A coupled analysis method and device for time-dependent crack evolution of high-stress hard rock in caverns

Abstract

The present application provides a coupled analysis method and device for the time-dependent fracture evolution of high-stress hard rock in a cavern, which is used to provide a high-precision, multi-scale and comprehensive analysis mechanism for the time-dependent fracture evolution of underground surrounding rock, and is particularly suitable for the analysis needs of the time-dependent fracture evolution of large-scale underground surrounding rock.

Description

Technical Field

[0001] The present application relates to the field of rock mechanics, and specifically to a coupled analysis method and device for the time-dependent fracture evolution of high-stress hard rock in a cavern. Background Art

[0002] The damage of underground surrounding rock caverns can be understood as the result of a violent excavation unloading response, resulting in a short-term internal rock mass stress adjustment value greater than the rock material strength, leading to local damage, such as spalling or rock burst, which are typical brittle failures under high stress. This type of damage often presents a gradual rather than sudden characteristic as the cavern construction work progresses. If no timely intervention is carried out, it is easy to cause large-scale rock mass peeling or instability, causing great harm to people, the environment, or engineering.

[0003] In the above context, the instability of cavern surrounding rock has certain timeliness and regularity, and the surrounding rock failure itself is closely related to the method, strength, timing and other aspects of on-site support. In order to avoid support damage or instability during excavation, it is obviously extremely important to understand the time-dependent deformation and failure laws of cavern surrounding rock.

[0004] In the existing technology, a series of in-depth studies have been conducted on the aging mechanical properties of rock during excavation and unloading, and fruitful research results have been achieved. The research methods include numerous conventional indoor tests, real-time on-site monitoring, and numerical simulation. Among them, conventional indoor macro tests include: uniaxial compression tests, conventional triaxial compression tests, and rheological tests, etc., and conventional indoor micro tests include SEM scanning electron microscopy, X-ray diffraction, optical microscopy, and energy spectrometer testing; real-time on-site monitoring includes surrounding rock acoustic wave testing, long-term borehole video observation, multi-point displacement meter monitoring, and stress meter monitoring; numerical simulation includes FLAC, PFC particle flow, and ABAQUS analysis. The overall research scale gradually evolves from micro to macro to ensure that the actual engineering practice matches the experimental results.

[0005] However, the inventors of the present application discovered that in the existing research and analysis schemes for the time-dependent fracture evolution of underground surrounding rock, there is a certain gap between the analysis accuracy and the actual situation. Obviously, the adopted scheme has limited accuracy. In further research, it was noted that the existing technology only analyzes the time-dependent fracture evolution of underground surrounding rock from a single perspective of microscopic or indoor experiments, and the model parameters therein are very limited. Summary of the Invention

[0006] The present application provides a coupled analysis method and device for the time-dependent fracture evolution of high-stress hard rock in a cavern, which is used to provide a high-precision, multi-scale and comprehensive analysis mechanism for the time-dependent fracture evolution of underground surrounding rock, and is particularly suitable for the analysis needs of the time-dependent fracture evolution of large-scale underground surrounding rock.

[0007] In a first aspect, the present application provides a coupled analysis method for the time-dependent crack evolution of high-stress hard rock in a cavern, the method comprising:

[0008] The mechanical properties of the grains and grain boundaries of the granite minerals related to the target cavern are obtained, along with the grain contact bonding aging model reflecting transgranular fracture, the grain cluster parallel bonding aging model reflecting intergranular fracture, and the filling geometry model of flexible grain clusters of different sizes and shapes.

[0009] Triaxial unloading creep tests on indoor rock samples related to the target cavern were carried out, and acoustic emission (AE) tests were performed simultaneously. The test results were combined with the mechanical properties of granite grains and grain boundaries, the particle contact bonding aging model reflecting transgranular fracture, the particle cluster parallel bonding aging model reflecting intergranular fracture, and the flexible particle cluster filling geometry model of different sizes and shapes to establish a 3D mesostructure model of granite at the indoor rock sample scale. PFC was carried out based on the 3D mesostructure model of granite at the indoor rock sample scale. 3D Through simulation, by calibrating the aging model parameters, the maximum size of flexible particle clusters and the size of the representative volume element (RVE) of the rock mass that reflect the mesoscopic evolution mechanism and mesoscopic structural characteristics of granite aging fracture are determined;

[0010] The initial cohesion weakening and friction strengthening (CWFS) model parameters are determined based on the maximum size of the flexible particle cluster and the rock mass RVE size. The initial CWFS model parameters are used as input parameters. Combined with the in-situ comprehensive monitoring of the deformation of the surrounding rock aging fracture zone, rock mass cracking and disturbance stress, the target CWFS model parameters of the macro-mechanical analysis area are inverted.

[0011] Based on the target CWFS model parameters in the macro-mechanical analysis area, FLAC 3D During the elastoplastic analysis, rock mass damage indicators are used to precisely locate the extent of the aging fracture zone. The largest flexible particle clusters are then filled into the aging fracture zone grid using existing grid particle filling techniques. The corresponding regional grid cells are then deleted to construct a grain-scale particle element-continuum partition coupling geometric model of the mesoscopic evolution mechanism of aging fracture in hard rock in underground caverns.

[0012] Based on the grain-scale particle element-continuum partition coupling geometric model, the FLAC 3D Under the same initial boundary and stress conditions as the elastic-plastic analysis, the particle clusters in the aging fracture zone are assigned the aging model parameters, and the rock mass outside the aging fracture zone is assigned the target CWFS model and parameters. Then, FLAC is used to calculate the aging model parameters.3D and PFC 3D A grain-scale particle element-continuum partition coupling analysis of the time-dependent fracture evolution mechanism of hard rock in underground caverns under high-stress layered excavation was carried out.

[0013] In conjunction with the first aspect of the present application, in a first possible implementation of the first aspect of the present application, the process of obtaining a particle contact bonding aging model reflecting transgranular fracture and a particle cluster parallel bonding aging model reflecting intergranular fracture includes the following:

[0014] According to the test results of grain intrusion creep and grain boundary intrusion creep related to the target cavity, based on the Burger's rheological contact model based on discontinuity theory, the elements reflecting the normal and tangential contact stiffness and strength characteristics are modified or added to establish the particle contact bonding aging model reflecting transgranular fracture and the particle cluster parallel bonding aging model reflecting intergranular fracture respectively. 3D Secondary development is realized.

[0015] In combination with the first aspect of the present application, in a second possible implementation of the first aspect of the present application, the process of acquiring the flexible particle cluster filling geometric model includes the following:

[0016] Through 3D microstructure data model and PFC 3D Based on the data interface, the point cloud data of the 3D microstructure model of the rock block that represents the fractal characteristics of grain embedding is imported into PFC 3D middle;

[0017] Using the grain boundaries in the 3D mesostructure model of the rock block as walls, a step-by-step filling method was used to fill flexible particle clusters composed of particles of different sizes, gradually filling from the grid boundary to the grid center, to construct an initial flexible particle cluster filling geometric model reflecting the mesostructure characteristics of granite.

[0018] Based on the initial flexible grain cluster filling geometric model, the self-similarity of the mineral grain embedding morphology, and fractal geometry theory, the size of the mesostructure model was expanded according to the same fractal characteristics, and 3D mesostructure models of granite of different scales and shapes were generated. Flexible grain clusters were then further filled in to construct a flexible grain cluster filling geometric model that characterizes the fractal characteristics of the mineral grain embedding and the filling of flexible grain clusters.

[0019] In combination with the first aspect of the present application, in a third possible implementation of the first aspect of the present application, the calibration process of the aging model parameters includes the following:

[0020] Based on the particle contact bonding aging model, the particle cluster parallel bonding aging model, and the mineral single crystal flexible particle cluster filling geometry model, experimental simulations of single crystal indentation creep and single crystal indentation creep were carried out;

[0021] Through parameter sensitivity analysis and data fitting, the model parameters of the particle contact bonding aging model and the particle cluster parallel bonding aging model were preliminarily determined.

[0022] Based on the model parameters of the flexible particle cluster filling geometry model, particle contact bonding aging model, and particle cluster parallel bonding aging model at the indoor rock sample scale, triaxial unloading creep test simulations of indoor rock samples under different stress paths were carried out.

[0023] By comparing and verifying the macroscopic stress-strain curve, crack initiation strength, peak strength, residual strength, AE characteristics and macro- and micro-fracture patterns with those of the indoor triaxial unloading creep test of rock samples, and combining the macroscopic strength and deformation equivalence principle, the mechanical parameters of the particle contact bonding aging model and the particle cluster parallel bonding aging model are calibrated, and the calibration of the aging model parameters is completed.

[0024] In combination with the first aspect of the present application, in a fourth possible implementation of the first aspect of the present application, the process of determining the flexible particle cluster with the largest particle size includes the following:

[0025] Based on a 3D mesostructure model of granite at the indoor rock sample scale, flexible particle clusters with proportionally increasing particle sizes were filled. The model parameters of the particle contact bonding aging model and the particle cluster parallel bonding aging model before and after calibration were used to further simulate indoor rock sample triaxial unloading creep tests and analyze the sensitivity of the filling particle size. By comparing the results with those of the indoor creep test, the flexible particle cluster with the largest particle size was determined under the principle of macroscopic strength and deformation equivalence.

[0026] In conjunction with the first aspect of the present application, in a fifth possible implementation of the first aspect of the present application, the determination of the rock mass RVE size includes the following:

[0027] Based on the flexible particle cluster filling geometric model, flexible particle clusters with the largest particle size are filled in. The model parameters of the particle contact bonding aging model and the particle cluster parallel bonding aging model before and after calibration are used to carry out triaxial strength test particle element simulation of RVE rock mass under different confining pressures. The instantaneous strength-model size curve is drawn to determine the RVE size of the rock mass that reflects the macroscopic mechanical properties of the granite rock mass.

[0028] In combination with the first aspect of the present application, in a sixth possible implementation of the first aspect of the present application, the inversion process of the target CWFS model parameters of the macro-mechanical analysis area includes the following contents:

[0029] The initial CWFS model parameters are obtained according to the rock mass RVE size and instantaneous strength;

[0030] The initial CWFS model parameters are used as the initial input parameters, and combined with the in-situ comprehensive monitoring of the deformation of the surrounding rock time-dependent fracture zone, rock cracking and disturbance stress, the target CWFS model parameters of the macro-mechanical analysis area are inverted.

[0031] In a second aspect, the present application provides a coupled analysis device for the time-dependent crack evolution of high-stress hard rock in a cavern, the device comprising:

[0032] The acquisition unit is used to obtain the mechanical properties of the grains and grain boundaries of the granite minerals related to the target cavern, the particle contact bonding aging model reflecting transgranular fracture, the particle cluster parallel bonding aging model reflecting intergranular fracture, and the flexible particle cluster filling geometric model of different sizes and shapes;

[0033] Determine the unit for carrying out triaxial unloading creep tests on indoor rock samples related to the target cavern and simultaneous acoustic emission detection (AE) tests. The test results are combined with the mechanical properties of granite grains and grain boundaries, the particle contact bonding aging model reflecting transgranular fracture, the particle cluster parallel bonding aging model reflecting intergranular fracture, and the flexible particle cluster filling geometry model of different sizes and shapes to establish a 3D mesostructure model of granite at the indoor rock sample scale. Based on the 3D mesostructure model of granite at the indoor rock sample scale, PFC is carried out. 3D Through simulation, by calibrating the aging model parameters, the maximum size of flexible particle clusters and rock mass RVE size reflecting the mesoscopic evolution mechanism and mesoscopic structural characteristics of granite aging fracture are determined;

[0034] The CWFS model parameter processing unit is used to determine the initial CWFS model parameters based on the maximum-size flexible particle cluster and the rock mass RVE size. The initial CWFS model parameters are used as input parameters, combined with the in-situ comprehensive monitoring of the deformation of the surrounding rock age-related fracture zone, rock mass cracking and disturbance stress, to invert the target CWFS model parameters of the macro-mechanical analysis area;

[0035] The elastic-plastic analysis unit is used to carry out FLAC based on the target CWFS model parameters of the macro-mechanical analysis area. 3D During the elastoplastic analysis, rock mass damage indicators are used to precisely locate the extent of the aging fracture zone. The largest flexible particle clusters are then filled into the aging fracture zone grid using existing grid particle filling techniques. The corresponding regional grid cells are then deleted to construct a grain-scale particle element-continuum partition coupling geometric model of the mesoscopic evolution mechanism of aging fracture in hard rock in underground caverns.

[0036] The coupled analysis unit is used based on the coupled geometric model of grain-scale particle element and continuous medium partition, and adopts the same method as FLAC. 3DUnder the same initial boundary and stress conditions as the elastic-plastic analysis, the particle clusters in the aging fracture zone are assigned the aging model parameters, and the rock mass outside the aging fracture zone is assigned the target CWFS model and parameters. Then, FLAC is used to calculate the aging model parameters. 3D and PFC 3D A grain-scale particle element-continuum partition coupling analysis of the time-dependent fracture evolution mechanism of hard rock in underground caverns under high-stress layered excavation was carried out.

[0037] In conjunction with the second aspect of the present application, in a first possible implementation of the second aspect of the present application, the process of obtaining a particle contact bonding aging model reflecting transgranular fracture and a particle cluster parallel bonding aging model reflecting intergranular fracture includes the following:

[0038] According to the test results of grain intrusion creep and grain boundary intrusion creep related to the target cavity, based on the Burger's rheological contact model based on discontinuity theory, the elements reflecting the normal and tangential contact stiffness and strength characteristics are modified or added to establish the particle contact bonding aging model reflecting transgranular fracture and the particle cluster parallel bonding aging model reflecting intergranular fracture respectively. 3D Secondary development is realized.

[0039] In conjunction with the second aspect of the present application, in a second possible implementation of the second aspect of the present application, the process of acquiring the flexible particle cluster filling geometric model includes the following:

[0040] Through 3D microstructure data model and PFC 3D Based on the data interface, the point cloud data of the 3D microstructure model of the rock block that represents the fractal characteristics of grain embedding is imported into PFC 3D middle;

[0041] Using the grain boundaries in the 3D mesostructure model of the rock block as walls, a step-by-step filling method was used to fill flexible particle clusters composed of particles of different sizes, gradually filling from the grid boundary to the grid center, to construct an initial flexible particle cluster filling geometric model reflecting the mesostructure characteristics of granite.

[0042] Based on the initial flexible grain cluster filling geometric model, the self-similarity of the mineral grain embedding morphology, and fractal geometry theory, the size of the mesostructure model was expanded according to the same fractal characteristics, and 3D mesostructure models of granite of different scales and shapes were generated. Flexible grain clusters were then further filled in to construct a flexible grain cluster filling geometric model that characterizes the fractal characteristics of the mineral grain embedding and the filling of flexible grain clusters.

[0043] In conjunction with the second aspect of the present application, in a third possible implementation of the second aspect of the present application, the calibration process of the aging model parameters includes the following:

[0044] Based on the particle contact bonding aging model, the particle cluster parallel bonding aging model, and the mineral single crystal flexible particle cluster filling geometry model, experimental simulations of single crystal indentation creep and single crystal indentation creep were carried out;

[0045] Through parameter sensitivity analysis and data fitting, the model parameters of the particle contact bonding aging model and the particle cluster parallel bonding aging model were preliminarily determined.

[0046] Based on the model parameters of the flexible particle cluster filling geometry model, particle contact bonding aging model, and particle cluster parallel bonding aging model at the indoor rock sample scale, triaxial unloading creep test simulations of indoor rock samples under different stress paths were carried out.

[0047] By comparing and verifying the macroscopic stress-strain curve, crack initiation strength, peak strength, residual strength, AE characteristics and macro- and micro-fracture patterns with those of the indoor triaxial unloading creep test of rock samples, and combining the macroscopic strength and deformation equivalence principle, the mechanical parameters of the particle contact bonding aging model and the particle cluster parallel bonding aging model are calibrated, and the calibration of the aging model parameters is completed.

[0048] In conjunction with the second aspect of the present application, in a fourth possible implementation of the second aspect of the present application, the process of determining the flexible particle cluster with the largest particle size includes the following:

[0049] Based on a 3D mesostructure model of granite at the indoor rock sample scale, flexible particle clusters with proportionally increasing particle sizes were filled. The model parameters of the particle contact bonding aging model and the particle cluster parallel bonding aging model before and after calibration were used to further simulate indoor rock sample triaxial unloading creep tests and analyze the sensitivity of the filling particle size. By comparing the results with those of the indoor creep test, the flexible particle cluster with the largest particle size was determined under the principle of macroscopic strength and deformation equivalence.

[0050] In conjunction with the second aspect of the present application, in a fifth possible implementation of the second aspect of the present application, the determination of the rock mass RVE size includes the following:

[0051] Based on the flexible particle cluster filling geometric model, flexible particle clusters with the largest particle size are filled in. The model parameters of the particle contact bonding aging model and the particle cluster parallel bonding aging model before and after calibration are used to carry out triaxial strength test particle element simulation of RVE rock mass under different confining pressures. The instantaneous strength-model size curve is drawn to determine the RVE size of the rock mass that reflects the macroscopic mechanical properties of the granite rock mass.

[0052] In conjunction with the second aspect of the present application, in a sixth possible implementation of the second aspect of the present application, the inversion process of the target CWFS model parameters of the macro-mechanical analysis area includes the following contents:

[0053] The initial CWFS model parameters are obtained according to the rock mass RVE size and instantaneous strength;

[0054] The initial CWFS model parameters are used as the initial input parameters, and combined with the in-situ comprehensive monitoring of the deformation of the surrounding rock time-dependent fracture zone, rock cracking and disturbance stress, the target CWFS model parameters of the macro-mechanical analysis area are inverted.

[0055] In a third aspect, the present application provides a processing device comprising a processor and a memory, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the method provided in the first aspect of the present application or any possible implementation of the first aspect of the present application is executed.

[0056] In a fourth aspect, the present application provides a computer-readable storage medium, which stores multiple instructions, and the instructions are suitable for loading by a processor to execute the method provided in the first aspect of the present application or any possible implementation of the first aspect of the present application.

[0057] From the above content, it can be concluded that this application has the following beneficial effects:

[0058] Regarding the time-dependent fracture evolution of underground surrounding rock, this application specifically focuses on the time-dependent fracture evolution of high-stress hard rock in caverns. The surrounding rock of cavern groups is taken as the research object, and is regarded as an aggregate composed of grains of different mineral components. Based on the mesoscopic fracture evolution mechanism of mineral grain scale, the time-dependent fracture evolution of high-stress hard rock in caverns is analyzed layer by layer and in an interrelated manner, from the mineral grain scale of micron to millimeter, the indoor rock sample scale of centimeter, the RVE scale of decimeter to meter, and the cavern group scale of ten to hundred meters. This provides a high-precision, multi-scale and comprehensive analysis mechanism for the time-dependent fracture evolution of underground surrounding rock, which is particularly suitable for the analysis needs of the time-dependent fracture evolution of large-scale underground surrounding rock. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0060] Figure 1 A schematic flow chart of a coupled analysis method for time-dependent crack evolution of high-stress hard rock in caverns in this application;

[0061] Figure 2 A schematic diagram of a 3D mesoscopic structural model of a rock block in this application;

[0062] Figure 3A schematic diagram of a model of the 3D microstructure of this application;

[0063] Figure 4 A schematic diagram of a 3D mesoscopic structural model of granite at the indoor rock sample scale for this application;

[0064] Figure 5 A schematic diagram of a scenario for in-situ integrated monitoring in this application;

[0065] Figure 6 A schematic diagram of a scenario for the scale of the research object of this application;

[0066] Figure 7 A schematic diagram of the structure of a coupled analysis device for time-dependent crack evolution of high-stress hard rock in a cavern according to the present application;

[0067] Figure 8 This is a structural diagram of the processing equipment for this application. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0069] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products or devices. The naming or numbering of steps in this application does not mean that the steps in the method flow must be executed in the time / logical sequence indicated by the naming or numbering. The process steps that have been named or numbered can be changed in the execution order according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved.

[0070] The division of modules in this application is a logical division. In actual application, there may be other division methods. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection between modules can be electrical or other similar forms, which are not limited in this application. Moreover, the modules or submodules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed into multiple circuit modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this application.

[0071] Before introducing the coupled analysis method for the time-dependent fracture evolution of high-stress hard rock in caverns provided by this application, the background content involved in this application is first introduced.

[0072] The coupled analysis method, device, and computer-readable storage medium for the time-dependent fracture evolution of high-stress hard rock in caverns provided in this application can be applied to processing equipment to provide a high-precision, multi-scale, and comprehensive analysis mechanism for the time-dependent fracture evolution of underground surrounding rock, and are particularly suitable for the analysis needs of the time-dependent fracture evolution of large-scale underground surrounding rock.

[0073] The coupled analysis method for the aging-induced cracking evolution of high-stress hard rock in a cavern, as described in this application, can be implemented by a coupled analysis device for the aging-induced cracking evolution of high-stress hard rock in a cavern, or by various types of processing devices, such as a server, physical host, or user equipment (UE), that integrates the coupled analysis device for the aging-induced cracking evolution of high-stress hard rock in a cavern. The coupled analysis device for the aging-induced cracking evolution of high-stress hard rock in a cavern can be implemented using hardware or software. The UE can specifically be a terminal device such as a smartphone, tablet computer, laptop computer, desktop computer, or personal digital assistant (PDA). The processing device can be configured as a device cluster.

[0074] The processing equipment can specifically be relevant equipment on site, so that the analysis of the time-dependent fracture evolution of underground surrounding rock can be carried out on site, or it can also be relevant equipment in a laboratory or other place used to perform analysis work, so that the analysis of the time-dependent fracture evolution of underground surrounding rock can be carried out in the background, and remote data guidance can be provided for the site.

[0075] Next, the coupled analysis method for the time-dependent crack evolution of high-stress hard rock in caverns provided by this application is introduced.

[0076] First, see Figure 1 , Figure 1A flow chart of the coupled analysis method for the aging-induced cracking evolution of high-stress hard rock in a cavern is shown. The coupled analysis method for the aging-induced cracking evolution of high-stress hard rock in a cavern provided by the present application may specifically include the following steps S101 to S105:

[0077] Step S101: Obtain the mechanical properties of the grains and grain boundaries of the granite minerals related to the target cavern, the grain contact bonding aging model reflecting transgranular fracture, the grain cluster parallel bonding aging model reflecting intergranular fracture, and the flexible grain cluster filling geometry model of different sizes and shapes;

[0078] It can be understood that the processing here corresponds to the analysis mechanism of the aging fracture evolution of underground surrounding rock at the mineral grain scale of micron to millimeter level involved in this application. This application targets the analysis target of this time, that is, the target cavern (which can be any cavern with analysis requirements, specifically a large cavern), and obtains data in four aspects: the mechanical properties of the grains and grain boundaries of the granite minerals, the particle contact bonding aging model reflecting transgranular fracture, the particle cluster parallel bonding aging model reflecting intergranular fracture, and the flexible particle cluster filling geometric model of different sizes and shapes, to provide a data basis for subsequent processing.

[0079] Among them, the particle contact bonding aging model reflecting transgranular fracture and the particle cluster parallel bonding aging model reflecting intergranular fracture can also be called micromechanical aging models.

[0080] Specifically, in the present application, the grains of granite minerals can be specifically breakable flexible particle clusters composed of spheres of different particle sizes, which can be understood as a collection of particle elements with the same mechanical properties, and the grain boundaries are the interfaces between grains (particle clusters).

[0081] The specific processing of measuring (or detecting) the mechanical properties of granite grains and grain boundaries can include the following:

[0082] (1) Scanning electron microscope (SEM) is used to scan and analyze the fresh fracture surfaces (non-primary) of the surrounding rock flakes collected at the initial excavation site of the target cavern / pilot tunnel. This allows the determination of the various mineral components and main grain microscopic fracture modes of the granite under high stress excavation disturbance, as well as the proportion of transgranular and intergranular fractures in each mineral grain.

[0083] (2) Combined use of micron computed tomography (CT) scanning and X-ray microanalyzer to detect the microstructure of a 5cm×5cm×5cm rock block taken from the slab, distinguish the internal pores, cracks and different mineral grains (such as quartz, mica, plagioclase, etc.) of the rock sample according to the grayscale, count the volume size and equivalent radius of each mineral grain, analyze the three-dimensional morphology and geometric fractal characteristics, and reconstruct the 3D microstructure model of the rock block that represents the distribution characteristics of the mineral grains, such as Figure 2 A schematic diagram of a model of the 3D microstructure model of a rock block of the present application is shown, wherein 1 is a particle contact unit, 2 is a crushable flexible particle cluster, 3 is a grain space, and 4 is a grain boundary bonding unit.

[0084] (3) Based on the fine grinding of rock slices under a microscope, rock samples containing the main mineral grains for microscopic mechanical properties testing are prepared. The main mineral grains are subjected to creep intrusion tests under graded loading using a nanomechanical probe. The intrusion load-depth, intrusion hardness-depth, and intrusion modulus-depth curves of the mineral grains under static load are plotted to obtain parameters such as the elastic modulus and creep compliance of the mineral grains. Based on the SEM scanning of the protrusion degree of the intrusion point and the morphology of the deformation zone below, the strength of the material fluidity of the grains under graded compression loading is analyzed. The experimental data required for the correction of the particle contact bonding aging model reflecting the mineral transgranular fracture is provided.

[0085] (4) Based on the fine grinding technology of rock slices under a microscope, the interface of a main mineral grain is exposed, and then the slices are prepared to prepare the microscopic mechanical properties test specimens containing the main mineral grain boundaries. The creep scratching test under graded tangential force loading is carried out on each mineral grain boundary using a nanoprobe, and the curves of the scratching depth-scratching position, tangential force-scratching position, etc. of the mineral grain boundary are drawn to obtain the parameters such as the scratching deformation and friction coefficient of the grain boundary; the scratch depth and debris peeling of the scratching point are scanned by SEM to analyze the strength of the grain boundary's anti-scratching ability under shear graded loading; and the experimental data required for the correction of the parallel bonding aging model of the particle cluster simulating the mineral intergranular fracture are provided.

[0086] For the mechanical aging model at the mineral grain scale, namely the particle contact bonding aging model reflecting transgranular fracture and the particle cluster parallel bonding aging model reflecting intergranular fracture, they characterize the 3D microstructure of the fractal characteristics of grain embedding. For details, please refer to Figure 3 A schematic diagram of a model of the 3D microstructure of the present application is shown, wherein: Figure 3 Transgranular fracture is simulated, 2 is a flexible particle cluster that can be broken, 3 is the grain space, and 5 is different mineral grains.

[0087] The process of obtaining both the particle contact bonding aging model reflecting transgranular fracture and the particle cluster parallel bonding aging model reflecting intergranular fracture can specifically include the following:

[0088] According to the test results of grain intrusion creep and grain boundary intrusion creep related to the target cavity, based on the Burger's rheological contact model based on discontinuity theory, the elements reflecting the normal and tangential contact stiffness and strength characteristics are modified or added to establish the particle contact bonding aging model reflecting transgranular fracture and the particle cluster parallel bonding aging model reflecting intergranular fracture respectively. 3D Secondary development is realized.

[0089] For this setting, it should be understood that for rocks, the cementation between particles will cause friction between internal particle boundaries and particle rotation under the action of external forces. To study the time-dependent deformation behavior of rocks under load, PFC is usually used. 3D The program's built-in discontinuous Burger's rheological contact model is a tandem Kelvin and Maxwell models in the normal and tangential directions, respectively. Therefore, by modifying or adding components reflecting the normal and tangential contact stiffness and strength characteristics based on creep test results, the rheological properties of the bonded rock particles can be more accurately characterized.

[0090] Among them, PFC 3D It can be understood as an analysis environment provided by existing products, and is also contrasted with the related analysis environment that follows.

[0091] Furthermore, a flexible particle cluster filling geometric model at the mineral grain scale is also involved here, and its acquisition process may specifically include the following:

[0092] Through the 3D microstructure data model (which can be understood as a general model of the initial state, a native model, such as PFC 3D Native model in the program) and PFC 3D Based on the data interface, the point cloud data of the 3D microstructure model of the rock block that represents the fractal characteristics of grain embedding is imported into PFC 3D middle;

[0093] Using the grain boundaries in the 3D mesostructure model of the rock block as walls, a step-by-step filling method was used to fill flexible particle clusters composed of particles of different sizes, gradually filling from the grid boundary to the grid center, to construct an initial flexible particle cluster filling geometric model reflecting the mesostructure characteristics of granite.

[0094] Based on the initial flexible grain cluster filling geometric model, the self-similarity of the mineral grain embedding morphology, and fractal geometry theory, the size of the mesostructure model was expanded according to the same fractal characteristics, and 3D mesostructure models of granite of different scales and shapes were generated. Flexible grain clusters were then added to construct a flexible grain cluster filling geometric model (also known as a multi-scale geometric model of granite) that characterizes the fractal characteristics of the mineral grain embedding and the filling of flexible grain clusters.

[0095] Step S102: Conduct triaxial unloading creep tests on indoor rock samples related to the target cavern and conduct simultaneous AE tests. The test results are combined with the mechanical properties of granite grains and grain boundaries, the particle contact bonding aging model reflecting transgranular fracture, the particle cluster parallel bonding aging model reflecting intergranular fracture, and the flexible particle cluster filling geometry model of different sizes and shapes to establish a 3D mesostructure model of granite at the indoor rock sample scale. PFC is then carried out based on the 3D mesostructure model of granite at the indoor rock sample scale. 3D Simulation, by calibrating the aging model parameters, determines the maximum size of flexible particle clusters and rock mass RVE size that reflects the mesoscopic evolution mechanism and mesoscopic structural characteristics of granite aging fracture (rock sample scale and RVE scale);

[0096] It can be understood that the processing here corresponds to the analysis mechanism of the time-dependent fracture evolution of underground surrounding rock at the centimeter-level indoor rock sample scale and the decimeter to meter-level RVE scale involved in this application.

[0097] Among them, the indoor rock sample scale granite 3D microstructure model corresponds to the indoor rock sample scale at the centimeter level. For details, please refer to Figure 4 A schematic diagram of a model of a 3D mesoscopic structure model of granite at the indoor rock sample scale of the present application is shown, wherein 2 is a crushable flexible particle cluster.

[0098] The process of establishing a 3D mesostructure model of granite at the indoor rock sample scale includes the following:

[0099] (1) Based on the non-time-dependent analysis simulation of continuous media during the layered excavation process of large-scale underground engineering, the range of the surrounding rock aging fracture zone and the stress state evolution law of typical parts of this area are given, the stress path of the indoor triaxial unloading creep test of rock samples is determined, and the values of the stress path control points are given;

[0100] (2) For cylindrical specimens (e.g., 5 cm in diameter and 10 cm in height) obtained by coring from geological drilling in the aging fracture zone, a triaxial rheometer (e.g., Top Industrie triaxial rheometer) is used to unload them step by step from high confining pressure levels, and triaxial unloading creep tests are conducted simultaneously with AE tests to track the initiation and propagation of cracks inside the rock specimens and the evolution of the position of the main fracture surface;

[0101] (3) SEM scanning was used to analyze the microscopic fracture pattern and distribution of the final failure surface of the rock sample, and compared with the previous grain-scale slab microscopic fracture pattern and distribution to verify the rationality of the experimental scheme. The time-dependent fracture evolution mechanism of hard rock at the rock sample scale was studied from the perspectives of internal crack propagation evolution and macro- and micro-fracture patterns of the fracture surface.

[0102] It should be understood that the content here does not involve the process of establishing a 3D mesostructure model of granite at the indoor rock sample scale, including the mechanical properties of granite grains and grain boundaries, the particle contact bonding aging model reflecting transgranular fracture, the particle cluster parallel bonding aging model reflecting intergranular fracture, and the filling geometric model of flexible particle clusters of different sizes and shapes. It mainly involves the acquisition of rock samples and the processing of synchronous AE.

[0103] The calibration of aging model parameters mentioned here refers to the calibration of model parameters of both the particle contact bonding aging model and the particle cluster parallel bonding aging model. The calibration process of the aging model parameters can specifically include the following:

[0104] Based on the particle contact bonding aging model, the particle cluster parallel bonding aging model, and the mineral single crystal flexible particle cluster filling geometry model, experimental simulations of single crystal indentation creep and single crystal indentation creep were carried out;

[0105] Through parameter sensitivity analysis and data fitting, the model parameters of the particle contact bonding aging model and the particle cluster parallel bonding aging model were preliminarily determined.

[0106] Based on the model parameters of the flexible particle cluster filling geometry model, particle contact bonding aging model, and particle cluster parallel bonding aging model at the indoor rock sample scale, triaxial unloading creep test simulations of indoor rock samples under different stress paths were carried out.

[0107] By comparing and verifying the macroscopic stress-strain curve, crack initiation strength, peak strength, residual strength, AE characteristics and macro- and micro-fracture modes with the indoor triaxial unloading creep test of rock samples, and combining the macroscopic strength and deformation equivalence principle, the mechanical parameters of the particle contact bonding aging model and the particle cluster parallel bonding aging model are calibrated, and the calibration of the microscopic mechanical aging model parameters is completed.

[0108] For this setting, it is easy to understand that different contact models in the numerical simulation program have different mesoscopic parameters, which respectively affect the deformation and failure characteristics of the specimen. Therefore, if a set of suitable mesoscopic parameters can be found to describe the test phenomenon, then a set of suitable mesoscopic parameters can be calibrated. However, the test phenomenon cannot be represented by only one set of parameters. Often, many sets of mesoscopic parameters are required to obtain numerical calculation results that are close to the test results. The process of mesoscopic parameter debugging is relatively complicated, and some mesoscopic parameters will also affect each other. Therefore, in order to calibrate the mesoscopic parameters, this application uses multi-faceted comparative verification to debug a set of parameters that are approximately close to the test results. Furthermore, for the flexible particles with the maximum particle size involved here, the content of the determination process may specifically include the following:

[0109] Based on a 3D mesostructure model of granite at the indoor rock sample scale, flexible particle clusters with proportionally increasing particle sizes were filled. The model parameters of the particle contact bonding aging model and the particle cluster parallel bonding aging model before and after calibration were used to further simulate indoor rock sample triaxial unloading creep tests and analyze the sensitivity of the filling particle size. By comparing the results with those of the indoor creep test, the flexible particle cluster with the largest particle size was determined under the principle of macroscopic strength and deformation equivalence.

[0110] For this setting, it can be understood that the selection of the basic particle size is determined by the particle size of the ore itself and the size of the screening particle size after crushing. Selecting an appropriate basic particle size combination can produce a more accurate model. Determining the maximum particle size flexible particle cluster through the above method not only improves the efficiency of particle generation, but also effectively characterizes the crushing characteristics, helps eliminate non-uniform forces between particles, better simulates rock crushing conditions, and makes the simulation results more consistent with the expected effect.

[0111] Furthermore, for the rock mass RVE size involved here, the content of determining the coarse particles may specifically include the following:

[0112] Based on the flexible particle cluster filling geometric model, flexible particle clusters with the largest particle size are filled in. The model parameters of the particle contact bonding aging model and the particle cluster parallel bonding aging model before and after calibration are used to carry out triaxial strength test particle element simulation of RVE rock mass under different confining pressures. The instantaneous strength-model size curve is drawn to determine the RVE size of the rock mass that reflects the macroscopic mechanical properties of the granite rock mass.

[0113] Among them, the establishment of RVE rock mass has certain degrees of freedom in terms of size and the number of particles it contains. Its calculation accuracy will be affected by the RVE size, and the existence of confining pressure will affect the size effect of the rock sample. Therefore, based on the determination of particle size, this application carried out triaxial strength test particle element simulation of RVE rock mass under multiple confining pressures to more accurately determine the RVE size of the rock mass that reflects the macroscopic mechanical properties of granite.

[0114] Step S103: Determine the initial CWFS model parameters based on the maximum-size flexible particle cluster and the rock mass RVE size. Use the initial CWFS model parameters as input parameters, and combine them with in-situ comprehensive monitoring of the deformation of the surrounding rock age-related fracture zone, rock mass cracking, and disturbance stress to invert the target CWFS model parameters of the macro-mechanical analysis area.

[0115] After determining the maximum-size flexible particle cluster and the rock mass RVE size, the relevant CWFS model parameters can be determined based on this through a two-layer parameter determination mechanism to obtain the final target CWFS model parameters.

[0116] It can be understood that the treatment here corresponds to the analysis mechanism of the time-dependent fracture evolution of underground surrounding rock at the cavern group scale of ten to one hundred meters involved in this application.

[0117] For the in-situ comprehensive monitoring of the cave group scale, taking a cavern in a hydropower station as an example, the following contents can be carried out:

[0118] (1) FLAC based on the layered excavation process of large underground caverns 3D Elastic-plastic analysis to roughly determine the time-dependent fracture zone after layered excavation of the cavern;

[0119] (2) Through the auxiliary caverns (such as drainage corridors, traffic tunnels, etc.), targeted geological drilling in the time-sensitive fracture zone, pre-buried disturbance stress and deformation monitoring devices, etc., to carry out in-hole ultrasonic testing and in-hole imaging observation, inter-hole CT scanning, disturbance stress testing and deep and shallow deformation monitoring of the rock mass in the time-sensitive fracture zone, such as Figure 5 Figure 6 shows a schematic diagram of a scenario for in-situ comprehensive monitoring of the present application, which illustrates the scale distribution and correlation of the research objects of the time-dependent fracture evolution mechanism of rock mass. Number 6 represents the time-dependent fracture zone, numbers 7 to 11 are all holes of the same size, number 7 is a disturbance stress test hole, numbers 8, 9, 10, and 11 are observation holes for acoustic wave testing and borehole photography, and numbers 7 and 8 are CT scanning test holes.

[0120] (3) Based on the monitoring results, the evolution mechanism of time-dependent fracture of hard rock in caverns under high-stress layered excavation is interpreted from the perspectives of surrounding rock wave velocity variation, crack propagation in the rock mass within the hole, microscopic damage of the rock mass between holes, stress redistribution, and deformation in deep and shallow layers.

[0121] The inversion process of the target CWFS model parameters in the macroscopic mechanical analysis area involved here can be implemented by algorithms such as neural networks, and specifically includes the following:

[0122] The initial CWFS model parameters are obtained according to the RVE size and instantaneous intensity;

[0123] The initial CWFS model parameters are used as the initial input parameters, and combined with the in-situ comprehensive monitoring of the deformation of the surrounding rock time-dependent fracture zone, rock cracking and disturbance stress, the target CWFS model parameters of the macro-mechanical analysis area are inverted.

[0124] Specifically, mechanical testing is an effective way to obtain rock mechanical parameters. However, due to the large scale of the rock mass, it is difficult to predict the mechanical properties of the rock mass through rock block strength and small-scale discontinuities. In addition, in-situ testing is expensive and difficult to carry out on a large scale. The rock mechanical parameter inversion method can integrate the various characteristics of the rock mass and the surrounding rock response information during the cavern excavation process, so that the obtained rock mechanical parameters are more accurate and relatively flexible. It has been widely used in many major projects and has been well received and recognized by industry experts. Therefore, in order to save time and economic costs, this application uses on-site in-situ comprehensive monitoring to establish a neural network, and obtains the target CWFS model parameters of the macroscopic mechanical analysis area through the autonomous evolution of learning errors and test errors through the algorithm.

[0125] Step S104: Based on the target CWFS model parameters of the macro-mechanical analysis area, FLAC 3D During the elastoplastic analysis, rock mass damage indicators are used to precisely locate the extent of the aging fracture zone. The largest flexible particle clusters are then filled into the aging fracture zone grid using existing grid particle filling techniques. The corresponding regional grid cells are then deleted to construct a grain-scale particle element-continuum partition coupling geometric model of the mesoscopic evolution mechanism of aging fracture in hard rock in underground caverns.

[0126] It can be understood that after obtaining the target CWFS model parameters, it can be based on FLAC 3D In this paper, a grain-scale particle element-continuum partition coupling geometric model of the mesoscopic evolution mechanism of time-dependent fracture of hard rock in underground caverns is constructed based on the analytical environment.

[0127] Step S105, based on the grain-scale particle element-continuum partition coupling geometric model, using the same 3D Under the same initial boundary and stress conditions as the elastic-plastic analysis, the particle clusters in the aging fracture zone are assigned the micromechanical aging model parameters, and the rock mass outside the aging fracture zone is assigned the target CWFS model and parameters. Then, FLAC is used to calculate the particle clusters in the aging fracture zone. 3D and PFC 3D A grain-scale particle element-continuum partition coupling analysis of the time-dependent fracture evolution mechanism of hard rock in underground caverns under high-stress layered excavation was carried out.

[0128] Understandably, when using FLAC 3D After obtaining the grain-scale particle element-continuum partition coupling geometric model through elastic-plastic analysis, it can be further combined with PFC 3DA grain-scale particle element-continuum partition coupling analysis of the time-dependent fracture evolution mechanism of hard rock in underground caverns under high-stress layered excavation was carried out to achieve the analysis goal, namely, the time-dependent fracture evolution of high-stress hard rock in the target caverns.

[0129] For the above content, you can also refer to Figure 6 By understanding the scenario diagram of the scale of the research object of this application, it can be intuitively seen that this application analyzes the time-dependent fracture evolution of high-stress hard rock in caverns in a progressive and interconnected manner from the mineral grain scale of micrometer to millimeter, the indoor rock sample scale of centimeter, the RVE scale of decimeter to meter, and the cave group scale of ten meters to one hundred meters, thus efficiently, highly and comprehensively integrating the analysis effects of various scales.

[0130] exist Figure 6 Under the analytical framework shown, in specific applications, the applicability and reliability of this scheme were verified by comparing the measured occurrence, density and aperture of newly formed macroscopic cracks in the borehole, the microscopic damage distribution and the deformation of the surrounding rock in the aging fracture zone. This can make a great contribution to further studying the deep and shallow deformation of the surrounding rock, the expansion and evolution of macroscopic and microscopic cracks, and the changing laws of energy dissipation, as well as the further prediction of the spalling range, depth and long-term aging mechanical behavior of the surrounding rock, and has high practical value.

[0131] As can be seen from the above content, with regard to the time-dependent fracture evolution of underground surrounding rock, this application specifically focuses on the time-dependent fracture evolution of high-stress hard rock in caverns. The surrounding rock of cavern groups is taken as the research object, which is regarded as an aggregate composed of grains of different mineral components. Based on the mesoscopic fracture evolution mechanism of mineral grain scale, the time-dependent fracture evolution of high-stress hard rock in caverns is analyzed in a progressive and interrelated manner from the mineral grain scale of micrometer to millimeter, the indoor rock sample scale of centimeter, the RVE scale of decimeter to meter, and the cavern group scale of ten to one hundred meters. This provides a high-precision, multi-scale and comprehensive analysis mechanism for the time-dependent fracture evolution of underground surrounding rock, which is particularly suitable for the analysis needs of the time-dependent fracture evolution of large-scale underground surrounding rock.

[0132] The above is an introduction to the coupled analysis method for the time-dependent fracture evolution of high-stress hard rock in caverns provided in this application. In order to better implement the coupled analysis method for the time-dependent fracture evolution of high-stress hard rock in caverns provided in this application, this application also provides a coupled analysis device for the time-dependent fracture evolution of high-stress hard rock in caverns from the perspective of functional modules.

[0133] See Figure 7 , Figure 7 This is a schematic diagram of a coupled analysis device for time-dependent cracking evolution of high-stress hard rock in a cavern according to the present application. In the present application, the coupled analysis device 700 for time-dependent cracking evolution of high-stress hard rock in a cavern may specifically include the following structure:

[0134] The acquisition unit 701 is used to respectively acquire the mechanical properties of the grains and grain boundaries of the granite minerals related to the target cavern, the grain contact bonding aging model reflecting transgranular fracture, the grain cluster parallel bonding aging model reflecting intergranular fracture, and the flexible grain cluster filling geometric model of different sizes and shapes;

[0135] Determine unit 702, which is used to carry out triaxial unloading creep tests on indoor rock samples related to the target cavern and synchronize acoustic emission detection (AE) tests. The test results are combined with the mechanical properties of granite grains and grain boundaries, the particle contact bonding aging model reflecting transgranular fracture, the particle cluster parallel bonding aging model reflecting intergranular fracture, and the flexible particle cluster filling geometry model of different sizes and shapes to establish a 3D mesostructure model of granite at the indoor rock sample scale. Based on the 3D mesostructure model of granite at the indoor rock sample scale, PFC is carried out. 3D Through simulation, by calibrating the aging model parameters, the maximum size of flexible particle clusters and rock mass RVE size reflecting the mesoscopic evolution mechanism and mesoscopic structural characteristics of granite aging fracture are determined;

[0136] The CWFS model parameter processing unit 703 is used to determine the initial CWFS model parameters based on the maximum-size flexible particle cluster and the rock mass RVE size, and use the initial CWFS model parameters as input parameters, combined with the in-situ comprehensive monitoring of the deformation of the surrounding rock age-related fracture zone, rock mass cracking and disturbance stress, to invert the target CWFS model parameters of the macro-mechanical analysis area;

[0137] The elastic-plastic analysis unit 704 is used to perform FLAC based on the target CWFS model parameters of the macro-mechanical analysis area. 3D During the elastoplastic analysis, rock mass damage indicators are used to precisely locate the extent of the aging fracture zone. The largest flexible particle clusters are then filled into the aging fracture zone grid using existing grid particle filling techniques. The corresponding regional grid cells are then deleted to construct a grain-scale particle element-continuum partition coupling geometric model of the mesoscopic evolution mechanism of aging fracture in hard rock in underground caverns.

[0138] The coupling analysis unit 705 is used to use the FLAC 3D Under the same initial boundary and stress conditions as the elastic-plastic analysis, the particle clusters in the aging fracture zone are assigned the aging model parameters, and the rock mass outside the aging fracture zone is assigned the target CWFS model and parameters. Then, FLAC is used to calculate the aging model parameters. 3D and PFC 3D A grain-scale particle element-continuum partition coupling analysis of the time-dependent fracture evolution mechanism of hard rock in underground caverns under high-stress layered excavation was carried out.

[0139] In an exemplary implementation, the process of obtaining a particle contact bonding aging model reflecting transgranular fracture and a particle cluster parallel bonding aging model reflecting intergranular fracture includes the following:

[0140] According to the test results of grain intrusion creep and grain boundary intrusion creep related to the target cavity, based on the Burger's rheological contact model based on discontinuity theory, the elements reflecting the normal and tangential contact stiffness and strength characteristics are modified or added to establish the particle contact bonding aging model reflecting transgranular fracture and the particle cluster parallel bonding aging model reflecting intergranular fracture respectively. 3D Secondary development is realized.

[0141] In another exemplary implementation, the process of acquiring the flexible particle cluster filling geometric model includes the following:

[0142] Through 3D microstructure data model and PFC 3D Based on the data interface, the point cloud data of the 3D microstructure model of the rock block that represents the fractal characteristics of grain embedding is imported into PFC 3D middle;

[0143] Using the grain boundaries in the 3D mesostructure model of the rock block as walls, a step-by-step filling method was used to fill flexible particle clusters composed of particles of different sizes, gradually filling from the grid boundary to the grid center, to construct an initial flexible particle cluster filling geometric model reflecting the mesostructure characteristics of granite.

[0144] Based on the initial flexible grain cluster filling geometric model, the self-similarity of the mineral grain embedding morphology, and fractal geometry theory, the size of the mesostructure model was expanded according to the same fractal characteristics, and 3D mesostructure models of granite of different scales and shapes were generated. Flexible grain clusters were then further filled in to construct a flexible grain cluster filling geometric model that characterizes the fractal characteristics of the mineral grain embedding and the filling of flexible grain clusters.

[0145] In another exemplary implementation, the calibration process of the aging model parameters includes the following:

[0146] Based on the particle contact bonding aging model, the particle cluster parallel bonding aging model, and the mineral single crystal flexible particle cluster filling geometry model, experimental simulations of single crystal indentation creep and single crystal indentation creep were carried out;

[0147] Through parameter sensitivity analysis and data fitting, the model parameters of the particle contact bonding aging model and the particle cluster parallel bonding aging model were preliminarily determined.

[0148] Based on the model parameters of the flexible particle cluster filling geometry model, particle contact bonding aging model, and particle cluster parallel bonding aging model at the indoor rock sample scale, triaxial unloading creep test simulations of indoor rock samples under different stress paths were carried out.

[0149] By comparing and verifying the macroscopic stress-strain curve, crack initiation strength, peak strength, residual strength, AE characteristics and macro- and micro-fracture patterns with those of the indoor triaxial unloading creep test of rock samples, and combining the macroscopic strength and deformation equivalence principle, the mechanical parameters of the particle contact bonding aging model and the particle cluster parallel bonding aging model are calibrated, and the calibration of the aging model parameters is completed.

[0150] In another exemplary implementation, the process of determining the flexible particle cluster with the largest particle size includes the following:

[0151] Based on a 3D mesostructure model of granite at the indoor rock sample scale, flexible particle clusters with proportionally increasing particle sizes were filled. The model parameters of the particle contact bonding aging model and the particle cluster parallel bonding aging model before and after calibration were used to further simulate indoor rock sample triaxial unloading creep tests and analyze the sensitivity of the filling particle size. By comparing the results with those of the indoor creep test, the flexible particle cluster with the largest particle size was determined under the principle of macroscopic strength and deformation equivalence.

[0152] In another exemplary implementation, the process of determining the size of the rock mass RVE includes the following:

[0153] Based on the flexible particle cluster filling geometric model, flexible particle clusters with the largest particle size are filled in. The model parameters of the particle contact bonding aging model and the particle cluster parallel bonding aging model before and after calibration are used to carry out triaxial strength test particle element simulation of RVE rock mass under different confining pressures. The instantaneous strength-model size curve is drawn to determine the RVE size of the rock mass that reflects the macroscopic mechanical properties of the granite rock mass.

[0154] In another exemplary implementation, the inversion process of the target CWFS model parameters in the macro-mechanical analysis area includes the following:

[0155] The initial CWFS model parameters are obtained according to the rock mass RVE size and instantaneous strength;

[0156] The initial CWFS model parameters are used as the initial input parameters, and combined with the in-situ comprehensive monitoring of the deformation of the surrounding rock time-dependent fracture zone, rock cracking and disturbance stress, the target CWFS model parameters of the macro-mechanical analysis area are inverted.

[0157] This application also provides a processing device from the perspective of hardware structure, see Figure 8 , Figure 8The schematic diagram of the structure of the processing device of the present application is shown. Specifically, the processing device of the present application may include a processor 801, a memory 802 and an input / output device 803. The processor 801 is used to execute the computer program stored in the memory 802 to implement the following Figure 1 The steps of the coupled analysis method for aging crack evolution of high-stress hard rock in a cavern in the corresponding embodiment; or, when the processor 801 is used to execute the computer program stored in the memory 802, the following is implemented: Figure 7 The memory 802 is used to store the functions of each unit in the embodiment corresponding to the processor 801. Figure 1 The computer program required for the coupled analysis method of the time-dependent crack evolution of high-stress hard rock in the corresponding embodiment.

[0158] For example, the computer program may be divided into one or more modules / units, one or more of which are stored in the memory 802 and executed by the processor 801 to complete the present application. One or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in a computer device.

[0159] The processing device may include, but is not limited to, a processor 801, a memory 802, and an input / output device 803. Those skilled in the art will appreciate that the illustrations are merely examples of processing devices and do not limit the processing device. The processing device may include more or fewer components than shown, or a combination of certain components, or different components. For example, the processing device may also include a network access device, a bus, etc., and the processor 801, the memory 802, the input / output device 803, etc. are connected via a bus.

[0160] The processor 801 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the processing device and connects various parts of the entire device using various interfaces and lines.

[0161] The memory 802 can be used to store computer programs and / or modules. The processor 801 implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory 802 and accessing the data stored in the memory 802. The memory 802 may mainly include a program storage area and a data storage area. The program storage area may store an operating system, at least one application required for a function, etc.; the data storage area may store data created based on the use of the processing device, etc. In addition, the memory may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0162] When the processor 801 is used to execute the computer program stored in the memory 802, it can specifically implement the following functions:

[0163] The mechanical properties of the grains and grain boundaries of the granite minerals related to the target cavern are obtained, along with the grain contact bonding aging model reflecting transgranular fracture, the grain cluster parallel bonding aging model reflecting intergranular fracture, and the filling geometry model of flexible grain clusters of different sizes and shapes.

[0164] Carry out triaxial unloading creep tests on indoor rock samples related to the target caverns and perform simultaneous acoustic emission (AE) tests. Combine the test results with the mechanical properties of granite grains and grain boundaries, the particle contact bonding aging model reflecting transgranular fracture, the particle cluster parallel bonding aging model reflecting intergranular fracture, and the flexible particle cluster filling geometry model of different sizes and shapes to establish a 3D mesostructure model of granite at the indoor rock sample scale. Based on the 3D mesostructure model of granite at the indoor rock sample scale, PFC is carried out. 3D Through simulation, by calibrating the aging model parameters, the maximum size of flexible particle clusters and rock mass RVE size reflecting the mesoscopic evolution mechanism and mesoscopic structural characteristics of granite aging fracture are determined;

[0165] The initial CWFS model parameters are determined based on the maximum-size flexible particle cluster and the rock mass RVE size. The initial CWFS model parameters are used as input parameters. Combined with the in-situ comprehensive monitoring of the deformation of the surrounding rock aging fracture zone, rock mass cracking, and disturbance stress, the target CWFS model parameters of the macro-mechanical analysis area are inverted.

[0166] Based on the target CWFS model parameters in the macro-mechanical analysis area, FLAC 3DDuring the elastoplastic analysis, rock mass damage indicators are used to precisely locate the extent of the aging fracture zone. The largest flexible particle clusters are then filled into the aging fracture zone grid using existing grid particle filling techniques. The corresponding regional grid cells are then deleted to construct a grain-scale particle element-continuum partition coupling geometric model of the mesoscopic evolution mechanism of aging fracture in hard rock in underground caverns.

[0167] Based on the grain-scale particle element-continuum partition coupling geometric model, the FLAC 3D Under the same initial boundary and stress conditions as the elastic-plastic analysis, the particle clusters in the aging fracture zone are assigned the aging model parameters, and the rock mass outside the aging fracture zone is assigned the target CWFS model and parameters. Then, FLAC is used to calculate the aging model parameters. 3D and PFC 3D A grain-scale particle element-continuum partition coupling analysis of the time-dependent fracture evolution mechanism of hard rock in underground caverns under high-stress layered excavation was carried out.

[0168] Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working process of the coupling analysis device, processing equipment and corresponding units of the above-described cavern high stress hard rock aging crack evolution can be referred to as follows: Figure 1 The description of the coupled analysis method for the aging-induced cracking evolution of high-stress hard rock in the corresponding embodiment will not be repeated here.

[0169] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be accomplished by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0170] To this end, the present application provides a computer-readable storage medium, which stores a plurality of instructions, which can be loaded by a processor to execute the present application as follows: Figure 1 The steps of the coupled analysis method for the evolution of aging cracking of high-stress hard rock in the corresponding embodiment can be referred to as follows for specific operations. Figure 1 The description of the coupled analysis method for the time-dependent crack evolution of high-stress hard rock in the cavern in the corresponding embodiment will not be repeated here.

[0171] The computer-readable storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0172] Due to the instructions stored in the computer readable storage medium, the present application can be executed as follows: Figure 1The steps of the coupled analysis method for the evolution of high-stress hard rock cracking in a cavern in the embodiment correspond to the steps of the coupled analysis method for the evolution of high-stress hard rock cracking in a cavern in the embodiment. Figure 1 The beneficial effects that can be achieved by the coupled analysis method for the aging-induced crack evolution of high-stress hard rock in the corresponding embodiment are detailed in the previous description and will not be repeated here.

[0173] The above is a detailed introduction to the coupled analysis method, device, processing equipment and computer-readable storage medium for the aging crack evolution of high-stress hard rock in caverns provided by this application. Specific examples are used herein to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, based on the ideas of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A coupled analysis method for the evolution of time-dependent cracking of high-stress hard rock in a cavern, characterized by: The method comprises: The mechanical properties of the grains and grain boundaries of the granite minerals related to the target cavern are obtained, along with the grain contact bonding aging model reflecting transgranular fracture, the grain cluster parallel bonding aging model reflecting intergranular fracture, and the filling geometry model of flexible grain clusters of different sizes and shapes. Carry out triaxial unloading creep test on indoor rock samples related to the target cavern and synchronous acoustic emission detection AE test, and combine the test results with the mechanical properties of the grains and grain boundaries of the granite minerals, the particle contact bonding aging model reflecting transgranular fracture, the particle cluster parallel bonding aging model reflecting intergranular fracture, and the flexible particle cluster filling geometric model of different sizes and shapes to establish a 3D mesostructure model of granite at the indoor rock sample scale. Based on the 3D mesostructure model of granite at the indoor rock sample scale, PFC is carried out. 3D Through simulation, by calibrating the parameters of the aging model, the maximum size of flexible particle clusters and the size of the representative volume unit RVE of the rock mass that reflects the mesoscopic evolution mechanism and mesoscopic structural characteristics of granite aging fracture are determined; Determine initial cohesion weakening and friction strengthening CWFS model parameters based on the maximum-size flexible particle cluster and the rock mass RVE size, and use the initial CWFS model parameters as input parameters. Combined with in-situ comprehensive monitoring of deformation in the surrounding rock age-related fracture zone, rock mass cracking, and disturbance stress, invert the target CWFS model parameters in the macro-mechanical analysis area. Based on the target CWFS model parameters of the macromechanical analysis area, FLAC 3D During the elastoplastic analysis, rock damage indicators are used to precisely locate the extent of the aging fracture zone. The largest-size flexible particle clusters are then filled into the aging fracture zone grid using existing grid particle filling techniques. The corresponding regional grid cells are then deleted to construct a grain-scale particle element-continuum partition coupling geometric model of the mesoscopic evolution mechanism of aging fracture in hard rock in underground caverns. Based on the grain-scale particle element-continuum partition coupling geometric model, the FLAC 3D The same initial boundary and stress conditions are used in the elastic-plastic analysis. The particle clusters in the aging fracture zone are assigned the aging model parameters. The rock mass outside the aging fracture zone is assigned the target CWFS model and parameters. Then, FLAC is used to calculate the aging model parameters. 3D and PFC 3D A grain-scale particle element-continuum partition coupling analysis of the time-dependent fracture evolution mechanism of hard rock in underground caverns under high-stress layered excavation was carried out.

2. The method according to claim 1, characterized in that The process of obtaining the particle contact bonding aging model reflecting transgranular fracture and the particle cluster parallel bonding aging model reflecting intergranular fracture includes the following: According to the test results of grain intrusion creep and grain boundary intrusion creep related to the target cavity, based on the Burger's rheological contact model based on discontinuity theory, the elements reflecting the normal and tangential contact stiffness and strength characteristics are modified or added to establish the particle contact bonding aging model reflecting transgranular fracture and the particle cluster parallel bonding aging model reflecting intergranular fracture, and the PFC 3D Secondary development is realized.

3. The method according to claim 1, characterized in that The process of obtaining the flexible particle cluster filling geometric model includes the following: Through the 3D microstructure data model and the PFC 3D Based on the data interface, the point cloud data of the 3D microstructure model of the rock block representing the fractal characteristics of the grain embedding is imported into the PFC 3D middle; Using the grain space boundaries in the 3D mesostructure model of the rock block as walls, a step-by-step filling method is used to fill flexible particle clusters composed of particles of different sizes, gradually filling from the grid boundary to the grid center, to construct an initial flexible particle cluster filling geometric model reflecting the mesostructure characteristics of granite; Based on the initial flexible particle cluster filling geometric model, the self-similarity of the mineral grain embedding morphology, and the fractal geometry theory, the size of the mesostructure model is expanded according to the same fractal characteristics, and 3D mesostructure models of granite of different scales and shapes are generated. Flexible particle clusters are further filled in to construct the flexible particle cluster filling geometric model that characterizes the fractal characteristics of the mineral grain embedding and the flexible particle cluster filling.

4. The method according to claim 1, wherein The calibration process of the aging model parameters includes the following contents: Based on the particle contact bonding aging model, the particle cluster parallel bonding aging model, and the mineral single crystal flexible particle cluster filling geometry model, experimental simulations of single crystal indentation creep and single crystal indentation creep were carried out; Through parameter sensitivity analysis and data fitting, the model parameters of the particle contact bonding aging model and the particle cluster parallel bonding aging model were preliminarily determined; Based on the indoor rock sample-scale flexible particle cluster filling geometry model, the particle contact bonding aging model, and the particle cluster parallel bonding aging model, triaxial unloading creep test simulations of indoor rock samples under different stress paths were carried out. By comparing and verifying the macroscopic stress-strain curve, crack initiation strength, peak strength, residual strength, AE characteristics and macro-microscopic fracture patterns of the indoor rock sample triaxial unloading creep test, and combining the macroscopic strength and deformation equivalence principle, the mechanical parameters of the particle contact bonding aging model and the particle cluster parallel bonding aging model are calibrated, and the calibration of the aging model parameters is completed.

5. The method according to claim 1, wherein The process of determining the maximum particle size flexible particle cluster includes the following: Based on the indoor rock sample-scale 3D mesoscopic structural model of granite, flexible particle clusters with proportionally increased particle sizes were filled. The model parameters of the particle contact bonding aging model and the particle cluster parallel bonding aging model before and after calibration were used to further carry out indoor rock sample triaxial unloading creep test simulation and filling particle size sensitivity analysis. Then, by comparing with the indoor creep test results, the flexible particle cluster with the maximum particle size was determined under the principle of macroscopic strength and deformation equivalence.

6. The method according to claim 1, characterized in that The determination process of the rock mass RVE size includes the following: Based on the flexible particle cluster filling geometric model, the flexible particle cluster with the maximum particle size is filled in. The model parameters of the particle contact bonding aging model and the particle cluster parallel bonding aging model before and after calibration are used to carry out triaxial strength test particle element simulation of RVE rock mass under different confining pressures, draw the instantaneous strength-model size curve, and determine the RVE size of the rock mass that reflects the macroscopic mechanical properties of the granite rock mass.

7. The method according to claim 1, characterized in that The inversion process of the target CWFS model parameters in the macro-mechanical analysis area includes the following: Obtaining the initial CWFS model parameters according to the rock mass RVE size and instantaneous strength; The initial CWFS model parameters are used as initial input parameters, combined with the in-situ comprehensive monitoring of the deformation of the surrounding rock age-related fracture zone, rock mass cracking and disturbance stress, to invert the target CWFS model parameters of the macro-mechanical analysis area.

8. A coupled analysis device for the evolution of high-stress hard rock cracking in a cavern, characterized by: The device comprises: The acquisition unit is used to obtain the mechanical properties of the grains and grain boundaries of the granite minerals related to the target cavern, the particle contact bonding aging model reflecting transgranular fracture, the particle cluster parallel bonding aging model reflecting intergranular fracture, and the flexible particle cluster filling geometric model of different sizes and shapes; A determination unit is used to carry out triaxial unloading creep tests on indoor rock samples related to the target cavern and synchronous acoustic emission detection (AE) tests, and to combine the test results with the mechanical properties of the grains and grain boundaries of the granite minerals, the particle contact bonding aging model reflecting transgranular fracture, the particle cluster parallel bonding aging model reflecting intergranular fracture, and the flexible particle cluster filling geometric model of different sizes and shapes to establish a 3D mesostructure model of granite at the indoor rock sample scale, and to carry out PFC based on the 3D mesostructure model of granite at the indoor rock sample scale. 3D Through simulation, by calibrating the parameters of the aging model, the maximum size of flexible particle clusters and the size of the representative volume unit RVE of the rock mass that reflects the mesoscopic evolution mechanism and mesoscopic structural characteristics of granite aging fracture are determined; a CWFS model parameter processing unit for determining initial cohesion weakening and friction strengthening CWFS model parameters based on the maximum-size flexible particle cluster and the rock mass RVE size, and using the initial CWFS model parameters as input parameters, in combination with in-situ comprehensive monitoring of deformation of the surrounding rock age-related fracture zone, rock mass cracking, and disturbance stress, to invert target CWFS model parameters in the macro-mechanical analysis area; The elastic-plastic analysis unit is used to carry out FLAC based on the target CWFS model parameters of the macro-mechanical analysis area. 3D During the elastoplastic analysis, rock damage indicators are used to precisely locate the extent of the aging fracture zone. The largest-size flexible particle clusters are then filled into the aging fracture zone grid using existing grid particle filling techniques. The corresponding regional grid cells are then deleted to construct a grain-scale particle element-continuum partition coupling geometric model of the mesoscopic evolution mechanism of aging fracture in hard rock in underground caverns. The coupling analysis unit is used to use the FLAC 3D The same initial boundary and stress conditions are used in the elastic-plastic analysis. The particle clusters in the aging fracture zone are assigned the aging model parameters. The rock mass outside the aging fracture zone is assigned the target CWFS model and parameters. Then, FLAC is used to calculate the aging model parameters. 3D and PFC 3D A grain-scale particle element-continuum partition coupling analysis of the time-dependent fracture evolution mechanism of hard rock in underground caverns under high-stress layered excavation was carried out.

9. A processing device, characterized in that The method comprises a processor and a memory, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the method according to any one of claims 1 to 7 is executed.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the method according to any one of claims 1 to 7.

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