Jointed rock mass tunnel deformation and instability prediction method, medium and equipment

Abstract

The present application relates to the technical field of tunnel and underground engineering, and discloses a jointed rock mass tunnel deformation and instability prediction method, medium and equipment, the prediction method comprising: based on the size of the to-be-tested tunnel and the deformation influence range of surrounding rock, a jointed rock mass tunnel multi-scale model comprising, from outside to inside, a continuous medium model area, a block discrete element model area and a bonded block model area is established; the jointed rock mass tunnel multi-scale model is used to perform step-by-step simulation of the construction steps of the to-be-tested tunnel according to the construction method and process of the actual project; and the jointed rock mass tunnel deformation and instability prediction results are obtained according to the displacement, deformation and stress change output at the specified monitoring points in the step-by-step simulation process.The present application realizes multi-scale analysis of macroscopic deformation, local damage and crack propagation of jointed rock mass through the hierarchical coupling of the continuous medium model, the block discrete element model and the bonded block model, and can truly reflect the mechanical response characteristics of rock mass under different scales.

Description

Technical Field

[0001] This invention relates to the field of tunnel and underground engineering technology, and in particular discloses a method, medium and equipment for predicting deformation and instability of jointed rock tunnels. Background Technology

[0002] In tunnel engineering, the stability of the surrounding rock is crucial to construction safety and the long-term performance of the structure. In rock masses with dense joints, layered structures, or weak structural planes, the integrity of the surrounding rock is poor, and stress disturbances can easily cause failure modes such as crack propagation, structural plane opening, block slippage, and displacement. This often leads to adverse deformation phenomena such as crown settlement, sidewall convergence, and local collapse, resulting in high construction risks.

[0003] Current tunnel deformation and stability analyses primarily employ continuous media methods such as the finite element method or finite difference method to calculate and analyze the overall deformation of the surrounding rock and the support response. However, jointed rock masses exhibit significant heterogeneity and discontinuity. Surrounding rock deformation is controlled by factors such as joint orientation, connectivity, and infill characteristics. Slippage, cracking, and through-failure between rocks are difficult to accurately reflect using continuous media theory. Furthermore, these methods typically employ small-deformation theories, making it difficult to handle large deformation processes following local rock mass instability. Consequently, the calculated results often deviate significantly from actual monitoring data.

[0004] With the improvement of computing power, the Discrete Element Method (DEM) has been applied in the field of rock mass analysis. It can directly describe the contact, friction, tensile cracking, and tensile-shear failure behavior between blocks, and can accurately simulate the separation and large deformation process of rock blocks, which is impossible for continuous medium models. At present, the coupling of particle flow and continuous models is commonly used for computational analysis. On the one hand, particle flow is suitable for accumulated or fragmented materials, but it is insufficient for characterizing the geometric features and overall failure modes of actual blocky jointed rock masses. On the other hand, the DEM has a large computational load and complex boundary treatment, making it difficult to balance efficiency and accuracy at the engineering scale.

[0005] In recent years, the block discrete element model has been increasingly applied to tunnel engineering. It can preserve the geometric characteristics of jointed structures and is more suitable for describing the sliding, shearing, and faulting behavior of large blocks controlled by joints. Compared to the granular flow model, its failure mode better reflects the actual situation of jointed rock masses. However, ordinary block discrete models typically treat rock masses as composed of rigid or elastic blocks, with no internal fracturing. The main manifestations are faulting and separation along joints or artificial structural surfaces, making it difficult to reflect the microscopic failure processes such as microcrack propagation and rock fracturing under stress concentration conditions.

[0006] Building upon this foundation, the BBM (Bound Block Model) has been further developed within the block discrete element model. This method refines the original large blocks into smaller bonded elements and sets tensile-shear contact parameters between the blocks, enabling the simulation of more realistic failure processes such as bond damage, crack propagation, and spalling, thus reflecting the microscopic fracturing and strength degradation mechanisms of rock masses. However, the BBM model struggles to meet the demands of large-scale, long-term, or multi-condition numerical simulations in tunnel engineering.

[0007] In summary, the continuous medium model is suitable for analyzing the overall mechanical response of the surrounding rock, the ordinary block discrete element model is suitable for large deformation and structural slip behavior controlled by joints, and the cohesive block model is suitable for describing local fracturing and crack evolution. However, all three types of models have obvious limitations in terms of applicable scale, computational cost, and expressive power. It is difficult to accurately characterize the entire mechanical response of jointed rock tunnels from overall deformation to local fracturing by relying on a single model.

[0008] Therefore, it is necessary to provide a new method, medium, and equipment for predicting the deformation and instability of jointed rock tunnels to solve the above-mentioned technical problems. Summary of the Invention

[0009] The main objective of this invention is to provide a method, medium, and equipment for predicting the deformation and instability of jointed rock tunnels, aiming to solve the problem that existing methods are unable to accurately characterize the entire mechanical response of jointed rock tunnels from overall deformation to local rupture.

[0010] To achieve the above objectives, this invention proposes a method for predicting the deformation and instability of jointed rock mass tunnels, comprising the following steps:

[0011] S1: A multi-scale model of the jointed rock mass tunnel is established based on the size of the tunnel to be tested and the influence range of the surrounding rock deformation. The multi-scale model of the jointed rock mass tunnel includes, from the outside to the inside, a continuous medium model region, a block discrete element model region, and a bonded block model region.

[0012] S2: A multi-scale model of jointed rock mass tunnels is used to simulate the construction steps of the tunnel under test in stages according to the actual construction methods and procedures of the project.

[0013] S3: Based on the displacement, deformation and stress changes at the specified monitoring points during the step-by-step simulation, output displacement cloud maps, stress cloud maps and crack propagation distribution to obtain the deformation and instability prediction results of jointed rock mass tunnels.

[0014] Optionally, S1 includes:

[0015] S1.1 Establish a multi-scale model framework for jointed rock mass tunnels;

[0016] S1.2. In the block discrete element model region of the multi-scale model architecture of jointed rock mass tunnel, joints are embedded and joint contact parameters are assigned to obtain the model architecture after joint embedding.

[0017] S1.3. Perform rock mass parameter calibration and assignment on the continuous medium model, block discrete element model and bonded block model in the jointed model architecture respectively to obtain a multi-scale model of jointed rock mass tunnel; where: when the jointed model architecture adopts different mesh division schemes, rock mass parameter calibration and assignment are required for each mesh size.

[0018] Optionally, S1.1 specifically includes:

[0019] A continuous medium model of the tunnel to be tested is established using finite element or finite difference software. The model range of the continuous medium model is 5-10 times the diameter of the tunnel to be tested.

[0020] A block discrete element model of the tunnel under test was established using block discrete metadata software. The model range of the block discrete element model is 2.5-3 times the diameter of the tunnel under test.

[0021] A coupling boundary is set at the intersection of the continuous medium model and the block discrete element model;

[0022] A square area within twice the diameter of the tunnel to be tested is designated as the core area. A bonded block model is established within the core area of ​​the tunnel to be tested to obtain a multi-scale model architecture for the jointed rock mass tunnel. Specifically: the bonded block model covers the entire core area of ​​the tunnel to be tested; or the bonded block model covers part of the core area with the tunnel axis as the center; or the model range of the bonded block model is set in a ring distribution with the tunnel axis as the center, and the size is 1.1-1.5 times the diameter of the tunnel to be tested. The tunnel excavation area within 1.1-1.5 times the diameter of the tunnel to be tested adopts a block discrete element model; or the tunnel excavation area within the core area adopts a block discrete element model, and the core area outside the tunnel excavation area adopts a bonded block model.

[0023] Optionally, setting a coupling boundary at the interface between the continuous medium model and the block discrete element model specifically involves: at the interface between the continuous medium model and the block discrete element model, using the surface displacement of the continuous medium model as the boundary constraint condition of the discrete element model, and simultaneously feeding back the contact force calculated by the discrete element model to the boundary surface of the continuous medium model to achieve bidirectional coupling.

[0024] Optionally, S1.2 includes:

[0025] S1.2.1 Establishing a joint geometric model, specifically:

[0026] The geometric characteristic parameters of main joints and random joints are obtained through statistical analysis of joint data from the tunnel under test. Specifically, for main joints, the geometric characteristic parameters of main joints measured in the field are directly used, as well as the geometric characteristic parameters of main joints in the joint geometric model. For random joints, the distribution parameters of random joints are determined based on field statistical data, and then the random joint network is generated using DFN technology to obtain the geometric characteristic parameters of random joints.

[0027] The joint mechanical parameters of the joint geometric model are determined through field tests or by referring to relevant engineering experience and literature.

[0028] A joint geometric model is established based on the joint geometric characteristic parameters and joint mechanical parameters of principal joints and random joints;

[0029] S1.2.2 Embed the joint geometric model into the block discrete element model region and assign joint contact parameters to obtain the model architecture after joint embedding; wherein: the joint contact parameters include parameters such as normal contact stiffness, tangential contact stiffness, internal friction angle and cohesion, to realize the simulation of joint slippage, tensile cracking and shear failure.

[0030] Optionally, in S1.3, the calibration and assignment of rock mass parameters for the continuous medium model and the block discrete element model in the model architecture after joint embedding specifically includes:

[0031] Rock mechanical parameters were obtained through indoor mechanical tests, on-site core sampling, and in-situ testing.

[0032] Assigning rock mechanics parameters to the block material in the block discrete element model;

[0033] The equivalent macroscopic mechanical parameters of the block discrete element model are determined by REV technology, including macroscopic internal friction angle, macroscopic cohesion, macroscopic elastic modulus, macroscopic Poisson's ratio and uniaxial compressive strength.

[0034] The macroscopic internal friction angle, macroscopic cohesion, and macroscopic Poisson's ratio are used as direct input parameters for the continuous medium model.

[0035] Optionally, determining the equivalent macroscopic mechanical parameters of the block discrete element model using REV technology includes:

[0036] ① Establish a cubic specimen with dimensions A×A×A. The cubic specimen adopts the same meshing scheme and joint distribution scheme as the discrete element model region of the block.

[0037] ② Determine the REV dimension of cubic sample one. Specifically, directly select 10m-15m as the REV dimension; or gradually adjust the A value according to the unit value until the friction angle and cohesion no longer change with A. The A value is the REV dimension.

[0038] ③ Based on the determined REV dimensions, numerical uniaxial and triaxial compression tests were performed to obtain the equivalent macroscopic mechanical parameters of the rock block and joints.

[0039] Optionally, in S1.3, the rock mass parameters of the bonded block model in the model architecture after joint embedding are calibrated and assigned, specifically including:

[0040] The REV dimensions of the binder block model were determined, and a second cubic specimen with dimensions of B×B×B was constructed. Using the same mesh generation scheme as the region of the binder block model, a numerical triaxial compression test was carried out on the second cubic specimen, and the friction angle and cohesion were obtained by fitting method.

[0041] The value of B is gradually adjusted according to the unit value until the macroscopic friction angle and cohesion no longer change with B. The value of B is the REV dimension of the adhesive block model.

[0042] Based on the determined REV dimensions, calibration specimens were established, and numerical uniaxial compression tests were performed on the calibration specimens. By iteratively adjusting the micro-parameters of the bonded block model, the errors between the simulated elastic modulus, Poisson's ratio, uniaxial compressive strength, and stress-strain curve and the equivalent macroscopic mechanical parameters of the discrete element model of the block were made to be no more than 5%, thus obtaining the successfully calibrated micro-parameters of the bonded block model. Among them, the micro-contact parameters of the bonded block model include the ratio of shear stiffness to normal stiffness, Poisson's ratio of the block, elastic modulus of the block, adjusted shear stiffness, normal stiffness, cohesion, friction angle, and tensile strength.

[0043] Specifically:

[0044] ① Check the macroscopic Poisson's ratio: Adjust the ratio of shear stiffness to normal stiffness and the Poisson's ratio of the block until the error between the simulated Poisson's ratio and the macroscopic Poisson's ratio does not exceed 5%;

[0045] ② Check the macroscopic elastic modulus: Adjust the elastic modulus of the block or the shear stiffness and normal stiffness until the error between the simulated elastic modulus and the macroscopic elastic modulus does not exceed 5%;

[0046] ③ Check uniaxial compressive strength: Adjust the cohesion, friction angle, and tensile strength until the error between the simulated compressive strength and the uniaxial compressive strength does not exceed 5%;

[0047] ④ Adjust the micro-contact parameters of the bonded block model so that the errors in Poisson's ratio, elastic modulus, and uniaxial compressive strength do not exceed 5%, thereby obtaining the successfully calibrated micro-parameters of the bonded block model.

[0048] In addition, the present invention provides a readable storage medium storing computer program instructions, which, when executed by a processor, implement the method for predicting deformation and instability of jointed rock mass tunnels as described above.

[0049] The present invention also provides an electronic device, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, wherein the computer program instructions are executed by the processor to perform the method for predicting deformation and instability of jointed rock mass tunnels as described above.

[0050] The application of the technical solution of the present invention has the following beneficial effects:

[0051] (1) This invention realizes multi-scale analysis of macroscopic deformation, local failure and crack propagation of jointed rock mass through the layered coupling of continuous medium model, block discrete element model and cohesive block model, which can truly reflect the mechanical response characteristics of rock mass at different scales.

[0052] (2) This invention clarifies the calibration and setting methods of various parameters in the multi-scale model, including the parameters of the continuous medium model, the parameters of the block discrete element model and the micro-parameters of the bonded block model. By combining indoor tests, field rock mass tests and iterative fitting, the macro-mechanical properties of each model region are kept consistent, thereby ensuring the model calculation accuracy and engineering applicability.

[0053] (3) By partitioning the multi-scale model, this invention applies the computationally intensive binder model only to the core area of ​​the tunnel, significantly reducing computational costs. Compared to using the binder model across the entire area, this invention can greatly shorten computation time while ensuring the simulation accuracy of key areas, achieving the best balance between computational efficiency and accuracy. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0055] Figure 1 This is a flowchart illustrating the method for predicting deformation and instability of jointed rock mass tunnels in an embodiment of the present invention.

[0056] Figure 2(a) is a schematic diagram of the region division of the multi-scale model of jointed rock mass tunnel in an embodiment of the present invention;

[0057] Figure 2(b) is a schematic diagram of the structure of the block discrete element model region in Figure 2(a);

[0058] Figure 2(c) is a schematic diagram of the structure of the adhesive block model region in Figure 2(a);

[0059] Figure 3(a) is a schematic diagram of the structure of the model architecture after joint embedding in an embodiment of the present invention;

[0060] Figure 3(b) is a schematic diagram of the structure of joint group one in Figure 3(a);

[0061] Figure 3(c) is a schematic diagram of the structure of joint group II in Figure 3(a);

[0062] Figure 3(d) is a schematic diagram of the block bonding structure in Figure 3(a);

[0063] Figure 4 This is a schematic diagram of the process for calibrating the REV dimension in an embodiment of the present invention;

[0064] Figure 5 This is a schematic diagram of the prediction results of the multi-scale model of jointed rock mass tunnel in an embodiment of the present invention;

[0065] Figure 6 This is a schematic diagram of the prediction results of the global block discrete element plus glued block model in an embodiment of the present invention.

[0066] Figure 7(a) is a schematic diagram of the coverage area of ​​the adhesive block model in an embodiment of the present invention;

[0067] Figure 7(b) is another schematic diagram of the coverage area of ​​the adhesive block model in an embodiment of the present invention;

[0068] Figure 7(c) is a schematic diagram of the coverage area of ​​the adhesive block model in an embodiment of the present invention;

[0069] Figure 8 This is a comparison of the calculation accuracy of the jointed rock mass tunnel deformation and instability prediction method of this invention with that of the conventional global block discrete element plus bonded block model.

[0070] Explanation of icon numbers:

[0071] 1. Continuous medium model region; 2. Block discrete element model region; 3. Bonded block model region; 21. Joint group one; 22. Joint group two; 23. Block bonding; 31. Multi-scale model of jointed rock mass tunnel; 32. Global block discrete element plus bonded block model.

[0072] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0073] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0074] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0075] Furthermore, in this invention, descriptions involving "first," "second," etc., are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0076] In this invention, unless otherwise explicitly specified and limited, the terms "connection," "fixed," etc., should be interpreted broadly. For example, "fixed" can mean a fixed connection, a detachable connection, or an integral part; it can mean a mechanical connection or an electrical connection; it can mean a direct connection or an indirect connection through an intermediate medium; it can mean the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0077] Furthermore, the technical solutions of the various embodiments of the present invention can be combined with each other, but only if they are based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0078] This invention proposes a method, medium, and equipment for predicting the deformation and instability of jointed rock tunnels, aiming to solve the problem that existing methods are unable to accurately characterize the entire mechanical response of jointed rock tunnels from overall deformation to local rupture.

[0079] This embodiment uses a tunnel project as an example. The tunnel traverses sandstone strata with well-developed joints, exhibiting poor surrounding rock integrity and complex joint orientations. During excavation, adverse geological phenomena such as block slippage, localized fracturing, and rockfall are prone to occur. According to on-site geological survey data and related information, the sandstone mass has a density of 2652 kg / m³, an elastic modulus of 3.8 GPa, a Poisson's ratio of 0.35, an internal friction angle of 27°, and a cohesion of 0.2 MPa. On-site investigation results indicate that there are two main sets of joints in the sandstone (Joint Set 1-21 and Joint Set 2-22). One set has a dip angle of 82° and a dip direction of 132°, while the other set has a dip angle of 12° and a dip direction of 73°. The joint friction angle is approximately 32°, and the cohesion is approximately 0.05 MPa. (See also...) Figure 1 The deformation and instability prediction method for jointed rock mass tunnels in this embodiment is used to predict the deformation and instability of the tunnel, including the following steps:

[0080] S1: Based on the size of the tunnel to be tested and the influence range of surrounding rock deformation, a multi-scale model of jointed rock mass tunnel is established 31; the multi-scale model of jointed rock mass tunnel includes, from the outside to the inside, a continuous medium model region 1, a block discrete element model region 2, and a bonded block model region 3; and a coupling boundary is set at the junction of the continuous medium model and the discrete element model to realize the displacement and force transmission between the continuous region and the discrete region;

[0081] S1 includes:

[0082] S1.1 Establish a multi-scale model framework for jointed rock mass tunnels;

[0083] Referring to Figures 2(a), 2(b), and 2(c), S1.1 specifically refers to:

[0084] A continuous medium model of the tunnel under test is established using finite element or finite difference software. The model range of the continuous medium model is 5-10 times the diameter of the tunnel under test. In this embodiment, based on the tunnel radius of 9m, the range of the continuous medium model area is taken as 5 times the tunnel diameter, that is, extending 45m outward from the center of the tunnel. The model range is from -45m to +45m in the X direction, from -45m to +45m in the Z direction, and from 0 to 20m in the Y direction. The overall size is 90m×90m×20m.

[0085] A block discrete element model of the tunnel under test was established using block discrete metadata software. The model range of the block discrete element model was 2.5-3 times the diameter of the tunnel. In this embodiment, the range of the block discrete element region was taken as 3 times the tunnel diameter, that is, extending 27m outward from the center of the tunnel. The model range was -27m to +27m in the X direction, -27m to +27m in the Z direction, and 0 to 20m in the Y direction, with an overall size of 54m × 54m × 20m. Tetrahedral meshing was used, with an average mesh element size of 2m. Furthermore, a random joint network was embedded using DFN technology to simulate the influence of joints on the mechanical properties of the rock mass.

[0086] A coupling boundary is set at the interface between the continuous medium model and the block discrete element model. Specifically, at the interface between the continuous medium model and the block discrete element model, the surface displacement of the continuous medium model is used as the boundary constraint condition of the discrete element model, and the contact force calculated by the discrete element model is fed back to the boundary surface of the continuous medium model to achieve bidirectional coupling. In this embodiment, the coupling boundary is achieved through nodal displacement coordination and force boundary transmission.

[0087] A square area within twice the diameter of the tunnel under test is designated as the core area. A bonded block model is established within the core area of ​​the tunnel under test to obtain a multi-scale model architecture for the jointed rock mass tunnel. Specifically: the bonded block model covers the entire core area of ​​the tunnel under test; or the bonded block model covers part of the core area with the tunnel axis as the center, see Figure 7(a); or the model range of the bonded block model is set in a ring distribution with the tunnel cross-section as the center, and the size is 1.1-1.5 times the diameter of the tunnel under test. The tunnel excavation area within 1.1-1.5 times the diameter of the tunnel under test adopts a block discrete element model, see Figure 7(c); or the tunnel excavation area within the core area adopts a block discrete element model, and the core area outside the tunnel excavation area adopts a bonded block model, see Figure 7(b). In this embodiment, the bonded block model is constructed by refining the blocks and setting bonded contacts between the blocks to simulate the local fracturing, crack propagation, and spalling process. In this embodiment, the range of the bonded block model is 1.2 times the tunnel diameter, that is, extending 10.8m outward from the tunnel center. The model range is from -10.8m to +10.8m in the X direction, from -10.8m to +10.8m in the Z direction, and from 0 to 20m in the Y direction, with an overall size of 21.6m × 21.6m × 20m. The microscopic fracturing process is simulated by setting bonded contacts between the bonded blocks 23. A tetrahedral meshing scheme is also used, with an average mesh size of 1.5m.

[0088] In this embodiment, after constructing the multi-scale model architecture of the jointed rock mass tunnel, it is necessary to analyze and evaluate the transmission effect of the coupling boundary (specifically, it is required that during the coupling calculation, the velocity difference between the adjacent continuous medium surface and the discrete element node on the boundary is less than 3%; otherwise, the mesh size is adjusted and the coupling relationship is re-established). By checking the displacement continuity and force balance state at the coupling interface, it is ensured that the coupling boundary effectively transmits displacement and force, and coupling failure is avoided in the subsequent calculation process.

[0089] S1.2. In the block discrete element model region of the multi-scale model architecture of jointed rock mass tunnel, joints are embedded and joint contact parameters are assigned to obtain the model architecture after joint embedding.

[0090] S1.2 includes:

[0091] S1.2.1 Establishing a joint geometric model, specifically:

[0092] The geometric characteristic parameters of main joints and random joints are obtained through statistical analysis of joint data from the test tunnel. Specifically, for main joints, the geometric characteristic parameters of the main joints measured in the field are directly used, as well as the geometric characteristic parameters of the main joints in the joint geometric model. For random joints, the distribution parameters of random joints are determined based on field statistical data, and then a random joint network is generated using DFN technology to obtain the geometric characteristic parameters of random joints. The geometric characteristic parameters of joints include joint attitude, spacing, length, etc. Among them, the determination of random joint distribution parameters includes the distribution function type of joint attitude (such as Fisher distribution, uniform distribution, etc.), mean, variance, concentration parameters, etc., and the distribution type of joint size (such as power law distribution, log-normal distribution, etc.). In this embodiment, main joints can be manually set, and a random joint network can be generated using Discrete Joint Network (DFN) technology, or main joints can be manually set and combined with DFN to simulate random joints. Referring to Figures 3(a), 3(b), 3(c), and 3(d), this embodiment uses DFN (Discrete Joint Network) technology to generate a random joint network to simulate two main groups of joints. The first group of joints is described using a Fisher function distribution, with a mean dip angle of 82°, a mean dip direction of 132°, and a concentration parameter set to 500. The second group of joints has a mean dip angle of 12°, a mean dip direction of 73°, and a concentration parameter also set to 500. The joint size distribution is described using a power-law distribution, with the power exponent set to 4. Considering computational efficiency, the DFN joint network is simplified to a minimum joint size of 1m and a maximum size of 4m. With the above DFN parameter settings, approximately 850 joint surfaces are generated in the discrete element region of the block and the bonded block model region.

[0093] The joint mechanical parameters of the joint geometric model are determined through field tests or by referring to relevant engineering experience and literature. Specifically, this involves directly measuring the peak friction angle, residual friction angle, cohesion, normal stiffness, and tangential stiffness of the joint through field direct shear tests; or determining the joint mechanical parameters through field joint scratch analysis, infill material analysis, and indoor similar material tests; or selecting values ​​based on recommended values ​​for similar lithology and joint characteristics found in relevant engineering experience and literature. Joint mechanical parameters include normal contact stiffness, tangential contact stiffness, internal friction angle, cohesion, and tensile strength. The joint contact constitutive model can adopt the Coulomb sliding model or the elastoplastic contact model as needed. In this embodiment, the joint mechanical parameters are set as follows: normal stiffness 1000 GPa / m, tangential stiffness 200 GPa / m, friction angle 20°, cohesion 0.5 MPa, and tensile strength 0.1 MPa. The joint contact constitutive model adopts the Coulomb sliding model.

[0094] A joint geometric model is established based on the joint geometric characteristic parameters and joint mechanical parameters of principal joints and random joints;

[0095] S1.2.2 Embed the joint geometric model into the block discrete element model region and assign joint contact parameters to obtain the model architecture after joint embedding; wherein: the joint contact parameters include parameters such as normal contact stiffness, tangential contact stiffness, internal friction angle and cohesion, to realize the simulation of joint slippage, tensile cracking and shear failure.

[0096] S1.3. Rock mass parameters are calibrated and assigned to the continuous medium model, block discrete element model, and bonded block model in the jointed model architecture to obtain a multi-scale model of the jointed rock mass tunnel. Specifically, when different meshing schemes are used in the jointed model architecture, rock mass parameters need to be calibrated and assigned for each mesh size. In this embodiment, parameter calibration is performed for each mesh size to obtain rock mass parameters adapted to that mesh size, ensuring that the macroscopic mechanical properties of regions with different mesh densities match the actual values.

[0097] In step S1.3, rock mass parameters are calibrated and assigned to the continuous medium model and the block discrete element model in the model architecture after joint embedding, specifically including:

[0098] Rock mechanical parameters are obtained through indoor mechanical tests, field coring, and in-situ testing. Specifically, rock mechanical parameters can be obtained through field tests or indoor tests, or estimated by combining rock parameters with empirical criteria. When directly obtained through tests, parameters such as the elastic modulus, Poisson's ratio, internal friction angle, and cohesion of the rock mass are obtained directly through uniaxial compression tests, triaxial compression tests, and direct shear tests. When estimated empirically, the equivalent mechanical parameters of the rock mass are estimated by using the strength parameters of intact rock, combined with empirical methods such as the Hoek-Brown criterion, the RMR rock mass classification system, or the GSI geological strength index.

[0099] Rock mechanics parameters are assigned to the bulk material in the discrete element model of the bulk material. In this embodiment, the constitutive model of the bulk material adopts the Mohr-Coulomb model, and the bulk parameters are: density of 2652 kg / m³, elastic modulus of 3.8 GPa, Poisson's ratio of 0.35, internal friction angle of 27°, and cohesion of 0.2 MPa.

[0100] The equivalent macroscopic mechanical parameters of the block discrete element model are determined by REV technology, including macroscopic internal friction angle, macroscopic cohesion, macroscopic elastic modulus, macroscopic Poisson's ratio and uniaxial compressive strength.

[0101] See Figure 4 The equivalent macroscopic mechanical parameters of the block discrete element model determined by REV technology include:

[0102] ① A cubic specimen with dimensions A×A×A (4m for the first time in this embodiment) was established. The cubic specimen adopted the same meshing and joint distribution scheme as the discrete element model region of the block (mesh element size 2m, DFN parameters as described above). In this embodiment, the confining pressure was set to three levels: 1MPa, 2MPa, and 8MPa, and the loading rate was 10⁻⁵ m / step. The friction angle and cohesion were obtained by fitting the stress-strain curve. The specimen size A was gradually adjusted to 5m, 6m, 7m, and 8m, and the triaxial test was repeated. The calibration results are shown in Table 1. As can be seen from Table 1, when A is 8m, the values ​​of internal friction angle and cohesion are small compared with those when A is 7m, and the parameters tend to be stable. Therefore, the REV size of the ordinary discrete element region was determined to be 8m.

[0103] Table 1. Calibration results of macroscopic mechanical parameters of rock mass under different REV sizes

[0104]

[0105] ② Determine the REV size of cubic sample one. Specifically, directly select 10m-15m as the REV size; or gradually adjust the A value according to the unit value until the friction angle and cohesion no longer change with A. The A value is the REV size. In this embodiment, the REV size is 8m×8m×8m.

[0106] ③ Based on the determined REV dimensions, numerical uniaxial and triaxial compression tests were performed to obtain the equivalent macroscopic mechanical parameters of the rock mass and joints. In this embodiment, joint parameters were set (normal stiffness 1000 GPa / m, tangential stiffness 200 GPa / m, friction angle 20°, cohesion 2.5 MPa, tensile strength 0.5 MPa). Numerical uniaxial compression tests were performed, and the equivalent macroscopic mechanical parameters of the rock mass and joints were obtained as follows: elastic modulus 3.49 GPa, Poisson's ratio 0.311, and uniaxial compressive strength 26.08 MPa. Triaxial compression tests were performed, and the equivalent cohesion was 0.042 MPa and the friction angle was 25.2°.

[0107] The macroscopic internal friction angle, macroscopic cohesion, and macroscopic Poisson's ratio are used as direct input parameters for the continuous medium model. In this embodiment, the continuous medium model adopts the Mohr-Coulomb model, with parameters set as follows: elastic modulus 3.49 GPa, Poisson's ratio 0.311, cohesion 0.042 MPa, and friction angle 25.2°, so that the continuous medium model can reflect the equivalent mechanical properties of jointed rock masses.

[0108] In step S1.3, rock mass parameters are calibrated and assigned to the bonded block model in the model architecture after joint embedding, specifically including:

[0109] The REV dimensions of the binder block model were determined, and a second cubic specimen with dimensions of B×B×B was constructed. Using the same mesh generation scheme as the region of the binder block model, a numerical triaxial compression test was carried out on the second cubic specimen, and the friction angle and cohesion were obtained by fitting method.

[0110] The value of B was gradually adjusted by unit until the macroscopic friction angle and cohesion no longer changed with B. This value of B is the REV dimension of the bonded block model. In this embodiment, cubic samples of different sizes were established for calibration: 6m, 8m, 10m, and 12m. The results showed that when the sample size was 10m, the cohesion and friction angle tended to stabilize. Therefore, the REV dimension of the bonded block model region was determined to be 10m.

[0111] Based on the determined REV dimensions, calibration specimens were established, and numerical uniaxial compression tests were performed on the calibration specimens. By iteratively adjusting the micro-parameters of the bonded block model, the errors between the simulated elastic modulus, Poisson's ratio, uniaxial compressive strength, and stress-strain curve and the equivalent macroscopic mechanical parameters of the discrete element model of the block were made to be no more than 5%, thus obtaining the successfully calibrated micro-parameters of the bonded block model. Among them, the micro-contact parameters of the bonded block model include the ratio of shear stiffness to normal stiffness, Poisson's ratio of the block, elastic modulus of the block, adjusted shear stiffness, normal stiffness, cohesion, friction angle, and tensile strength.

[0112] Specifically:

[0113] ① Check the macroscopic Poisson's ratio: Adjust the ratio of shear stiffness to normal stiffness and the Poisson's ratio of the block until the error between the simulated Poisson's ratio and the macroscopic Poisson's ratio does not exceed 5%;

[0114] ② Check the macroscopic elastic modulus: Adjust the elastic modulus of the block or the shear stiffness and normal stiffness until the error between the simulated elastic modulus and the macroscopic elastic modulus does not exceed 5%;

[0115] ③ Check uniaxial compressive strength: Adjust the cohesion, friction angle, and tensile strength until the error between the simulated compressive strength and the uniaxial compressive strength does not exceed 5%;

[0116] ④ Adjust the micro-contact parameters of the bonded block model so that the errors in Poisson's ratio, elastic modulus, and uniaxial compressive strength do not exceed 5%, thereby obtaining the successfully calibrated micro-parameters of the bonded block model.

[0117] In this embodiment, after iterative calibration, the block constitutive model adopts an elastic model with the following parameters: elastic modulus 3.9 GPa, Poisson's ratio 0.32, and density 2652 kg / m³. The bond contact parameters are: normal stiffness 8 GPa / m, tangential stiffness 4 GPa / m, friction angle 25°, cohesion 5.25 MPa, and tensile strength 2.5 MPa. The bond contact constitutive model adopts the Mohr-Coulomb sliding model. The corresponding macroscopic mechanical parameters are: elastic modulus 3.462 MPa, Poisson's ratio 0.304, and uniaxial compressive strength 26.065 MPa. These parameters differ from the equivalent rock mass's elastic modulus (3.49 GPa), Poisson's ratio (0.311), and uniaxial compressive strength (26.08 MPa) by less than 5%. This achieves the unification of multi-scale, cross-model rock mechanical parameters.

[0118] S2: A multi-scale model of jointed rock mass tunnels is used to simulate the construction steps of the tunnel under test in stages according to the actual construction methods and procedures of the project. The boundary conditions in this embodiment are set as follows: the surrounding normal and bottom normal are constrained, and the top is free. Initial ground stress setting: the gravitational acceleration is set to 9.81 m / s², and the self-weight stress field is calculated. After the initial stress field is calculated, the excavation unloading process is simulated by relaxation excavation. Figure 5 The figure shows the deformation cloud map of the surrounding rock during tunnel excavation in the embodiment. As can be seen from the figure, the multi-scale coupling analysis method of this invention achieves cross-scale numerical simulation analysis of the tunnel. The displacement fields of the continuous medium model region, the block discrete element model region, and the bonded block region transition continuously without obvious abrupt changes, indicating that the coupling boundary effectively transmits force and displacement constraints.

[0119] Due to the low strength of sandstone, the tunnel has a large excavation span and is prone to collapse without support. The bonded block model area clearly shows the large deformation characteristics of the upper part of the tunnel, as well as the instability and collapse phenomenon under unsupported conditions.

[0120] S3: Based on the displacement, deformation, and stress changes at designated monitoring points during the step-by-step simulation, output displacement cloud maps, stress cloud maps, and crack propagation distributions to obtain the predicted results of jointed rock mass tunnel deformation and instability. In this embodiment, designated monitoring points are set at the tunnel arch crown, arch shoulder, arch waist, arch foot, and arch bottom to monitor surrounding rock deformation, support stress, and joint propagation.

[0121] To verify the computational efficiency advantage of the method of this invention, this embodiment also establishes a global block discrete element method plus glued block model 32 as a comparison model. This embodiment was computed on a desktop computer equipped with an NVIDIA GeForce GTX 1660 SUPER graphics card and an Intel i5-12400F processor. Using the method of this embodiment, the model contains 15929 block elements and 7680 continuous medium mesh elements, and the computation time is 33 minutes and 18 seconds. In comparison, the scheme using a global block discrete element method plus glued block model contains 16588 block elements, and the computation time is 48 minutes and 39 seconds. The results show that compared with the traditional global discrete element method, the method of this embodiment improves the computational efficiency by approximately 31.5% with a similar number of elements, verifying that multi-scale model partitioning can significantly reduce computational costs.

[0122] See Figure 5 , Figure 6 and Figure 8 The diagram shows a comparison of the calculation results of the multi-scale method in this embodiment with the conventional global block discrete element method plus bonded block model. Specifically, it compares the vertical displacement measurement lines extending 21m horizontally from the tunnel's center as the origin. Figure 8It can be seen that the calculation results of the two methods are similar, with a difference of no more than 1% at a distance of 10m from the tunnel center; however, the calculation results closer to the tunnel's free face show significant differences. The calculation result using the method of this embodiment is slightly larger than the calculation result using the bonded block model + discrete element block model, with a maximum difference of 0.6mm, which is acceptable on an engineering scale. Therefore, it can be considered that the method proposed in this embodiment significantly improves computational efficiency while ensuring numerical calculation accuracy.

[0123] This embodiment provides a readable storage medium storing computer program instructions, which, when executed by a processor, implement the method for predicting deformation and instability of jointed rock mass tunnels as described above.

[0124] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0125] This embodiment also includes an electronic device, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, wherein the computer program instructions are executed by the processor to perform the jointed rock mass tunnel deformation and instability prediction method as described above.

[0126] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0127] The electronic device can be a mobile phone, desktop computer, laptop, handheld computer, cloud server, or other computing device. The electronic device may include, but is not limited to, processors and memory. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0128] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0129] The memory can be used to store the computer program and / or modules. The processor implements the computer program by running or executing the computer program and / or modules stored in the memory, and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function (such as finite element or finite difference software, block discrete element software, data processing programs, etc.). The data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0130] If the modules / units integrated in the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0131] The above description is only a preferred embodiment of the present invention and does not limit the scope of the present invention. All equivalent structural transformations made under the inventive concept of the present invention using the contents of the present invention specification and drawings, or direct / indirect applications in other related technical fields, are included within the protection scope of the present invention.

Claims

1. A method for predicting deformation and instability of jointed rock mass tunnels, characterized in that, Includes the following steps: S1: Establish a multi-scale model of jointed rock mass tunnel based on the size of the tunnel to be tested and the influence range of surrounding rock deformation; The multi-scale model of jointed rock mass tunnels includes, from the outside to the inside, a continuous medium model region, a block discrete element model region, and a bonded block model region. S1 includes: S1.1 Establish a multi-scale model framework for jointed rock mass tunnels; Specifically, S1.1 is: A continuous medium model of the tunnel to be tested is established using finite element or finite difference software. The model range of the continuous medium model is 5-10 times the diameter of the tunnel to be tested. A block discrete element model of the tunnel under test was established using block discrete metadata software. The model range of the block discrete element model is 2.5-3 times the diameter of the tunnel under test. A coupling boundary is set at the intersection of the continuous medium model and the block discrete element model; A square area within twice the diameter of the tunnel to be tested is designated as the core area. A bonded block model is established within the core area of ​​the tunnel to be tested to obtain a multi-scale model architecture for the jointed rock mass tunnel. Specifically: the bonded block model covers the entire core area of ​​the tunnel to be tested; or the bonded block model covers part of the core area with the tunnel axis as the center; or the model range of the bonded block model is set in a ring distribution with the tunnel axis as the center, and the size is 1.1-1.5 times the diameter of the tunnel to be tested. The tunnel excavation area within 1.1-1.5 times the diameter of the tunnel to be tested adopts a block discrete element model; or the tunnel excavation area within the core area adopts a block discrete element model, and the core area outside the tunnel excavation area adopts a bonded block model. S1.

2. In the block discrete element model region of the multi-scale model architecture of jointed rock mass tunnel, joints are embedded and joint contact parameters are assigned to obtain the model architecture after joint embedding. S1.

3. The rock mass parameters of the continuous medium model, block discrete element model and bonded block model in the joint-embedded model architecture are calibrated and assigned respectively to obtain the multi-scale model of jointed rock mass tunnel; where: when the joint-embedded model architecture adopts different mesh division schemes, the rock mass parameters need to be calibrated and assigned separately for each mesh size. S2: A multi-scale model of jointed rock mass tunnels is used to simulate the construction steps of the tunnel under test in stages according to the actual construction methods and procedures of the project. S3: Based on the displacement, deformation and stress changes at the specified monitoring points during the step-by-step simulation, output displacement cloud maps, stress cloud maps and crack propagation distribution to obtain the deformation and instability prediction results of jointed rock mass tunnels.

2. The method for predicting deformation and instability of jointed rock mass tunnels according to claim 1, characterized in that, Setting a coupling boundary at the interface between the continuous medium model and the block discrete element model specifically involves using the surface displacement of the continuous medium model as the boundary constraint condition of the discrete element model at the interface between the continuous medium model and the block discrete element model, while feeding back the contact force calculated by the discrete element model to the boundary surface of the continuous medium model to achieve bidirectional coupling.

3. The method for predicting deformation and instability of jointed rock mass tunnels according to claim 2, characterized in that, S1.2 includes: S1.2.1 Establishing a joint geometric model, specifically: The geometric characteristic parameters of main joints and random joints are obtained through statistical analysis of joint data from the tunnel under test. Specifically, for main joints, the geometric characteristic parameters of the main joints measured in the field are directly used as the geometric characteristic parameters of the main joints in the joint geometric model; for random joints, the distribution parameters of random joints are determined based on field statistical data, and then the random joint network is generated using DFN technology to obtain the geometric characteristic parameters of the random joints. The joint mechanical parameters of the joint geometric model are determined through field tests or by referring to relevant engineering experience and literature. A joint geometric model is established based on the joint geometric characteristic parameters and joint mechanical parameters of principal joints and random joints; S1.2.2 Embed the joint geometric model into the block discrete element model region and assign joint contact parameters to obtain the model architecture after joint embedding; wherein: the joint contact parameters include normal contact stiffness, tangential contact stiffness, internal friction angle and cohesion parameters, to realize the simulation of joint slippage, tensile cracking and shear failure.

4. The method for predicting deformation and instability of jointed rock mass tunnels according to claim 3, characterized in that, In step S1.3, rock mass parameters are calibrated and assigned to the continuous medium model and the block discrete element model in the model architecture after joint embedding, specifically including: Rock mechanical parameters were obtained through indoor mechanical tests, on-site core sampling, and in-situ testing. Assigning rock mechanics parameters to the block material in the block discrete element model; The equivalent macroscopic mechanical parameters of the block discrete element model are determined by REV technology, including macroscopic internal friction angle, macroscopic cohesion, macroscopic elastic modulus, macroscopic Poisson's ratio and uniaxial compressive strength. The macroscopic internal friction angle, macroscopic cohesion, and macroscopic Poisson's ratio are used as direct input parameters for the continuous medium model.

5. The method for predicting deformation and instability of jointed rock mass tunnels according to claim 4, characterized in that, The equivalent macroscopic mechanical parameters of the block discrete element model determined by REV technology include: ① Establish a cubic specimen with dimensions A×A×A. The cubic specimen adopts the same meshing scheme and joint distribution scheme as the discrete element model region of the block. ② Determine the REV dimension of cubic sample one. Specifically, directly select 10m-15m as the REV dimension; or gradually adjust the A value according to the unit value until the friction angle and cohesion no longer change with A. The A value is the REV dimension. ③ Based on the determined REV dimensions, numerical uniaxial and triaxial compression tests were performed to obtain the equivalent macroscopic mechanical parameters of the rock block and joints.

6. The method for predicting deformation and instability of jointed rock mass tunnels according to claim 5, characterized in that, In step S1.3, the rock mass parameter calibration and assignment for the bonded block model in the joint-embedded model architecture specifically includes: The REV dimensions of the binder block model were determined, and a second cubic specimen with dimensions of B×B×B was constructed. Using the same mesh generation scheme as the region of the binder block model, a numerical triaxial compression test was carried out on the second cubic specimen, and the friction angle and cohesion were obtained by fitting method. The value of B is gradually adjusted according to the unit value until the macroscopic friction angle and cohesion no longer change with B. The value of B is the REV dimension of the adhesive block model. Based on the determined REV dimensions, calibration specimens were established, and numerical uniaxial compression tests were performed on the calibration specimens. By iteratively adjusting the micro-parameters of the bonded block model, the errors between the simulated elastic modulus, Poisson's ratio, uniaxial compressive strength, and stress-strain curve and the equivalent macroscopic mechanical parameters of the discrete element model of the block were made to be no more than 5%, thus obtaining the successfully calibrated micro-parameters of the bonded block model. Among them, the micro-contact parameters of the bonded block model include the ratio of shear stiffness to normal stiffness, Poisson's ratio of the block, elastic modulus of the block, adjusted shear stiffness, normal stiffness, cohesion, friction angle, and tensile strength. Specifically: ① Check the macroscopic Poisson's ratio: Adjust the ratio of shear stiffness to normal stiffness and the Poisson's ratio of the block until the error between the simulated Poisson's ratio and the macroscopic Poisson's ratio does not exceed 5%; ② Check the macroscopic elastic modulus: Adjust the elastic modulus of the block or the shear stiffness and normal stiffness until the error between the simulated elastic modulus and the macroscopic elastic modulus does not exceed 5%; ③ Check uniaxial compressive strength: Adjust the cohesion, friction angle, and tensile strength until the error between the simulated compressive strength and the uniaxial compressive strength does not exceed 5%; ④ Adjust the micro-contact parameters of the bonded block model so that the errors in Poisson's ratio, elastic modulus, and uniaxial compressive strength do not exceed 5%, thereby obtaining the successfully calibrated micro-parameters of the bonded block model.

7. A readable storage medium, characterized in that, It stores computer program instructions, which, when executed by a processor, implement the method for predicting deformation and instability of jointed rock mass tunnels as described in any one of claims 1 to 6.

8. An electronic device, characterized in that, include: The method for predicting deformation and instability of jointed rock mass tunnels as described in any one of claims 1 to 7 includes at least one processor, at least one memory, and computer program instructions stored in the memory.

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