A method and system for identifying full-face excavation parameters of a large-section tunnel in broken rock

Abstract

The present application relates to a kind of broken surrounding rock large section tunnel full-face excavation parameter identification method and system, it is related to novel information technology service technical field, the method includes: obtaining historical construction data, including the original geological characteristics of historical broken surrounding rock section and full-face excavation parameter set, full-face excavation parameter at least includes cycle footage, blast hole depth, blast hole layout and charge weight;According to historical construction data, determine multiple related geological parameters, obtain the related geological characteristics of current broken surrounding rock section;Based on historical construction data and the related geological characteristics of current broken surrounding rock section, determine the multiple alternative full-face excavation parameter set of current broken surrounding rock section;Through finite element analysis and genetic algorithm, based on the multiple alternative full-face excavation parameters of current broken surrounding rock section, determine the full-face excavation parameter set of current broken surrounding rock section, with the advantage of improving the accuracy of broken surrounding rock large section tunnel full-face excavation parameter.

Description

Technical Field

[0001] This invention relates to the field of new information technology service technology, and in particular to a method and system for identifying parameters of full-section excavation of large-section tunnels in fractured surrounding rock. Background Technology

[0002] In tunnel engineering, surrounding rock refers to the rock mass within a certain range around the tunnel. Fractured surrounding rock means that this rock mass has become broken and loose due to geological tectonic movements, weathering, etc., resulting in poor mechanical properties and low stability. Tunnel construction under fractured surrounding rock conditions faces greater risks of collapse and deformation, requiring higher standards for construction techniques and parameter control. Large-section tunnels refer to tunnels with a larger cross-sectional area during excavation. Compared to small-section tunnels, large-section tunnel construction causes a greater disturbance to the surrounding rock, resulting in more complex stress redistribution and increased construction difficulty and risk. For example, some subway platform tunnels and sections of large traffic tunnels are large-section tunnels. Full-face excavation is a tunnel construction method that involves excavating the entire rock or soil section of the tunnel in one go, followed by subsequent lining and support processes. Compared to sectional excavation (such as bench excavation, ring excavation with reserved core soil, etc.), full-face excavation has advantages such as faster construction speed and simpler procedures, but it also places higher demands on surrounding rock conditions, construction equipment, and construction technology. In large-section tunnels with fractured surrounding rock, full-face excavation is required, necessitating precise control of excavation parameters to ensure construction safety and project quality.

[0003] Existing technologies mostly rely on empirical formulas or linear regression models (such as the BQ grading method and RMR rock mass quality score), which make it difficult to capture the nonlinear and multi-factor coupling characteristics of fractured surrounding rock (such as the nonlinear relationship between the degree of joint development in surrounding rock and the charge amount). The interaction between geological parameters and excavation parameters (such as cycle advance and blast hole layout) has not been explicitly quantified, resulting in a lack of theoretical support for parameter recommendations.

[0004] Therefore, there is a need to provide a method and system for identifying full-section excavation parameters of large-section tunnels in fractured surrounding rock, in order to improve the accuracy of full-section excavation parameters of large-section tunnels in fractured surrounding rock. Summary of the Invention

[0005] This invention provides a method for identifying full-section excavation parameters for large-section tunnels in fractured surrounding rock, comprising: acquiring historical construction data, wherein the historical construction data includes the original geological characteristics of historical fractured surrounding rock sections and a set of full-section excavation parameters, the full-section excavation parameters including at least cycle advance, borehole depth, borehole layout, and charge quantity; determining multiple relevant geological parameters based on the historical construction data; acquiring relevant geological characteristics of the current fractured surrounding rock section based on the multiple relevant geological parameters; determining multiple alternative full-section excavation parameter sets for the current fractured surrounding rock section based on the historical construction data and the relevant geological characteristics of the current fractured surrounding rock section; and determining the full-section excavation parameter set for the current fractured surrounding rock section based on the multiple alternative full-section excavation parameters of the current fractured surrounding rock section through finite element analysis and genetic algorithm.

[0006] Furthermore, the original geological features of the historical fractured surrounding rock section are obtained, including: obtaining ultrasonic detection data at multiple locations of the historical fractured surrounding rock section; and determining the original geological features of the historical fractured surrounding rock section based on the ultrasonic detection data at multiple locations of the historical fractured surrounding rock section, wherein the original geological features of the historical fractured surrounding rock section include at least the fracture density index and the anisotropy coefficient.

[0007] Furthermore, based on historical construction data, multiple relevant geological parameters are determined, including: identifying multiple candidate geological parameters; for each geological parameter, determining the correlation coefficient between the geological parameter and each full-section excavation parameter based on historical construction data; for each full-section excavation parameter, determining the weight of the full-section excavation parameter based on historical construction data; for each geological parameter, determining a score for the geological parameter based on the correlation coefficient between the geological parameter and each full-section excavation parameter and the weight of each full-section excavation parameter; and determining multiple relevant geological parameters based on the score of each geological parameter.

[0008] Furthermore, based on historical construction data, the weights of the full-section excavation parameters are determined, including: determining the discrete values ​​of the full-section excavation parameters based on historical construction data; establishing an undirected graph of correlation coefficients based on historical construction data, wherein the nodes of the undirected graph of correlation coefficients represent the full-section excavation parameters, and the weights of the edges of the undirected graph of correlation coefficients represent the correlation coefficients between the two nodes connected by the edges; and determining the weights of the full-section excavation parameters based on the undirected graph of correlation coefficients.

[0009] Furthermore, based on the correlation coefficient undirected graph, the weights of the full-section excavation parameters are determined, including: for each node, the degree centrality of the node is calculated based on the correlation coefficient undirected graph; the mean and discrete edge weights of the node are calculated based on the weights of the edges directly connected to the node; and the weights of the full-section excavation parameters corresponding to the node are calculated based on the degree centrality, mean and discrete edge weights of the node.

[0010] Furthermore, based on multiple relevant geological parameters, the relevant geological characteristics of the current fractured surrounding rock section are obtained, including: obtaining ultrasonic detection data from multiple points of the current fractured surrounding rock section; and extracting the relevant geological characteristics of the current fractured surrounding rock section from the ultrasonic detection data from multiple points of the current fractured surrounding rock section based on multiple relevant geological parameters.

[0011] Furthermore, ultrasonic testing data from multiple points on the current fractured surrounding rock section are obtained, including: determining multiple initial points; obtaining ultrasonic testing data from multiple initial points; calculating the geological dispersion value of the current fractured surrounding rock section based on the ultrasonic testing data from multiple initial points; determining whether secondary testing is needed based on the geological dispersion value of the current fractured surrounding rock section; if so, determining multiple new points based on the ultrasonic testing data from multiple initial points, and obtaining ultrasonic testing data from multiple new points.

[0012] Furthermore, based on historical construction data and the relevant geological characteristics of the current fractured surrounding rock section, multiple alternative full-section excavation parameter sets for the current fractured surrounding rock section are determined, including: for each historical fractured surrounding rock section, extracting relevant geological characteristics of the historical fractured surrounding rock section based on multiple relevant geological parameters; calculating the similarity of geological characteristics between the current fractured surrounding rock section and the historical fractured surrounding rock section based on the relevant geological characteristics of the current fractured surrounding rock section and the historical fractured surrounding rock section; determining multiple similar historical fractured surrounding rock sections based on the similarity of geological characteristics between the current fractured surrounding rock section and each historical fractured surrounding rock section; and determining multiple alternative full-section excavation parameter sets for the current fractured surrounding rock section based on the full-section excavation parameter sets of the similar historical fractured surrounding rock sections.

[0013] Furthermore, through finite element analysis and genetic algorithms, based on multiple alternative full-section excavation parameter sets for the current fractured surrounding rock section, the full-section excavation parameter set for the current fractured surrounding rock section is determined. This includes: determining multiple parameter evaluation indices; establishing a finite element model of the current fractured surrounding rock section; for each alternative full-section excavation parameter set, determining the parameter evaluation value of the alternative full-section excavation parameter set based on the finite element model of the current fractured surrounding rock section and multiple parameter evaluation indices; and using a genetic algorithm, determining the full-section excavation parameter set for the current fractured surrounding rock section based on the parameter evaluation value of each alternative full-section excavation parameter set.

[0014] This invention provides a system for identifying full-section excavation parameters for large-section tunnels in fractured surrounding rock, comprising: a sample acquisition module for acquiring historical construction data, wherein the historical construction data includes the original geological characteristics of historical fractured surrounding rock sections and a full-section excavation parameter set, wherein the full-section excavation parameter set includes at least cycle advance, borehole depth, borehole layout, and charge quantity; a parameter determination module for determining multiple relevant geological parameters based on historical construction data; a feature acquisition module for acquiring relevant geological features of the current fractured surrounding rock section based on multiple relevant geological parameters; and a parameter determination module for determining multiple alternative full-section excavation parameter sets for the current fractured surrounding rock section based on historical construction data and relevant geological features of the current fractured surrounding rock section, and determining the full-section excavation parameter set for the current fractured surrounding rock section based on the multiple alternative full-section excavation parameter sets through finite element analysis and genetic algorithm.

[0015] Compared with existing technologies, the method and system for identifying full-section excavation parameters of large-section tunnels in fractured surrounding rock provided in this specification have at least the following advantages:

[0016] 1. By acquiring the original geological features and full-section excavation parameter set from historical construction data, and determining relevant geological parameters to obtain the geological characteristics of the current fractured surrounding rock section, multiple alternative full-section excavation parameter sets are determined. Finally, finite element analysis and genetic algorithms are used to determine the optimal parameter set. This series of processes fully considers the actual geological conditions of the current fractured surrounding rock section, ensuring precise matching between excavation parameters and geological conditions. This avoids safety accidents such as surrounding rock instability and collapse caused by unreasonable parameters, ensuring the safety of construction personnel and equipment. By determining the full-section excavation parameter set through scientific methods, the blindness and experience-based approach to parameter selection in traditional construction are avoided. Reasonable parameters such as cycle advance, borehole depth, borehole layout, and charge quantity can improve excavation speed and cycle operation efficiency, reduce downtime and adjustment time during construction, and thus shorten the overall project duration. Since the excavation parameters are optimized and determined based on the actual geological characteristics of the current fractured surrounding rock section, they can better adapt to changes in geological conditions, reduce changes in construction plans and rework caused by discrepancies between geological conditions and expectations, ensure the continuity and smoothness of construction, and improve construction efficiency.

[0017] 2. By determining the correlation coefficients between geological parameters and each full-section excavation parameter, and further combining the weights of the full-section excavation parameters to calculate the scores of the geological parameters, the influence of each geological parameter on the excavation parameters is quantitatively assessed. This quantitative assessment avoids the bias of subjective judgment and can more accurately identify relevant geological parameters that have a significant impact on the excavation process and results, providing key basis for subsequent construction. Based on the scores of the geological parameters, multiple relevant geological parameters are identified, and those with little or no impact on the excavation parameters are filtered out, focusing on key factors. This helps simplify the complexity of problem analysis, improves the efficiency of subsequent analysis and decision-making, and also enables more precise monitoring and control of key geological parameters, ensuring construction safety and quality. An undirected graph of correlation coefficients is established, with the full-section excavation parameters as nodes and the correlation coefficients between parameters as edge weights, intuitively displaying the interrelationships between parameters. Determining parameter weights based on this graph can fully consider the correlation and mutual influence between parameters. Weights are determined by calculating indicators such as the degree centrality of nodes, the mean of edge weights, and the discrete value of edge weights. This approach not only considers the characteristics of the parameters themselves but also takes into account their relationship with other parameters, thereby improving the accuracy and rationality of weight determination.

[0018] 3. By calculating the similarity of geological features between the current fractured surrounding rock section and historical fractured surrounding rock sections, the most similar historical cases to the current geological conditions can be accurately located from a large amount of historical data. This matching method based on geological feature similarity fully considers the complex geological characteristics of fractured surrounding rock, such as rock strength, degree of joint and fracture development, and groundwater conditions, making the selected similar historical fractured surrounding rock sections more representative. This provides a reliable basis for determining the subsequent candidate excavation parameter set. Establishing a finite element model of the current fractured surrounding rock section can realistically simulate the mechanical properties and stress-strain state of the fractured surrounding rock. Combining the candidate parameter set determined by similar historical sections, simulation analysis in the finite element model can more accurately predict the response of the surrounding rock under different parameter sets, thereby ensuring that the final determined excavation parameter set is highly matched with the current geological conditions, improving the pertinence and effectiveness of construction. A genetic algorithm is used to optimize the candidate full-section excavation parameter set. The genetic algorithm has the advantages of strong global search capability and fast convergence speed, and can quickly find the optimal solution in a complex parameter space. By using a genetic algorithm to iteratively optimize based on parameter evaluation values, the set of full-section excavation parameters for the current fractured surrounding rock section can be determined efficiently, avoiding the tedious trial-and-error process in traditional methods. Attached Figure Description

[0019] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0020] Figure 1 This is a flowchart illustrating a method for identifying full-section excavation parameters of a large-section tunnel in fractured surrounding rock, as shown in one embodiment of this application.

[0021] Figure 2 This is a schematic diagram of an undirected graph of correlation coefficients shown in one embodiment of this application;

[0022] Figure 3 This is a block diagram of a full-section excavation parameter identification system for large-section tunnels in fractured surrounding rock, as shown in one embodiment of this application. Detailed Implementation

[0023] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0024] Figure 1 This is a flowchart illustrating a method for identifying parameters during full-section excavation of a large-section tunnel in fractured surrounding rock, as shown in one embodiment of this application. Figure 1 As shown, a method for identifying full-section excavation parameters of a large-section tunnel in fractured surrounding rock belongs to a new type of information technology service and may include the following steps.

[0025] Step 110: Obtain historical construction data.

[0026] Among them, historical construction data includes the original geological characteristics of historical fractured surrounding rock sections and the full-section excavation parameter set. The full-section excavation parameters include at least cycle advance, blast hole depth, blast hole layout and charge quantity.

[0027] Cycle advance refers to the distance traveled by a tunneling machine or drilling and blasting method during a single excavation cycle. The size of the cycle advance needs to be comprehensively considered based on factors such as the geological conditions of the surrounding rock, the performance of the excavation equipment, and the construction schedule requirements. A larger cycle advance can improve construction efficiency but may increase disturbance and deformation of the surrounding rock, increasing construction safety risks; a smaller cycle advance can improve construction safety but reduce construction efficiency.

[0028] The depth of a blast hole refers to the distance from the opening to the bottom of the blast hole. The choice of blast hole depth affects the blasting effect and excavation efficiency. Deeper blast holes can increase the amount of rock blasted per blast, improving excavation efficiency, but require more precise control of blasting parameters and higher drilling technology requirements; shallower blast holes are relatively easier to drill and control blasting, but have lower excavation efficiency.

[0029] The layout of blast holes includes their arrangement, spacing, and angle. A reasonable blast hole layout can distribute blasting energy evenly in the rock, achieving good fragmentation, reducing the generation of large rock fragments, and minimizing disturbance to the surrounding rock. Different geological conditions of the surrounding rock and the shape of the excavation cross-section require different blast hole layouts.

[0030] Charge quantity refers to the amount of explosives loaded into each blast hole. The amount of explosives directly affects the power and effect of the blast. Too much explosives may lead to excessive fragmentation and flyrock, increasing safety risks and causing significant disturbance to the surrounding rock; too little explosives may fail to achieve the expected fragmentation effect, affecting excavation efficiency.

[0031] In some embodiments, obtaining the original geological features of a historical fractured surrounding rock section includes:

[0032] Acquire ultrasonic detection data at multiple locations on historical fractured surrounding rock sections;

[0033] Based on ultrasonic testing data from multiple locations of the historical fractured surrounding rock section, the original geological characteristics of the historical fractured surrounding rock section are determined. The original geological characteristics of the historical fractured surrounding rock section include at least the fracture density index and the anisotropy coefficient.

[0034] Specifically, when ultrasound propagates through rock, its speed, frequency, and amplitude are affected by the internal structure and properties of the rock. By transmitting and receiving ultrasound at multiple locations on a fractured rock section and measuring the propagation parameters, information reflecting the internal structural characteristics of the rock can be obtained.

[0035] Using ultrasonic testing equipment, test points are set up on the fractured surrounding rock section. Ultrasonic transmitting and receiving probes are placed at each test point, and ultrasonic waves are emitted and their propagation time and waveform data are recorded. To obtain accurate data, multiple tests are required at different locations. Data from multiple locations comprehensively reflects the geological feature distribution of the fractured surrounding rock section, avoiding data deviations caused by local anomalies, and providing a reliable basis for accurately determining the original geological features.

[0036] The fracture density index reflects the degree of fracture development in the surrounding rock. A higher fracture density index indicates more fractures in the surrounding rock, resulting in poorer rock integrity and consequently reduced strength and stability. For example, in a fractured rock section with a high fracture density index, the surrounding rock may be more prone to fracturing and collapse.

[0037] By analyzing ultrasonic testing data, the density of fractures can be estimated using the propagation characteristics of ultrasound waves in fractured rocks (such as decreased velocity and increased attenuation). The greater the fracture density, the lower the propagation velocity and the greater the attenuation of ultrasound waves. By establishing a mathematical model between ultrasonic propagation parameters and the fracture density index, the fracture density index can be calculated based on measured ultrasonic data.

[0038] Specifically, ultrasonic testing is performed at multiple locations on the historical fractured surrounding rock section, and the propagation speed of ultrasonic waves is recorded at each location. For each testing location, the attenuation rate of the ultrasonic wave velocity is calculated. The mean and standard deviation of the attenuation rate of the ultrasonic wave velocity at each testing location are calculated to obtain the mean and standard deviation of the attenuation rate. Through experimental or theoretical analysis, a mathematical relationship between the fracture density index and the mean and standard deviation of the attenuation rate is established. The mean and standard deviation of the attenuation rate of the historical fractured surrounding rock section are substituted into this mathematical relationship to calculate the fracture density index of the historical fractured surrounding rock section.

[0039] The anisotropy coefficient describes the degree of difference in the physical and mechanical properties of surrounding rock in different directions. When ultrasound propagates in fractured surrounding rock, its velocity is affected by the internal structure of the surrounding rock (such as the distribution and orientation of fractures). If the surrounding rock is anisotropic, meaning that its physical properties (including properties affecting the propagation velocity of ultrasound) differ in different directions, then the ultrasound velocities detected at different locations will have a certain degree of dispersion. Ultrasonic testing is performed at multiple locations on a historical fractured surrounding rock section, and the propagation velocity of ultrasound at each location is recorded. The standard deviation of the ultrasound velocity at each testing location is calculated to obtain the standard deviation of the ultrasound velocity. Through experimental or theoretical analysis, a mathematical relationship between the anisotropy coefficient and the standard deviation of the ultrasound velocity is established. Substituting the standard deviation of the ultrasound velocity of the historical fractured surrounding rock section into this mathematical relationship, the anisotropy coefficient of the historical fractured surrounding rock section is calculated.

[0040] Step 120: Determine multiple relevant geological parameters based on historical construction data.

[0041] Specifically, it includes:

[0042] Identify multiple candidate geological parameters;

[0043] For each geological parameter, the correlation coefficient between the geological parameter and each full-section excavation parameter is determined based on historical construction data;

[0044] For each full-section excavation parameter, the weight of the full-section excavation parameter is determined based on historical construction data;

[0045] For each geological parameter, the score of the geological parameter is determined based on the correlation coefficient between the geological parameter and each full-section excavation parameter and the weight of each full-section excavation parameter;

[0046] Based on the score of each geological parameter, multiple related geological parameters are determined.

[0047] Specifically, the determination of candidate geological parameters is to comprehensively cover all kinds of geological factors that may affect the full-section excavation construction, and to provide a rich selection basis for the subsequent screening of key geological parameters that are truly closely related to the excavation parameters.

[0048] Collect geological survey reports from similar past projects and extract candidate geological parameters, such as rock type (e.g., granite, limestone, shale, etc., different types of rocks have different physical and mechanical properties, which will affect the excavation difficulty and efficiency), rock strength (including uniaxial compressive strength, tensile strength, etc., the higher the strength, the higher the energy and equipment requirements for excavation may be), rock integrity coefficient (reflects the degree of rock fragmentation, a low integrity coefficient indicates rock fragmentation, and problems such as collapse are likely to occur during excavation), degree of fracture development (the number, spacing, and opening of fractures, etc., fracture development will affect the stability of the rock and the excavation progress), and groundwater level (the level of groundwater level will affect the drainage difficulty and the stability of the surrounding rock during the excavation process), etc.

[0049] Obtain the measured values ​​of each geological parameter and the corresponding actual values ​​of the full-section excavation parameters from historical construction data. For example, collect rock strength data for different construction sections, as well as excavation parameter data such as excavation progress and blasting charge usage during the same period.

[0050] The collected data is cleaned to remove outliers and missing values. For data of different scales, standardization is performed to make them comparable. For example, Z-score standardization is used to convert the data into a standard normal distribution with a mean of 0 and a standard deviation of 1.

[0051] For each geological parameter and full-section excavation parameter, the values ​​of the candidate geological parameters and full-section excavation parameters for each historical fractured surrounding rock section are substituted into the correlation coefficient calculation formula (e.g., Pearson correlation coefficient, Spearman correlation coefficient, etc.) to obtain the correlation coefficient between the geological parameters and the full-section excavation parameters.

[0052] In some embodiments, the weights of the full-section excavation parameters are determined based on historical construction data, including:

[0053] Based on historical construction data, determine the discrete values ​​of the full-section excavation parameters;

[0054] Based on historical construction data, an undirected graph of correlation coefficients was constructed, in which... Figure 2 This is a schematic diagram of an undirected graph of correlation coefficients shown in one embodiment of this application, as follows: Figure 2As shown, the nodes of the correlation coefficient undirected graph represent the full-section excavation parameters, and the weights of the edges in the correlation coefficient undirected graph represent the correlation coefficient between the two nodes connected by the edge.

[0055] The weights of the full-section excavation parameters are determined based on the undirected graph of correlation coefficients.

[0056] Specifically, the discrete values ​​reflect the degree of fluctuation of the full-face excavation parameters during historical construction. The greater the dispersion of a parameter, the more drastic its changes under different construction conditions, and the more complex and critical its impact on the construction process, requiring careful consideration when determining its weights. Conversely, parameters with smaller dispersion tend to have more stable changes and may have a relatively smaller impact on construction. For each full-face excavation parameter, the standard deviation of its value at each historical fractured rock section can be calculated to obtain the discrete value of the full-face excavation parameter.

[0057] Undirected correlation graphs can visually demonstrate the relationships between parameters in a full-section excavation. The graph clearly shows which parameters are strongly correlated and which are relatively independent, providing an intuitive basis for determining parameter weights. The edge weights (correlation coefficients) quantitatively describe the degree of association between parameters; a larger correlation coefficient indicates a stronger linear relationship between the two parameters, suggesting potential mutual influence and constraints during construction. These relationships need to be considered when determining weights.

[0058] For any two full-section excavation parameters, substitute the values ​​of the two full-section excavation parameters at each historical fractured surrounding rock section into the correlation coefficient calculation formula (e.g., Pearson correlation coefficient, Spearman correlation coefficient, etc.) to obtain the correlation coefficient of the two full-section excavation parameters. If the absolute value of the correlation coefficient of the two full-section excavation parameters is greater than the correlation coefficient threshold (e.g., 0.5), then the nodes representing the two full-section excavation parameters are connected by edges. The correlation coefficient threshold can be determined based on experimental data.

[0059] In some embodiments, the weights of the full-section excavation parameters are determined based on the correlation coefficient undirected graph, including:

[0060] For each node, based on the undirected graph with correlation coefficients, the degree centrality of the node is calculated. According to the weights of the edges directly connected to the node, the mean and discrete values ​​of the edge weights of the node are calculated. Based on the degree centrality, mean and discrete values ​​of the edge weights of the node, the weights of the full-section excavation parameters corresponding to the node are calculated.

[0061] Specifically, degree centrality is an indicator that measures the number of nodes directly connected to other nodes in a graph. In an undirected graph with correlation coefficients, the higher the degree centrality of a node, the more direct the relationship between the full-section excavation parameters represented by that node and other parameters. This reflects a wider range of influence of this parameter during construction, and it needs to be given more attention when determining its weight, as it may have a more comprehensive impact on the overall construction.

[0062] The degree centrality of a node can be calculated using the following formula:

[0063]

[0064] in, Let i be the degree centrality of the i-th node. Let be the total number of edges connected to the i-th node. This represents the total number of nodes.

[0065] Understandably, the range of the total number of edges connected to the i-th node depends on the size of the network (total number of nodes m). In a graph with m nodes, the maximum possible number of connections for a node is m-1 (because it cannot be connected to itself). Normalization can be achieved by dividing the total number of edges connected to the i-th node by m-1.

[0066] The mean edge weight reflects the average correlation between other parameters connected to a node and the parameter represented by that node. The larger the mean edge weight, the stronger the average correlation between that parameter and other parameters, the closer its interaction with other parameters during construction, and the more significant its impact on construction.

[0067] The edge weight discrete value measures the degree of fluctuation in the weights of edges connected to a node. A larger discrete value indicates a greater difference in correlation between this parameter and other parameters, meaning that the degree of correlation between this parameter and different parameters varies drastically under different conditions. This may indicate that the parameter is affected by various complex factors during construction. The edge weight discrete value of a node can be obtained by calculating the standard deviation of its edges.

[0068] The weights of the full-section excavation parameters corresponding to the nodes can be calculated using the following formula:

[0069]

[0070] in, Let be the weight of the full-section excavation parameters corresponding to the i-th node. , and To preset weights, , and Greater than 0, , Let $i$ be the average edge weight corresponding to the $i$-th node. This represents the discrete value of the edge weight corresponding to the i-th node. For example, =0.3, =0.4. =0.3.

[0071] Understandably, this formula combines three different factors—degree centrality, mean edge weights, and the inverse of the discrete edge weights—to calculate the weights, thus providing a more comprehensive and accurate reflection of the importance of full-face excavation parameters during construction. Degree centrality considers the range of influence of a parameter, the mean edge weights consider the average correlation between a parameter and other parameters, and the inverse of the discrete edge weights considers the stability of the correlation between a parameter and other parameters. The organic combination of these three factors makes the weight calculation more scientific and reasonable.

[0072] The geological parameters can be scored by weighting and summing the correlation coefficients between the geological parameters and each full-section excavation parameter according to the weight of each full-section excavation parameter.

[0073] Geological parameters with scores greater than a scoring threshold (e.g., 1) can be used as relevant geological parameters. The scoring data can be determined based on experimental data.

[0074] Step 130: Obtain the relevant geological characteristics of the current fractured surrounding rock section based on multiple relevant geological parameters.

[0075] Specifically, it includes:

[0076] Acquire ultrasonic detection data at multiple points on the current fractured surrounding rock section;

[0077] Based on multiple relevant geological parameters, the relevant geological features of the current fractured surrounding rock section are extracted from ultrasonic detection data at multiple points on the current fractured surrounding rock section.

[0078] In some embodiments, ultrasonic detection data at multiple points on the current fractured surrounding rock section are acquired, including:

[0079] Determine multiple initial points;

[0080] Acquire ultrasonic detection data from multiple initial points;

[0081] Based on ultrasonic detection data from multiple initial points, the geological discrete value of the current fractured surrounding rock section is calculated.

[0082] Determine whether secondary testing is needed based on the geological discrete value of the current fractured surrounding rock section. If so, determine multiple new points based on the ultrasonic testing data of multiple initial points, and obtain the ultrasonic testing data of multiple new points.

[0083] Specifically, based on the characteristics and testing experience of fractured surrounding rock sections in similar past projects, the approximate distribution pattern of initial points is determined. For example, in tunnel construction, initial points can be evenly distributed in key areas such as the arch crown, arch waist, and sidewalls. The number of initial points can be around 5 to 10.

[0084] The standard deviation of the ultrasonic propagation velocity at each initial point can be calculated as the geological discrete value of the current fractured surrounding rock section.

[0085] Based on engineering experience and relevant specifications, a threshold for geological dispersion values ​​is set. For example, if the calculated geological dispersion value (standard deviation) is greater than 200 m / s, it is determined that a secondary test is required; if it is less than or equal to 200 m / s, the geological properties of the surrounding rock section are considered relatively uniform, and a secondary test can be temporarily omitted.

[0086] The newly added monitoring points are locations selected from the initial points to supplement the testing in areas with large geological dispersion and significant differences in geological properties. By acquiring ultrasonic testing data from these new points, the geological understanding of these areas can be further refined, improving the accuracy of the overall geological condition assessment of the surrounding rock cross-section.

[0087] Analyze the distribution of geological dispersion values ​​of the initial points on the surrounding rock cross section, identify areas with large geological dispersion values, and appropriately add new points around these areas. For example, if it is found that the geological dispersion values ​​of several adjacent points in the initial points are significantly larger than those of other areas, then new points are arranged around these points at certain intervals (e.g., 0.5-1 meters).

[0088] Extracting relevant geological features of the current fractured surrounding rock section can include the values ​​of multiple relevant geological parameters. The method for extracting the values ​​of multiple relevant geological parameters of the current fractured surrounding rock section from ultrasonic testing data at multiple points on the current fractured surrounding rock section can be found in the description of step 120, and will not be repeated here.

[0089] Step 140: Based on historical construction data and the relevant geological characteristics of the current fractured surrounding rock section, determine a set of multiple alternative full-section excavation parameters for the current fractured surrounding rock section.

[0090] Specifically, it includes:

[0091] For each historical fractured surrounding rock section, based on multiple relevant geological parameters, the relevant geological features of the historical fractured surrounding rock section are extracted. Based on the relevant geological features of the current fractured surrounding rock section and the relevant geological features of the historical fractured surrounding rock section, the similarity of the geological features between the current fractured surrounding rock section and the historical fractured surrounding rock section is calculated. The relevant geological features of the historical fractured surrounding rock section may include the values ​​of multiple relevant geological parameters.

[0092] Based on the similarity of the geological characteristics of the current fractured surrounding rock section with each historical fractured surrounding rock section, multiple similar historical fractured surrounding rock sections are identified.

[0093] Based on the full-section excavation parameter set of similar historical fractured surrounding rock sections, multiple alternative full-section excavation parameter sets are determined for the current fractured surrounding rock section.

[0094] Specifically, the Euclidean distance between the relevant geological features of the current fractured surrounding rock section and the relevant geological features of the historical fractured surrounding rock section can be calculated to obtain the similarity of the geological features between the current fractured surrounding rock section and the historical fractured surrounding rock section.

[0095] Based on actual engineering needs and experience, a reasonable geological feature similarity threshold should be set. For example, the similarity threshold can be set to 0.7. When the geological feature similarity between the current fractured surrounding rock section and the historical fractured surrounding rock section is greater than or equal to 0.7, the historical fractured surrounding rock section can be regarded as a similar historical fractured surrounding rock section.

[0096] The full-section excavation parameter set of similar historical fractured surrounding rock sections can be used as the alternative full-section excavation parameter set for the current fractured surrounding rock section.

[0097] Step 150: Using finite element analysis and genetic algorithm, determine the set of full-section excavation parameters for the current fractured surrounding rock section based on multiple alternative full-section excavation parameters for the current fractured surrounding rock section.

[0098] Specifically, it includes:

[0099] Determine multiple parameter evaluation indicators;

[0100] Establish a finite element model of the current fractured surrounding rock section;

[0101] For each alternative full-section excavation parameter set, the parameter evaluation values ​​of the alternative full-section excavation parameter set are determined based on the finite element model of the current fractured surrounding rock section and multiple parameter evaluation indices.

[0102] Using a genetic algorithm, the full-section excavation parameter set for the current fractured surrounding rock section is determined based on the parameter evaluation values ​​of each alternative full-section excavation parameter set.

[0103] Specifically, parameter evaluation indicators are key criteria for assessing the quality of full-face excavation parameters. Since the excavation of fractured surrounding rock involves multiple performance aspects and requirements, such as construction safety, economy, construction efficiency, and impact on surrounding rock stability, it is necessary to determine multiple different types of evaluation indicators to comprehensively evaluate the rationality of the candidate excavation parameter set from multiple perspectives, providing a scientific basis for subsequently selecting the optimal parameter set. Examples include maximum principal stress evaluation indicators, minimum principal stress evaluation indicators, shear stress evaluation indicators, displacement evaluation indicators, construction cost evaluation indicators, and excavation speed evaluation indicators.

[0104] Finite element models can simulate the mechanical behavior and deformation of fractured surrounding rock sections during excavation. By establishing an accurate finite element model, the stress, strain, and displacement responses of the surrounding rock under different alternative excavation parameter sets can be predicted, providing a basis for calculating parameter evaluation values ​​and thus helping to assess the rationality and feasibility of excavation parameters. Based on on-site geological survey data and design drawings, the geometry and dimensions of the fractured surrounding rock section are determined, including the section outline and the distribution of rock strata. Geometric modeling is performed using professional finite element software (such as ABAQUS, ANSYS, etc.), abstracting the actual fractured surrounding rock section into geometric elements within the finite element model. Physical and mechanical parameters of the fractured surrounding rock, such as elastic modulus, Poisson's ratio, internal friction angle, and cohesion, are obtained through indoor rock mechanics tests and field tests. These parameters are input into the finite element model to accurately simulate the mechanical properties of the surrounding rock. Boundary conditions of the finite element model are determined based on actual construction conditions, such as fixed boundaries, displacement boundaries, and stress boundaries. For example, in tunnel excavation, the rock mass boundary around the tunnel is usually set as a fixed boundary to simulate the constraint effect of the rock mass. The finite element model is divided into a finite number of elements and nodes. The coarseness of the mesh affects the accuracy and efficiency of the calculation results. Generally speaking, a finer mesh should be used in critical areas (such as faults and joints) to improve calculation accuracy, while a coarser mesh can be used in other areas to reduce the amount of computation.

[0105] By performing finite element analysis on each alternative excavation parameter set and calculating the corresponding evaluation value based on the determined parameter evaluation indicators, the merits of each parameter set can be quantified. Each alternative full-section excavation parameter set (such as excavation advance, blasting parameters, support parameters, etc.) is input into the finite element model to simulate the excavation process. The finite element software calculates the stress, strain, displacement, and other responses of the surrounding rock during the excavation process based on the input parameters and set boundary conditions. Based on the results of the finite element analysis, calculations are performed according to the determined parameter evaluation indicators. For example, for the maximum principal stress of the surrounding rock in the safety indicators, the principal stress values ​​of each node in the surrounding rock can be extracted using the post-processing function of the finite element software, and then the maximum value is found as the evaluation value of this indicator. For the construction cost in the economic indicators, cost accounting can be performed based on the excavation equipment usage time, support material usage, etc., corresponding to the alternative parameter sets, to obtain the cost evaluation value.

[0106] A fitness function is designed based on parameter evaluation values. This function measures the quality of each individual. For example, the evaluation values ​​of safety indicators can be normalized and then negatively evaluated, and the evaluation values ​​of economic indicators can also be normalized. These values ​​can then be weighted and summed to form the fitness function, ensuring that individuals with higher fitness values ​​are better. The fitness value of each individual is calculated using this function. Then, methods such as roulette wheel selection or tournament selection are used to select a certain number of individuals from the current population as parents for reproduction. The purpose of selection is to increase the probability of selecting individuals with high fitness, thus passing on superior genes to the next generation. A crossover operation is performed on the selected parents, randomly selecting two parents and exchanging some of their genes to generate new offspring. Crossover increases population diversity and helps in finding the global optimum. A certain probability is used to mutate some genes in the offspring, changing their values. Mutation prevents the algorithm from getting trapped in local optima and improves its global search capability. When the preset termination conditions are met (such as maximum number of iterations, fitness value convergence, etc.), the genetic algorithm stops running, and the individual with the highest fitness value is selected from the final population as the optimal solution, which is the full-section excavation parameter set of the current broken surrounding rock section.

[0107] Figure 3 This is a block diagram of a full-section excavation parameter identification system for large-section tunnels in fractured surrounding rock, as shown in one embodiment of this application. Figure 3 As shown, a parameter identification system for full-section excavation of large-section tunnels in fractured surrounding rock may include a sample acquisition module, a parameter determination module, a feature acquisition module, and a parameter determination module.

[0108] The sample acquisition module is used to acquire historical construction data, which includes the original geological characteristics of the historical fractured surrounding rock section and the full-section excavation parameter set. The full-section excavation parameter set includes at least the cycle advance, blast hole depth, blast hole layout and charge amount.

[0109] The parameter determination module is used to determine multiple relevant geological parameters based on historical construction data.

[0110] The feature acquisition module is used to acquire the relevant geological features of the current fractured surrounding rock section based on multiple relevant geological parameters;

[0111] The parameter determination module is used to determine multiple alternative full-section excavation parameter sets for the current broken rock section based on historical construction data and relevant geological characteristics of the current broken rock section. It then uses finite element analysis and genetic algorithms to determine the full-section excavation parameter set for the current broken rock section based on these alternative full-section excavation parameter sets.

[0112] A parameter identification system for full-section excavation of large-section tunnels in fractured surrounding rock can be used to implement a method for identifying parameters for full-section excavation of large-section tunnels in fractured surrounding rock, which will not be elaborated here.

[0113] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A method for identifying full-section excavation parameters of a large-section tunnel in fractured surrounding rock, characterized in that, include: Acquire historical construction data, which includes the original geological characteristics of historical fractured surrounding rock sections and the full-section excavation parameter set. The full-section excavation parameters include at least cycle advance, blast hole depth, blast hole layout and charge quantity. Based on historical construction data, several relevant geological parameters were determined; Based on multiple relevant geological parameters, the relevant geological characteristics of the current fractured surrounding rock section are obtained; Based on historical construction data and the relevant geological characteristics of the current fractured surrounding rock section, a variety of alternative full-section excavation parameter sets for the current fractured surrounding rock section are determined. By using finite element analysis and genetic algorithm, the set of full-section excavation parameters for the current fractured surrounding rock section is determined based on multiple alternative full-section excavation parameters for the current fractured surrounding rock section. Based on historical construction data, several relevant geological parameters were determined, including: Identify multiple candidate geological parameters; For each geological parameter, the correlation coefficient between the geological parameter and each full-section excavation parameter is determined based on historical construction data; For each full-section excavation parameter, the weight of the full-section excavation parameter is determined based on historical construction data; For each geological parameter, the score of the geological parameter is determined based on the correlation coefficient between the geological parameter and each full-section excavation parameter and the weight of each full-section excavation parameter; Based on the score of each geological parameter, multiple related geological parameters are determined.

2. The method for identifying full-section excavation parameters of a large-section tunnel in fractured surrounding rock according to claim 1, characterized in that, Obtain the original geological features of the historical fractured surrounding rock section, including: Acquire ultrasonic detection data at multiple locations on historical fractured surrounding rock sections; Based on ultrasonic testing data from multiple locations of the historical fractured surrounding rock section, the original geological characteristics of the historical fractured surrounding rock section are determined. The original geological characteristics of the historical fractured surrounding rock section include at least the fracture density index and the anisotropy coefficient.

3. The method for identifying full-section excavation parameters of a large-section tunnel in fractured surrounding rock according to claim 1, characterized in that, Based on historical construction data, the weights of the full-section excavation parameters are determined, including: Based on historical construction data, determine the discrete values ​​of the full-section excavation parameters; Based on historical construction data, an undirected graph of correlation coefficients is established. In this graph, the nodes represent the full-section excavation parameters, and the weights of the edges represent the correlation coefficients between the two nodes connected by the edges. The weights of the full-section excavation parameters are determined based on the undirected graph of correlation coefficients.

4. The method for identifying full-section excavation parameters of a large-section tunnel in fractured surrounding rock according to claim 3, characterized in that, Based on the undirected graph of correlation coefficients, the weights of the full-section excavation parameters are determined, including: For each node, based on the undirected graph with correlation coefficients, the degree centrality of the node is calculated. According to the weights of the edges directly connected to the node, the mean and discrete values ​​of the edge weights of the node are calculated. Based on the degree centrality, mean and discrete values ​​of the edge weights of the node, the weights of the full-section excavation parameters corresponding to the node are calculated.

5. A method for identifying full-section excavation parameters of a large-section tunnel in fractured surrounding rock according to any one of claims 1-4, characterized in that, Based on multiple relevant geological parameters, the relevant geological characteristics of the current fractured surrounding rock section are obtained, including: Acquire ultrasonic detection data at multiple points on the current fractured surrounding rock section; Based on multiple relevant geological parameters, the relevant geological features of the current fractured surrounding rock section are extracted from ultrasonic detection data at multiple points on the current fractured surrounding rock section.

6. The method for identifying full-section excavation parameters of a large-section tunnel in fractured surrounding rock according to claim 5, characterized in that, Acquire ultrasonic testing data from multiple points on the current fractured surrounding rock section, including: Determine multiple initial points; Acquire ultrasonic detection data from multiple initial points; Based on ultrasonic detection data from multiple initial points, the geological discrete value of the current fractured surrounding rock section is calculated. Determine whether secondary testing is needed based on the geological discrete value of the current fractured surrounding rock section. If so, determine multiple new points based on the ultrasonic testing data of multiple initial points, and obtain the ultrasonic testing data of multiple new points.

7. A method for identifying full-section excavation parameters of a large-section tunnel in fractured surrounding rock according to any one of claims 1-4, characterized in that, Based on historical construction data and the relevant geological characteristics of the current fractured surrounding rock section, several alternative full-section excavation parameter sets for the current fractured surrounding rock section are determined, including: For each historical fractured surrounding rock section, based on multiple relevant geological parameters, the relevant geological features of the historical fractured surrounding rock section are extracted. Based on the relevant geological features of the current fractured surrounding rock section and the relevant geological features of the historical fractured surrounding rock section, the similarity of the geological features between the current fractured surrounding rock section and the historical fractured surrounding rock section is calculated. Based on the similarity of the geological characteristics of the current fractured surrounding rock section with each historical fractured surrounding rock section, multiple similar historical fractured surrounding rock sections are identified. Based on the full-section excavation parameter set of similar historical fractured surrounding rock sections, multiple alternative full-section excavation parameter sets are determined for the current fractured surrounding rock section.

8. A method for identifying full-section excavation parameters of a large-section tunnel in fractured surrounding rock according to any one of claims 1-4, characterized in that, Based on multiple alternative full-section excavation parameter sets for the current fractured surrounding rock section using finite element analysis and genetic algorithms, the full-section excavation parameter set for the current fractured surrounding rock section is determined, including: Determine multiple parameter evaluation indicators; Establish a finite element model of the current fractured surrounding rock section; For each alternative full-section excavation parameter set, the parameter evaluation values ​​of the alternative full-section excavation parameter set are determined based on the finite element model of the current fractured surrounding rock section and multiple parameter evaluation indices. Using a genetic algorithm, the full-section excavation parameter set for the current fractured surrounding rock section is determined based on the parameter evaluation values ​​of each alternative full-section excavation parameter set.

9. A parameter identification system for full-section excavation of large-section tunnels in fractured surrounding rock, characterized in that, The method for identifying full-section excavation parameters of a large-section tunnel in fractured surrounding rock as described in claim 1 includes: The sample acquisition module is used to acquire historical construction data, which includes the original geological characteristics of the historical fractured surrounding rock section and the full-section excavation parameter set. The full-section excavation parameter set includes at least the cycle advance, blast hole depth, blast hole layout and charge amount. The parameter determination module is used to determine multiple relevant geological parameters based on historical construction data. The feature acquisition module is used to acquire the relevant geological features of the current fractured surrounding rock section based on multiple relevant geological parameters; The parameter determination module is used to determine multiple alternative full-section excavation parameter sets for the current broken rock section based on historical construction data and relevant geological characteristics of the current broken rock section. It then uses finite element analysis and genetic algorithms to determine the full-section excavation parameter set for the current broken rock section based on these alternative full-section excavation parameter sets.

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