A dynamic simulation method for blasting in surrounding rock tunnels

Abstract

The present invention discloses a method for dynamic simulation of tunnel blasting in surrounding rock, belonging to the field of data simulation technology. The method comprises: acquiring three-dimensional geological data; constructing an initial mechanical parameter field of the surrounding rock based on the three-dimensional geological data; discretizing the surrounding rock region using the finite element method based on the initial mechanical parameter field, dividing the grid and initializing the mechanical properties of each unit; performing blasting simulation based on the mechanical properties, monitoring the stress distribution state of the surrounding rock in real time during the simulation process, and determining whether stress concentration occurs; calculating the propagation path and attenuation rate of the stress wave generated by the explosive explosion in the surrounding rock through numerical simulation based on the determination result; analyzing the fracture pattern and fragment distribution characteristics of the surrounding rock based on the propagation path and attenuation rate of the stress wave; and obtaining a final blasting simulation result based on the stress distribution state, fracture pattern, and fragment distribution characteristics of the surrounding rock. The present invention can reflect the dynamic response of the surrounding rock and predict the blasting effect.

Description

Technical Field

[0001] The invention belongs to the technical field of data simulation, and in particular relates to a dynamic simulation method for surrounding rock tunnel blasting. Background Art

[0002] A key technical challenge in the dynamic simulation of tunnel blasting in surrounding rock is how to accurately capture the influence of surrounding rock properties on blasting stress wave propagation, especially under complex geological conditions. The physical and mechanical properties of the surrounding rock, such as rock density, elastic modulus, Poisson's ratio, cohesion, and internal friction angle, directly determine the propagation path, attenuation rate, and dynamic response of the stress wave during blasting. However, these properties often exhibit spatial heterogeneity and anisotropy, making it difficult for traditional homogeneous models to accurately reflect the actual blasting effects. For example, in high-stress areas, the elastic modulus of the surrounding rock can vary significantly due to stress concentration, thereby affecting the propagation velocity and energy distribution of the stress wave. Furthermore, the cohesion and internal friction angle of the surrounding rock may undergo nonlinear changes under dynamic loading conditions, further affecting the surrounding rock fracture pattern and the distribution of blasting fragments. Therefore, accurately describing the spatial variability and dynamic response characteristics of surrounding rock properties in dynamic simulations has become a technical challenge that needs to be addressed urgently. Simultaneously, the interaction between the surrounding rock and the explosive is extremely complex, involving factors such as the explosive energy release rate, the expansion of explosive gases, and the transient stress state of the surrounding rock, all of which must be considered in the simulation. Combining these complex interaction mechanisms with the surrounding rock properties to construct a simulation model that can both reflect the dynamic response of the surrounding rock and accurately predict blasting effects remains a core issue in current dynamic simulation technology for tunnel blasting in surrounding rock. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention proposes a dynamic simulation method for surrounding rock tunnel blasting, which can reflect the dynamic response of the surrounding rock and predict the blasting effect.

[0004] The present invention provides a method for dynamic simulation of surrounding rock tunnel blasting, comprising:

[0005] Acquire 3D geological data;

[0006] constructing an initial mechanical parameter field of surrounding rock based on the three-dimensional geological data;

[0007] Based on the initial mechanical parameter field, the surrounding rock region is discretized using the finite element method, the mesh is divided, and the mechanical properties of each unit are initialized;

[0008] Based on the mechanical properties, a blasting simulation is performed, and during the simulation, the stress distribution state of the surrounding rock is monitored in real time to determine whether there is a stress concentration phenomenon;

[0009] Based on the judgment results, the propagation path and attenuation rate of the stress wave generated by the explosive explosion in the surrounding rock are calculated through numerical simulation;

[0010] Analyze the fracture pattern and fragment distribution characteristics of the surrounding rock based on the propagation path and attenuation rate of the stress wave;

[0011] Based on the stress distribution state, fracture mode and fragment distribution characteristics of the surrounding rock, the final blasting simulation results are obtained.

[0012] Optionally, the three-dimensional geological data includes: rock density, elastic modulus and Poisson's ratio measurement data.

[0013] Optionally, constructing an initial mechanical parameter field of surrounding rock based on the three-dimensional geological data includes:

[0014] Processing the rock density using a Kriging interpolation method to generate a continuous distribution map;

[0015] constructing a rock mass mechanical property model based on the elastic modulus and Poisson's ratio measurement data;

[0016] Data fusion is performed on the data having corresponding relationships between the continuous distribution map and the rock mass mechanical property model to establish an initial mechanical parameter field of the surrounding rock.

[0017] Optionally, based on the initial mechanical parameter field, the finite element method is used to discretize the surrounding rock region, divide the grid, and initialize the mechanical properties of each unit, including:

[0018] Obtain the geometric structure data of the surrounding rock area, discretize the area using the finite element method, and generate a grid model;

[0019] Determining a mechanical property value of each grid unit in the grid model according to data in the initial mechanical parameter field;

[0020] If there is a correspondence between the grid cells and the spatial distribution in the parameter field, the corresponding mechanical properties are assigned;

[0021] The mechanical data of the missing areas in the parameter field are processed by interpolation method to supplement the attribute values ​​of the grid cells;

[0022] Use machine learning algorithms to optimize the mechanical properties of grid cells and correct outliers;

[0023] Update the mechanical parameters of the mesh model according to the optimized mechanical property values.

[0024] Optionally, real-time monitoring of the stress distribution of the surrounding rock to determine whether there is stress concentration includes:

[0025] Obtain real-time data on surrounding rock stress distribution and extract stress values ​​at monitoring points;

[0026] The stress value of the monitoring point is calculated using the analytical model to generate a stress distribution diagram;

[0027] If there is an area in the stress distribution diagram where the stress value exceeds a preset threshold, it is determined that stress concentration exists.

[0028] Optionally, if there is an area in the stress distribution map where the stress value exceeds a preset threshold, determining that there is a stress concentration phenomenon includes:

[0029] Using the preset threshold, it is determined whether the stress value exceeds the threshold area and marked as a concentration area;

[0030] If the concentrated area coincides with a historical anomaly point, update the anomaly point data and record the correction value;

[0031] The elastic modulus of the affected units is adjusted according to the correction value through a dynamic adjustment algorithm;

[0032] An iterative algorithm is used to iteratively calculate the adjusted elastic modulus to generate an updated stress distribution diagram;

[0033] Reanalyze the stress values ​​based on the updated stress distribution diagram to determine whether there is a new concentration area;

[0034] If there is a new concentration area, repeat the adjustment and iteration process until the stress distribution tends to be uniform.

[0035] Optionally, based on the judgment result, the propagation path and attenuation rate of the stress wave generated by the explosive explosion in the surrounding rock are calculated by numerical simulation, including:

[0036] Get the initial parameters of explosive explosion;

[0037] Set the initial value of the calculation domain according to the material parameters and boundary conditions of the surrounding rock mass;

[0038] Performing dynamic change analysis based on the initial parameters and initial values ​​to calculate the propagation path of the stress wave generated by the explosive explosion in the surrounding rock;

[0039] The decay rate of the stress wave is calculated based on the nodes along the propagation path.

[0040] Optionally, the decay rate of the stress wave is calculated based on the nodes along the propagation path, including:

[0041] If the stress value of a node exceeds the preset threshold, it is marked as a high stress area;

[0042] According to the distribution of the high stress areas, the material parameters of the surrounding rock mass are adjusted to re-obtain the attenuation rate of the stress wave.

[0043] Optionally, based on the propagation path and attenuation rate of the stress wave, the analysis of the fracture mode and fragment distribution characteristics of the surrounding rock includes:

[0044] Divide the surrounding rock fracture area according to the stress wave peak value and duration;

[0045] Extract the size and distribution of blasting fragments and establish a fragment distribution model;

[0046] Combined with the fracture area and fragment distribution model, the blasting effect is analyzed to obtain the fracture pattern and fragment distribution characteristics.

[0047] Optionally, obtaining the final blasting simulation result based on the stress distribution state, fracture mode, and fragment distribution characteristics of the surrounding rock includes:

[0048] Obtain explosive energy release rate data and explosion gas expansion parameters, combine them with the mechanical properties of the surrounding rock, and update the stress distribution state of the surrounding rock;

[0049] The data corresponding to the stress distribution state, fracture mode and fragment distribution characteristics are coupled to obtain the final blasting simulation results.

[0050] Compared with the prior art, the present invention has the following advantages and technical effects:

[0051] The present invention first obtains the three-dimensional geological data of the surrounding rock, constructs the initial mechanical parameter field, and then discretizes the surrounding rock using the finite element method. During the simulation process, the stress distribution state is monitored in real time, and the elastic modulus of the affected unit is dynamically adjusted according to the stress level to ensure uniform stress distribution. The propagation path and attenuation rate of the stress wave generated by the explosion of the explosives are calculated through numerical simulation, and the surrounding rock fracture mode and the distribution characteristics of the blasting fragments are analyzed. Finally, the energy release rate of the explosives and the expansion process of the explosion gas are integrated, the instantaneous stress state of the surrounding rock is updated, the dynamic response characteristics of the surrounding rock are coupled with the blasting effect, and the final blasting simulation results are output. The present invention can accurately simulate the surrounding rock blasting process under complex geological conditions, provide a reliable basis for blasting design and construction, and effectively improve the blasting effect and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0053] Figure 1 This is a flow chart of a dynamic simulation method for surrounding rock tunnel blasting according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0055] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0056] The present invention proposes a dynamic simulation method for surrounding rock tunnel blasting, such as Figure 1 As shown, the specific steps include:

[0057] Acquire 3D geological data;

[0058] Based on 3D geological data, the initial mechanical parameter field of surrounding rock is constructed;

[0059] Based on the initial mechanical parameter field, the finite element method is used to discretize the surrounding rock area, divide the grid and initialize the mechanical properties of each unit;

[0060] Based on mechanical properties, blasting simulation is carried out. During the simulation, the stress distribution of the surrounding rock is monitored in real time to determine whether there is stress concentration.

[0061] Based on the judgment results, the propagation path and attenuation rate of the stress wave generated by the explosive explosion in the surrounding rock are calculated through numerical simulation;

[0062] Analyze the fracture pattern and fragment distribution characteristics of the surrounding rock based on the propagation path and attenuation rate of the stress wave;

[0063] The final blasting simulation results are obtained based on the stress distribution state, fracture mode and fragment distribution characteristics of the surrounding rock.

[0064] Specifically, three-dimensional geological data are obtained from the surrounding rock area, including distribution parameters of rock density, elastic modulus, and Poisson's ratio;

[0065] Based on 3D geological data, the initial mechanical parameter field of the surrounding rock is constructed to determine the spatial distribution of cohesion and internal friction angle;

[0066] The finite element method is used to discretize the surrounding rock area, divide the grid and initialize the mechanical properties of each unit. The mechanical properties are derived from the initial mechanical parameter field.

[0067] During the simulation process, the stress distribution of the surrounding rock is monitored in real time to determine whether there is stress concentration.

[0068] If stress concentration is detected, the elastic modulus of the affected unit is dynamically adjusted according to the stress level to ensure that the stress distribution tends to be uniform;

[0069] The propagation path and attenuation rate of the stress wave generated by explosive explosion in the surrounding rock are calculated through numerical simulation;

[0070] Based on the stress wave propagation results, the fracture mode of the surrounding rock and the distribution characteristics of the blasting fragments are analyzed;

[0071] Integrate the explosive energy release rate and the explosion gas expansion process to update the instantaneous stress state of the surrounding rock;

[0072] The dynamic response characteristics of the surrounding rock are coupled with the blasting effect to output the final blasting simulation results, including stress distribution, fracture mode and fragment distribution.

[0073] Furthermore, the three-dimensional geological data includes rock density, elastic modulus and Poisson's ratio measurement data.

[0074] Furthermore, based on the 3D geological data, the initial mechanical parameter field of the surrounding rock is constructed, including:

[0075] Kriging difference method is used to process rock density and generate a continuous distribution map;

[0076] Construct a rock mass mechanical properties model based on elastic modulus and Poisson's ratio measurement data;

[0077] The data with corresponding relationship between the continuous distribution map and the rock mass mechanical property model are fused to establish the initial mechanical parameter field of the surrounding rock.

[0078] Specifically, rock density, elastic modulus, and Poisson's ratio data were obtained. The drilling sampling depth ranged from the surface to 300 meters underground, with a spacing of 50 meters, forming a grid distribution. When processing rock density distribution using the Kriging interpolation algorithm, discrete measurement point data was converted into a continuous distribution model. Through Kriging interpolation, the spatial gradient relationship of rock density was visualized. In the construction of the mechanical property model, the elastic modulus of the collected rock samples was between 20 and 50 GPa, and the Poisson's ratio was between 0.2 and 0.3. Using the finite element method, the sampling area was divided into thousands of grid cells, and each cell was assigned corresponding mechanical parameters to form a complete mechanical property model. During the data fusion process, it was found that rock density and elastic modulus showed a positive correlation.

[0079] Furthermore, based on the initial mechanical parameter field, the finite element method is used to discretize the surrounding rock area, divide the grid and initialize the mechanical properties of each unit, including:

[0080] Obtain the geometric structure data of the surrounding rock area, discretize the area using the finite element method, and generate a grid model;

[0081] Determine the mechanical property value of each grid unit in the grid model according to the data in the initial mechanical parameter field;

[0082] If there is a correspondence between the grid cells and the spatial distribution in the parameter field, the corresponding mechanical properties are assigned;

[0083] The mechanical data of the missing areas in the parameter field are processed by interpolation method to supplement the attribute values ​​of the grid cells;

[0084] Use machine learning algorithms to optimize the mechanical properties of grid cells and correct outliers;

[0085] Update the mechanical parameters of the mesh model according to the optimized mechanical property values.

[0086] Specifically, the geometric structural data of the surrounding rock area includes key information such as geological fractures, rock stratification, and fissure orientation. For an underground cavern project, for example, the collected geometric data indicates three major joints in the rock mass, with orientations of 45 degrees north-east, 30 degrees north-west, and near-horizontal. This data directly influences the precision of the meshing. During the finite element discretization process, tetrahedral mesh elements are selected based on the structural characteristics of the surrounding rock mass. The mesh size is set to 0.5 meters near the fault fracture zone and 1.5 meters in intact rock mass areas, ensuring both accuracy and conserving computing resources. For a typical underground cavern project, approximately 100,000 mesh elements may be required to adequately describe the surrounding rock structure. The mechanical properties of the mesh elements are mapped based on the distribution characteristics of the initial mechanical parameter field. This spatial variability in mechanical parameters directly reflects the heterogeneity of the rock mass strength. For areas with missing data in the parameter field, the inverse distance weighted method is used for interpolation. For example, in the area between two boreholes, the parameter values ​​of the interpolated points are calculated based on the mechanical parameter values ​​of the known points and their spatial relationship. This method is particularly suitable for areas with relatively continuous geological variations. During the mechanical property optimization phase, a random forest algorithm was used to identify outliers. For example, if the elastic modulus of a grid cell is 30 GPa, while the surrounding cells are generally 15 GPa, this significant deviation from the surrounding values ​​requires reasonable correction based on the geological structure characteristics. The optimization process takes into account the anisotropic characteristics of the rock mass, making the distribution of mechanical parameters more consistent with actual geological conditions. The mechanical properties of the grid model are verified through transient dynamic analysis, and boundary conditions equivalent to the actual engineering loads are applied. In the underground powerhouse project of a hydropower station, the deformation of the surrounding rock reached the millimeter level during excavation, and the error with the on-site monitoring data was within 15%, confirming the rationality of the discretized model. This verification process not only ensures the accuracy of the model, but also provides a reliable basis for the subsequent optimization of the construction plan.

[0087] Furthermore, real-time monitoring of the stress distribution of the surrounding rock and determination of whether stress concentration exists include:

[0088] Obtain real-time data on surrounding rock stress distribution and extract stress values ​​at monitoring points;

[0089] The stress value of the monitoring point is calculated using the analytical model to generate a stress distribution diagram;

[0090] If there is an area in the stress distribution diagram where the stress value exceeds a preset threshold, it is determined that stress concentration exists.

[0091] Specifically, surrounding rock stress monitoring typically uses multiple real-time displacement sensors and intelligent pressure gauges for data collection. For example, in a tunnel project, four sets of displacement sensors and six sets of pressure gauges were deployed, with monitoring points spaced every ten meters. The displacement sensors primarily monitor surrounding rock deformation, while the pressure gauges record stress distribution along the depth. The sensor data is transmitted to the monitoring center via a data collector, forming a real-time stress data stream. Stress distribution maps are generated using three-dimensional interpolation modeling techniques, converting discrete monitoring point data into a continuous distribution field. Kriging interpolation is used to spatially interpolate the monitoring point data to construct a stress field distribution model. Preset thresholds are typically determined based on the surrounding rock grade and project experience, and stress concentration areas are identified using regional clustering analysis. When the stress value in a region exceeds the threshold, the system automatically marks it as a stress concentration area. A historical anomaly database records all previously discovered stress anomaly areas, including location coordinates and stress values. A spatial matching algorithm is used to determine whether newly discovered concentration areas coincide with historical anomalies. Data for overlapping areas is then updated to the database. The correction algorithm is primarily based on elastic-plastic mechanics theory and takes into account the nonlinear characteristics of the surrounding rock. The optimization iteration uses the gradient descent method, with the goal of minimizing the discontinuity of the stress field. In each round of iteration, the system calculates the continuity index of the corrected stress field and stops the iteration when the improvement in the index is less than the set threshold. The optimized stress distribution diagram shows a more reasonable stress transition relationship and avoids stress mutation. The determination of new anomalies is based on the updated stress distribution diagram using the dynamic threshold method. The system automatically identifies areas with abnormal stress value growth and, combined with the deformation rate of the surrounding rock, determines whether new anomalies have formed. By analyzing and confirming new anomalies, timely support and reinforcement measures can be taken to effectively prevent the instability of the surrounding rock.

[0092] Furthermore, if there is an area in the stress distribution diagram where the stress value exceeds a preset threshold, then it is determined that there is a stress concentration phenomenon including:

[0093] Using the preset threshold, it is determined whether the stress value exceeds the threshold area and marked as a concentration area;

[0094] If the concentrated area coincides with a historical anomaly point, update the anomaly point data and record the correction value;

[0095] The elastic modulus of the affected units is adjusted according to the correction value through a dynamic adjustment algorithm;

[0096] An iterative algorithm is used to iteratively calculate the adjusted elastic modulus to generate an updated stress distribution diagram;

[0097] Reanalyze the stress values ​​based on the updated stress distribution diagram to determine whether there is a new concentration area;

[0098] If there is a new concentration area, repeat the adjustment and iteration process until the stress distribution tends to be uniform.

[0099] Furthermore, based on the judgment results, the propagation path and attenuation rate of the stress wave generated by the explosive explosion in the surrounding rock are calculated through numerical simulation, including:

[0100] Get the initial parameters of explosive explosion;

[0101] Set the initial value of the calculation domain according to the material parameters and boundary conditions of the surrounding rock mass;

[0102] Based on the initial parameters and initial values, dynamic change analysis is performed to calculate the propagation path of the stress wave generated by the explosive explosion in the surrounding rock;

[0103] The decay rate of the stress wave is calculated based on the nodes along the propagation path.

[0104] Specifically, the explosive detonation process involves a rapid release of energy. For example, black powder, commonly used in tunneling, has a detonation velocity of approximately 3,000 meters per second and releases up to 3 gigajoules of energy per unit volume. Selecting the appropriate explosive type and parameters is crucial for controlling the blasting effect. For example, in underground mines, lower-power emulsion explosives are often used to minimize damage to the surrounding rock. The 3D geometric modeling of the surrounding rock mass must consider the actual geological conditions. For example, in a certain mining area, the surrounding rock mass is primarily granite, which has relatively good rock integrity. The model size can be set to a 20-meter radius around the detonation epicenter, using hexahedral meshing. The element size gradually increases from the detonation epicenter outward, with a minimum mesh size of 5 mm near the epicenter. This gradient meshing method ensures both accuracy and efficiency. Regarding material parameter settings, the elastic modulus of granite is 50 GPa, the Poisson's ratio is 0.25, and the density is 2.6 tons per cubic meter. Non-reflecting boundaries are used to prevent reflection interference from stress waves. When setting initial conditions, the pressure at the center of the explosion is set to the peak explosion pressure of approximately 200 MPa. Stress wave propagation in the surrounding rock follows the wave equation, solved using an explicit dynamics algorithm. Stress waves attenuate during propagation, and the rate of attenuation is related to material damping and wavefront expansion. For example, in a deep tunnel blasting incident, monitoring showed that the peak stress decreased by approximately 40% after the stress wave propagated one meter. The criteria for determining high-stress areas must consider the strength characteristics of the surrounding rock, with 70% of the uniaxial compressive strength typically used as a warning value. When stress in a specific area exceeds this warning value, the surrounding rock may experience damage or cracking. This can be controlled by adjusting blasting parameters, such as reducing the charge weight per stage or adopting a loosely coupled charge structure. Regarding material parameter adjustments, a damage factor is introduced to account for the damage effects of blast loading on the surrounding rock. When the surrounding rock is subjected to high stress, its elastic modulus decreases. Experience has shown that a damage factor of 0.3 reduces the elastic modulus of the material by approximately 40%. This dynamic adjustment method more accurately simulates the actual mechanical response of the surrounding rock during blasting. Through repeated iterative calculations, blasting and surrounding rock parameters are continuously optimized, ultimately achieving a reasonable stress distribution. This optimization process effectively controls blasting effects and reduces surrounding rock damage, which is of great significance in guiding engineering practice.

[0105] Furthermore, based on the nodes on the propagation path, the attenuation rate of the stress wave is calculated including:

[0106] If the stress value of a node exceeds the preset threshold, it is marked as a high stress area;

[0107] According to the distribution of high stress areas, the material parameters of the surrounding rock mass are adjusted to regain the attenuation rate of the stress wave.

[0108] Furthermore, based on the propagation path and attenuation rate of stress waves, the fracture mode and fragment distribution characteristics of the surrounding rock are analyzed, including:

[0109] Divide the surrounding rock fracture area according to the stress wave peak value and duration;

[0110] Extract the size and distribution of blasting fragments and establish a fragment distribution model;

[0111] Combined with the fracture area and fragment distribution model, the blasting effect is analyzed to obtain the fracture pattern and fragment distribution characteristics.

[0112] Specifically, the stress wave propagation path reflects the diffusion of blasting energy. By constructing a surrounding rock fracture model, blasting effects can be predicted. In typical blasting projects, the initial peak stress wave can reach hundreds of MPa and decay exponentially with increasing propagation distance. For example, in a mining blasting project, the peak stress wave at the blast source was measured to be 300 MPa, which decayed to 30 MPa at a distance of 20 meters. This attenuation characteristic directly influences the delineation of surrounding rock fracture zones. Delineation of surrounding rock fracture zones requires consideration of the strength characteristics of the rock mass. For example, granite has a compressive strength of approximately 120 MPa. When the peak stress wave exceeds this threshold, fractures will form in the rock mass. Finite element analysis can generate a stress distribution cloud map under the action of stress waves, allowing for the delineation of fracture zones, fissure zones, and elastic deformation zones. In actual projects, the fracture zone typically takes on an ellipsoidal shape, with the major axis aligned with the direction of the maximum principal stress. Establishing a fragmentation distribution model requires extensive field data. The distribution of fragments after blasting at a quarry showed that 50% of the fragments were between 10 and 30 cm in size, 30% were smaller than 10 cm, and 20% were larger than 30 cm. This distribution pattern is closely related to blasting parameters, including explosive consumption per unit, charge structure, and detonation sequence. By establishing a fragment distribution database, it can provide a reference for blasting design under different working conditions. Machine learning algorithms play an important role in predicting blasting results. Using algorithms such as support vector machines, fragment distribution can be predicted by inputting characteristics such as explosive type, charge quantity, and rock mass parameters. A project demonstrated that a model trained using historical data can achieve an 85% accuracy rate in predicting fragment distribution. This predictive capability is crucial for optimizing blasting parameters. When analyzing blasting results, multiple indicators must be considered comprehensively. Key indicators include fragment size uniformity, the proportion of oversized fragments, and flyaway rock. In an open-pit mine blast, adjustments to the charge structure reduced the proportion of oversized fragments larger than one meter from 15% to 5%, while also limiting flyaway rock distance to within 50 meters. This optimization process requires continuous adjustment and verification in engineering practice. Data analysis techniques can reveal the correlation between blasting parameters and fragmentation effectiveness. For example, under the same lithologic conditions, an increase of 0.1 kg per cubic meter in explosives consumption reduces the average fragmentation size by approximately 20%. Establishing this quantitative relationship provides a scientific basis for optimizing blasting parameters. By continuously accumulating and analyzing data, we can continuously refine our prediction models and improve blasting effectiveness.

[0113] Furthermore, based on the stress distribution state, fracture pattern and fragment distribution characteristics of the surrounding rock, the final blasting simulation results are obtained, including:

[0114] Obtain explosive energy release rate data and explosion gas expansion parameters, combine them with the mechanical properties of the surrounding rock, and update the stress distribution state of the surrounding rock;

[0115] The data corresponding to the stress distribution state, fracture mode and fragment distribution characteristics are coupled to obtain the final blasting simulation results.

[0116] Specifically, the dynamic response characteristics of the surrounding rock and blasting effect data are obtained, coupled with stress distribution, fracture pattern, and fragment distribution, and the blasting simulation results are output. A finite element method is used to establish a surrounding rock stress field model. By inputting surrounding rock dynamics and blasting force parameters, a surrounding rock stress distribution map is obtained. Based on the surrounding rock stress distribution map, the stress peak region and fracture zone are divided, and the surrounding rock fracture pattern class is determined by combining the fragment volume and regional volume data. Based on the fracture pattern class and stress peak region data, a machine learning algorithm is used to predict the expansion trend of the surrounding rock fracture zone. If the expansion trend of the surrounding rock fracture zone exceeds the preset threshold, the blasting force parameters are adjusted, the surrounding rock stress field model is optimized, and the stress distribution map is recalculated. Based on the optimized stress distribution map, the surrounding rock fracture zone and fragment volume data are updated, the coupling simulation value is output, and the surrounding rock dynamic response characteristics analysis results are generated. A deep learning algorithm is used to predict the surrounding rock fragment distribution and fracture zone expansion trend based on the surrounding rock dynamic response characteristics and blasting effect data, and the final blasting simulation results are output.

[0117] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A dynamic simulation method for surrounding rock tunnel blasting, characterized in that: include: Acquire 3D geological data; constructing an initial mechanical parameter field of surrounding rock based on the three-dimensional geological data; Based on the initial mechanical parameter field, the surrounding rock region is discretized using the finite element method, the mesh is divided, and the mechanical properties of each unit are initialized; Based on the mechanical properties, a blasting simulation is performed, and during the simulation, the stress distribution state of the surrounding rock is monitored in real time to determine whether there is a stress concentration phenomenon; Based on the judgment results, the propagation path and attenuation rate of the stress wave generated by the explosive explosion in the surrounding rock are calculated through numerical simulation; Analyze the fracture pattern and fragment distribution characteristics of the surrounding rock based on the propagation path and attenuation rate of the stress wave; Based on the stress distribution state, fracture mode and fragment distribution characteristics of the surrounding rock, the final blasting simulation results are obtained.

2. A method for dynamic simulation of surrounding rock tunnel blasting according to claim 1, characterized in that: The three-dimensional geological data includes rock density, elastic modulus and Poisson's ratio measurement data.

3. A method for dynamic simulation of surrounding rock tunnel blasting according to claim 2, characterized in that: Based on the three-dimensional geological data, the initial mechanical parameter field of the surrounding rock is constructed, including: Processing the rock density using a Kriging interpolation method to generate a continuous distribution map; constructing a rock mass mechanical property model based on the elastic modulus and Poisson's ratio measurement data; Data fusion is performed on the data having corresponding relationships between the continuous distribution map and the rock mass mechanical property model to establish an initial mechanical parameter field of the surrounding rock.

4. A method for dynamic simulation of surrounding rock tunnel blasting according to claim 3, characterized in that: Based on the initial mechanical parameter field, the finite element method is used to discretize the surrounding rock area, divide the grid and initialize the mechanical properties of each unit, including: Obtain the geometric structure data of the surrounding rock area, discretize the area using the finite element method, and generate a grid model; Determining a mechanical property value of each grid unit in the grid model according to data in the initial mechanical parameter field; If there is a correspondence between the grid cells and the spatial distribution in the parameter field, the corresponding mechanical properties are assigned; The mechanical data of the missing areas in the parameter field are processed by interpolation method to supplement the attribute values ​​of the grid cells; Use machine learning algorithms to optimize the mechanical properties of grid cells and correct outliers; Update the mechanical parameters of the mesh model according to the optimized mechanical property values.

5. The method for dynamic simulation of surrounding rock tunnel blasting according to claim 1, characterized in that: Real-time monitoring of the stress distribution of the surrounding rock and determination of whether there is stress concentration include: Obtain real-time data on surrounding rock stress distribution and extract stress values ​​at monitoring points; The stress value of the monitoring point is calculated using the analytical model to generate a stress distribution diagram; If there is an area in the stress distribution diagram where the stress value exceeds a preset threshold, it is determined that stress concentration exists.

6. A method for dynamic simulation of surrounding rock tunnel blasting according to claim 5, characterized in that: If there is an area in the stress distribution diagram where the stress value exceeds a preset threshold, then determining that there is a stress concentration phenomenon includes: Using the preset threshold, it is determined whether the stress value exceeds the threshold area and marked as a concentration area; If the concentrated area coincides with a historical anomaly point, update the anomaly point data and record the correction value; The elastic modulus of the affected units is adjusted according to the correction value through a dynamic adjustment algorithm; An iterative algorithm is used to iteratively calculate the adjusted elastic modulus to generate an updated stress distribution diagram; Reanalyze the stress values ​​based on the updated stress distribution diagram to determine whether there is a new concentration area; If there is a new concentration area, repeat the adjustment and iteration process until the stress distribution tends to be uniform.

7. The method for dynamic simulation of surrounding rock tunnel blasting according to claim 1, characterized in that: Based on the judgment results, the propagation path and attenuation rate of the stress wave generated by the explosive explosion in the surrounding rock are calculated through numerical simulation, including: Get the initial parameters of explosive explosion; Set the initial value of the calculation domain according to the material parameters and boundary conditions of the surrounding rock mass; Performing dynamic change analysis based on the initial parameters and initial values ​​to calculate the propagation path of the stress wave generated by the explosive explosion in the surrounding rock; The decay rate of the stress wave is calculated based on the nodes along the propagation path.

8. A method for dynamic simulation of surrounding rock tunnel blasting according to claim 7, characterized in that: Calculating the decay rate of stress waves based on the nodes along the propagation path includes: If the stress value of a node exceeds the preset threshold, it is marked as a high stress area; According to the distribution of the high stress areas, the material parameters of the surrounding rock mass are adjusted to re-obtain the attenuation rate of the stress wave.

9. The method for dynamic simulation of surrounding rock tunnel blasting according to claim 1, characterized in that: Based on the propagation path and attenuation rate of stress waves, the fracture mode and fragment distribution characteristics of the surrounding rock are analyzed, including: Divide the surrounding rock fracture area according to the stress wave peak value and duration; Extract the size and distribution of blasting fragments and establish a fragment distribution model; Combined with the fracture area and fragment distribution model, the blasting effect is analyzed to obtain the fracture pattern and fragment distribution characteristics.

10. The method for dynamic simulation of surrounding rock tunnel blasting according to claim 1, characterized in that: Based on the stress distribution state, fracture mode and fragment distribution characteristics of the surrounding rock, the final blasting simulation results are obtained including: Obtain explosive energy release rate data and explosion gas expansion parameters, combine them with the mechanical properties of the surrounding rock, and update the stress distribution state of the surrounding rock; The data corresponding to the stress distribution state, fracture mode and fragment distribution characteristics are coupled to obtain the final blasting simulation results.

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