Multi-modal intelligent grouting and plugging method for karst area with shallow overburden

Abstract

This invention discloses a multimodal intelligent grouting and plugging method for shallow-buried, overburdened karst areas. It includes the following steps: Step S1: Conducting hydrogeological surveys of the proposed area; Step S2: Conducting geophysical surveys within the proposed area to determine the extent, depth, and scale of karst development; Step S3: Drilling key anomaly areas and verifying the geophysical survey results; Step S4: Verifying the connectivity and seepage type of karst conduits through tracer tests; Step S5: Integrating geological data using Visual Modflow to establish a three-dimensional model of karst fissures with a permeability coefficient field, and calibrating the connectivity of the karst caves in the three-dimensional model using tracer tests; Step S6: Generating a grouting priority heatmap using an entropy-based fusion algorithm; Step S7: Adjusting the grouting pressure in different zones and segments. This invention has the advantage of preventing grout leakage.

Description

Technical Field

[0001] This invention relates to grouting and sealing engineering, specifically a multimodal intelligent grouting and leak-stopping method for shallow-buried, covered karst areas. More specifically, it is a quantitative control reverse grouting and sealing method for fissure-pipe type dispersed seepage in shallow-buried, covered karst areas, applicable to fissure-pipe type dispersed seepage sealing projects in canal excavation projects in shallow-buried, covered karst areas and exposed karst regions. Background Technology

[0002] In shallow-buried karst development areas, numerous karst fissures and conduits exist underground. Due to the overlying Quaternary strata, drilling alone cannot clearly reveal the development of these fissures and conduits. Before constructing cofferdams for artificial canals in such areas, these fissures and conduits must be treated. The primary purpose is to ensure the safe construction of the canal cofferdam project, preventing seepage and leakage caused by karst fissures during construction, thus avoiding the inability to create dry construction conditions and the instability of canal bank slopes, among other karst water-related problems.

[0003] Currently, engineering measures for preventing and controlling karst water hazards mainly focus on two aspects: first, sealing groundwater channels through methods such as grouting to reduce seepage; and second, altering groundwater dynamic conditions. However, because altering groundwater dynamic conditions remains challenging in experiments, it is not commonly used in practice. Therefore, sealing groundwater channels through methods such as grouting to reduce seepage is one of the most commonly used methods in karst water hazard prevention engineering.

[0004] Currently, the common grouting construction techniques in karst areas are mainly as follows: ① Through design drawings and actual on-site pilot hole drilling, further exploration of the overburden and rock strata within the karst treatment area is conducted to fully understand and grasp the degree of underlying karst development and spatial distribution. ② Using a drilling rig and a grouting pump to pressurize and inject the prepared cement grout into karst fissures or karst conduits, driving away water from the fissures and filling them. Although the above grouting methods play a certain role in reinforcement, the grout diffusion radius dispersion reaches ±40%, and the grout diffusion distance is uneven, which easily leads to grout leakage, hindering the treatment of karst water hazards.

[0005] Therefore, it is necessary to develop a grouting and plugging method for shallow-buried karst areas to prevent grout leakage. Summary of the Invention

[0006] The purpose of this invention is to provide a multimodal intelligent grouting and plugging method for shallow-buried karst areas, which is a construction method for karst grouting that effectively avoids regional influence and prevents grout outflow; overcoming the problems of mutual influence between regions and grout outflow in the current technology.

[0007] To achieve the above objectives, the technical solution of the present invention is: a multimodal intelligent grouting and plugging method for shallow-buried karst areas, comprising the following steps. Step S1: Conduct hydrogeological surveys of the proposed area to provide basic data for subsequent delineation of grouting range and grouting depth; In step S1, the present invention uses remote sensing interpretation data to determine the spatial distribution range of karst; The remote sensing interpretation data is not only part of the karst planar distribution survey results, but also shows the distribution and development characteristics of karst, which can corroborate the geophysical exploration results to a certain extent. Step S2: Based on the hydrogeological survey results, various geophysical explorations are conducted in the proposed construction area to determine the extent, depth, and scale of karst development within the proposed construction area. The geophysical methods used in this invention include audio-frequency magnetotellurics and ground-penetrating radar. First, the audio-frequency magnetotellurics method is used to detect the study area and delineate a key anomaly area. Then, the ground-penetrating radar method is used for detection. Finally, the anomaly areas detected by the two methods are comprehensively analyzed and judged to determine key anomaly areas such as karst caves, soil caves, and karst fissures. Step S3: Based on the geophysical exploration results, drilling is conducted in key anomaly areas to verify the geophysical exploration results and determine the karst development status, karst burial depth, and fracture conditions within the proposed construction area (i.e., the proposed canal excavation area). This invention uses boreholes to verify the geophysical interpretation results, determine the accuracy of the geophysical results, and also provide basic data for the subsequent establishment of a three-dimensional model. Hydrological tests are conducted in the boreholes to obtain hydrological parameters and preliminarily determine the permeability of water-bearing rock groups, karst caves, etc., providing basic parameters for subsequent tracer tests and the establishment of a three-dimensional model. This invention obtains the location of karst seepage points through hydrogeological surveys; geophysical exploration can detect key anomaly areas such as karst caves and karst conduits, and boreholes are used to verify these key anomaly areas. Boreholes can serve as the source of tracer release; by combining geological data, geophysical data, and borehole data, karst seepage channels are preliminarily determined. Step S4: Conduct tracing tests on the sources and channels of seepage revealed by geophysical exploration and drilling within the proposed area. The tracing tests can verify the connectivity of karst conduits and the type of seepage. Verifying the karst channels provides a reference for the subsequent comprehensive determination of grouting locations. The grouting and sealing processes differ for different types of seepage. Fissure-type seepage has a smaller seepage rate and can be directly grouted; while for conduit-type seepage, it is necessary to first fill the area with gravel to slow down the water flow before grouting. Step S5: Integrate geological data using Visual Modflow and establish a 3D model of karst fissures with a permeability field based on multi-source data fusion detection technology to determine seepage paths and identify seepage types. Use tracer tests to calibrate the connectivity of karst caves in the 3D model. Use this 3D model of karst fissures with a permeability field to perform preliminary simulation of the grouting priority distribution map generated by the entropy algorithm during the construction period to preliminarily verify the sealing effect. The geological data includes stratigraphic distribution information, lithology, elevation, etc. Step S6: The entropy method fusion algorithm generates a grouting priority heatmap; a. Constructing a decision matrix containing six evaluation indicators: The method of constructing the decision matrix in this invention is highly objective, avoids the subjectivity of human scoring, and can also eliminate the influence of dimensions; these six evaluation indicators are important factors affecting the grouting effect. By calculating the entropy value, the grouting priority of each unit can be divided, thereby rationally arranging the construction sequence, improving the overall grouting effect, improving grouting accuracy, and preventing grout leakage. The six evaluation indicators are as follows: 1) Crack aperture (0-10cm); 2) Pipe diameter (0-50cm); 3) Permeability coefficient (10^-4 - 10^-2 cm / s); 4) Water flow rate (0-50L / s); 5) Cover thickness (0-15m); 6) Distance from the excavation face (0-30m); b. Calculate the index weights using the coefficient of variation method: wi = Vi / Vi∑k; Vi = (σi / μi); wi=(σi / μi) / {σi / μi∑k}; Where Vi is the coefficient of variation of the i-th index, σ i Let μ be the standard deviation of the i-th indicator. i Let be the mean of the i-th indicator; ∑k is the sum of the k indicators; w i Let be the weight of the i-th indicator; this formula is a statistical method for determining the weight of indicators. It determines the weight of each indicator by calculating the information included in the data, and improves the grouting accuracy and grouting effect through objective weighting. c. A dynamic update of the grouting priority heat map is output from the 3D model of karst fissures with a permeability coefficient field to guide the borehole layout for grouting construction. The 3D model of karst fissures with a permeability coefficient field outputs accurate karst fissure channels, karst connectivity, and leakage conditions. Based on the accurate karst fissure channels, the layout of grouting holes can be targeted. The karst connectivity can prevent excessive grouting pressure from affecting surrounding grouting holes. The leakage conditions can reflect the grouting sealing effect and further provide feedback to optimize the grouting sequence. This invention, based on the grouting of priority areas defined by the initial thermal map, observes changes in leakage. It then dynamically updates the grouting priority thermal map using a 3D karst fissure model with a permeability field and an algorithm. Specifically, the 3D karst fissure model with a permeability field predicts the leakage paths and identifies leakage types for each groundwater leak. The algorithm distinguishes priority grouting areas and performs grouting accordingly, effectively sealing a specific leakage channel. After sealing, the sealing effect can be inferred from the sealing results and the leakage conditions at other points, thereby optimizing the simulated leakage path and grouting sequence. The dynamically updated grouting priority thermal map achieves better grouting results, optimizes grout usage, and prevents grout waste. Step S7: Adjust the grouting pressure in sections and zones; a. Establish a three-tiered pressure control system: 1) Low-pressure zone (0.3-0.5MPa): Used for grouting of pore-type fractures; 2) Medium pressure zone (0.5-1.2MPa): Used for grouting of pipeline channels; 3) High-pressure zone (1.2-2.0MPa): Used for grouting concentrated leakage zones; Different leakage conditions require different optimal grouting pressures. This invention establishes a three-level pressure control system to select the optimal grouting pressure according to the leakage type. For pore-type fissures, low-pressure grouting is used; for pipe-type channels, medium-pressure grouting is used; and for concentrated leakage zones, high-pressure grouting is used, effectively improving grouting utilization. b. Based on the leakage type and condition output from the 3D model of karst fissures with permeability coefficient field, an IoT-based intelligent grouting system is used to perform intelligent grouting according to the grouting priority heat map and a three-level pressure control system. The system dynamically adjusts the grouting pressure or rate based on real-time feedback from the formation (such as pressure changes), achieving the following: 1) High real-time flow monitoring accuracy (accuracy ±0.1L / min); 2) Automatic pressure regulation has a fast response time (<3s); 3) The online detection error of slurry consistency is small (error ≤ 5%); Grouting ends when the leakage termination condition is met; When the leakage termination condition is not met, proceed to step S5. Adjust the 3D model of karst fissures with permeability coefficient field according to the leakage situation of the leakage point, dynamically update the grouting priority heat map to guide the borehole layout and perform zoned and segmented control of grouting pressure until the leakage termination condition is met. Achieve continuous dynamic and closed-loop automatic control of the grouting construction sequence, grouting pressure or rate and grouting volume throughout the entire process, improve grouting accuracy, improve sealing effect, achieve precise, efficient and economical grouting sealing, and solve the problem of grout leakage in large areas.

[0008] The leakage termination conditions are: (1) When the grouting pressure reaches or approaches the design final pressure, the injection rate continues to decrease and stabilizes below the design end injection rate (for example, ordinary cement grout can be set to 1-3 L / min, depending on the project requirements and geological conditions); (2) Under the design final pressure, the injection rate continues to decrease and can be stably maintained for a period of time (usually 10-30 minutes); (3) In the grouting process curve, there is an obvious "fast pressure increase and slow grout absorption" characteristic, that is, the pressure rises steadily to the design value, while the injection rate curve drops steadily to the end standard.

[0009] In the above technical solution, in step S2, the geophysical methods include audio-frequency magnetotellurics (AMT) and ground-penetrating radar (GPR). First, the study area is probed using AMT to obtain the AMT resistivity profile. In this invention, the AMT is combined with the karst distribution and development characteristics obtained from the aforementioned survey to delineate an initial survey network and conduct overall exploration of the target area, thereby preliminarily determining the karst groundwater runoff pathways in shallow-covered karst areas over a large area. Then, ground-penetrating radar (GPR) is used to detect and obtain the bedrock elevation. This invention uses ground-penetrating radar to conduct local detection of karst development areas in order to further obtain the karst development range, preliminary scale and bedrock surface undulation, thereby clarifying the karst groundwater runoff pathway and karst groundwater runoff target area.

[0010] In the above technical solution, in step S5, a three-dimensional model of karst fissures with a permeability coefficient field is established based on multi-source data fusion detection technology, including the following steps: (1) After integrating remote sensing interpretation data, AMT resistivity profile, GPR detection results (i.e. GPR bedrock elevation) and tracer test data, the fused geophysical interpretation results including the spatial distribution of stratigraphic interfaces, cross sections and karst channels are obtained; This invention establishes different forward models to study and analyze different types of ground-penetrating radar attribute data, and then uses correlation analysis and karst properties to optimize the ground-penetrating radar attributes; The optimized ground-penetrating radar attributes are preprocessed, and the preprocessed ground-penetrating radar data and magnetotelluric data are registered in the horizontal and vertical directions respectively, and finally the registered ground-penetrating radar data and magnetotelluric data are fused using the RGB fusion principle; Based on hydrogeological surveys and fused geophysical data, the karst groundwater runoff pathways and karst seepage groundwater runoff areas are inferred, and these suspected areas are preliminarily verified by drilling methods combined with pumping tests and injection tests; (2) Generate a GRID file from the integrated geophysical interpretation results (such as the spatial distribution of stratigraphic interfaces, cross sections, and karst channels) and import it into the conceptual model module of Visual Modflow; use Visual Modflow to generate a three-dimensional stratigraphic structure, divide the layers with different permeability, use the hydrogeological parameters obtained by the pumping test to assign values ​​to the layers with different permeability, and obtain a preliminary three-dimensional model of karst fractures with permeability coefficient field; (3) Based on the above three-dimensional model of karst fissures with permeability coefficient field, the water flow simulation module of Visual Modflow is used to first simulate the stable or unstable groundwater flow field to ensure that the basic elements including water flow direction and water level are roughly consistent with the actual situation; then, the solute transport simulation module is used to simulate the injection point of the tracer, define the injection concentration and injection time, and set the observation point at the actual water outlet of the tracer to obtain the tracer monitoring curve simulated by the three-dimensional model of karst fissures with permeability coefficient field. (4) Model calibration and inversion: The tracer monitoring curve simulated by the three-dimensional model of karst fissures with permeability coefficient field is compared with the actual monitoring curve obtained by the tracer test in step S4. If the two do not match, the leakage path is adjusted by adjusting the hydrological parameters of the abnormal zone interpreted by geophysical exploration until the simulation result of the tracer monitoring curve is highly consistent with the measured data in step S4, and a highly reliable three-dimensional model of karst fissures with permeability coefficient field is obtained. This invention makes the simulation result highly consistent with the measured data by continuous trial and error or by using the automatic calibration tool PEST. When the three-dimensional model of karst fissures with permeability coefficient field is successfully calibrated by the tracer test data, the model is a highly reliable representation of the actual groundwater flow system. The three-dimensional model of karst fissures with permeability coefficient field can improve the detection accuracy, reduce the detection cost and improve the detection efficiency, and accurately identify the karst groundwater runoff path and determine the leakage path in the case of shallow buried karst development and very complex karst groundwater. It can also quantitatively predict the elevation coupling relationship between karst pipelines and canal water levels and improve the risk warning capability.

[0011] In the above technical solution, the inputs to the 3D model of karst fissures with a permeability coefficient field include elevation information, stratigraphic distribution information based on multi-source fusion, permeability coefficients of each stratum, and tracer test data. The elevation information can be obtained from the owner, and the stratigraphic distribution information based on multi-source fusion can be obtained through steps S2 and S3. The permeability coefficients of each stratum are obtained from hydrogeological surveys, and the tracer test data are obtained through tracer test monitoring in step S4. The presence of permeability coefficients of each stratum in the above input data indicates that the 3D model of karst fissures has a "permeability coefficient field." The permeability coefficient, as an important parameter for the inversion of the 3D model of karst fissures, participates in the determination of important aspects such as grouting priority, grouting reordering, and grouting termination pressure, thereby improving the accuracy and safety performance of grouting volume control and enhancing the sealing effect and sealing efficiency. The three-dimensional model of karst fissures with permeability coefficient field outputs accurate information on karst fissure channels, karst connectivity, and leakage.

[0012] This invention completes source identification and leakage network deconstruction through steps S1-S5, transforming "blind zoning" into "as-needed zoning." Then, step S6 precisely allocates grouting construction zones and construction sequence, thereby eliminating the possibility of high-pressure grout flowing between different areas. Finally, step S7 adjusts the grouting pressure in different zones and segments to ensure that the grout is mainly consumed in the areas that need to be filled, greatly reducing the possibility of its overflow. This overcomes the problems of mutual influence between areas in current technology (e.g., traditional grouting uses uniform high pressure for construction. When grouting in one area (area A), the high-pressure grout will break through the rock resistance and "flow" along the connected pipes to another nearby area (area B), causing the boreholes in area B to be contaminated by cross-grouting, forcing a work stoppage, resulting in construction chaos and low efficiency; in addition, traditional zoning is planar and conceptual, without effective isolation in three-dimensional space, and the construction sequence is also linear, making it difficult to handle complex underground connections) and the problem of grout outflow (i.e., grout leakage, which affects the grouting effect and thus the seepage prevention of the cofferdam).

[0013] The present invention has the following advantages: (1) The dispersion of the diffusion radius of the present invention is expected to be within ±15% to ±20%, or even better; This invention achieves true uniformity and control by using targeted filling and segmented control to make the slurry diffusion behavior within the same geological unit regular and predictable. Through IoT closed-loop control, precise pressure grading (low, medium, and high pressure), and pressure-flow dual-compliance termination conditions, it fundamentally eliminates the problem of slurry runoff caused by pressure loss. It overcomes the problems of existing technologies where the slurry diffusion radius dispersion reaches ±40%, the slurry diffusion distance is uneven, and slurry runoff is prone to occur, which is detrimental to the treatment of karst water hazards. (2) Improved accuracy in grout volume control: Implementation cases show that grout waste is reduced by about 62% compared to traditional methods; At the exploration level (reducing ineffective grout application by about 20-30%): through tracer tests and 3D modeling, areas that need to be treated are accurately identified, eliminating at least 60% of areas that do not require grouting or only require minimal treatment, thus avoiding ineffective grout application from the source. At the planning level (reducing ineffective grout application by about 10-15%): through intelligent sorting and targeted filling, grout is prioritized for use at core leak plugging points, avoiding the waste of resources caused by "average effort" in secondary channels or low-risk areas; At the control level (reducing ineffective slurry delivery by about 25-35%): Through the dynamic control of "pressure-flow dual target" of the Internet of Things, the waste caused by slurry runaway, cross-contamination and "overtime work" due to uncontrolled pressure and excessive flow is eliminated. Every cubic meter of slurry is precisely injected and stays at the target position, with extremely high utilization rate. At the management level (reducing ineffective grout application by approximately 5-10%): Full-process digital management enables continuous, dynamic, and closed-loop automatic control of the grouting construction sequence, grouting pressure or rate, and grouting volume, reducing additional waste caused by human estimation errors and improper operation; (3) Good sealing effect and improved sealing efficiency: leakage reduction rate >95%, and construction period shortened by about 40%; This invention first advances extensive exploration and planning work, accurately identifying seepage channels using a 3D model of karst fissures with a permeability field. This ensures that over 90% of the grout is used on the seepage channels. Second, it utilizes an entropy algorithm to prioritize the sealing of critical seepage channels with the highest risk and best connectivity, avoiding the predicament of injecting a large amount of grout into one seepage channel only to have it flow away to other connected seepage channels, leading to sealing failure. This overcomes the limitations of traditional methods that involve exploration and sealing simultaneously, which cannot guarantee that all grout is used on the seepage channels. The leakage reduction rate is approximately 60%–80%, but the construction period is relatively long, generally around 2–3 months. (4) Improved safety performance: the rate of cross-contamination between adjacent boreholes is <5%; This invention primarily utilizes multi-source exploration data and a three-dimensional model of karst fissures with a permeability coefficient field to accurately identify leakage channels. Then, it employs an entropy algorithm to determine the grouting priority level for each region and node, thereby reducing the occurrence of grout crosstalk between adjacent boreholes. This overcomes the problems of existing technologies where the probability of grout crosstalk between adjacent boreholes is relatively high, ranging from approximately 10% to 40%, which can lead to grout waste, uncontrolled grouting volume, and borehole abandonment or scrapping. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the cross-section of the shallow-buried, covered karst area grouting reinforcement method according to the present invention; Figure 2 Interpretation map of ground-penetrating radar detection; Figure 3 This is a flowchart illustrating the invention.

[0015] exist Figure 2 In the diagram, the red dashed line represents the inferred fault, and the blue and pink dashed boxes represent the inferred karst caves; the horizontal axis represents the station number, and the vertical axis represents the depth.

[0016] In the diagram, 1-ground line, 2-grouting straight hole, 3-overburden layer, 4-fissure channel, 5-karst cave, 6-bedrock, 7-soil-rock interface, 8-connecting flange, 9-grouting pipe, 10-grouting machine. Detailed Implementation

[0017] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, these descriptions do not constitute a limitation of the present invention and are merely illustrative. The advantages of the present invention will become clearer and easier to understand through this description.

[0018] This invention achieves data fusion through steps S1-S4, enabling "clear visibility." Step S5 utilizes multi-source data fusion technology to construct and calibrate a high-precision three-dimensional geological model, providing a unified platform for decision-making. Step S6 uses an entropy algorithm to fuse multiple geological parameters, achieving "clear differentiation," accurately and efficiently generating grouting priorities, establishing a "geological type-pressure parameter" matching model, and using an IoT system to achieve precise control of flow rate, pressure, and consistency, thus realizing the multimodal grouting and leak sealing of this invention. Simultaneously, relying on the established precise three-dimensional geological model, this invention uses an entropy algorithm combined with multiple geological parameters to scientifically calculate the priority of the grouting range, grouting flow rate, pressure, and consistency. Its entropy-based heat map serves as a construction instruction directly guiding the construction sequence, achieving dynamic, fully automated, and intelligent grouting without human intervention in decision-making and operation. This overcomes the problems of existing technologies that focus on monitoring and auxiliary decision-making during the construction process, where final decision-making and operation still require human judgment and execution, resulting in strong subjectivity and low grouting accuracy.

[0019] This invention relates to a reverse grouting sealing method for quantitative control of dispersed seepage in shallow-buried karst areas with fissure-pipe type seepage. The quantitative control of dispersed seepage is achieved through multimodal exploration in steps S1-S4, which digitizes the dispersed seepage problem data and obtains key quantitative parameters such as fissure aperture, permeability coefficient, and water flow rate, providing a basis for control. Step S6 uses an entropy algorithm model to intelligently analyze massive amounts of data, generating a grouting priority distribution map to quantify the decision-making process for construction sequence. Step S7 utilizes a three-level pressure system and an IoT-based intelligent grouting system to control the grouting volume (i.e., the quantitative grouting of this invention is a dynamic and closed-loop self-control system, which dynamically updates the heat map (spatially optimizing the construction sequence) and receives real-time feedback from the strata (such as pressure changes) through the IoT system). (Real-time control) dynamically adjusts grouting pressure or rate and grouting volume, adapting to changes in the strata during the grouting process (i.e., automatically adjusting grouting parameters such as grouting pressure and grouting volume according to changes in the strata). Through continuous dynamic and closed-loop full-process self-control, it solves the persistent problem of grout leakage in large-area grouting, making it suitable for systematic prevention and control in complex geological areas; it overcomes the problem of existing technologies being unable to adapt to changes in the strata during grouting. Its reverse grouting, through the fusion of multi-source data, accurately locates the leakage path, transforming the construction target from relying on vague trial and error based on experience to clear, efficient, and precise filling. Furthermore, step S6 determines the priority placement of boreholes, and step S7's precise pressure control ensures that the vast majority of the grout remains within the target area requiring sealing. This invention is "reverse" from traditional grouting methods at the logical, strategic, and execution levels, thereby achieving precise, efficient, and cost-effective treatment results.

[0020] Preventing slurry leakage is an inevitable result of the key measures taken during implementation to achieve "quantitative control of slurry diffusion radius." Behind this lies the systemic, holistic challenge of addressing the global engineering problem of "dispersed leakage," characterized by inefficiency, high cost, and disorder in its management. This invention establishes a novel intelligent grouting paradigm, truly elevating karst treatment from reliance on experience to reliance on data and algorithms. Specifically, this invention transforms the traditional engineer-based judgment of "where to drill first" and "which hole to grout first" into a quantifiable and computable mathematical model based on multi-source data (6 evaluation indicators) (i.e., decision modeling). Its entropy-based heat map serves as a direct guide to the construction sequence, achieving the highest level of intelligent decision-making for intelligent construction. This invention achieves automated control through an IoT closed loop, automatically executing real-time flow and pressure adjustments based on the entropy-based heat map via the IoT system, thus achieving intelligent execution. Simultaneously, this invention achieves refined zoning based on a three-level pressure system, pre-setting precise pressure ranges according to geological type (pores, pipes, concentrated seepage), enabling precise and effective IoT automated control rather than blind adjustments. This achieves intelligent top-level decision-making (entropy-based heat map) and integrated process execution control (IoT closed loop), demonstrating strong systematicity, forward-looking approach, and integration. This improves grouting accuracy and effect, saves construction time, and prevents grout leakage. This invention primarily constructs a three-dimensional model of karst fissures with a permeability coefficient field through steps S1-S5. This model accurately detects leakage channels, precisely identifies the fissure aperture and type that need to be filled, and confirms the target space (i.e., the target area) to be filled. This allows for precise placement into the known target space. Steps S6 and S7 establish a correspondence between geological type, pressure, and diffusion, enabling model-based quantitative control. This ensures that the grout flow rate, pressure, and consistency achieve a level of precision that accurately matches the leakage type. For pore-type fissures, the diffusion radius dispersion of this invention can reach a level of precision of ±15% to ±20% or even better. For pipe-type channels, since the grout mainly flows along the pipe, its effective diffusion distance is closer to the length and connectivity of the pipe, rather than radial diffusion. Therefore, its dispersion is mainly reflected in the fullness of the grout, while the dispersion of the target filling range in this invention is extremely low.

[0021] Example: The present invention will be described in detail here by taking the application of the present invention to grouting and plugging of a certain Pinglu Canal as an example. The present invention also has guiding significance for the application of the present invention to grouting and plugging in other karst areas.

[0022] In this embodiment, 13 karst seepage points were found on the right bank of the diversion cofferdam in the Pinglu Canal section from K54+151 to K55+211. According to the exploration results: the planar cave rate was 28.72%, the linear karst rate was 9.88%, there were 26 fully filled caves, accounting for 23.00% of all caves, the filling material was mainly brownish-yellow plastic clay, containing a small amount of undissolved limestone blocks; there were 48 semi-filled caves, accounting for 41.74% of all caves, the filling material was mainly brownish-yellow, grayish-black, and brownish-red fluid-soft plastic clay and sand, with a small amount of medium and coarse grains visible locally; there were 41 unfilled caves, accounting for 35.65% of all caves.

[0023] Because the karst seepage types in the examples were quite complex, the conventional grouting scheme initially adopted resulted in the grout being rapidly diluted after grouting in karst conduit seepage areas. This caused a delay of approximately 15 days in the construction period, taking about three months, and the seepage prevention effect was very poor. Furthermore, improper grouting sequence during grouting led to increased seepage rates in some areas during later grouting. Moreover, when using existing technology to address karst conduit seepage, typically only a small amount of grout is visible on the conduit wall, with most of the grout having been washed away, resulting in poor sealing. (Only some fissure-type seepage channels were relatively densely filled).

[0024] like Figure 3 As shown, this embodiment uses the multimodal intelligent grouting and plugging method provided by the present invention for grouting and plugging. The specific process method is as follows: This embodiment uses the high-density resistivity method of the present invention to conduct key investigations of karst development areas, discovering multiple karst development zones; further exploration of these karst development zones is carried out using ground-penetrating radar (e.g., Figure 2 As shown in the figure, a karst cave was found between K54+556.8 and K54+560.8, with a width of about 4.0 meters and a burial depth of 6~14m. It is speculated that the seepage channel at this location is a seepage channel that originates from 255° along the bottom karst conduit.

[0025] Two tracer experiments were conducted on the sources and channels of seepage points SL16 and SL17, namely Tracer Experiment 1 and Tracer Experiment 2. The results of Tracer Experiment 1 show that SL15 is connected to the injection point and is the seepage source of SL15. Due to the short duration of the tracer, this point can be identified as a karst conduit-type seepage with a relatively fast flow rate. The results of Tracer Experiment 2 show that SL16 is connected to the injection point and is the seepage source of SL16. Due to the short duration of the tracer, this point can be identified as a karst fissure-conduit-type seepage with a relatively fast flow rate.

[0026] Based on the three-dimensional model of karst fissures with permeability coefficient field established by combining exploration results with geophysical results and tracer tests, the grouting range is designed as follows: the vertical seepage channel extends 5m to both sides in the plane, and the vertical grouting range is 2m below the bottom of the channel; the grouting material is 42.5 grade ordinary Portland cement, and the water-cement ratio of the cement slurry is 0.6:1 to 1:1, thinning first and thickening later; the grouting pressure is 0.3-1.2MPa, 0.3-0.5MPa for pore-type fissures, and 0.5-1.2MPa for pipe-type channels; the grouting holes are distributed in an array with a spacing of 2m.

[0027] This embodiment uses the present invention to reinforce the cross-section of a shallowly buried karst area with grouting, as shown in the example. Figure 1 As shown, the grouting straight hole 2 passes sequentially through the ground line 1, the overburden layer 3, the soil-rock interface 7, and the bedrock 6 (which is limestone; this invention is applicable to soluble carbonate rock formations), and extends into the karst cave 5; one end of the grouting pipe 9 is connected to the grouting machine 10, and the other end extends into the grouting straight hole 2 and enters the karst cave 5. The grouting pipe 9 has multiple sections, which are connected and fixed by the connecting flange 8; during grouting, the grout enters the fissure channel 4 through the karst cave 5.

[0028] like Figure 2 As shown, during grouting, according to the grouting priority heat map, SL15-SL18 (K54+852~K54+967) (red area); SL03 / SL04 (K54+218 / K54+277), SL08-SL10 (K55+081~K55+221) (orange area); SL07 / SL11 / SL12 (K54+698~K54+713), SL13 / 14 (K54+548 / K54+443) (yellow area) are grouted in sequence. Grouting of the hole can be terminated when the grout reaches one of the following standards: (1) When the injection rate of continuous grouting for 10 minutes under the final injection pressure is not greater than 5L / min, grouting can be terminated. (2) When the grouting point has moved 3-5m outside the grouting range. (3) When the bedrock of the grouting borehole is intact, or when grouting has been performed multiple times and the borehole pressure exceeds 1.5MPa. (4) When the grouting volume of a single hole reaches 1.5 to 2.0 times the average grouting volume, and the grouting volume is significantly reduced.

[0029] The grouting effect was tested using both the inspection hole method and the leakage observation method. In this embodiment, the inspection hole method was used for the first time after grouting according to the present invention. Core sampling revealed that the karst cavern was densely filled and the cement grout was well bonded to the rock. On-site observation of leakage points showed only occasional small amounts of seepage. This indicates that the grouting effect was significant and achieved the desired leakage prevention. For porous fractures, the dispersion of the diffusion radius after using this invention in this embodiment reached ±15% to ±20%; the dispersion of pipe-type channels was extremely low; no cross-contamination between adjacent holes was found; the leakage reduction rate was greater than or equal to 96%, and the construction period was approximately one month.

[0030] All other unspecified parts belong to the prior art.

Claims

1. A multi-modal intelligent grouting and plugging method for shallow buried cover type karst areas, characterized in that: Includes the following steps, Step S1: Conduct hydrogeological survey of the proposed area; Step S2: Based on the hydrogeological survey results, conduct geophysical exploration in the proposed construction area to determine the extent, depth, and scale of karst development within the proposed construction area; Step S3: Based on the geophysical exploration results, drill in key anomaly areas, verify the geophysical exploration results, determine the karst development status, karst burial depth, and fracture status in the proposed area; and conduct hydrological tests in the boreholes to obtain hydrological parameters. Step S4: Conduct tracing tests on the sources and channels of seepage revealed by geophysical exploration and drilling in the proposed area to verify the connectivity of karst conduits and the type of seepage through the tracing tests; Step S5: Integrate geological data through Visual Modflow, establish a three-dimensional model of karst fissures with permeability field based on multi-source data fusion detection technology, and use tracer experiments to calibrate the connectivity of karst caves in the three-dimensional model; Step S6: The entropy method fusion algorithm generates a grouting priority heatmap; a. Construct a decision matrix containing six evaluation indicators; The six evaluation indicators include: fissure aperture, pipe diameter, permeability coefficient, water flow rate, overburden thickness, and distance from the excavation face; b. Calculate the index weights using the coefficient of variation method: w i =V i / V i ∑k; V i = (σ i / μ i ); w i = (σ i / μ i ) / {σ i / μ i ∑k}; where V i is the coefficient of variation for the ith indicator, σ i is the standard deviation for the ith indicator, μ i is the mean for the ith indicator;∑k is the summation of k indicators; w i is the weight for the ith indicator; c. Output a dynamically updated grouting priority thermal map from a 3D model of karst fissures with a permeability field to guide borehole layout; Step S7: Adjust the grouting pressure in sections and zones; a. Establish a three-tiered pressure control system: 1) Low-pressure zone, 0.3-0.5MPa: used for grouting of pore-type fractures; 2) Medium pressure zone, 0.5-1.2MPa: used for grouting of pipeline channels; 3) High-pressure zone, 1.2-2.0MPa: used for grouting of concentrated leakage zones; b. Based on the leakage type and leakage situation output by the 3D model of karst fissures with permeability coefficient field, the Internet of Things intelligent grouting system is used to carry out intelligent grouting according to the grouting priority heat map and the three-level pressure control system, so as to achieve high accuracy of real-time flow monitoring, fast response of automatic pressure adjustment, and small error of online detection of grout consistency. Grouting ends when the leakage termination condition is met; If the leakage termination condition is not met, proceed to step S5. Adjust the three-dimensional model of karst fissures with permeability coefficient field according to the leakage situation of the leakage point, dynamically update the grouting priority heat map to guide the borehole layout and perform grouting pressure control in sections until the leakage termination condition is met.

2. The multi-modal intelligent grouting and plugging method for shallow-buried cover type karst areas according to claim 1, characterized in that: In step S2, the geophysical exploration methods include audio-frequency magnetotellurics (AMT) and ground-penetrating radar (GPR). First, the study area is explored using AMT to obtain the AMT resistivity profile. The ground-penetrating radar (GPR) method was then used to detect and obtain the bedrock elevation.

3. The multi-modal intelligent grouting and plugging method for shallow-buried cover type karst areas according to claim 2, characterized in that: In step S5, a three-dimensional model of karst fractures with a permeability field is established based on multi-source data fusion detection technology, including the following steps: (1) After integrating remote sensing interpretation data, AMT resistivity profile, GPR bedrock elevation and tracer test data, the fused geophysical interpretation results including the spatial distribution of stratigraphic interfaces, cross sections and karst channels were obtained. (2) Generate a GRID file from the geophysical interpretation results and import it into the conceptual model module of Visual Modflow; use Visual Modflow to generate a three-dimensional stratigraphic structure, divide the layers with different permeability, use the hydrogeological parameters obtained by the pumping test to assign values ​​to the layers with different permeability, and obtain a preliminary three-dimensional model of karst fractures with permeability coefficient field. (3) Based on the above three-dimensional model, the water flow simulation module of Visual Modflow is used to first simulate the stable or unstable groundwater flow field to ensure that the basic elements including the flow direction and water level are roughly consistent with the actual situation; then the solute transport simulation module is used to simulate the injection point of the tracer, define the injection concentration and injection time, set the observation point at the actual water outlet of the tracer, and obtain the simulated tracer monitoring curve. (4) Model calibration and inversion: The simulated tracer monitoring curve is compared with the actual monitoring curve obtained by the tracer test in step S4. If the two do not match, the seepage path is adjusted by adjusting the hydrological parameters of the abnormal zone interpreted by the geophysical exploration until the simulation result of the tracer monitoring curve is highly consistent with the measured data in step S4, and a highly reliable three-dimensional model of karst fissures with permeability coefficient field is obtained.

4. The multi-modal intelligent grouting and plugging method for shallow-buried cover type karst areas according to claim 3, characterized in that: The inputs to the 3D model of karst fissures with permeability field are elevation information, stratigraphic distribution information based on multi-source fusion, permeability coefficients of each stratum, and tracer test data; The three-dimensional model of karst fissures with permeability coefficient field outputs accurate karst fissure channels, karst connectivity, and leakage conditions.

5. The multi-modal intelligent grouting and plugging method for shallow-buried cover type karst areas according to claim 4, characterized in that: In step S6, the six evaluation indicators include: 1) Crack aperture, 0-10cm; 2) Pipe diameter, 0-50cm; 3) permeability coefficient, 10 -4 -10 -2 cm / s; 4) Water flow rate, 0-50L / s; 5) Cover thickness: 0-15m; 6) Distance from the excavation face: 0-30m.

6. The multi-modal intelligent grouting and plugging method for shallow-buried cover type karst areas according to claim 5, characterized in that: In step S7, the real-time flow rate monitoring accuracy is ±0.1 L / min; the response time of automatic pressure regulation is less than 3 s; and the error of online slurry consistency detection is less than or equal to 5%.

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