Method and system for setting parameters of tunnel surrounding rock blasting excavation

Abstract

The present invention provides a method and system for setting tunnel surrounding rock blasting excavation parameters, relating to the technical field of tunnel construction. The method comprises: obtaining geological property data, blasting excavation history data, and excavation profile data of the surrounding rock; performing sparse subspace clustering processing based on the blasting excavation history data to obtain a feature data set; performing stress state analysis based on the geological property data, excavation profile data, and feature data set to obtain dynamic stress state and topological change data of the surrounding rock; performing association rule mining based on the blasting excavation history data and feature data set to obtain optimized decision rules; performing nonlinear finite element analysis based on the dynamic stress state and topological change data to obtain an equivalent material model of the surrounding rock; and obtaining a blasting excavation parameter setting scheme based on the equivalent material model and the optimized decision rules. The present invention achieves real-time monitoring of the surrounding rock under different blasting conditions through dynamic response analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel construction, and in particular to a method and system for setting tunnel surrounding rock blasting excavation parameters. Background Art

[0002] In recent years, with the continuous advancement of transportation networks, tunnel projects spanning complex geological conditions, such as mountainous areas and submarine areas, have become increasingly numerous, and the role of tunnel construction in various infrastructure projects has become increasingly prominent. Faced with increasingly complex geological conditions and stringent engineering requirements, the safety and efficiency of tunnel excavation have become critical issues that need to be addressed in engineering design and construction. Blasting excavation of surrounding rock is a core step in tunnel construction, and the proper setting of its parameters is crucial for ensuring surrounding rock stability, reducing deformation during construction, and controlling construction costs.

[0003] In the existing technology, the setting of surrounding rock blasting excavation parameters mainly relies on traditional manual analysis and static mechanical models. Specifically, engineers collect geological property data of the surrounding rock and previous blasting history records, use simple static stress models to infer the blasting effect, and then set blasting parameters such as blasthole arrangement, explosive dosage, and detonation sequence. However, the existing technology still has significant shortcomings. First, the traditional static model cannot effectively capture the nonlinear characteristics of the surrounding rock under the action of blasting loads, resulting in inaccurate predictions of the dynamic behavior of the surrounding rock. Secondly, the existing technology relies on empirical methods and lacks scientific quantitative analysis. It usually only considers the initial state of the surrounding rock, and fails to monitor and adjust the dynamic changes during the construction process in real time.

[0004] To address the above problems, the present invention proposes a tunnel surrounding rock blasting excavation parameter setting method and system thereof, aiming to optimize the blasting excavation parameter setting by real-time analysis of the surrounding rock dynamic response, thereby improving the safety and efficiency of tunnel construction. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for setting parameters for tunnel surrounding rock blasting excavation to improve the above-mentioned problems. To achieve the above-mentioned purpose, the technical solutions adopted by the present invention are as follows:

[0006] In a first aspect, the present application provides a method for setting tunnel surrounding rock blasting excavation parameters, comprising:

[0007] Obtain geological characteristics data of surrounding rock, blasting excavation history and excavation profile data;

[0008] Performing sparse subspace clustering processing based on the blasting excavation history records, and obtaining a feature data set by performing sparse subspace decomposition on surrounding rock characteristics and blasting effects in the historical data;

[0009] performing stress state analysis based on the geological characteristic data, the excavation profile data, and the characteristic data set, modeling the stress state and spatial variation of the surrounding rock using a time-dependent topological analysis method by simulating the stress variation process of the surrounding rock at different time points, and obtaining dynamic stress state and topological variation data of the surrounding rock;

[0010] performing association rule mining based on the blasting excavation history records and the feature data set, and obtaining an optimization decision rule by mining the intrinsic association between the excavation sequence and the blasthole layout plan;

[0011] performing nonlinear finite element analysis based on the dynamic stress state and the topological change data, establishing a mechanical behavior model of the surrounding rock and performing numerical calculations to obtain an equivalent material model of the surrounding rock;

[0012] According to the equivalent material model and the optimization decision rule, after processing with a preset viscoelastic fluid model, by simulating the stress release and deformation process of the surrounding rock under all rounds of blasting, a blasting and excavation parameter setting scheme is obtained. The blasting and excavation parameter setting scheme includes an excavation sequence, a blasthole arrangement scheme, and a detonation sequence.

[0013] Secondly, the present application also provides a tunnel surrounding rock blasting excavation parameter setting system, including:

[0014] An acquisition module is used to obtain geological characteristic data of surrounding rocks, blasting excavation history records and excavation profile data;

[0015] A clustering module is used to perform sparse subspace clustering processing based on the blasting excavation history records, and obtain a feature data set by performing sparse subspace decomposition on the surrounding rock characteristics and blasting effects in the historical data;

[0016] an analysis module for performing stress state analysis based on the geological characteristic data, the excavation profile data, and the characteristic data set, by simulating the stress change process of the surrounding rock at different time points, using a time-dependent topological analysis method to model the stress state and spatial change of the surrounding rock, and obtaining dynamic stress state and topological change data of the surrounding rock;

[0017] a mining module for mining association rules based on the blasting excavation history records and the feature data set, and obtaining an optimization decision rule by mining the intrinsic association between the excavation sequence and the blasthole layout plan;

[0018] A construction module is used to perform nonlinear finite element analysis based on the dynamic stress state and the topological change data, and obtain an equivalent material model of the surrounding rock by establishing a mechanical behavior model of the surrounding rock and performing numerical calculations;

[0019] The output module is used to obtain a blasting and excavation parameter setting scheme based on the equivalent material model and the optimization decision rule, after processing with a preset viscoelastic fluid model, by simulating the stress release and deformation process of the surrounding rock under all rounds of blasting. The blasting and excavation parameter setting scheme includes an excavation step sequence, a blasthole arrangement scheme, and a detonation sequence.

[0020] The beneficial effects of the present invention are:

[0021] The present invention performs dynamic response analysis by acquiring real-time geological property data of the surrounding rock, blasting excavation history records, and excavation profile data, thereby achieving real-time monitoring of the surrounding rock under different blasting conditions. By performing nonlinear finite element analysis on the dynamic stress state of the surrounding rock, a more realistic surrounding rock mechanical behavior model is established, thereby effectively evaluating and predicting the stress changes and potential risks of the surrounding rock during the excavation process.

[0022] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the embodiments of the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 Schematic diagram of the flow of the method for setting tunnel surrounding rock blasting excavation parameters according to an embodiment of the present invention;

[0025] Figure 2 Flowchart of the density-based clustering algorithm described in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0027] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are used only to distinguish the description and should not be understood as indicating or implying relative importance.

[0028] Example 1:

[0029] This embodiment provides a method for setting tunnel surrounding rock blasting excavation parameters.

[0030] See also Figure 1 , the figure shows that the method includes steps S100 to S600.

[0031] Step S100: Acquire geological property data of surrounding rock, blasting excavation history records, and excavation profile data;

[0032] It is understood that geological property data includes information such as the rock type, strength, fracture distribution, porosity, and groundwater level of the surrounding rock. Through the use of geological exploration techniques (such as drilling, geological radar, and seismic reflection methods), detailed surrounding rock information can be obtained to support subsequent dynamic analysis. At the same time, blasting excavation history records, including blasting parameters and effect feedback from past construction, can provide valuable reference for current projects and help identify past successes and failures. The integration of these data can provide a historical basis for the dynamic response of the surrounding rock, making subsequent analysis more targeted and scientific. In addition, excavation profile data describes the geometry and dimensions of the tunnel design, providing guidance for actual operations during construction.

[0033] Step S200: performing sparse subspace clustering processing based on the blasting excavation history records, and obtaining a feature data set by performing sparse subspace decomposition on the surrounding rock characteristics and blasting effects in the historical data;

[0034] It's important to explain that the basic idea behind sparse subspace clustering is to map high-dimensional data into a low-dimensional space to identify the intrinsic correlation between surrounding rock properties and blasting effectiveness. In this process, the algorithm analyzes the relationship between surrounding rock parameters (such as rock type, strength, and porosity) in historical records and the corresponding blasting effects (such as excavation quality, surrounding rock stability, and excavation efficiency) to model the relationship. Specifically, sparse subspace decomposition can effectively isolate historical records with similar characteristics and cluster them into several subspaces, each of which reflects the changes in surrounding rock properties under specific conditions and their impact on blasting effectiveness.

[0035] Step S300: Perform stress state analysis based on the geological characteristic data, excavation profile data, and characteristic data set. By simulating the stress change process of the surrounding rock at different time points, a time-dependent topological analysis method is used to model the stress state and spatial variation of the surrounding rock, thereby obtaining dynamic stress state and topological change data of the surrounding rock.

[0036] It can be understood that this step uses a time-dependent topological analysis method, which can effectively capture the stress state of the surrounding rock under dynamic loading and its spatial variation. The method first uses the finite volume method or the finite element method to spatially discretize the surrounding rock to generate a high-fidelity numerical model. This model can reflect the stress distribution and deformation of the surrounding rock at each time point. Then, through the time-stepping method, the blasting load is applied to the model, and the simulation of stress redistribution is gradually promoted to obtain the stress state of the surrounding rock at each moment during the excavation process. Using time-dependent topological analysis, especially the persistent homology technology, stress concentration areas and newly formed fractures generated in the stress field over time can be identified. This process not only reveals the dynamic stress state of the surrounding rock at different time points, but also provides information on the overall stability of the surrounding rock and potential unstable areas.

[0037] Step S400: mining association rules based on the blasting excavation history records and feature data sets, and obtaining optimization decision rules by mining the intrinsic association between the excavation sequence and the blasthole layout plan;

[0038] As can be understood, characteristic sequence analysis is first used to convert the stress evolution processes of different sequences into time series matrices for pattern comparison and identification. Dynamic time warping can handle time series of varying lengths and without synchronization, matching similar stress variations and thereby revealing potential similarities between sequences. After obtaining the stress evolution sequences, a clustering algorithm is used to classify sequences with similar stress variation characteristics to identify typical stress patterns under different excavation conditions. Bayesian network modeling is then used to quantify the clustering results, revealing the conditional dependencies and probability distributions between excavation sequences and blasthole placements. This allows for a description of the impact and success probability of each blasthole placement scheme under specific conditions. This network model represents the interactions between complex variables through node and edge relationships, enabling the system to quantify the effect of each placement scheme on stress evolution and assist in developing scientifically sound combinations of excavation sequences and blasthole placements.

[0039] Step S500: performing nonlinear finite element analysis based on the dynamic stress state and topological change data, establishing a mechanical behavior model of the surrounding rock and performing numerical calculations to obtain an equivalent material model of the surrounding rock;

[0040] It is important to note that this process captures the dynamic changes in the surrounding rock as stress is gradually released and redistributed after blasting and excavation, reflecting the nonlinear behavior of the material. Finally, through inversion of the numerical analysis results, an equivalent material model of the surrounding rock is constructed. This equivalent model not only simplifies subsequent calculations but also comprehensively expresses the overall mechanical properties of the surrounding rock, providing a more concise and reliable basis for setting parameters for subsequent construction.

[0041] Step S600: Based on the equivalent material model and the optimization decision rule, after processing with a preset viscoelastic fluid model, the blasting and excavation parameter setting scheme is obtained by simulating the stress release and deformation process of the surrounding rock under all rounds of blasting. The blasting and excavation parameter setting scheme includes the excavation sequence, the blasthole arrangement scheme, and the detonation sequence.

[0042] As can be understood, the viscoelastic fluid model is used to capture the stress release and deformation processes of the surrounding rock during different blasting cycles. This model simultaneously considers the elastic recovery capacity and accumulated plastic deformation of the surrounding rock. By abstracting the deformation characteristics of the rock mass into a viscoelastic body, it simulates the time-dependent behavior of the actual excavation process. Based on this model, the stress wave propagation, rock mass fracture, and stress release processes generated by each blasting cycle are simulated step by step, and the dynamic response of the surrounding rock under different hole layouts and detonation sequences is calculated. Ultimately, the simulation process generates a blasting excavation parameter setting scheme that includes a detailed excavation sequence, blasthole layout plan, and detonation sequence. Each parameter combination is selected through simulation evaluation and optimization to ensure that the scheme can maximize the control of surrounding rock deformation, ensure construction safety, and reduce the number of blasting cycles and construction costs. This setting scheme provides specific guidance for on-site construction, making stress management during the construction process more scientific and efficient.

[0043] Furthermore, step S200 includes steps S210 to S240.

[0044] Step S210: Perform data dimensionality reduction processing based on the blasting excavation history records, extract surrounding rock characteristic data and corresponding blasting effect data through principal component analysis, and obtain a low-dimensional feature set;

[0045] Understandably, during blasting and excavation, historical data contains a vast amount of multidimensional information, such as surrounding rock geological properties (lithology, strength, porosity, etc.), blasting parameters (blasthole layout, explosive charge, detonation sequence, etc.), and construction results (surrounding rock stability, deformation, excavation efficiency, etc.). This data is multi-dimensional and contains a certain degree of redundancy. Direct analysis and processing results in excessive computational complexity and makes it difficult to identify valid information. Therefore, dimensionality reduction is necessary to simplify the data structure. Principal component analysis (PCA) is a dimensionality reduction method that maps the original high-dimensional data into a low-dimensional space through a linear transformation, ensuring that the data variance is preserved to the greatest extent possible. In its implementation, the covariance matrix of the original data is first calculated to analyze the correlations between different dimensions. Eigenvalue decomposition is then used to extract several principal components from the data. Each principal component is a linear combination of the original data dimensions and is sorted by the variance they explain. Principal components with larger variance retain the key characteristics of the data, while components with smaller variance represent redundant information and can be discarded. Through principal component analysis, a low-dimensional feature set can be extracted from complex historical records, which covers the main change patterns between surrounding rock properties and blasting effects.

[0046] Step S220: performing sparse coding processing on the low-dimensional feature set, performing sparse representation on the features through Lasso regression, and obtaining a sparse feature matrix;

[0047] It should be noted that Lasso regression is a regression model with L1 regularization, which selects features while performing regression analysis. Specifically, Lasso regression introduces L1 norm constraints in the loss function, causing some regression coefficients to tend to zero, thereby automatically screening out important features and ignoring features that have little impact on the results. In this step, Lasso regression is used to identify and retain variables that have a key impact on the blasting effect. The sparse coding process includes: first, building a regression model based on the data in the low-dimensional feature set to predict the contribution of each feature in the blasting effect; then, by introducing L1 regularization constraints, the model compresses the coefficients of certain features to zero during the optimization process, retaining only the most influential features. Finally, these screened features are organized into a sparse feature matrix, where each column in the matrix represents an important feature, and most irrelevant features are eliminated or set to zero.

[0048] Step S230: performing similarity measurement based on the sparse feature matrix, calculating the similarity between different historical blasting records by cosine similarity, and obtaining a similarity matrix;

[0049] In this step, each pair of burst records in the sparse feature matrix is ​​compared as a vector, and the cosine similarity between them is calculated. Because these features are sparse after Lasso regression processing, the calculation process is more efficient and the results are more interpretable. The similarity calculation results between all two records are organized into a similarity matrix, where each element represents the cosine similarity between a pair of records.

[0050] Step S240 : performing sparse subspace clustering processing according to the similarity matrix, dividing the blasting records into at least two related subspaces by a sparse subspace clustering algorithm, and obtaining a feature data set.

[0051] In practice, the goal of sparse subspace clustering is to partition records into distinct subspaces, such that the records in each subspace are highly correlated along certain dimensions. The algorithm first constructs an adjacency graph based on the similarity matrix generated in the previous step. In this graph, records with high similarity are connected by edges with high weights. The sparse subspace clustering algorithm then creates a sparse representation for these records, assuming that each record can be represented by a linear combination of a small number of other records in the same subspace. By optimizing the sparsity of these linear combinations, the algorithm automatically discovers the distinct subspaces to which these records belong.

[0052] The key to this process is capturing local characteristics in multidimensional data: even if some records are not highly similar across all dimensions, they may exhibit strong correlations within specific dimensions or conditions (such as specific geological structures, blasthole arrangements, or blasting effects). Sparse subspace clustering algorithms can identify these local patterns and partition the records into subspaces associated with these characteristics. Ultimately, the clustering results generate a feature dataset, where each subspace represents a cluster of blasting records with similar characteristics.

[0053] Furthermore, step S300 includes steps S310 to S340.

[0054] Step S310: Perform spatial discretization processing based on the geological characteristic data and the excavation profile data, and perform grid discretization on the tunnel surrounding rock using the finite volume method to obtain a spatial discretization model;

[0055] Preferably, the finite volume method (FVM) is used in this embodiment to perform gridding and discretization of the surrounding rock. The core idea of ​​the finite volume method is to divide the solution area into multiple finite control volumes (i.e., grid cells) and perform calculations through the conservation laws within these cells. In the scenario of tunnel construction, geological characteristic data (such as rock type, density, porosity, etc.) and excavation profile data (geometric size and shape of the tunnel) jointly determine the grid division method. Important structural details and stress concentration areas in the area will use finer grid division to ensure the accuracy of the calculation, while coarser grids can be used in relatively stable areas to improve calculation efficiency.

[0056] The advantage of the finite volume method is that it ensures the conservation of mass, momentum, and energy by integrating fluxes at the boundaries of each control volume. This method demonstrates high adaptability when simulating complex geometries (such as irregular tunnel cross-sections) and heterogeneous materials (such as combinations of different surrounding rock types). Furthermore, the finite volume method can better capture changes in localized stress concentrations or cracked areas, making it particularly effective for addressing the dynamic stress redistribution that occurs during tunnel construction.

[0057] Step S320: Calculate the initial stress field according to the spatial discrete model, calculate the surrounding rock deadweight and formation pressure and use Gaussian numerical integration to calculate stress, and obtain the initial stress distribution state of the surrounding rock;

[0058] It should be noted that this step calculates the initial stress field of the tunnel surrounding rock based on a spatial discrete model. This stress field is formed by the combined action of the surrounding rock's own weight and formation pressure, reflecting the natural equilibrium state of the surrounding rock before construction. The surrounding rock's own weight stress increases linearly with increasing depth. The specific calculation depends on the density, gravitational acceleration and depth of the rock mass, while the formation pressure takes into account factors such as geological structure and lateral pressure coefficient, especially in fault zones or high stress areas. Through the Gaussian numerical integration method, the stress value is calculated at the Gaussian integration point of each grid unit and summed according to the preset weights, so as to accurately approximate the stress distribution of each unit and generate a complete initial stress distribution state. This stress field represents the stress situation of each unit in different directions in the form of a tensor, providing reliable initial conditions for stress redistribution simulation in subsequent construction.

[0059] Step S330: Perform dynamic stress change simulation based on the initial stress distribution state, gradually promote stress redistribution of the surrounding rock during the excavation process, and simulate stress release and redistribution caused by rock removal at different time points during the excavation process to obtain time series stress distribution data;

[0060] It's important to note that as tunnel excavation progresses, the stress state of the surrounding rock undergoes redistribution. Therefore, simulations are needed to gradually advance the stress evolution process to predict the stress release and redistribution behavior of the surrounding rock at each stage. In practice, the simulation is performed in stages according to the excavation sequence. Each excavation step removes a portion of the rock mass. This removal leads to a rapid release of local stress and a subsequent redistribution of stress within the remaining rock mass.

[0061] Furthermore, the simulation process uses a step-by-step loading and iterative solution method, imposing new boundary conditions at each time step to reflect the removed rock mass area. At this time, the new mechanical equilibrium state is calculated, and the corresponding stress changes are recorded in each grid cell. This process is nonlinear because as the rock mass is continuously removed, stress may be concentrated in certain specific areas, leading to the formation of new cracks or the emergence of potential unstable areas. Therefore, each step of the simulation needs to be iterated until the mechanical equilibrium of each stage converges. In addition, this simulation of dynamic stress changes generates time series stress distribution data, which records the stress state of the surrounding rock at different time points. The time series stress distribution data not only shows how stress changes as construction progresses, but also reveals stress peak areas and potential risk areas at critical time points.

[0062] Step S340: Perform time-dependent topological analysis based on the time series stress distribution data, identify changes in stress concentration areas and newly formed fractures in the stress field over time through a persistent homology method, and obtain dynamic stress state and topological change data.

[0063] Furthermore, as excavation progresses, stress is continuously released and redistributed, potentially leading to stress concentrations or new fractures in localized areas. These changes directly impact tunnel stability. To identify these dynamic changes, we introduce the persistent homology method, a topological data analysis tool that quantifies changes in topological structure at different time scales.

[0064] During the specific analysis process, the persistent homology method tracks stress concentration areas in time series data and regards them as key features in the topological structure. For example, as excavation progresses, certain areas may experience stress concentration, crack expansion, or the formation of new fractures. Persistent homology can record when these phenomena appear and disappear, and quantify their "persistence." The higher the persistence, the greater the impact of the stress concentration area or fracture on the stability of the surrounding rock during the entire construction process. By identifying these topological features, not only can obvious fractures and concentration areas be discovered, but also some hidden but subtle changes that may have an impact in subsequent construction can be captured. The final generated dynamic stress state and topological change data reveal the key evolution patterns and potential unstable areas in the surrounding rock stress field.

[0065] Furthermore, step S400 includes steps S410 to S440.

[0066] Step S410: Perform feature sequence analysis based on the blasting excavation history records and feature data sets, compare the surrounding rock stress evolution patterns under different step sequence conditions through dynamic time warping, and obtain the surrounding rock stress sequence matrix under different excavation step sequence conditions;

[0067] It should be noted that dynamic time warping is an algorithm for nonlinear matching of time series data. It can effectively handle stress evolution sequences of different lengths and time steps. In blasting excavation, different excavation sequence causes surrounding rock stress to be released or concentrated at different time points, and the patterns of these stress changes may not be completely synchronized in time. Dynamic time warping aligns these time series and finds the optimal matching path between them. Even in the presence of time lags or asynchronies, it can accurately compare the similarities in stress evolution.

[0068] Specifically, the stress evolution data under each step condition is first extracted from the blasting history records and combined with the relevant features in the feature data set to form an initial time series data set. Then, dynamic time warping compares these sequences one by one and calculates the similarity of stress evolution between different sequences. This process not only identifies which step conditions have the most similar stress change patterns, but also reveals which steps may cause abnormal stress concentration under specific conditions. Finally, the comparison results are integrated into a surrounding rock stress sequence matrix, in which each row represents an excavation step and each column corresponds to the stress state at a different time step. This matrix clearly shows the impact of different sequences on the stress evolution of the surrounding rock, providing a quantitative basis for subsequent analysis.

[0069] Step S420: performing stress pattern clustering according to the surrounding rock stress sequence matrix, and obtaining a clustering result of excavation step sequence and stress evolution by clustering step combinations with similar stress evolution characteristics;

[0070] Preferably, this step adopts an improved density-based clustering algorithm. Specifically, first, the stress sequence matrix generated by the historical blasting data and the characteristic data set is used to directly analyze the changes in the stress data under different step conditions. The improved density clustering method does not require a preset number of clusters, but automatically discovers clusters based on the density of the data points. The stress evolution characteristics corresponding to each excavation step are regarded as a data point, and whether it belongs to a high-density area is judged based on the number of its neighboring points. When a data point of a step is located in a high-density area, it is regarded as a core point and is classified into a cluster together with other points in its neighborhood. On the contrary, if there are not enough neighboring points near a point (that is, the density is not high enough), the point is marked as a noise point and removed from the cluster. This feature is particularly suitable for processing complex stress evolution data during excavation, where some steps may exhibit abnormal stress concentration patterns, while other steps exhibit relatively smooth stress distributions.

[0071] like Figure 2 As shown, Figure 2 This is a flowchart of the density-based clustering algorithm. When applying this method, a neighborhood radius parameter (∈) and a minimum number of neighborhood points (MinPts) are first defined for each data point to control the clustering density requirement. By gradually traversing the data points, high-density areas are found, and points that meet the density requirements are grouped into the same cluster. The density-based clustering algorithm not only identifies steps with similar stress characteristics but also filters out steps with anomalous stress evolution behavior, thereby improving the stability and accuracy of the clustering results. Through this density-driven clustering, a clustering result is ultimately generated that shows each excavation step and its corresponding stress evolution pattern.

[0072] Step S430: Analyze blasthole layout association rules based on the clustering results, model the probabilistic dependency between excavation sequence and blasthole layout through Bayesian network, and quantify the impact of each blasthole layout on the stress evolution of a specific sequence to obtain probabilistic association rules;

[0073] It can be understood that in a Bayesian network, each node represents a random variable. The step sequence nodes describe different stress evolution characteristics, while the blasthole layout nodes cover design parameters such as blasthole location, spacing, depth, and detonation sequence. Specifically, in this step, structural and parameter learning is first performed using historical data to automatically generate a network structure and calculate a conditional probability table (CPT), quantifying the probability distribution of different layout schemes under a specific step sequence. Then, utilizing the probabilistic inference capabilities of the Bayesian network, the optimal layout scheme and its corresponding stress evolution effect are inferred based on the given step sequence, thereby generating practical probabilistic association rules. These rules not only reveal the dependency between step sequence and blasthole layout, but also quantify the stress control effect of different schemes under each step sequence and their combined impact on blasting efficiency, stability, and cost.

[0074] Step S440: Perform multi-objective optimization processing according to the probabilistic association rule, simulate and evaluate the stress evolution and surrounding rock stability by simulating the combination of different blasthole schemes and step sequences, and screen the tunnel surrounding rock with stability and economy as the reward function to obtain the optimization decision rule.

[0075] As can be understood, various combinations of blasthole placements and sequence are first constructed based on probabilistic association rules, and each combination is then subjected to detailed simulation evaluation. The simulation process encompasses stress variation patterns under different geological conditions, the evolution of stress concentration areas, and the expansion of fractures, ensuring the adaptability of the schemes in various scenarios. Key evaluations include the stress release efficiency of the surrounding rock, the effectiveness of controlling local stress concentrations, and stability changes to quantify the mechanical performance of each scheme.

[0076] In the multi-objective optimization process, rock mass stability and construction economics serve as the primary reward functions. The stability metric focuses on evaluating the solution's ability to control rock mass deformation and stress redistribution, thereby preventing instability and sudden fracture. The economic metric, on the other hand, measures the construction cost of each combination, including factors such as explosive usage, construction schedule, and labor and equipment costs. In actual optimization, these two objectives often conflict. Therefore, a multi-objective optimization algorithm is used to find a balance between the two, ensuring a construction solution that is both safe, stable, and cost-effective.

[0077] The resulting optimization decision rules do not directly specify the parameters for each specific blasthole, but rather provide a guiding design framework. These rules, expressed mathematically, provide guidance for construction under diverse geological conditions. These rules form a scientific and adaptable design framework, providing theoretical support and optimization paths for specific parameter setting schemes, thereby ensuring cost control and improved efficiency while meeting safety requirements.

[0078] Furthermore, step S500 includes steps S510 to S540.

[0079] Step S510: fitting nonlinear material property parameters according to the dynamic stress state and topological change data, optimizing and fitting the nonlinear constitutive relationship of the surrounding rock material through a genetic algorithm, and obtaining the nonlinear material parameters of the surrounding rock;

[0080] It's understandable that the nonlinear mechanical behavior of the surrounding rock, including plastic deformation, stress relaxation, and crack propagation, changes during construction as stress release and redistribution occur. Therefore, to accurately describe these complex behaviors, a fitting process is necessary to identify the most realistic material parameters. These parameters cannot be obtained through direct measurement; instead, they must be fitted through numerical optimization, combining actual stress data with theoretical models.

[0081] Specifically, the genetic algorithm simulates the process of natural selection and evolution. It first generates multiple possible parameter combinations as initial solutions and evaluates their fitness by calculating the error between each combination and the actual stress data. The higher the fitness, the more accurately the parameter combination reflects the actual stress response of the surrounding rock. The algorithm then selects the best-performing parameter combinations for crossover and mutation to generate new parameter combinations. Crossover simulates information exchange between different solutions, while mutation introduces random perturbations, helping the algorithm escape the trap of local optimal solutions. Through continuous iteration and gradual optimization, the algorithm finds a stable parameter combination with the lowest error. The resulting nonlinear material parameters can truly reflect the deformation and mechanical properties of the surrounding rock under various stress conditions.

[0082] Step S520: dividing the nonlinear finite element unit grid according to the nonlinear material parameters, and optimizing the grid of the spatial discrete model of the surrounding rock using an adaptive grid generation algorithm to obtain a finite element grid model containing nonlinear characteristics;

[0083] It should be noted that because the surrounding rock undergoes complex stress redistribution and nonlinear deformation during blasting, conventional uniform meshing cannot accurately capture the details of critical areas. To this end, this step uses an adaptive mesh generation algorithm to dynamically optimize the mesh based on the characteristics of nonlinear material parameters and the stress concentration of the surrounding rock.

[0084] Specifically, the nonlinear material parameters obtained through previous fitting are first used to define the mechanical behavior characteristics of each region. Based on these parameters, regions likely to experience stress concentration, plastic deformation, or crack propagation are identified, and finer mesh elements are created in these regions to ensure computational accuracy. Furthermore, larger mesh elements are used in regions with less stress variation or more stable behavior, reducing computational effort and improving simulation efficiency. This meshing approach ensures model accuracy while optimizing computational time and resources. The adaptive mesh generation algorithm works by performing mesh optimization through iterative analysis. After the initial mesh generation is completed, a preliminary mechanical calculation is performed, and the mesh structure is adjusted based on the results. The mesh is automatically refined for regions with high stress concentration or dramatic variations, while mesh elements are merged or expanded for regions with less pronounced stress variations. This adaptive adjustment process continues until the mesh structure meets the requirements for computational accuracy and efficiency. The resulting finite element mesh model not only captures the geometric structure and material properties of the surrounding rock but also reflects its nonlinear mechanical behavior during blasting excavation.

[0085] Step S530: Perform nonlinear mechanical behavior simulation based on the finite element mesh model. Based on the initial stress state of the surrounding rock and the mesh model, gradually and iteratively solve the stress and strain of each unit until a mechanical equilibrium state is reached, thereby obtaining the stress-strain distribution result of the surrounding rock.

[0086] Specifically, the initial stress state of the surrounding rock is first imported as the initial condition for the model. Initially, the stress distribution within each mesh element is set based on the surrounding rock's deadweight and the formation pressure. As construction progresses, the stress in the surrounding rock is gradually released and redistributed after blasting removes some of the rock mass. This nonlinear process requires a stepwise iterative solution to simulate: at each iteration, the stresses and strains of all mesh elements are calculated and matched to the nonlinear constitutive relationship of the material.

[0087] The iterative solution process uses a nonlinear finite element solver and uses a progressive loading method to gradually apply the external force boundary conditions of blasting and excavation. In each round of iteration, the equilibrium state of all units will be checked to determine whether they meet the convergence conditions. If the mechanical equilibrium between the units has not yet been achieved, the mesh will be adjusted according to the calculated stress and strain results, and the next round of iteration will be carried out. This process will continue until the internal and external forces of all units reach a state of equilibrium, that is, the mechanical convergence criteria are met. The final stress-strain distribution results not only include the stress and strain magnitude of each unit, but also reveal the stress concentration areas, potential instability areas and deformation modes in the entire surrounding rock structure. This step-by-step iterative simulation method can dynamically capture the response behavior of the surrounding rock at different stages of the excavation process, providing reliable support for subsequent blasting and excavation design.

[0088] Step S540: construct an equivalent material model based on the stress-strain distribution results, determine the equivalent material properties of the surrounding rock through an inversion analysis algorithm, and obtain the equivalent material model.

[0089] It should be noted that since the actual material properties of the surrounding rock often exhibit strong nonlinearity, such as local cracks, stress concentration, and bedding structure, directly using a complete nonlinear model will result in complex and time-consuming calculations. Therefore, it is necessary to use an inversion algorithm to map the complex mechanical properties into a simplified equivalent model. First, the stress-strain data obtained from the nonlinear finite element simulation are imported. These data reflect the stress distribution and deformation mode of the surrounding rock in different areas. Then, the inversion analysis algorithm is used to adjust the parameters of the equivalent model so that its stress-strain response is as consistent as possible with the actual results. In each round of iteration, by comparing the simulation results with the actual data, the error is calculated and the material parameters are updated until the error converges to an acceptable range. The final equivalent material model contains simplified key parameters, such as equivalent elastic modulus, Poisson's ratio, and shear strength. These parameters can comprehensively describe the average response behavior of the surrounding rock while reducing the computational complexity, making it suitable for subsequent construction analysis and optimization.

[0090] Furthermore, step S600 includes steps S610 to S640.

[0091] Step S610: Perform dynamic response analysis based on the equivalent material model, model the viscoelastic characteristics of the surrounding rock by applying a piecewise linearization method, and obtain a dynamic response function;

[0092] Understandably, the stress and deformation behavior of surrounding rock materials over different time periods exhibits complex viscoelastic properties. This means that the material's stress response is related not only to its current strain state but also to its past stress history. Therefore, an efficient method is needed to describe its dynamic changes. Directly simulating the entire process using a nonlinear model is computationally complex and time-consuming. However, the piecewise linearization method simplifies the calculation by dividing the nonlinear viscoelastic behavior into multiple small intervals, approximating a linear response within each interval.

[0093] Step S620: Use the time domain integration method to simulate the dynamic response of the surrounding rock, by applying the dynamic response function to the time history of each blasting round, and gradually integrating the time to calculate the stress and deformation at each moment, to obtain a stress-deformation curve;

[0094] Specifically, the dynamic response function obtained in the previous stage is first imported. This function describes the stress and strain evolution characteristics of the surrounding rock over different time periods. To simulate the dynamic response during actual construction, the external force generated by each round of blasting is used as an input signal and applied to the dynamic response model. After each round of blasting, the surrounding rock undergoes stress release, redistribution, and deformation accumulation, and these processes change over time. To capture these changes, the time domain integration method decomposes the entire time history into several tiny time steps, calculating the current stress and deformation in each time step.

[0095] Furthermore, based on the dynamic response function in the viscoelastic model, the stress state in each time step is integrated, the deformation and stress changes of the previous time step are accumulated, and the results are used as the initial conditions of the next time step. This step-by-step integration method can dynamically capture the propagation path of stress, the deformation mode of the surrounding rock, and the potential risk of instability. As time goes on, the calculation results are continuously superimposed, and finally a comprehensive stress-deformation curve is obtained, which reflects the mechanical evolution of the surrounding rock during the entire construction process. The stress-deformation curve not only shows the deformation amplitude and stress concentration degree of the surrounding rock at different time points, but also provides a basis for engineers to identify key time points in the blasting process, such as the time when the stress peak occurs and the possible deformation inflection point. The calculation formulas involved include:

[0096]

[0097] (σ,ε)={(σ0,ε0),(σ1,ε1),…,(σ n ,ε n )};

[0098] Where t represents the current time point; τ represents the past time point; n represents the sequence number of the discrete time step; σ(t) represents the stress value of the surrounding rock at time t; ε(t) represents the strain of the surrounding rock at time t; E(t-τ) represents the viscoelastic modulus, which describes the elastic and viscous properties of the surrounding rock material within the time difference t-τ; σ0 represents the initial stress state; ε0 represents the initial strain state; C(τ) is the energy dissipation function, which represents the energy lost by the surrounding rock at time τ due to internal friction, plastic deformation or material damage; Δt represents the time step; F ext,n is the external blasting load, which represents the blasting force applied in the nth time step; σ n represents the stress value at the nth time step; σ n+1 represents the stress value at the n+1th time step; ε n is the strain variable at the nth time step; ε n+1 is the strain variable at the n+1th time step; E n is the dynamic modulus at the nth time step, which indicates the elastic recovery capacity of the surrounding rock in this time step and is used to reflect the transient response of the material; C n is the energy dissipation at the nth time step, which is used to quantify the energy dissipated by the surrounding rock due to plastic deformation or material damage in this time step; (σ, ε) is the coordinate pair of the stress-deformation curve, which records the stress and strain at each time step and shows the evolution of the mechanical state of the surrounding rock during the entire construction process; (σ0, ε0) is the initial stress and strain, which represents the initial state of the surrounding rock at the beginning of the simulation; (σ1, ε1) is the stress and strain at the first time step, which reflects the initial response after the blasting load is applied; (σ n ,ε n ) is the stress and strain at the nth time step, which records the stress and deformation evolution of the surrounding rock during the advancement of each time step.

[0099] Step S630: Perform critical state analysis based on the stress-strain curve, compare the maximum stress in the curve with the shear strength of the surrounding rock using the limit equilibrium analysis method, and obtain the safety factor of the surrounding rock;

[0100] It can be understood that this step first extracts the maximum stress value of each key time point from the stress-deformation curve, that is, the stress peak value of the surrounding rock in each blasting stage and time step. These maximum stress values ​​reflect the most severe mechanical state that the surrounding rock withstands during the construction process. Next, these stress values ​​are compared with the shear strength of the surrounding rock. Shear strength is the maximum stress that the surrounding rock material can withstand before reaching shear failure. It depends on material properties such as the internal friction angle, cohesion and pore pressure of the surrounding rock. By comparing the maximum stress and shear strength, it can be determined whether the surrounding rock is within a safe range. Next, the safety factor of the surrounding rock is calculated using the limit equilibrium analysis method. The calculation of the safety factor is used to quantify the stability of the surrounding rock under the current stress state and is defined as the ratio of shear strength to maximum stress. By analyzing the maximum stress and safety factor at multiple time points, not only can the high-risk areas of the surrounding rock be identified, but also the possible instability time period of the surrounding rock can be predicted. The calculation formula is:

[0101]

[0102] τ c =c+σ0·tan(φ)-u;

[0103] Among them, SF n represents the safety factor of the nth time step; τ c Indicates the shear strength of surrounding rock; σ max,n represents the maximum stress value at the nth time step; c represents the bond strength between surrounding rock particles; σ0 represents the normal stress; φ represents the internal friction angle between surrounding rock particles; and u represents the fluid pressure in the pores inside the rock mass.

[0104] Step S640: Parameter optimization is performed according to the safety factor and the optimization decision rule. A fuzzy comprehensive evaluation model is established based on a preset fuzzy comprehensive evaluation algorithm. The safety factor, construction cost, and explosive usage are used as inputs. Fuzzy rules are set and evaluated through the weighted average method to obtain a blasting excavation parameter setting plan.

[0105] In this step, a fuzzy comprehensive evaluation model is established using safety factor, construction cost, and explosives usage as input variables to comprehensively assess the interplay and trade-offs between these factors under different conditions. Because the relationships between these factors are inherently uncertain and complex, fuzzy language is used to describe them, such as "high," "medium," and "low." Fuzzy rules based on expert experience or historical data are then constructed. These rules provide a logical basis for selecting alternatives under different scenarios. A weighted average method is used for comprehensive evaluation, assigning weights based on the importance of each factor in different scenarios. For example, safety factor is prioritized in high-risk areas, while cost is given greater weight under budgetary constraints. The results of each rule are weighted averaged to generate a comprehensive score, which is used to evaluate the safety, cost-effectiveness, and rationality of explosives use of the current alternative. Based on this comprehensive score, blasting parameters, including sequence design, blasthole layout, and detonation sequence, are further optimized. If the score indicates an insufficient safety factor, the blasthole density is increased or the detonation sequence is adjusted. If the cost is too high, the explosives usage is reduced or the excavation sequence is simplified. After multiple rounds of iterative optimization, a specific blasting and excavation parameter setting scheme was generated, specifying the location, depth, spacing, explosive dosage, and detonation sequence of blastholes. This scheme not only ensures a balance between construction safety and economic efficiency, but also offers flexibility and adaptability, allowing dynamic adjustments based on on-site feedback, providing scientific and efficient operational guidance for the construction process.

[0106] Furthermore, step S610 includes steps S611 to S614.

[0107] Step S611: Define viscoelastic properties according to the equivalent material model, derive the viscoelastic constitutive equation of the surrounding rock through analytical methods, and obtain a dynamic material model of the surrounding rock;

[0108] Specifically, the parameters in the equivalent material model are first used as input to capture the viscous and elastic characteristics of the surrounding rock and establish the mathematical form of the viscoelastic constitutive relationship. In the derivation process, the generalized Maxwell model is preferably used to describe the viscous damping effect and elastic recovery ability of the material. The Maxwell model represents the stress response of the material as a combination of viscous elements and elastic elements, and describes it through time-dependent differential equations. The key to the analytical derivation is to map the combination relationship of viscoelastic elements into a standardized equation to ensure that the model can accurately reflect the dynamic response of the surrounding rock under different stress states and loading times. During the derivation process, the stress and strain are solved in stages, and the loading rate and time effects are incorporated into the equation to generate a dynamic material model covering elasticity, viscosity and stress relaxation characteristics. The final viscoelastic constitutive equation can describe the evolution of stress and deformation of the surrounding rock during dynamic construction, and predict the mechanical performance of the surrounding rock under different construction conditions. The viscoelastic constitutive equation is:

[0109]

[0110] Among them, σ(t) represents the stress value at time t, which describes the internal force state of the surrounding rock at that moment; ε(t) represents the strain at time t, which describes the deformation degree of the surrounding rock; E1 is the elastic modulus, which represents the elastic recovery capacity in the Maxwell model; η1 is the viscous damping coefficient, which represents the viscous effect of the material; λ1 is the time constant.

[0111] Step S612: performing transient response calculation based on the dynamic material model, solving the dynamic response of the surrounding rock under the blasting load by the explicit finite difference method, and obtaining the dynamic response data of the surrounding rock at each time step;

[0112] It should be noted that the load applied to the surrounding rock during the blasting process is a short-term, high-intensity impact load, which will produce complex stress wave propagation and stress redistribution in the surrounding rock. Therefore, it is necessary to gradually solve its dynamic response through efficient numerical methods.

[0113] Specifically, this step first applies the blasting load as a boundary condition to the surrounding rock model, and divides the entire calculation process into multiple small time steps to capture the stress and deformation state of the surrounding rock at every moment during the blasting process. The advantage of the explicit finite difference method is that its calculation steps are clear, and the results in each time step only depend on the data of the previous time step. This step-by-step solution method is particularly suitable for handling short-term dynamic impact loads. In each time step, the stress and strain of each unit are calculated according to the dynamic material model, and the results are passed to the next time step to ensure that the stress wave propagation and stress redistribution process are accurately simulated.

[0114] During the calculation process, the mechanical state of each element, including stress, strain, and deformation rate, is continuously updated to track the impact of the blasting load on the surrounding rock. This time-step calculation not only captures the deformation trajectory of the surrounding rock but also identifies areas of stress concentration and potential points of instability, providing engineers with critical construction safety information. Ultimately, the calculation results from all time steps are integrated to form a dynamic response dataset, which details the stress and deformation of the surrounding rock at each point in time during the blasting process.

[0115] Step S613: Perform energy analysis based on the dynamic response data, and obtain the energy dissipation characteristics of the surrounding rock by calculating the product of stress and strain;

[0116] It should be noted that during blasting, the transfer and dissipation of energy significantly influences the deformation, crack propagation, and stability of the surrounding rock. Therefore, energy analysis is necessary to assess the surrounding rock's response and instability risk. Specifically, this step first extracts the stress and strain state of each element from the dynamic response data at each time step. For each time step, the instantaneous energy density of each element is calculated—the product of stress and strain per unit volume—reflecting the elastic energy absorbed and released by that element. This instantaneous energy data is gradually accumulated to form a complete energy evolution process, demonstrating the energy dissipation path of the surrounding rock throughout the blasting process. During the analysis, energy is primarily divided into elastic energy and dissipated energy. Elastic energy reflects the resilience of the surrounding rock and can be partially recovered when stress is released. Dissipated energy, on the other hand, reflects the energy consumed by permanent deformation and internal friction in the material and cannot be recovered. By calculating the dissipated energy of each element, areas of the surrounding rock that may undergo plastic deformation or crack propagation can be identified. These areas are often concentrated with high dissipated energy and are prone to becoming areas of potential instability. Finally, the energy data of each unit are summarized into an energy dissipation characteristic dataset, which shows the energy distribution and evolution law of the surrounding rock at different stages.

[0117] Step S614: construct a function based on the dynamic response data and energy dissipation characteristics, establish a mathematical model using regression analysis, and use stress, strain and energy as input to obtain a dynamic response function through fitting.

[0118] It is understandable that this step utilizes the dynamic response data obtained from the previous step, including stress, strain and energy dissipation data in each time step, and uses these variables as the input of the model. Since the relationship between stress and strain exhibits nonlinear characteristics, and energy dissipation reflects the complex deformation and damage behavior inside the material, it is necessary to find the best matching relationship between the variables through regression analysis. Preferably, a regression algorithm in machine learning (such as ridge regression or support vector regression) is used to capture these complex mechanical relationships. In the specific fitting process, training is performed based on the dynamic data, and by adjusting the model parameters, the error between the predicted value and the actual observed data is gradually reduced to ensure that the final dynamic response function can accurately reflect the true mechanical behavior of the surrounding rock. The regression model can not only describe the evolution of stress and strain over time, but also reveal the relationship between energy dissipation and surrounding rock response under different construction conditions. The dynamic response function finally fitted is a simplified but efficient mathematical expression that can quickly predict the dynamic response behavior of the surrounding rock under given stress, strain and energy conditions.

[0119] Example 2:

[0120] This embodiment provides a system for setting tunnel surrounding rock blasting excavation parameters, the system comprising:

[0121] An acquisition module is used to obtain geological characteristic data of surrounding rocks, blasting excavation history records and excavation profile data;

[0122] Clustering module, used to perform sparse subspace clustering based on the blasting excavation history records, and obtain feature data sets by performing sparse subspace decomposition on the surrounding rock characteristics and blasting effects in the historical data;

[0123] The analysis module is used to analyze the stress state based on geological characteristic data, excavation profile data, and characteristic data sets. By simulating the stress change process of the surrounding rock at different time points, the stress state and spatial change of the surrounding rock are modeled using a time-dependent topological analysis method to obtain the dynamic stress state and topological change data of the surrounding rock;

[0124] The mining module is used to mine association rules based on blasting excavation history records and feature data sets. By mining the inherent associations between excavation steps and blasthole layout plans, the optimized decision rules are obtained.

[0125] The construction module is used to perform nonlinear finite element analysis based on dynamic stress state and topological change data. By establishing a mechanical behavior model of the surrounding rock and performing numerical calculations, an equivalent material model of the surrounding rock is obtained.

[0126] The output module is used to obtain the blasting excavation parameter setting plan based on the equivalent material model and optimization decision rules, after processing with a preset viscoelastic fluid model, by simulating the stress release and deformation process of the surrounding rock under all rounds of blasting. The blasting excavation parameter setting plan includes the excavation step sequence, blasthole arrangement plan and detonation sequence.

[0127] In a specific embodiment disclosed in this application, the clustering module includes:

[0128] The first dimensionality reduction unit is used to perform data dimensionality reduction processing based on the blasting excavation history records, extract surrounding rock characteristic data and corresponding blasting effect data through principal component analysis, and obtain a low-dimensional feature set;

[0129] The first encoding unit is used to perform sparse coding processing based on the low-dimensional feature set, sparsely represent the features through Lasso regression, and obtain a sparse feature matrix;

[0130] A first calculation unit is used to perform similarity measurement based on the sparse feature matrix, calculate the similarity between different historical blasting records by cosine similarity, and obtain a similarity matrix;

[0131] The first clustering unit is used to perform sparse subspace clustering processing according to the similarity matrix, and divide the blasting records into at least two related subspaces through the sparse subspace clustering algorithm to obtain a feature data set.

[0132] In a specific embodiment disclosed in this application, the analysis module includes:

[0133] The first processing unit is used to perform spatial discretization processing based on geological characteristic data and excavation profile data, and to perform grid discretization on the tunnel surrounding rock using the finite volume method to obtain a spatial discretization model;

[0134] The second calculation unit is used to calculate the initial stress field according to the spatial discrete model, calculate the surrounding rock deadweight and formation pressure and use Gaussian numerical integration to calculate the stress, and obtain the initial stress distribution state of the surrounding rock;

[0135] The first simulation unit is used to simulate dynamic stress changes based on the initial stress distribution state. By gradually promoting the stress redistribution of the surrounding rock during the excavation process and simulating the stress release and redistribution caused by the removal of rock mass at different time points during the excavation process, time series stress distribution data are obtained.

[0136] The first analysis unit is used to perform time-dependent topological analysis based on the time series stress distribution data, and identify the changes in stress concentration areas and newly formed fractures in the stress field that occur over time through the persistent homology method, thereby obtaining dynamic stress state and topological change data.

[0137] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for setting parameters for tunnel surrounding rock blasting excavation, characterized in that: include: Obtain geological characteristics data of surrounding rock, blasting excavation history and excavation profile data; Performing sparse subspace clustering processing based on the blasting excavation history records, and obtaining a feature data set by performing sparse subspace decomposition on surrounding rock characteristics and blasting effects in the historical data; performing stress state analysis based on the geological characteristic data, the excavation profile data, and the characteristic data set, modeling the stress state and spatial variation of the surrounding rock using a time-dependent topological analysis method by simulating the stress variation process of the surrounding rock at different time points, and obtaining dynamic stress state and topological variation data of the surrounding rock; performing association rule mining based on the blasting excavation history records and the feature data set, and obtaining an optimization decision rule by mining the intrinsic association between the excavation sequence and the blasthole layout plan; performing nonlinear finite element analysis based on the dynamic stress state and the topological change data, establishing a mechanical behavior model of the surrounding rock and performing numerical calculations to obtain an equivalent material model of the surrounding rock; According to the equivalent material model and the optimization decision rule, after processing with a preset viscoelastic fluid model, by simulating the stress release and deformation process of the surrounding rock under all rounds of blasting, a blasting excavation parameter setting scheme is obtained, wherein the blasting excavation parameter setting scheme includes an excavation step sequence, a blasthole arrangement scheme, and an initiation sequence; According to the equivalent material model and the optimization decision rule, after processing with a preset viscoelastic fluid model, by simulating the stress release and deformation process of the surrounding rock under all rounds of blasting, a blasting excavation parameter setting scheme is obtained, including: Performing dynamic response analysis based on the equivalent material model, modeling the viscoelastic properties of the surrounding rock using a piecewise linearization method, and obtaining a dynamic response function; The dynamic response of the surrounding rock is simulated using the time domain integration method. The stress-deformation curve is obtained by applying the dynamic response function to the time history of each blasting round and calculating the stress and deformation at each moment through step-by-step time integration. Perform critical state analysis based on the stress-strain curve, compare the maximum stress in the curve with the shear strength of the surrounding rock through the limit equilibrium analysis method, and obtain the safety factor of the surrounding rock; Parameter optimization is performed according to the safety factor and the optimization decision rule, and a fuzzy comprehensive evaluation model is established based on a preset fuzzy comprehensive evaluation algorithm. The safety factor, construction cost, and explosive quantity are used as inputs, fuzzy rules are set, and evaluation is performed through a weighted average method to obtain a blasting excavation parameter setting plan; The dynamic response analysis is performed based on the equivalent material model, and the viscoelastic characteristics of the surrounding rock are modeled by applying the piecewise linearization method to obtain the dynamic response function, including: The viscoelastic properties are defined according to the equivalent material model, and the viscoelastic constitutive equation of the surrounding rock is derived by an analytical method to obtain a dynamic material model of the surrounding rock; Performing transient response calculations based on the dynamic material model, solving the dynamic response of the surrounding rock under blasting loads by an explicit finite difference method, and obtaining dynamic response data of the surrounding rock at each time step; Performing energy analysis based on the dynamic response data, and obtaining energy dissipation characteristics of the surrounding rock by calculating the product of stress and strain; Function construction processing is performed based on the dynamic response data and energy dissipation characteristics, a mathematical model is established using regression analysis, and stress, strain and energy are used as inputs to obtain a dynamic response function through fitting.

2. The method for setting tunnel surrounding rock blasting excavation parameters according to claim 1, characterized in that: Sparse subspace clustering is performed based on the blasting excavation history records. By performing sparse subspace decomposition on the surrounding rock characteristics and blasting effects in the historical data, a feature data set is obtained, including: Performing data dimensionality reduction processing based on the blasting excavation historical records, extracting surrounding rock characteristic data and corresponding blasting effect data through principal component analysis to obtain a low-dimensional feature set; Performing sparse coding processing on the low-dimensional feature set, and sparsely representing the features through Lasso regression to obtain a sparse feature matrix; Performing similarity measurement based on the sparse feature matrix, calculating the similarity between different historical blasting records by cosine similarity, and obtaining a similarity matrix; Sparse subspace clustering is performed according to the similarity matrix, and the blasting records are divided into at least two related subspaces by a sparse subspace clustering algorithm to obtain a feature data set.

3. The method for setting tunnel surrounding rock blasting excavation parameters according to claim 1, characterized in that: A stress state analysis is performed based on the geological characteristic data, the excavation profile data, and the characteristic data set. By simulating the stress change process of the surrounding rock at different time points, a time-dependent topological analysis method is used to model the stress state and spatial change of the surrounding rock, thereby obtaining dynamic stress state and topological change data of the surrounding rock, including: Performing spatial discretization processing based on the geological characteristic data and the excavation profile data, and performing grid discretization on the tunnel surrounding rock using the finite volume method to obtain a spatial discretization model; Calculating the initial stress field according to the spatial discrete model, calculating the surrounding rock deadweight and formation pressure and using Gaussian numerical integration to calculate stress, and obtaining the initial stress distribution state of the surrounding rock; Performing a dynamic stress change simulation based on the initial stress distribution state, gradually promoting stress redistribution of the surrounding rock during the excavation process, and simulating stress release and redistribution of the surrounding rock due to rock mass removal at different time points during the excavation process, thereby obtaining time series stress distribution data; A time-dependent topological analysis is performed based on the time series stress distribution data, and the changes in stress concentration areas and newly formed fractures generated over time in the stress field are identified by a persistent homology method to obtain dynamic stress state and topological change data.

4. The method for setting tunnel surrounding rock blasting excavation parameters according to claim 1, characterized in that: Association rule mining is performed based on the blasting excavation history records and the feature data set. By mining the intrinsic association between the excavation sequence and the blasthole layout plan, an optimization decision rule is obtained, including: Performing feature sequence analysis based on the blasting excavation history records and feature data sets, comparing surrounding rock stress evolution patterns under different step sequence conditions through dynamic time warping, and obtaining surrounding rock stress sequence matrices under different excavation step sequence conditions; Stress pattern clustering is performed according to the surrounding rock stress sequence matrix, and clustering steps with similar stress evolution characteristics is performed to obtain clustering results of excavation step sequence and stress evolution; An association rule analysis of blasthole layout is performed based on the clustering results, and a probabilistic dependency relationship between excavation sequence and blasthole layout is modeled by a Bayesian network, and the influence of each blasthole layout on the stress evolution of a specific sequence is quantified to obtain a probabilistic association rule. Multi-objective optimization is performed according to the probabilistic association rules. The stress evolution and surrounding rock stability are simulated and evaluated by simulating the combination of different blasthole schemes and step sequences. The stability and economy of the tunnel surrounding rock are used as the reward function for screening to obtain the optimization decision rule.

5. The method for setting tunnel surrounding rock blasting excavation parameters according to claim 1, characterized in that: Nonlinear finite element analysis is performed based on the dynamic stress state and the topological change data, and an equivalent material model of the surrounding rock is obtained by establishing a mechanical behavior model of the surrounding rock and performing numerical calculations, including: Performing nonlinear material property parameter fitting based on the dynamic stress state and the topological change data, optimizing and fitting the nonlinear constitutive relationship of the surrounding rock material through a genetic algorithm to obtain the nonlinear material parameters of the surrounding rock; Dividing the nonlinear finite element unit grid according to the nonlinear material parameters, optimizing the grid of the spatial discrete model of the surrounding rock through an adaptive grid generation algorithm, and obtaining a finite element grid model containing nonlinear characteristics; Performing nonlinear mechanical behavior simulation according to the finite element mesh model, based on the initial stress state of the surrounding rock and the mesh model, gradually and iteratively solving the stress and strain of each unit until a mechanical equilibrium state is reached, thereby obtaining the stress-strain distribution result of the surrounding rock; An equivalent material model is constructed based on the stress-strain distribution results, and the equivalent material properties of the surrounding rock are determined through an inversion analysis algorithm to obtain the equivalent material model.

6. A tunnel surrounding rock blasting excavation parameter setting system, characterized in that: include: An acquisition module is used to obtain geological characteristic data of surrounding rocks, blasting excavation history records and excavation profile data; A clustering module is used to perform sparse subspace clustering processing based on the blasting excavation history records, and obtain a feature data set by performing sparse subspace decomposition on the surrounding rock characteristics and blasting effects in the historical data; an analysis module for performing stress state analysis based on the geological characteristic data, the excavation profile data, and the characteristic data set, by simulating the stress change process of the surrounding rock at different time points, using a time-dependent topological analysis method to model the stress state and spatial change of the surrounding rock, and obtaining dynamic stress state and topological change data of the surrounding rock; a mining module for mining association rules based on the blasting excavation history records and the feature data set, and obtaining an optimization decision rule by mining the intrinsic association between the excavation sequence and the blasthole layout plan; A construction module is used to perform nonlinear finite element analysis based on the dynamic stress state and the topological change data, and obtain an equivalent material model of the surrounding rock by establishing a mechanical behavior model of the surrounding rock and performing numerical calculations; an output module for deriving a blasting and excavation parameter setting scheme based on the equivalent material model and the optimization decision rule, through processing with a preset viscoelastic fluid model, and by simulating the stress release and deformation process of the surrounding rock under all rounds of blasting, wherein the blasting and excavation parameter setting scheme includes an excavation sequence, a blasthole arrangement scheme, and an initiation sequence; According to the equivalent material model and the optimization decision rule, after processing with a preset viscoelastic fluid model, by simulating the stress release and deformation process of the surrounding rock under all rounds of blasting, a blasting excavation parameter setting scheme is obtained, including: Performing dynamic response analysis based on the equivalent material model, modeling the viscoelastic properties of the surrounding rock using a piecewise linearization method, and obtaining a dynamic response function; The dynamic response of the surrounding rock is simulated using the time domain integration method. The stress-deformation curve is obtained by applying the dynamic response function to the time history of each blasting round and calculating the stress and deformation at each moment through step-by-step time integration. Perform critical state analysis based on the stress-strain curve, compare the maximum stress in the curve with the shear strength of the surrounding rock through the limit equilibrium analysis method, and obtain the safety factor of the surrounding rock; Parameter optimization is performed according to the safety factor and the optimization decision rule, and a fuzzy comprehensive evaluation model is established based on a preset fuzzy comprehensive evaluation algorithm. The safety factor, construction cost, and explosive quantity are used as inputs, fuzzy rules are set, and evaluation is performed through a weighted average method to obtain a blasting excavation parameter setting plan; The dynamic response analysis is performed based on the equivalent material model, and the viscoelastic characteristics of the surrounding rock are modeled by applying the piecewise linearization method to obtain the dynamic response function, including: The viscoelastic properties are defined according to the equivalent material model, and the viscoelastic constitutive equation of the surrounding rock is derived by an analytical method to obtain a dynamic material model of the surrounding rock; Performing transient response calculations based on the dynamic material model, solving the dynamic response of the surrounding rock under blasting loads by an explicit finite difference method, and obtaining dynamic response data of the surrounding rock at each time step; Performing energy analysis based on the dynamic response data, and obtaining energy dissipation characteristics of the surrounding rock by calculating the product of stress and strain; Function construction processing is performed based on the dynamic response data and energy dissipation characteristics, a mathematical model is established using regression analysis, and stress, strain and energy are used as inputs to obtain a dynamic response function through fitting.

7. The tunnel surrounding rock blasting excavation parameter setting system according to claim 6, characterized in that: The clustering module includes: A first dimensionality reduction unit is used to perform data dimensionality reduction processing based on the blasting excavation history records, extract surrounding rock characteristic data and corresponding blasting effect data through principal component analysis to obtain a low-dimensional feature set; A first encoding unit is configured to perform sparse coding processing on the low-dimensional feature set, and sparsely represent the features through Lasso regression to obtain a sparse feature matrix; A first calculation unit is used to perform similarity measurement based on the sparse feature matrix, calculate the similarity between different historical blasting records by cosine similarity, and obtain a similarity matrix; The first clustering unit is used to perform sparse subspace clustering processing according to the similarity matrix, and divide the blasting records into at least two related subspaces by a sparse subspace clustering algorithm to obtain a feature data set.

8. The tunnel surrounding rock blasting excavation parameter setting system according to claim 6, characterized in that: The analysis module includes: A first processing unit is configured to perform spatial discretization processing based on the geological characteristic data and the excavation profile data, and to perform grid discretization on the tunnel surrounding rock using a finite volume method to obtain a spatial discretization model; A second calculation unit is used to calculate the initial stress field according to the spatial discrete model, calculate the surrounding rock deadweight and formation pressure and use Gaussian numerical integration to perform stress calculation to obtain the initial stress distribution state of the surrounding rock; a first simulation unit for performing a dynamic stress change simulation based on the initial stress distribution state, by gradually promoting stress redistribution of the surrounding rock during the excavation process, and simulating stress release and redistribution of the surrounding rock due to rock mass removal at different time points during the excavation process, thereby obtaining time series stress distribution data; The first analysis unit is used to perform time-dependent topological analysis based on the time series stress distribution data, identify stress concentration area changes and newly formed fractures in the stress field that occur over time through a persistent homology method, and obtain dynamic stress state and topological change data.

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