one correspondence, both are taken as 0.5 in this embodiment; p = [p 1 , p 2,...,p 13 are the 13 constitutive parameters to be optimized, represents the stress-strain values obtained by simulation under the given inversion parameter p, represents the stress value obtained from the test, ε i corresponding strain;

[0118] b. Using the test stress-strain curve and the simulated stress-strain curve The absolute value of the difference in the integral area is taken as the objective function:

[0119]

[0120] where J S is the single-objective optimization function, [ε min , ε max is the value range of the stress-strain curve; since the actual data is discrete, the trapezoidal method is used to obtain the area:

[0121]

[0122] The final objective function is expressed as:

[0123] J S = |A exp - A sim |

[0124] The objective function determined by the above equation is a single-objective function. Since the stress-strain curve obtained by numerical simulation does not correspond one-to-one with the points in the test, the 'interp' function is needed to ensure the matching of the corresponding numerical points.

[0125] Step 6. Perform inversion through the particle swarm optimization algorithm to obtain the inversion parameters of the tunnel surrounding rock.

[0126] Step 6.1 After setting the upper and lower limits of the parameters, set the initial population number of the particle swarm to 30 and the number of iterations to 50 times, and assign the parameters to be inverted to the objective function; use the *PARAMETER keyword to define the parameters to be inverted, and use the "&" symbol to reference the parameters to be inverted in the k file to achieve dynamic modification of the parameters.

[0127] Step 6.2 Use system(command) in Matlab to call the LS-DYNA software, where command needs to be replaced with the installation path of the root directory of the LS-DYNA software; according to the specific positions of the strain gauges in the SHPB test, set the output of the element results, and the output point interval is f is the sampling frequency, and the specific keyword is: *DATABASE_HISTORY_SOLID_ID. The specific output file is the binary binout file. For the interaction process between software, please refer to Figure 2 ;

[0128] After the simulation results are output in Step 6.3, use the written m function file to read the binary binout file in LS-DYNA, obtain the strain values at the output points, and calculate the signed equivalent strain ε vM :

[0129]

[0130] where ε x , ε y , ε z represent the normal strains in the x, y, and z directions respectively, and γ xy , γ yz , γ zx represent the shear strains in the xy, yz, and zx planes respectively. Sign is the sign function; plot ε vM The incident wave, reflected wave, and transmitted wave will be obtained, as Figure 3 shown; the stress-strain curve can be calculated by the two-wave method only after the three waves are aligned.

[0131] Please refer to Figure 4 , which mainly realizes the functions of three-wave separation and automatic wavehead alignment; for the calculated three waves, use the max command to find the maximum values corresponding to the incident wave and the reflected wave. The wave speed C of the rod in the simulation is the incident rod length divided by the time difference corresponding to the wave peak and wave valley 1 :

[0132] C 1 = L / (T R - T I )

[0133] where L is the incident rod length; T R is the time corresponding to the minimum value of the incident wave; T I is the time corresponding to the maximum value of the reflected wave; determine the wavehead times t 1 , t 2 , t 3 :

[0134]

[0135] t 2 = t 1 + (2L 1 / C 1 )

[0136] t3 = t 1 + (L 1 + L 2 ) / C 1 + L s / C s

[0137] where L 1 , L 2 are the distances from the strain gauges to the left and right ends of the specimen respectively; C s is the wave velocity of the stress wave in the specimen, and L s is the length of the specimen;

[0138] Step 6.5 Determine ε i , ε r and ε t After the wavefronts of the three waves, judge whether the three waves are aligned by translating left and right. The evaluation index is:

[0139] min[ε t - (ε i+ΔT + ε r+ΔT )]

[0140] where ΔT is the left - right translation value during the alignment of the wavefronts. When the minimum value of the three aligned waves meets the condition, the two - wave method can be selected to draw the stress - strain curve.

[0141] The implementation processes described above are all completed in MATLAB, and this process is also applicable to other programming software such as Python.

[0142] Example 2:

[0143] This example provides a tunnel surrounding rock parameter inversion system under multi - disaster coupling, including:

[0144] A specimen preparation module to obtain in - situ soft rock and prepare soft rock specimens;

[0145] A test module to group the soft rock specimens and conduct laboratory tests;

[0146] A parameter and curve acquisition module to obtain the basic static mechanical parameters of the in - situ soft rock and the test stress - strain curve under dynamic action according to the laboratory test results;

[0147] A value - taking module to determine the constitutive parameters to be inverted and obtain the upper and lower limits of the values of each parameter according to experience;

[0148] A target function determination module to obtain the target function based on the test stress - strain curve and the simulated stress - strain curve;

[0149] An inversion module to perform inversion through the particle swarm optimization algorithm to obtain the tunnel surrounding rock inversion parameters.

[0150] Embodiment 3:

[0151] An electronic device includes a memory, a processor, and a computer program running on the memory. When the processor executes the program, it implements the above-mentioned method for inverting tunnel surrounding rock parameters under multi-disaster coupling, including:

[0152] Obtain in-situ soft rock and prepare soft rock specimens;

[0153] Group the soft rock specimens and conduct laboratory tests;

[0154] Obtain the basic static mechanical parameters of the in-situ soft rock and the test stress-strain curve under dynamic action according to the laboratory test results;

[0155] Determine the constitutive parameters to be inverted, and obtain the upper and lower limits of the values of each parameter according to experience;

[0156] Obtain the objective function based on the test stress-strain curve and the simulated stress-strain curve;

[0157] Perform inversion through the particle swarm optimization algorithm to obtain the inverted parameters of the tunnel surrounding rock.

[0158] Embodiment 4:

[0159] A computer-readable storage medium stores a computer program. When the program is executed by a processor, it implements the above-mentioned method for inverting tunnel surrounding rock parameters under multi-disaster coupling, including:

[0160] Obtain in-situ soft rock and prepare soft rock specimens;

[0161] Group the soft rock specimens and conduct laboratory tests;

[0162] Obtain the basic static mechanical parameters of the in-situ soft rock and the test stress-strain curve under dynamic action according to the laboratory test results;

[0163] Determine the constitutive parameters to be inverted, and obtain the upper and lower limits of the values of each parameter according to experience;

[0164] Obtain the objective function based on the test stress-strain curve and the simulated stress-strain curve;

[0165] Perform inversion through the particle swarm optimization algorithm to obtain the inverted parameters of the tunnel surrounding rock.

[0166] Those skilled in the art should understand that the above-mentioned modules or steps of the present disclosure can be implemented by a general-purpose computer device. Optionally, they can be implemented by program codes executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module for implementation. The present disclosure is not limited to any specific combination of hardware and software.

[0167] The above are only the preferred embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0168] Although the specific implementation manners of the present disclosure have been described above in conjunction with the accompanying drawings, it is not a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications or deformations that can be made without creative efforts by those skilled in the art on the basis of the technical solution of the present disclosure are still within the protection scope of the present disclosure.

Claims

1. A tunnel surrounding rock parameter inversion method under multiple disaster coupling, characterized in that: The following steps are involved: Obtain soft rock on site and prepare soft rock specimens; The soft rock specimens were grouped and subjected to indoor tests; According to the indoor test results, the basic static mechanical parameters of soft rock on site and the test stress-strain curve under dynamic action are obtained; Determine the constitutive parameters to be inverted, and obtain the upper and lower limits of each parameter based on experience; Obtaining the objective function based on the experimental stress-strain curve and the simulated stress-strain curve; The inversion parameters of the tunnel surrounding rock are obtained by using the particle swarm optimization algorithm.

2. According to claim 1, a tunnel surrounding rock parameter inversion method under multiple disaster coupling is characterized in that: The soft rock specimens are divided into uniaxial compression test group, triaxial compression test group, Brazilian splitting test group, shear test group and SHPB test group. The uniaxial compression test group, triaxial compression test group, Brazilian splitting test group and shear test group are used to obtain basic static mechanical parameters, and the SHPB test group is used to obtain the experimental stress-strain curve under dynamic action.

3. According to claim 2, a tunnel surrounding rock parameter inversion method under multiple disaster coupling is characterized in that: The basic static mechanical parameters include material density ρ, uniaxial compressive strength f c , tensile strength f t , shear strength f s , initial porosity α0, Poisson's ratio υ, elastic modulus E, shear wave velocity and longitudinal wave velocity, after completing the calibration of basic static mechanical parameters, determine the p-α state equation parameters and the parameters characterizing the failure surface.

4. According to claim 1, a tunnel surrounding rock parameter inversion method under multiple disaster coupling is characterized in that: The experimental stress-strain curve under dynamic action is obtained by the two-wave method: where ε t =ε r +ε i ; ε i is the incident strain; A e A is the cross-sectional area of ​​the rod; s is the cross-sectional area of ​​the specimen; σ s is stress; L s is the length of the rock specimen; C e is the wave velocity of the incident wave in the impact rod; ε t is the transmission strain; ε s For strain; is the strain rate; the stress obtained in this step is the engineering stress. When simulating the results numerically, the incident stress wave needs to be converted into the true stress σ Ture : s Ture =s s (1+e t )。 5. According to claim 1, a tunnel surrounding rock parameter inversion method under multiple disaster coupling is characterized in that: The constitutive parameters to be inverted include A, N, f s * , f t * , Q0, ξ,D1, A f , n f , p comp and n, where g c * is the compressive yield surface parameter, f s * ,f t * is the shear modulus, ξ is the shear modulus reduction factor, D1 is the damage evolution parameter, is the minimum equivalent plastic strain, A f ,n f is the residual stress surface parameter, p comp is the pressure when the material pores are compacted, and n is the compression index.

6. According to claim 1, a tunnel surrounding rock parameter inversion method under multiple disaster coupling is characterized in that: The objective function is obtained by method 1 or method 2: Method 1: Test stress-strain curve Simulated stress-strain curve Make the difference, and then use the root mean square error as the evaluation indicator: J M =ω1f1(p)+ω2f2(p) Among them J M is a multi-objective optimization function, ω1 and ω2 are optimization weights; p = [p1, p2, ..., p 13 ] is the constitutive parameter to be inverted, represents the stress-strain value obtained by simulation under a given inversion parameter p, represents the stress value obtained from the test, ε i Corresponding strain; Method 2: Test stress-strain curve Simulated stress-strain curve The absolute value of the difference in integrated area is the objective function: Among them J S is a single objective optimization function, [ε min ,ε max ] is the range of stress-strain curve; since the actual data is discrete, the trapezoidal method is used to obtain the area: The final objective function is expressed as: I S =|A exp -HAS sim | 7. The method for inversion of tunnel surrounding rock parameters under multiple disaster coupling according to claim 1, characterized in that: The inversion parameters of the tunnel surrounding rock are obtained by using the particle swarm optimization algorithm, including: Pass the parameter to be inverted to the objective function; use the *PARAMETER keyword to define the parameter to be inverted, and use the "&" symbol in the k file to reference the parameter to be inverted to achieve dynamic modification of the parameter; Use system(command) in Matlab to call the LS-DYNA solver, where command is replaced by the root directory installation path of the LS-DYNA solver; according to the specific location of the strain gauge in the SHPB test, set the unit result output, and the output point interval is f is the sampling frequency, the specific keyword is: *DATABASE_HISTORY_SOLID_ID, and the specific output file is a binary binout file; After the simulation results are output, the prepared m function file is used to read the binary binout file in LS-DYNA, obtain the strain value of the output point, and obtain the signed equivalent strain ε vM : Among them, ε x ,ε y ,ε z denote the normal strain in the x, y, and z directions, respectively, xy ,γ yz ,γ zx Respectively represent the shear strain in the xy, yz, and zx planes, Sign is the sign function; plot ε vM The incident wave, reflected wave and transmitted wave will be obtained; For the three waves obtained, use the max command to find the maximum values ​​corresponding to the incident wave and the reflected wave. The length of the incident rod divided by the time difference corresponding to the crest and trough is the wave velocity C1 of the rod in the simulation: C1=L / (T R -T I ) Where, L is the incident rod length; T R is the time corresponding to the minimum value of the incident wave; T I is the time corresponding to the maximum value of the reflected wave; determine the wave head times t1, t2, and t3 of the incident wave, reflected wave, and transmitted wave: t2=t1+(2L1 / C1) <h2 style=";text-align:left;direction:ltr">t3 = t1 + (L1 + L2) / C1 + L<h2 style=";text-align:left;direction:ltr"> s <h2 style=";text-align:left;direction:ltr"> / C<h2 style=";text-align:left;direction:ltr"> s Where L1 and L2 are the distances from the strain gauge to the left and right ends of the specimen respectively; C s is the wave velocity of stress wave in the specimen, L s is the length of the specimen; Determine ε i , ε r and ε t After the three waves have reached their heads, we can judge whether the three waves are aligned by translating left and right. The evaluation indicators are: min[e t -(e i+ΔT +e r+ΔT )] Where ΔT is the left-right translation value during the wave head alignment process. When the minimum value of the three waves after alignment meets the conditions, the two-wave method is selected to draw the stress-strain curve.

8. A tunnel surrounding rock parameter inversion system under multiple disaster coupling, characterized in that: include: The specimen preparation module obtains soft rock on site and prepares soft rock specimens; The test module groups the soft rock specimens and conducts indoor tests; The parameter and curve acquisition module obtains the basic static mechanical parameters of soft rock on site and the test stress-strain curve under dynamic action based on the indoor test results; The value module determines the constitutive parameters to be inverted and obtains the upper and lower limits of each parameter based on experience; An objective function determination module obtains the objective function based on the experimental stress-strain curve and the simulated stress-strain curve; Inversion module, inversion is performed through particle swarm optimization algorithm to obtain the inversion parameters of tunnel surrounding rock.

9. An electronic device comprising a memory, a processor and a computer program stored and running on the memory, characterized in that: When the processor executes the program, the method for inverting tunnel surrounding rock parameters under multiple disaster coupling is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a tunnel surrounding rock parameter inversion method under multiple disaster coupling is implemented.

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