A method, system, device and medium for evaluating the effect of advance grouting of a phosphate mine shaft

Abstract

This invention discloses a method, system, equipment, and medium for evaluating the effectiveness of pre-grouting reinforcement in complex geological phosphate mine shafts. The method includes: Step 1, collecting data on surrounding rock geological, hydrological, and mechanical parameters before, during, and after pre-grouting in the phosphate mine shaft; Step 2, constructing a comprehensive evaluation index system for the pre-grouting reinforcement effect; Step 3, standardizing the collected data and assigning weights according to the importance of each index; Step 4, using the superior-inferiority distance method-rank sum ratio combined method, combining the standardized data with the weights to calculate a comprehensive grouting effect score; Step 5, classifying the comprehensive score according to preset standards, outputting the grouting effect evaluation results, and continuously optimizing the results. This invention can ensure that pre-grouting reinforcement of complex geological phosphate mine shafts is carried out at the lowest economic cost, and minimizes the damage to the pre-reinforced shaft and surrounding rock caused by multiple cyclic blasting, ensuring safe and economical shaft excavation.

Description

Technical Field

[0001] This invention belongs to the field of mine shaft and tunnel water hazard prevention technology, specifically involving a method, system, equipment and medium for evaluating the effect of advanced grouting reinforcement of newly built or renovated vertical shafts in complex geological phosphate mines. Background Technology

[0002] Vertical shaft construction in complex geological phosphate mines often faces severe challenges posed by the Dengying Formation aquifer. This formation is characterized by its large thickness (approximately 277-300m), high fracture rate (0.7%-2.95%), high permeability (permeability coefficient 0.103-0.151 m / d), and high water pressure (up to 6.4MPa), making it highly susceptible to water inrush accidents and threatening construction safety and progress. Existing technologies mainly rely on methods such as surface pre-grouting, working face grouting, and backfill grouting. However, these methods suffer from limitations in evaluation, relying solely on isolated indicators such as residual leakage or pressure tests, lacking a systematic assessment of the synergistic effects of multiple materials. For example, some projects only use the water shut-off rate as the acceptance criterion, neglecting the long-term compressive strength of the consolidated body, leading to secondary water damage caused by subsequent formation deformation. Summary of the Invention

[0003] To address existing problems in safety, technology, and processes during and after shaft excavation, this invention provides a method, system, equipment, and medium for evaluating the effectiveness of pre-grouting reinforcement in complex geological conditions of phosphate mine shafts. This enables a systematic and precise evaluation of the effectiveness of pre-grouting reinforcement in complex geological conditions of phosphate mine shafts, providing technical support for safe construction.

[0004] To achieve the above objectives, the present invention provides the following integrated solution: A method for evaluating the effectiveness of pre-grouting reinforcement in phosphate mine shafts includes: Step 1: Before, during, and after grouting construction in complex geological phosphate mine shafts, collect geological, hydrological, and mechanical parameter data of the surrounding rock. Step 2: Construct a comprehensive evaluation index system for the effect of pre-grouting reinforcement of vertical shafts; Step 3: Standardize the collected data and assign weights according to the importance of each indicator; Step 4: Using the superior-inferior solution distance method-rank sum ratio combined method, combine the standardized data with weights to calculate the comprehensive score of grouting effect; Step 5: Classify the comprehensive score according to the preset standards, output the grouting effect evaluation results, and continuously optimize them.

[0005] As a preferred option, the comprehensive evaluation index system for the effect of pre-grouting reinforcement of vertical shafts includes: rock mass reinforcement index, water plugging and seepage prevention index, grout diffusion controllability index, and engineering economic index.

[0006] As a preferred option, in step three, the weight allocation is determined using the improved AHP-CRITIC method.

[0007] This invention also provides a system for evaluating the effect of pre-grouting reinforcement in phosphate mine shafts, comprising: The first processing module is used to collect geological, hydrological and mechanical parameter data of the surrounding rock before, during and after grouting construction in the phosphate mine shaft. The second processing module is used to construct a comprehensive evaluation index system for the effect of pre-grouting reinforcement of vertical shafts; The third processing module is used to standardize the collected data and assign weights according to the importance of each indicator. The fourth processing module is used to combine the standardized data with weights using the superior-inferior solution distance method-rank sum ratio joint method to calculate the comprehensive score of grouting effect; The fifth processing module is used to classify the comprehensive score according to preset standards, output the grouting effect evaluation results, and continuously optimize them.

[0008] As a preferred option, the comprehensive evaluation index system for the effect of pre-grouting reinforcement of vertical shafts includes: rock mass reinforcement index, water plugging and seepage prevention index, grout diffusion controllability index, and engineering economic index.

[0009] As a preferred option, the third processing module uses the improved AHP-CRITIC method to determine the weight allocation.

[0010] The present invention also provides a device for evaluating the effect of pre-grouting reinforcement of phosphate mine shafts, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes a method for evaluating the effect of pre-grouting reinforcement of phosphate mine shafts when executed by the processor.

[0011] The present invention also provides a storage medium storing a computer program, which, when running, executes a method for evaluating the effect of pre-grouting reinforcement in phosphate mine shafts.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: The technical solution of this invention can evaluate the effect of advanced grouting reinforcement on typical geological defects such as karst caves, cavities, and fissures during the construction of phosphate mine shafts under complex geological conditions. It can be used in mine shaft engineering with high water pressure, large water inflow, and karst fissure development, such as the Dengying Formation of the Sinian System. Through a systematic and quantitative evaluation process, it provides a scientific basis for construction decision-making. Attached Figure Description

[0013] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a flowchart illustrating the method for evaluating the effect of pre-grouting reinforcement of phosphate mine shafts according to an embodiment of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] Example 1: This invention provides a method for evaluating the effect of pre-grouting reinforcement of phosphate mine shafts, including the construction and renovation of shafts, comprising: Step 1: Before, during, and after grouting in the phosphate mine shaft, collect, analyze, and summarize data on the geological, hydrological, and mechanical parameters of the surrounding rock in different strata. Step 2: Construct a comprehensive evaluation index system for the effect of pre-grouting reinforcement of vertical shafts; including the following indicators: 1) Rock mass reinforcement indicators: rock mass integrity coefficient improvement rate after grouting (obtained by sonic wave velocity or borehole television), rock mass strength improvement rate (point load test or rebound value), and fracture filling fullness (core observation, qualitative indicators quantified). 2) Water-blocking and seepage prevention indicators: reduction rate of unit inflow (pressure test / inflow observation), reduction rate of permeability coefficient; 3) Slurry diffusion controllability indicators: design diffusion radius achievement rate (judged by inspecting the slurry vein distribution in the inspection hole), slurry loss rate (the ratio of injected volume to theoretical filling volume, the smaller the better the controllability). 4) Engineering economic indicators: consumption of grouting materials per cubic meter of rock mass, grouting period efficiency (grouting time per linear meter). Step 3: Standardize the collected data and assign weights according to the importance of each indicator; the specific steps are as follows: 1) Data normalization: Positively adjust the above indicators (e.g., the higher the "improvement rate", the better, and the lower the "consumption" is, the better) and normalize them; 2) Determine the weights: The weights are determined using the AHP-CRITIC method, and the specific steps are as follows: 1) Establish a hierarchical structure: Determine the indicators that need to be evaluated (such as the aforementioned wave velocity increase rate, water flow reduction rate, material consumption, etc.). 2) Parallel weight calculation: Experts in geology, grouting, construction and other fields were invited to obtain a set of subjective weight vectors Ws based on the key points of the project using the AHP method; combined with the actual or simulated grouting data of the construction section or test section of the newly built or renovated vertical shaft of phosphate mine or other mine under various geological conditions, the data were input into the CRITIC model to calculate a set of objective weight vectors Wo.

[0018] 3) Combined weight calculation: Using game theory or a linear weighted combination model, Ws and Wo are combined to obtain the final combined weight vector Wc. This Wc incorporates both the expert's experience and strategic intent, and faithfully reflects the objective laws reflected in the pre-grouting data of mine shafts under various geological conditions.

[0019] 4) The specific calculation process for data standardization and combined weighting (AHP-CRITIC method) is as follows: (1) Data normalization processing For data of different properties (positive / negative) and dimensions in the indicator system, the range transformation method is used for standardization, mapping all indicator values ​​to the [0,1] interval.

[0020] Positive indicators (the higher the indicator value, the better the grouting effect, such as: rock mass integrity coefficient improvement rate, rock mass strength improvement rate, unit inflow rate reduction rate, permeability coefficient reduction rate, and design diffusion radius achievement rate): in, : The original value of the i-th evaluation unit (a certain grouting section) on the j-th index. : The standardized value.

[0021] Negative indicators (the smaller the indicator value, the better the grouting effect, such as: grout loss rate, grouting material consumption per unit rock mass, and grouting time per linear meter) Note: If some indicators are qualitative (e.g., fracture filling fullness), they can be quantified through qualitative description. Establish a value assignment standard: Observe the proportion and degree of cementation of the fracture filling material through core drilling, and classify them into "Poor (<30% filling, no cementation)", "Medium (30%-70% filling, partially cemented)", and "Good (>70% filling, densely cemented)", assigning values ​​of 1, 3, and 5 respectively, which will be used as quantitative data in the above normalization calculation.

[0022] (2) Calculation of combined weights (AHP-CRITIC method) To address the limitations of a single weighting method, a combined weighting strategy that integrates subjective and objective approaches is adopted, ensuring that the weights reflect both engineering experience and the inherent differences and conflicts in the data.

[0023] First, experts were invited to conduct pairwise comparisons (based on indicators such as rock mass reinforcement and water shut-off / seepage prevention). A judgment matrix was then constructed. Where m is the number of indicators.

[0024] Calculate the geometric mean of each row of the judgment matrix: Normalization yields subjective weights: Subjective weight vector: Calculate the consistency ratio: If the conditions are met, the matrix is ​​considered to have passed the consistency test.

[0025] The CRITIC method uses the strength of indicator contrast (standard deviation) and the degree of indicator conflict (correlation coefficient) to determine objective weights. First, a standardized matrix is ​​constructed: Where N is the number of evaluation units and m is the number of indicators. The standard deviation of the indicators is calculated using: in, Let be the average value of the j-th indicator. A larger standard deviation indicates a stronger discriminative ability of the indicator. Calculate the correlation coefficient (conflict coefficient) among the indicators: in, Let be the Pearson correlation coefficient between indicator k and indicator j. A higher conflict coefficient indicates stronger independence of the indicator information. Calculate the information content: This represents the information content of the j-th indicator. The formula for determining the objective weights is: Furthermore, the objective weight vector is derived as follows: (3) Combinatorial weighting based on game theory Introducing game theory concepts, in the context of subjective weighting and objective weight The goal is to find a consensus or compromise between the combined weights and the basic weights, that is, to minimize the deviation between the combined weights and the basic weights, and to obtain the optimal combined coefficients.

[0026] The linear combination model is: in, and These are the combination coefficients.

[0027] Establish an optimization model with the objective of minimizing the deviation between the combined weights and the two weights: Solving for and The normalization scheme is as follows: Therefore, the final weight combination is determined as follows: Right now: This process quantifies qualitative indicators into quantitative calculations and uses game theory to assign weights, effectively integrating expert experience and data patterns to provide a scientific weighting basis for the subsequent TOPSIS-RSR comprehensive evaluation.

[0028] For example: Four evaluation units (S1, S2, S3, and S4) were selected, along with four evaluation indicators: rock mass integrity improvement rate. The data includes: unit inflow rate reduction rate (C2), slurry loss rate (C3), and design diffusion radius achievement rate (C4). The original data matrix is ​​as follows: The subjective weight vector is calculated using the AHP method. The consistency test result is CR=0.0263<0.1, which meets the consistency requirements. The objective weight vector is calculated using the CRITIC method. The combination coefficients are obtained using the combinatorial weighting method of game theory. , The final combined weight vector is obtained as follows: The higher weights of the rock mass integrity improvement rate and the unit inflow reduction rate indicate that the rock mass reinforcement effect and water blocking effect play a major role in the comprehensive evaluation. This combined weight can be used as the weight input for the subsequent TOPSIS-RSR comprehensive evaluation.

[0029] Step 4: Utilizing the TOPSIS-RSR method (Top-Sum Ratio-Rank-Sum Distance Method), this step combines two classic evaluation models, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and RSR (Rank-sum Ratio), to achieve complementary advantages. Standardized data is combined with weights to calculate a comprehensive score for grouting effectiveness. The specific process is as follows: Detection or simulated grouting data of actual pre-grouting in mine shafts under various geological conditions → TOPSIS processing to obtain the proximity C value (continuous superiority / inferiority score) → RSR analysis (non-parametric grading and verification) using the C value as a new single indicator → Obtaining the final grading evaluation result. This method can provide a multi-indicator, comparable, and gradable quantitative evaluation of the grouting effect of each / section of pre-grouting in shafts, and the evaluation results serve as a direct basis for optimizing the design parameters of the next grouting cycle. The steps for performing RSR analysis on the proximity C value obtained from TOPSIS processing are as follows: 1) Ranking: Sort all evaluation units by C-value from largest to smallest and rank them (R); 2) Calculate the RSR value: RSR = R / N (N is the total number of cells); 3) Grading: Using RSR value as the dependent variable and Probit as the independent variable, perform regression analysis to establish a grading model (usually divided into 3-5 grades: excellent, good, average, poor, and bad). 4) Obtain the final evaluation conclusion: not only do we know the ranking of the grouting effects of each section of the pre-grouting of the vertical shaft under different geological conditions in different strata of phosphate mines, but we also clarify its quality grade (such as "Class II Good").

[0030] 5) The specific calculation process of the superior-inferior solution distance method-rank sum ratio joint method (TOPSIS-RSR method) is as follows: Suppose there are m evaluation units and n evaluation indicators. The original evaluation matrix is ​​constructed as follows: in, Let i represent the original observation value of the i-th evaluation unit on the j-th evaluation index, i=1, 2, …, m; j=1, 2, …, n.

[0031] a. Standardization of indicators Since the dimensions and orders of magnitude of the various evaluation indicators are different, the original evaluation matrix needs to be dimensionless to obtain a standardized matrix: For benefit-type indicators, i.e., the larger the indicator value, the better the grouting effect, the following formula is used for standardization: For cost-related indicators, where a smaller value indicates better grouting results, the following formula is used for standardization: After standardization, ,and The larger the value, the better the performance of the corresponding indicator for that evaluation unit.

[0032] b. Construct a weighted normalization matrix Let the weight vector of each evaluation indicator be: Where: Combining the standardized matrix with the weights yields the weighted standardized matrix: in, c. Determine the positive and negative ideal solutions In the TOPSIS method, the positive ideal solution represents the optimal combination of each evaluation index, and the negative ideal solution represents the worst combination of each evaluation index.

[0033] The ideal solution is expressed as: in: The negative ideal solution is represented as: in: d. Calculate the distance between each evaluation unit and the positive and negative ideal solutions. The Euclidean distance between the i-th evaluation unit and the positive ideal solution is: The Euclidean distance between the i-th evaluation unit and the negative ideal solution is: e. Calculate proximity Calculate the proximity of each evaluation unit to the positive and negative ideal solutions: in: The larger the value, the closer the i-th evaluation unit is to the optimal solution and the better its grouting effect; The smaller the value, the worse the grouting effect. Therefore, the continuous comprehensive evaluation results based on the TOPSIS method are obtained: f. Based on proximity Perform RSR analysis The closeness obtained by the TOPSIS method As a new single indicator, RSR analysis was performed on all evaluation units. The RSR of all evaluation units was analyzed. Sort the values ​​in descending order and assign them a rank. To ensure that the RSR value correlates with the quality of grouting, i.e., a higher RSR value indicates a better evaluation result, it can be calculated using the following formula: in, Let be the rank of the i-th evaluation unit, and m be the total number of evaluation units. Equivalently, the RSR value is maximized when the rank of the best evaluation unit is 1, and minimized when the rank of the worst evaluation unit is m. The RSR values ​​of each evaluation unit are statistically analyzed, and their cumulative frequencies are calculated. These cumulative frequencies are then converted to their corresponding probability units (Probits), forming an "RSR—Cumulative Frequency—Probit" correspondence table. A linear regression model is established with RSR value as the dependent variable and Probit as the independent variable: Where a and b are regression coefficients. Based on the established regression model and combined with the preset grading standards, all evaluation units are divided into several grades, preferably into 3 to 5 grades, such as five grades: "Excellent, Good, Medium, Poor, and Inferior," or three grades: "Excellent, Good, and Medium." Based on the RSR value of each evaluation unit and its corresponding grade range, the final grouting effect grade of each evaluation unit is obtained. This not only provides a ranking of the pre-grouting effects of each section of the shaft under different strata and geological conditions, but also clarifies its quality grade, such as "Class I Excellent," "Class II Good," and "Class III Medium," thus providing a direct basis for optimizing grouting parameters in the next cycle. To facilitate understanding, this invention further illustrates the specific calculation process of the TOPSIS-RSR method with a set of example data.

[0034] Five evaluation units were set up, denoted as A1, A2, A3, A4, and A5; three evaluation indicators were selected: the rate of reduction in permeability coefficient after grouting, the rate of achievement of grout diffusion radius, and the rate of improvement in rock mass strength. All three indicators are benefit-oriented. The original data are shown in Table 1: Table 1

[0035] Let the weights of the three indicators be: First, the raw data is standardized. Using the standardization formula for benefit-type indicators, the standardized matrix can be obtained: Then, combine the weights to construct a weighted standardized matrix: From this, we can obtain the positive and negative ideal solutions. Further calculations are made of the distances between each evaluation unit and the positive and negative ideal solutions, and then the proximity of each evaluation unit is calculated accordingly. The TOPSIS proximity results for the five evaluation units at this point are: Sort by proximity from highest to lowest, and you get: The corresponding ranks are as follows: Further calculation of the RSR values ​​yields the following RSR ranking results, as shown in Table 2: Table 2

[0036] Then, a regression equation is established based on the cumulative frequency and the Probit value, and the data is categorized according to preset evaluation criteria. For example, it can be divided into five categories: Therefore, the final evaluation result of each evaluation unit in this example can be expressed as: As can be seen from the above calculation process, the TOPSIS-RSR combined method proposed in this invention can not only continuously rank the grouting effects of different evaluation units, but also further realize grading and classification, so that the evaluation results are quantitative, comparable and engineering-usable, and are suitable for comprehensive evaluation of the advanced grouting effect of mine shafts under different strata and geological conditions.

[0037] Step 5: Classify the final evaluation conclusions obtained in Step S4 according to the preset standards, and output the evaluation results of advanced grouting under different geological conditions in different strata of phosphate mines; sort the grouting effects of each section and clarify their quality level (such as "Class II Good"), so as to continuously optimize the selection of different grouting materials and different single and combined grouting methods (single liquid grouting, double liquid grouting, ultrafine cement grouting, chemical grouting, etc.) for advanced grouting sections under different geological conditions in different strata.

[0038] Case Study: Application of Intelligent Dynamic Evaluation and Optimization System for Pre-grouting in Vertical Shafts of Lianhuashan Phosphate Mine, Zhongxiang City, Hubei Province 1. Project Overview and Core Concept Project Background: The Lianhuashan phosphate mine in Zhongxiang City, Hubei Province, is constructing a new vertical shaft. The main shaft is designed to a depth of 577m, and the auxiliary shaft is designed to a depth of 487m. The surface outcrops the Upper Sinian Dengying Formation (Sanya-Liuya section), Cambrian, Ordovician, and Quaternary strata. The concealed strata are the Mesoproterozoic Kongling Group, the Lower Sinian Doushantuo Formation, and the Lower-Upper Second Section of the Upper Sinian Dengying Formation. The main strata for this construction are the Dengying Formation of the Sinian System. The shaft will be excavated using blasting. To ensure construction safety and efficiency when passing through high-risk strata rich in groundwater, a "segmented advanced grouting reinforcement" scheme will be adopted. A closed-loop intelligent decision-making system will be established, encompassing "geological identification → differentiated design → construction execution → multi-source monitoring → intelligent evaluation → dynamic optimization." The AHP-CRITIC method will be used to determine scientific weights, and the TOPSIS-RSR method will be used for effect classification and diagnosis, driving continuous optimization of grouting parameters and processes.

[0039] 2. Constructing an evaluation system and combining weights (application of the AHP-CRITIC method) 1) First step: Establish a hierarchical evaluation index system Target layer (A): Overall effect of advanced grouting Criterion Layer (B): B1 Rock Mass Reinforcement Quality (Core Safety), B2 Water Blocking and Seepage Prevention Effect (Core Safety), B3 Grout Diffusion Controllability (Economy and Environmental Protection), B4 Engineering Economic Efficiency (Cost and Construction Period) Indicator Layer (C): A total of 8 quantifiable indicators are selected: C1 Sonic wave velocity improvement rate (%) -> Belongs to B1, C2 Core recovery rate and RQD improvement rate (%) -> Belongs to B1, C3 Borehole water inflow reduction rate (%) -> Belongs to B2, C4 Permeability coefficient reduction factor -> Belongs to B2, C5 Design diffusion radius achievement rate (%) -> Belongs to B3, C6 Grout loss rate (%) (lower is better) -> Belongs to B3, C7 Cement consumption per linear meter (t / m) (lower is better) -> Belongs to B4, C8 Grouting operation efficiency (m / day) -> Belongs to B4; 2) Second step: AHP-CRITIC combined weighting calculation (1) Calculation of AHP subjective weights (Ws): The organization assembled a panel of five experts (geology, grouting, construction, safety, and cost) to conduct pairwise comparisons of the indicators for each layer, based on the principle of "prioritizing water blocking, emphasizing reinforcement, and balancing controllability and economy" for this project.

[0040] Example judgment matrices (criterion layer B versus target layer A) are shown in Table 3: Table 3

[0041] Consistency check: CR = 0.02 < 0.1, passed. Similarly, calculate the weight of each indicator relative to its respective criterion, and finally synthesize the global subjective weight vector Ws.

[0042] (2) Calculation of CRITIC objective weight (Wo): Data collection: Measured data of 8 indicators from 5 typical sections (covering large karst caves, fracture zones, etc.) that have completed grouting tests were selected to form a 5×8 data matrix.

[0043] Information Calculation: Comparison Strength (Standard Deviation): The data for C3 (reduction rate of water inflow) and C4 (reduction factor of permeability coefficient) showed the largest fluctuations (large standard deviations), indicating significant differences in water-blocking effects under different geological conditions. Conflict Factors (Correlation Coefficient): Analysis revealed a strong negative correlation between C5 (diffusion radius achievement rate) and C6 (slurry loss rate) (r=-0.88), indicating high conflict; while C1 and C2 (both reflecting reinforcement) showed a strong positive correlation (r=0.92), indicating information overlap.

[0044] Weight Calculation: An objective weight vector Wo is obtained by combining the intensity of comparison and the degree of conflict. The results show that C3, C4, and C6, which have large data differences and strong independence, received higher objective weights.

[0045] 3) Game theory combinatorial weighting: Establish an optimization model and solve for ||Wc - Ws|| 2 + ||Wc - Wo|| 2 The smallest overall weight vector Wc.

[0046] Example of final combined weights: Wc = [C1:0.10, C2:0.08, C3:0.22, C4:0.18, C5:0.12, C6:0.15, C7:0.08, C8:0.07]; the water-blocking indicators (C3, C4) have the highest weights (total 0.40), which aligns with the project's safety-first strategy; the slurry controllability indicator (C6) also has a relatively high weight (0.15), reflecting that "controlling leakage" is the primary challenge revealed by objective data. The weighting system achieves a unity of subjective and objective factors.

[0047] 3. First round of construction, evaluation and optimization (application of TOPSIS-RSR method) 1) First Phase: Construction of the First Section (S1 Section) – Large Karst Cave Area (1) Geological advance prediction and design: Geology: The predicted cave is a 3m high filled karst cave, filled with mud and sand, and rich in water.

[0048] Differentiated design: Grout: First, use CS two-component grout (cement-water glass) to quickly seal the water flow, and then use cement grout for reinforcement; Pressure: Low pressure slow injection, with the final pressure being 1.3 times the hydrostatic pressure; Section length: Shortened to 3m; Process: Forward segmented grouting, with grout stop at the orifice pipe.

[0049] (2) Construction and monitoring: According to the design, the pressure and flow rate were recorded during the grouting process. After grouting, three inspection holes were drilled in the area to conduct acoustic testing, core sampling, and water pressure testing to obtain the actual values ​​of eight indicators.

[0050] (3) Evaluation of TOPSIS-RSR effect: Data normalization and TOPSIS calculation: The measured values ​​of the 8 indicators in segment S1 were normalized, substituted into the TOPSIS model, and the overall closeness C = 0.72 was calculated using the combined weight Wc.

[0051] RSR grading (cumulative analysis): The C-values ​​of segment S1 and the previous 5 experimental segments (6 samples in total) were used for RSR analysis. The samples were sorted, ranked, and RSR values ​​were calculated. Probability unit regression was performed to determine the grading criteria (e.g., Probit ≥ 6.5 for excellent, 5.5-6.5 for good, 4.5-5.5 for average, and ≤ 4.5 for poor). Evaluation results: The Probit corresponding to the RSR value of segment S1 was 5.8, and it was rated as "Class II (Good)".

[0052] (4) Correlation analysis and optimization decision-making: ① In-depth diagnosis: Why is it only "good" instead of "excellent"? Analysis of the scores of each item in section S1: C3 (water inflow reduction rate) and C4 (permeability coefficient reduction factor) have very high scores (above 0.9), indicating that the water plugging was successful; however, C6 (grout loss rate) has a very low score (0.3), and the inspection record found that there was a brief grout loss phenomenon in the early stage of grouting; C7 (cement consumption) is also too high.

[0053] ② Optimization feedback: For large-scale filling karst caves, the cause was determined to be poor matching between the initial grout gelation time and the formation grout absorption rate used in advance grouting.

[0054] ③ Optimization Measures (for the next similar geological section): First, process optimization: adopt a composite grouting process of "first sealing the wall with fast-setting grout, then filling with slow-setting grout". First, inject a two-component grout with a short gelling time (30-60 seconds) to form a sealing shell around the borehole, and then inject cement grout for the main filling; Second, parameter fine-tuning: reduce the grouting pressure of the first two-component grout by 10%.

[0055] 4. Dynamic Cycles and System Evolution Phase Two: Construction of the Next Section (S2 Section) – Densely Fractured Zone 1) Geology and Design: (1) Geology: The forecast is that the network of fissures is well-developed and the water content is moderate to slightly high.

[0056] (2) Differentiated design: Based on the optimization experience of the previous cycle. Grout selection: Use ultra-fine cement single liquid grout to balance injectability and durability; Pressure setting: Use 1.5-1.8 times the hydrostatic pressure to facilitate grout diffusion; Grouting section length optimization: Appropriately increase to 5m.

[0057] 2) Evaluation and further optimization: (1) After the construction of section S2, monitoring and TOPSIS-RSR evaluation were also carried out. Assuming that its C value is 0.81 and the RSR is classified as "Class I (Excellent)".

[0058] (2) Correlation analysis: It was found that all indicators were balanced and C6 (slurry loss rate) was well controlled.

[0059] (3) Knowledge consolidation: The successful parameter combination of "network fracture zone + ultrafine cement + medium and high pressure grouting" is stored in the "Excellent Case Library".

[0060] (4) Overall system evolution: ① Rich database: As construction progresses, the number of evaluation samples (N) continues to increase, the regression equation for RSR classification becomes more and more robust, and the classification results become more and more reliable.

[0061] ② Dynamic weight update: After each complete stratum (such as a medium-layer fractured dolomite stratum), the CRITIC method can be rerun using all the latest accumulated data to update the objective weights and recombine them with the AHP weights. This allows the weights to dynamically reflect new patterns in the progress of the project.

[0062] ③ Intelligent Recommendation: The system can eventually automatically recommend historically validated and optimized grouting parameters and process packages for different geological defects (large karst caves, fracture zones, and broken zones).

[0063] 5. Evaluation Summary of Advanced Grouting This case study, based on the design and construction evaluation of pre-grouting in the vertical shaft of the Lianhuashan Phosphate Mine in Zhongxiang City, Hubei Province, constructs a data-driven, dynamically optimized intelligent management system for pre-grouting in vertical shafts. 1) AHP-CRITIC: Provides a scientific scale (weight) that is both in line with strategic intent and respects objective data.

[0064] 2) TOPSIS-RSR: A diagnostic instrument that provides accurate measurements (C-value) and clear scales (grading).

[0065] 3) Closed-loop feedback: The diagnostic results are linked to the construction parameters, realizing the transformation from "experience-driven" to "data-driven optimization".

[0066] Through the above evaluation methods, the engineering team can not only answer "How effective is this grouting?", but also scientifically answer "Why is it like this? How can we do better in the next section?", thereby achieving refined and adaptive management of grouting projects under complex geological conditions. Ultimately, this ensured that the main and auxiliary shafts of the newly built Lianhuashan phosphate mine safely, economically, and efficiently traversed high-risk strata rich in groundwater.

[0067] Example 2 This invention also provides a system for evaluating the effect of pre-grouting reinforcement in phosphate mine shafts, comprising: The first processing module is used to collect geological, hydrological and mechanical parameter data of the surrounding rock before, during and after grouting construction in the phosphate mine shaft. The second processing module is used to construct a comprehensive evaluation index system for the effect of pre-grouting reinforcement of vertical shafts; The third processing module is used to standardize the collected data and assign weights according to the importance of each indicator. The fourth processing module is used to combine the standardized data with weights using the superior-inferior solution distance method-rank sum ratio joint method to calculate the comprehensive score of grouting effect; The fifth processing module is used to classify the comprehensive score according to preset standards, output the grouting effect evaluation results, and continuously optimize them.

[0068] As one embodiment of the present invention, the comprehensive evaluation index system for the pre-grouting reinforcement effect of vertical shafts includes: rock mass reinforcement index, water plugging and seepage prevention index, grout diffusion controllability index, and engineering economic index.

[0069] As one embodiment of the present invention, the third processing module uses the improved AHP-CRITIC method to determine the weight allocation.

[0070] Example 3 The present invention also provides a device for evaluating the effect of pre-grouting reinforcement of phosphate mine shafts, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes a method for evaluating the effect of pre-grouting reinforcement of phosphate mine shafts when executed by the processor.

[0071] Example 4 The present invention also provides a storage medium storing a computer program, which, when running, executes a method for evaluating the effect of pre-grouting reinforcement in phosphate mine shafts.

[0072] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for evaluating the effect of pre-grouting reinforcement in phosphate mine shafts, characterized in that, include: Step 1: Collect geological, hydrological and mechanical parameter data of the surrounding rock in multiple batches throughout the entire process of pre-grouting reinforcement of the vertical shaft of a complex geological phosphate mine, including before, during and after grouting. Step 2: Construct a comprehensive evaluation index system for the effect of advanced grouting reinforcement of vertical shafts in complex geological phosphate mines; Step 3: Standardize the collected data and assign weights according to the importance of each indicator; Step 4: Using the superior-inferior solution distance method-rank sum ratio combined method, combine the standardized data with weights to calculate the comprehensive score of grouting effect; Step 5: Classify the comprehensive score according to the preset standards, output the grouting effect evaluation results, and continuously optimize them.

2. The method for evaluating the effect of pre-grouting reinforcement of phosphate mine shafts as described in claim 1, characterized in that, The comprehensive evaluation index system for the effect of pre-grouting reinforcement of vertical shafts includes: rock mass reinforcement index, water plugging and seepage prevention index, grout diffusion controllability index, and engineering economic index.

3. The method for evaluating the effect of pre-grouting reinforcement of phosphate mine shafts as described in claim 2, characterized in that, In step three, the weight allocation is determined using the improved AHP-CRITIC method.

4. A system for evaluating the effectiveness of pre-grouting reinforcement in complex geological phosphate mine shafts, characterized in that, include: The first processing module is used to collect geological, hydrological and mechanical parameter data of the surrounding rock before, during and after the grouting reinforcement of the phosphate mine shaft. The second processing module is used to construct a comprehensive evaluation index system for the effect of pre-grouting reinforcement of vertical shafts; The third processing module is used to standardize the collected data and assign weights according to the importance of each indicator. The fourth processing module is used to combine the standardized data with weights using the superior-inferior solution distance method-rank sum ratio joint method to calculate the comprehensive score of grouting effect; The fifth processing module is used to classify the comprehensive score according to preset standards, output the grouting effect evaluation results, and continuously optimize them.

5. The evaluation system for the effect of advanced grouting reinforcement of phosphate mine shafts as described in claim 4, characterized in that, The comprehensive evaluation index system for the effect of pre-grouting reinforcement of vertical shafts includes: rock mass reinforcement index (compressive strength, shear strength, tensile strength, etc.), water plugging and seepage prevention index (permeability), grout diffusion controllability index, and engineering economic index.

6. The evaluation system for the effect of advanced grouting reinforcement of phosphate mine shafts as described in claim 5, characterized in that, The third processing module uses an improved weighting evaluation method based on the combined AHP (Analytic Hierarchy Process) and CRITIC (AHP-CRITIC method, or Analytic Hierarchy Process-Criteria Importance Through Intercriteria Correlation) to determine the weight allocation.

7. A device for evaluating the effect of pre-grouting reinforcement in phosphate mine shafts, characterized in that, include: The system includes a memory and a processor, wherein the memory stores a computer program that is executed by the processor, and the computer program, when executed by the processor, performs the method for evaluating the effect of advanced grouting reinforcement of phosphate mine shafts as described in any one of claims 1-3.

8. A storage medium, characterized in that, The storage medium stores a computer program, which, when running, executes the method for evaluating the effect of pre-grouting reinforcement of phosphate mine shafts as described in any one of claims 1-3.

Images