Karst plugging pipeline type quantitative classification and plugging measure decision method and system

Abstract

The present application provides a karst plugging pipeline type quantitative classification and plugging measure decision method and system, first builds a karst plugging control element, a power type calculation, a pipeline quantitative classification and a measure classification model, then constructs a plugging decision model, and for the karst to be plugged, the pipeline type and the corresponding optimal plugging strategy are output through the operation of each model. The present application can clearly divide the karst pipeline type by quantifying the basic control elements and the power control elements of the success of the quantitative pipeline type karst plugging, match the steady flow, the diameter reduction and the closed type exclusive plugging strategy, realize the factor system integration, the parameter accurate quantification and the measure accurate adaptation, reduce the experience dependence, and improve the plugging success rate and the engineering efficiency of the reservoir, the dam and other projects in the karst area.

Description

Technical Field

[0001] This invention belongs to the field of water conservancy engineering technology, and relates to a quantitative classification of pipe blockage types and a decision-making method and system for treatment measures. Background Technology

[0002] When constructing reservoirs, dams, or tunnels in karst areas, underground caverns and pipeline networks are prone to sudden leaks, leading to reservoir depletion, dam foundation erosion, or even collapse. Existing karst sealing technologies for reservoirs and dams have certain limitations: First, traditional karst sealing methods rely heavily on engineering experience, lack systematic quantitative models, and do not fully quantify the impact of hydrodynamic parameters (such as flow velocity and flow rate) on the stability of sealing materials; second, the selection of karst treatment measures is somewhat subjective: the most reasonable sequence of karst sealing measures has not yet been matched according to the type of karst pipeline (such as pipeline diameter, flow velocity, and flow rate), often resulting in redundant or insufficient measures. Summary of the Invention

[0003] To address the problems mentioned in the background section regarding existing karst sealing technologies for reservoirs and dams, which rely heavily on engineering experience, lack systematic quantitative models, and exhibit a degree of subjectivity in the selection of karst treatment measures, this invention provides a method and system for quantitative classification of karst pipe types and decision-making on sealing measures. This overcomes the shortcomings of existing karst sealing technologies, such as insufficient factor quantification and reliance on experience in decision-making. By constructing a systematic model, it achieves scientific classification of karst pipe types and accurate decision-making on sealing measures, improving the success rate of sealing and engineering efficiency. It is applicable to the design and construction of sealing pipe-type seepage in reservoirs, dams, and other projects in karst areas.

[0004] A first aspect of the present invention provides a method for quantitative classification of karst-blocked pipeline types and decision-making on blocking measures, comprising: Based on the fundamental and dynamic control elements for the success of pipeline-type karst plugging, a karst plugging control element model is established. Based on the hydrodynamic parameters of karst pipelines, the physical properties of sealing materials, and the geometric characteristics of the pipelines, a calculation model for dynamic control elements is established. Based on the karst plugging control element model and the dynamic control element calculation model, and combined with engineering practice, a quantitative classification model for karst plugged pipeline types is established. Based on the flow stabilization, diameter reduction, and closure measures for karst plugging, and combined with the requirements for various types of karst plugging in the quantitative classification model of karst plugging pipeline types, a classification model for karst plugging measures is established. A decision-making model for pipeline-type karst blocking is established based on a quantitative classification model of karst blocking pipeline types and a classification model of karst blocking measures. For karst to be sealed, based on the above-mentioned karst sealing control element model, dynamic control element calculation model, quantitative classification model of karst sealing pipeline type, karst sealing measure classification model, and pipeline-type karst sealing decision model, the karst sealing pipeline type and the corresponding optimal sealing measure strategy are output.

[0005] Furthermore, the karst sealing control element model is as follows: (1), In the formula, A This is a set of elements for controlling karst sealing, comprising two subsets: A 1 =Collection of basic class elements, including a 11 =Diameter of karst conduit D ; A 2 =A collection of dynamic control elements, including a 21 =flow velocity coefficient K v , a 22 =Flow Intensity Index I q .

[0006] Furthermore, the calculation model for the dynamic control element is as follows: (2), In the formula, K v for a 21 =Flow velocity coefficient, dimensionless ρ w The density of water; V max This represents the maximum flow velocity of water in karst-type pipelines. ρ m The density of the coarse-particle sealing material such as sand and gravel being delivered; g is the acceleration due to gravity. d m denoted as characteristic particle size of coarse-grained sealing materials such as sand and gravel; C is the material shape and fit coefficient, which is 0.95-1 for sand and gravel. I q for a 22 =Flow intensity index, dimensionless, where D is the pipe diameter; Q b This is a constant for determining the scale of leakage.

[0007] Furthermore, the quantitative classification model for the karst-blocked pipeline type is as follows: (3), In the formula, f(D, K v , I q ) This is a classification function for karst-blocked pipeline types, whose output value is the type of karst-blocked pipeline, respectively. P 11 , P 12 , P 21 and P 22 .

[0008] Furthermore, the classification model for karst sealing measures is as follows: (4), In the formula, T This is a classification set of karst sealing measures, comprising three subsets: T 1 =A collection of flow stabilization-type blocking measures, T 2 =A collection of diameter reduction and blocking measures T 3 =A set of closed-off containment measures; T 1 include t 11 , t 11 =Filled with sand and gravel; T 2 include t 21 and t 22 , t 21 =Membrane bag concrete grouting, t 22 =Grouting of expansive concrete; T 3 include t 31 , t 32 and t 33 , t 31 =Cement grouting, t 32 =Clay grouting t 33 =Organic material grouting.

[0009] Furthermore, the pipeline-type karst plugging decision model is as follows: (5), In the formula, g(D, K v , I q ) For pipeline-type karst plugging decision function, S 11 、S 12 、S 21 、S 22 Strategies for sealing pipeline-type karst; For P 11 Pipe-type karst, g(D, K v , I q )=S 11 =t 11 →t 21 →t 31 ， That is: first use t 11 Filling the karst conduits with gravel and sand to stabilize the flow and reduce the flow velocity to less than 1 m / s; then employing... t 21 The membrane bag concrete grouting diameter reduction sealing measure further reduces the flow velocity to below 0.5 m / s; finally, it adopts... t 31 Cement grouting sealing measures are used to seal karst pipes. For P 12 Pipe-type karst, g(D, K v , I q )=S 12 =t 22 →t 31 ， That is: first adopt t 22 Expansive concrete grouting for diameter reduction and sealing measures reduces the flow velocity to below 0.5 m / s; then... t 31 Cement grouting sealing measures are used to seal karst pipes. For P 21 Pipe-type karst, g(D, K v , I q )=S 21 =t11 →t 31 or t 32 ， That is: first use t 11 Filling the karst conduits with gravel and sand to stabilize the flow and reduce the flow velocity to less than 0.5 m / s; then employing... t 31 Cement grouting or t 32 =Clay grouting sealing measures are used to seal karst pipes; For P 22 Pipe-type karst, g(D, K v , I q )= S 22 =t 31 or t 32 or t 33 ， That is: directly adopt t 31 Cement grouting or t 32 = Clay grouting or t 33 =The sealing and plugging measures using organic material grouting achieve the sealing of karst pipes.

[0010] Furthermore, for karst to be sealed, the process of obtaining the type of karst-sealed pipeline and the corresponding optimal sealing strategy includes: Based on the karst plugging control element model, basic control elements and dynamic control elements are obtained; Based on the calculation model of dynamic control elements, the dynamic control elements are calculated; Based on the calculation results of the basic control elements and the dynamic control elements, the karst-blocked pipeline type is output using a quantitative classification model based on the karst-blocked pipeline type. Based on the type of karst-sealed pipeline, and using a karst sealing measure classification model and a pipeline-type karst sealing decision model, the corresponding optimal sealing measure strategy is output.

[0011] A second aspect of the present invention provides a quantitative classification system for karst-sealed pipeline types and a decision-making system for sealing measures, applicable to the method described above, comprising: The karst plugging control element model building module establishes a karst plugging control element model based on the basic and dynamic control elements that determine whether pipeline-type karst plugging can be successful. The module for establishing a dynamic control element calculation model establishes a dynamic control element calculation model based on the hydrodynamic parameters of the karst pipeline, the physical properties of the sealing material, and the geometric features of the pipeline. The module for establishing a quantitative classification model of karst-blocked pipeline types is based on the calculation results of the karst-blocked control element model and the dynamic control element calculation model, combined with engineering practice, to establish a quantitative classification model of karst-blocked pipeline types. The module for establishing a classification model of karst plugging measures establishes a classification model of karst plugging measures based on the flow stabilization, diameter reduction, and closure of karst plugging measures, combined with the requirements of various types of pipeline karst plugging in the quantitative classification model of karst plugging pipeline types. The pipeline-type karst plugging decision-making model establishment module establishes a pipeline-type karst plugging decision-making model based on a quantitative classification model of karst plugging pipeline types and a classification model of karst plugging measures. The optimal closure strategy output module, for karst to be closed, outputs the karst closure pipeline type and the corresponding optimal closure strategy based on the above-mentioned karst closure control element model, dynamic control element calculation model, karst closure measure classification model, karst closure pipeline type quantitative classification model, and pipeline karst closure decision model.

[0012] A third aspect of the present invention provides an electronic device comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to realize the quantification classification of karst-blocked pipeline types and decision-making method for blocking measures as described above.

[0013] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the karst-blocking pipeline type quantitative classification and blocking measure decision-making method as described above.

[0014] Compared with the prior art, the present invention has the following advantages: (1) Comprehensive factor integration: Traditional karst plugging methods often analyze various factors affecting the plugging effect in isolation without forming a systematic correlation. This invention constructs a karst plugging control element model and clearly classifies the key influencing factors of pipeline karst plugging into basic and dynamic categories, thus realizing the systematic integration of the core influencing factors of plugging. This classification and integration method not only covers the geometric characteristics of the pipeline itself, but also incorporates the relevant parameters of water flow dynamics, avoiding the one-sidedness of single factor analysis, and providing a comprehensive and systematic basis for the precise design of subsequent plugging strategies. (2) High accuracy and quantification of dynamic parameters: Existing technologies lack systematic quantification of hydrodynamic parameters, making it difficult to accurately assess their impact on the stability of sealing materials. This invention establishes a calculation model for dynamic control elements, based on the hydrodynamic parameters of karst pipelines, the physical properties of sealing materials, and the geometric features of pipelines, to achieve quantitative calculation of dynamic control elements. By replacing traditional experience-based judgment with quantitative analysis, it can more accurately reflect the impact of hydrodynamics on the sealing process, providing objective and quantifiable support for pipeline type classification and sealing measure selection. (3) Refined classification of pipeline types and precise matching with treatment measures: Traditional technology does not scientifically classify karst pipelines, resulting in strong subjectivity in the selection of sealing measures and easy redundancy or insufficiency of measures; This invention establishes a quantitative classification model based on the control element model and the dynamic element calculation model, combined with engineering practice, to clearly classify the types of karst pipelines; At the same time, it sorts out the three core measures of flow stabilization, diameter reduction and closure through the sealing measure classification model, and constructs a decision model to match exclusive sealing strategies for each type of pipeline; This "classification → matching" logic realizes the precise adaptation of sealing measures, reduces the reliance on human experience, and effectively improves the sealing success rate and engineering efficiency; (4) Systematized technical system and strong engineering applicability: Existing karst plugging technology lacks a complete systematized model support, and its application scenarios are limited; This invention constructs a complete technical system of “control element model → dynamic element calculation model → pipeline quantitative classification model → measure classification model → plugging decision model”, forming a closed loop from factor quantification, pipeline classification to measure decision; This system is applicable to the pipeline leakage plugging design and construction of reservoirs, dams and other projects in karst areas. By replacing the traditional scattered experience-based operation with a standardized and systematic process, it improves the technology’s scalability and engineering application value.

[0015] In summary, this invention addresses the problems of existing karst sealing methods relying on experience and lacking quantitative models by constructing a complete system consisting of "control element model - dynamic calculation model - pipeline quantitative classification model - measure classification model - sealing decision model". By quantifying the basic and dynamic control elements that determine the success of pipeline-type karst sealing, it clearly classifies karst pipeline types and matches specific sealing strategies for flow stabilization, diameter reduction, and closure. This achieves systematic integration of factors, precise parameter quantification, and accurate adaptation of measures, reducing reliance on experience and improving the success rate and efficiency of sealing projects such as reservoirs and dams in karst areas. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0017] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0018] Example 1 Quantitative classification of karst-sealed pipeline types and decision-making methods for sealing measures, flowchart as follows: Figure 1 As shown, the specific steps are as follows.

[0019] Based on the fundamental and dynamic control elements for the success of pipeline-type karst plugging, a karst plugging control element model is established.

[0020] The success of karst sealing is constrained by multiple factors, mainly including two categories: the first is basic control elements, which mainly refer to the diameter of pipe-type karst. D Second, dynamic control elements: including the flow velocity coefficient of water within karst formations. K v Used to determine the scouring ability of water flow on sealing materials, and the flow intensity index. I q This characterizes the leakage intensity of karst conduits.

[0021] Specifically, the karst sealing control element model is as follows: (1), In the formula, A This is a set of elements for controlling karst sealing, comprising two subsets: A 1 =Collection of basic class elements, including a 11 =Diameter of karst conduit D ; A 2 =A collection of dynamic control elements, including a 21 =flow velocity coefficient K v , a 22 =Flow Intensity Index I q .

[0022] Based on the hydrodynamic parameters of karst pipelines, the physical properties of sealing materials, and the geometric characteristics of the pipelines, a calculation model for dynamic control elements is established.

[0023] By establishing a calculation model for dynamic control elements, dynamic control elements can be realized. a 21 and a22 The quantification of this data provides a reliable basis for the precise selection of karst sealing measures.

[0024] Specifically, the calculation model for dynamic control elements is as follows: (2), In the formula, K v for a 21 =Flow velocity coefficient, dimensionless ρ w The density of water, in kg / m³ 3 ; V max The maximum flow velocity of water in karst-type pipelines is expressed in m / s. ρ m The density of coarse-particle sealing materials such as sand and gravel, expressed in kg / m³. 3 g is the acceleration due to gravity, which is 9.8 m / s². 2 ; d m For coarse-grained sealing materials such as sand and gravel, the median particle size can be taken as the characteristic particle size, in meters; C is the material shape and fit coefficient, which is 0.95-1 for sand and gravel; I q for a 22 = Flow intensity index, dimensionless; D is the pipe diameter, in meters; Q b As a constant for determining the leakage scale, 2m is taken. 3 / s.

[0025] Generally, Q b The specific values ​​are determined with reference to engineering practices regarding the classification of seepage hazards in karst seepage treatment in water conservancy and hydropower projects. Based on the relevant provisions of the "Guidelines for Safety Evaluation of Reservoir Dams" (SL 258), the "Code for Geological Investigation of Water Conservancy and Hydropower Projects" (GB 50487), and the "Technical Specification for Grouting of Rock Mass in Dam Foundation of Hydropower and Water Conservancy Projects" (DL / T 5148), and the experience of reservoir seepage treatment projects in karst areas of my country, for karst seepage treatment projects of medium and smaller reservoir dam foundations, a single-point (single-pipe) concentrated seepage exceeding 2m³ is considered acceptable. 3 A leakage rate of / s is considered a major leakage hazard, requiring special handling; therefore, this invention... Q b Take 2m 3 / s.

[0026] Flow rate coefficient K vThe physical meaning of this criterion is the ratio of the drag force of the water flow on the sealing particle material to the underwater gravity of the particles. It is a dimensionless criterion based on the principle of particle dynamics. Its numerator represents the hydrodynamic pressure exerted by the water flow on a unit area of ​​particles (its dimension is consistent with the water flow impact pressure), and the denominator represents the effective gravity of a unit area of ​​particles underwater (including shape adaptation correction). The form of this criterion is consistent with the dimensionless form of the classic Shields number and the Izbash boulders stability formula, the latter being the fundamental formula in hydraulic engineering for determining the stability of boulders and gravel under dynamic water conditions. K v When <1, the drag force of the water flow is less than the underwater weight of the particles, and the particles can stably accumulate inside the pipe; when K v When = 1, the particle is in the critical starting state; when K v When the water flow is greater than 1, the water flow is sufficient to agitate and carry the particles, preventing the sealing material from remaining stably within the pipe. Therefore, this invention takes... K v = 1 is used as the threshold for determining whether the sealing material can exist stably. This value has a clear basis for particle dynamics.

[0027] Flow intensity index I q The physical meaning of is: the ratio of the actual leakage of the pipeline being evaluated to the leakage scale determination constant; it is a dimensionless criterion for leakage scale. When I q When the value is ≤1, the actual leakage does not exceed the allowable concentrated leakage threshold of the project. The single-point injection flow rate of conventional diameter-reducing and sealing grouting materials can cover the leakage, and the material can accumulate stably in the pipeline; when I q When the value is greater than 1, the actual leakage exceeds the benchmark value. The single-point injection flow rate of conventional diameter-reducing and sealing materials is already less than the leakage flow rate, and the material cannot remain stably. Therefore, pre-emptive measures such as flow stabilization (filling with gravel) must be taken to reduce the flow cross-section and velocity. Therefore, this invention adopts... I q = 1 is used as the threshold for determining whether "pre-current stabilization measures are needed".

[0028] Therefore, in practical applications, when the present invention is used... K v When the value is greater than 1, it indicates that the water flow is sufficient to impede and carry away the sealing material. For karst pipes with a diameter greater than 0.5m, flow stabilization measures should be taken first. K v When ≤ 1, the material can exist stably and no flow stabilization measures are required; when I qA value >1 indicates that the karst pipe leakage is a high-flow-rate leakage. For karst pipes with a diameter greater than 0.5m, flow stabilization measures should be taken first. I q When the value is ≤1, it indicates that the karst pipe leakage is a low-flow leakage.

[0029] The threshold for the diameter D of karst pipes is set at 0.5m because: First, the "Code for Geological Investigation of Highway Engineering in Karst Areas" clearly stipulates that the borehole encounter rate refers to the percentage of boreholes encountering karst cavities (height greater than 0.5m) compared to the total number of soluble rock boreholes. This means that in karst engineering investigation, 0.5m is the engineering boundary scale between karst caves and fissures recognized by national industry standards; only cavities with a height exceeding 0.5m are statistically considered karst caves of engineering significance. Second, in the uncertainty analysis of karst pipe leakage, 0.5m is used as the minimum upper limit for pipe diameter classification studies. The average leakage of a pipe system with a diameter upper limit of 0.5m is approximately 0.8 m. 3 / s. Third, the "Technical Specification for Pulsating Grouting in Water Conservancy and Hydropower Engineering" stipulates that the inner diameter of the grouting pipe should be greater than 50mm, and the inner diameter of the grout delivery pipe should be greater than 50mm. When the pipe diameter D>0.5m, the pipe diameter reaches more than 10 times the inner diameter of the grouting pipe, and conventional grouting slurry is difficult to stably retain and accumulate in the pipe. Flow stabilization measures (filling with sand and gravel to reduce flow velocity) must be taken first. When D≤0.5m, grouting sealing measures can be directly adopted. Fourth, in engineering practice, the maximum aggregate particle size generally does not exceed 1 / 10 to 1 / 3 of the pipe diameter; using the commonly used characteristic particle size d of sand and gravel... m (5~30mm) Calculation: When D=0.5m, D / d m With a diameter of approximately 17 times, coarse-grained materials can be smoothly delivered and stockpiled; when D < 0.5m, smaller-diameter aggregates can still be delivered or grouting can be performed directly.

[0030] Based on the karst plugging control element model and the dynamic control element calculation model, and combined with engineering practice, a quantitative classification model for karst-plugged pipeline types is established.

[0031] Specifically, the quantitative classification model for karst-blocked pipeline types is as follows: (3), In the formula, f(D, K v , I q ) This is a classification function for karst-blocked pipeline types, whose output value is the type of karst-blocked pipeline, respectively. P 11 , P 12 , P 21 and P22 .

[0032] Based on the flow stabilization, diameter reduction, and closure measures for karst plugging, and combined with the requirements for various types of karst plugging in the quantitative classification model of karst plugging pipeline types, a classification model for karst plugging measures is established.

[0033] Karst sealing measures can be categorized into three types based on their specific functions: flow stabilization, diameter reduction, and closure. Flow stabilization measures involve filling with gravel; diameter reduction measures include membrane bag concrete grouting and expansive concrete grouting; and closure measures include cement grouting, clay grouting, and grouting with organic materials such as soybeans. A classification model for karst sealing measures can meet the following requirements. P 11 , P 12 , P 21 and P 22 Requirements for sealing various types of pipeline-type karst.

[0034] Specifically, the classification model for karst sealing measures is as follows: (4), In the formula, T This is a classification set of karst sealing measures, comprising three subsets: T 1 =A collection of flow stabilization-type blocking measures, T 2 =A collection of diameter reduction and blocking measures T 3 =A set of closed-off containment measures; T 1 include t 11 , t 11 =Filled with sand and gravel; T 2 include t 21 and t 22 , t 21 =Membrane bag concrete grouting, t 22 =Grouting of expansive concrete; T 3 include t 31 , t 32 and t 33 , t 31 =Cement grouting, t 32=Clay grouting t 33 =Organic material grouting, wherein the organic material can be soybeans, etc.

[0035] A decision-making model for pipeline-type karst plugging is established based on a quantitative classification model of karst plugging pipeline types and a classification model of karst plugging measures.

[0036] Specifically, the decision model for pipeline-type karst plugging is as follows: (5), In the formula, g(D, K v , I q ) For pipeline-type karst plugging decision function, S 11 、S 12 、S 21 、S 22 Strategies for sealing pipeline-type karst; For P 11 Pipe-type karst, g(D, K v , I q )=S 11 =t 11 →t 21 →t 31 ， That is: first use t 11 Filling the karst conduits with gravel and sand to stabilize the flow and reduce the flow velocity to less than 1 m / s; then employing... t 21 The membrane bag concrete grouting diameter reduction sealing measure further reduces the flow velocity to below 0.5 m / s; finally, it adopts... t 31 Cement grouting sealing measures are used to seal karst pipes. For P 12 Pipe-type karst, g(D, K v , I q )=S 12 =t 22 →t 31 ， That is: first adopt t 22Expansive concrete grouting for diameter reduction and sealing measures reduces the flow velocity to below 0.5 m / s; then... t 31 Cement grouting sealing measures are used to seal karst pipes. For P 21 Pipe-type karst, g(D, K v , I q )=S 21 =t 11 →t 31 or t 32 ， That is: first use t 11 Filling the karst conduits with gravel and sand to stabilize the flow and reduce the flow velocity to less than 0.5 m / s; then employing... t 31 Cement grouting or t 32 =Clay grouting sealing measures are used to seal karst pipes; For P 22 Pipe-type karst, g(D, K v , I q )= S 22 =t 31 or t 32 or t 33 ， That is: directly adopt t 31 Cement grouting or t 32 = Clay grouting or t 33 =The sealing and plugging measures using organic material grouting achieve the sealing of karst pipes.

[0037] The engineering basis for the aforementioned flow velocity threshold of 1 m / s is as follows: The "Construction Technology for Seepage Prevention and Emergency Treatment of Cofferdam in the Repair Project of Guide Wall of CHE Hydropower Station" clearly states that: Section 9.02 of the "Technical Specification for High-Pressure Jet Grouting in Hydropower and Water Conservancy Projects" (DL / T5200-2004) stipulates that when the water flow velocity in the stratum is too high, water blocking treatment should be carried out first, followed by high-pressure jet grouting. This engineering practice directly provides 1 m / s as the upper limit of the operable flow velocity for dynamic water grouting—when the groundwater flow velocity does not exceed 1 m / s, the grout and aggregate can stably exist within the karst conduit and will not be washed away by the water flow. This value comes from the experience summary of high-pressure jet grouting engineering practice and has clear engineering guiding significance.

[0038] The engineering basis for the aforementioned flow velocity threshold of 0.5 m / s is: The study "Experimental Research and Engineering Application of Anti-dispersion Properties of Underwater Karst Grout" (Zhang Cong et al., System Experimental Research) provides grout retention rate data at different flow velocities: At a dynamic water flow velocity of 0.2 m / s, the grout retention rate reaches 96.2%, but when the water flow velocity increases to 0.8 m / s, the grout retention rate drops sharply to only 62.62%. The anti-dispersion performance of modified clay-cement paste is significantly affected by factors such as the grout water-to-solid ratio, the karst filling material, and the karst water flow velocity. At karst water flow velocities not exceeding 0.8 m / s, the grout exhibits good anti-dispersion performance. However, when the karst water flow velocity v > 1.0 m / s, the modified clay-cement paste fails to achieve the effect of sealing water and plugging leaks. When the karst water velocity is 0.5 m / s ≤ v ≤ 0.8 m / s, for karst caves containing filling materials, a higher grout retention rate and better retention body performance can be achieved by adjusting the water-to-solid ratio. For karst caves without filling materials, a higher grout retention rate and better retention body performance can be achieved by adding sand (fine sand content should not exceed 30%). 0.5 m / s is the dividing point between "good sealing conditions" and "requiring special mix adjustments". When the flow velocity is below 0.5 m / s, effective sealing can be achieved directly using cement grout with a conventional water-to-solid ratio. When the flow velocity is between 0.5 and 0.8 m / s, it is necessary to adjust the mix ratio or add sand as an auxiliary agent.

[0039] For karst to be sealed, based on the above-mentioned karst sealing control element model, dynamic control element calculation model, quantitative classification model of karst sealing pipeline type, karst sealing measure classification model, and pipeline-type karst sealing decision model, the karst sealing pipeline type and the corresponding optimal sealing measure strategy are output.

[0040] Specifically, for karst to be sealed, the process of obtaining the type of karst-sealed pipeline and the corresponding optimal sealing strategy is as follows: Based on the karst plugging control element model, basic control elements and dynamic control elements are obtained; Based on the calculation model of dynamic control elements, the dynamic control elements are calculated; Based on the calculation results of the basic control elements and the dynamic control elements, the karst-blocked pipeline type is output using a quantitative classification model based on the karst-blocked pipeline type. Based on the type of karst-sealed pipeline, and using a karst sealing measure classification model and a pipeline-type karst sealing decision model, the corresponding optimal sealing measure strategy is output.

[0041] Example 2 The Quantitative Classification and Decision-Making System for Karst Pipeline Blocking Types consists of a karst blocking control element model establishment module, a dynamic control element calculation model, a quantitative classification model for karst blocking pipeline types, a karst blocking measure classification model establishment module, a pipeline-type karst blocking decision-making model establishment module, and an optimal blocking measure strategy output module.

[0042] The karst plugging control element model building module establishes a karst plugging control element model based on the basic and dynamic control elements that determine whether pipeline-type karst plugging can be successful.

[0043] A dynamic control element calculation model is established based on the hydrodynamic parameters of the karst pipeline, the physical properties of the sealing material, and the geometric characteristics of the pipeline. A quantitative classification model for karst-blocked pipeline types is established based on the calculation results of the karst blocking control element model and the dynamic control element calculation model, combined with engineering practice.

[0044] The module for establishing a classification model of karst plugging measures establishes a classification model of karst plugging measures based on the flow stabilization, diameter reduction, and closure measures of karst plugging, combined with the requirements of various types of pipeline karst plugging in the quantitative classification model of karst plugging pipeline types.

[0045] The module for establishing a decision-making model for pipeline-type karst plugging is based on a quantitative classification model of karst plugging pipeline types and a classification model of karst plugging measures, to establish a decision-making model for pipeline-type karst plugging.

[0046] The optimal closure strategy output module, for karst to be closed, outputs the karst closure pipeline type and the corresponding optimal closure strategy based on the above-mentioned karst closure control element model, dynamic control element calculation model, karst closure measure classification model, karst closure pipeline type quantitative classification model, and pipeline karst closure decision model.

[0047] The specific implementation methods of each module in this system are the same as those described in Example 1, and will not be repeated here.

[0048] Example 3 An electronic device includes a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to implement the karst pipe type quantitative classification and pipe-blocking measure decision-making method as described in Embodiment 1 above, and the karst pipe type quantitative classification and pipe-blocking measure decision-making system as described in Embodiment 2.

[0049] Example 4 A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the karst-blocking pipeline type quantitative classification and blocking measure decision-making method as described in Embodiment 1 above, and the karst-blocking pipeline type quantitative classification and blocking measure decision-making system as described in Embodiment 2.

[0050] Example 5 For a certain karst, the present invention is used to obtain the type of karst-blocked pipeline and the corresponding optimal blocking strategy, the steps of which are as follows.

[0051] I. Data Acquisition and Parameter Calculation: Based on geological and geophysical data, the diameter D of the karst conduit was calculated. Based on monitoring data, the maximum flow velocity V within the karst conduit was calculated. max The parameters such as the median particle size, density, and material shape and fit coefficient C of the sand and gravel material to be delivered are determined.

[0052] II. Calculation of Dynamic Control Elements: A calculation model for dynamic control elements is used to calculate the flow velocity coefficient. K v and flow intensity index I q ,judge K v and I q Threshold: If Kv > 1, corresponding to karst formations with a diameter greater than 50cm, flow stabilization measures should be prioritized; if I q > 1. For large-flow leakage, flow stabilization measures should be given priority.

[0053] III. Determining the type of karst-blocked pipeline: The D values ​​calculated in steps 1 and 2... K v , I q Parameters, substituted into the pipeline-type karst sealing classification function f(D, K v , I q ) Determine the type of karst conduit ( P 11 , P 12 , P 21 or P 22 ).

[0054] IV. Based on the pipeline type, invoke the decision function to determine the optimal blocking strategy: f(D, K v , I q) Substitute into the pipeline type to call the decision function g(D, K v , I q ) Determine the sealing measures and strategies for various types of karst-sealed pipelines: for P 11 Pipe-type karst, g(D, K v , I q )=S 11 =t 11 →t 21 →t 31 ， That is: first adopt t 11 Filling the karst conduits with gravel and sand to stabilize the flow and reduce the flow velocity to less than 1 m / s; then employing... t 21 The diameter-reduction sealing measures for membrane bag concrete grouting further reduce the flow velocity to below 0.5 m / s; finally, the following measures are adopted. t 31 Cement grouting and sealing methods are used to seal karst pipes.

[0055] for P 12 Pipe-type karst, g(D, K v , I q )=S 12 =t 22 →t 31 ， That is: first adopt t 22 For expansive concrete grouting, diameter reduction sealing measures are used to reduce the flow velocity to below 0.5 m / s; then... t 31 Cement grouting is used as a sealing and plugging method to seal karst pipes.

[0056] for P 21 Pipe-type karst, g(D, K v , I q )=S 21 =t 11 →t31 or t 32 ， That is: first use t 11 Filling the karst conduits with gravel and sand to stabilize the flow and reduce the flow velocity to less than 0.5 m / s; then employing... t 31 Cement grouting or t 32 Clay grouting is used as a sealing and plugging method to seal karst conduits.

[0057] for P 22 Pipe-type karst, g(D, K v , I q )=S 22 =t 31 or t 32 or t 33 ， That is: directly adopt t 31 Cement grouting or t 32 = Clay grouting or t 33 =The sealing and plugging measures using soybean organic material grouting achieve the sealing of karst pipes.

[0058] Through the above steps, scientific decision-making and efficient construction of karst sealing can be achieved, ensuring the safety and reliability of the project.

[0059] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented in various computer languages, such as object-oriented programming languages ​​like Java, C++, Python, and interpreted scripting languages ​​like JavaScript.

[0060] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, electronic devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing electronic device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing electronic device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0061] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing electronic device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0062] These computer program instructions can also be loaded onto a computer or other programmable data processing electronic device to cause a series of operational steps to be performed on the computer or other programmable electronic device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable electronic device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0063] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0064] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A quantitative classification method for karst-sealed pipeline types and a decision-making method for sealing measures, characterized in that, include: Based on the fundamental and dynamic control elements for the success of pipeline-type karst plugging, a karst plugging control element model is established. Based on the hydrodynamic parameters of karst pipelines, the physical properties of sealing materials, and the geometric characteristics of the pipelines, a calculation model for dynamic control elements is established. Based on the calculation results of the karst plugging control element model and the dynamic control element calculation model, and combined with engineering practice, a quantitative classification model of karst plugging pipeline types is established. Based on the flow stabilization, diameter reduction, and closure measures for karst plugging, and combined with the requirements for various types of karst plugging in the quantitative classification model of karst plugging pipeline types, a classification model for karst plugging measures is established. A decision-making model for pipeline-type karst blocking is established based on a quantitative classification model of karst blocking pipeline types and a classification model of karst blocking measures. For karst to be sealed, based on the above-mentioned karst sealing control element model, dynamic control element calculation model, quantitative classification model of karst sealing pipeline type, karst sealing measure classification model, and pipeline-type karst sealing decision model, the karst sealing pipeline type and the corresponding optimal sealing measure strategy are output.

2. The method for quantitative classification of karst-sealed pipeline types and decision-making on sealing measures according to claim 1, characterized in that: The karst sealing control element model is as follows: （1）， In the formula, A This is a set of elements for controlling karst sealing, comprising two subsets: A 1 =Collection of basic class elements, including a 11 =Diameter of karst conduit D ; A 2 =A collection of dynamic control elements, including a 21 =flow velocity coefficient K v , a 22 =Flow Intensity Index I q .

3. The method for quantitative classification of karst-sealed pipeline types and decision-making on sealing measures according to claim 2, characterized in that: The calculation model for the dynamic control elements is as follows: （2）， In the formula, K v for a 21 =Flow velocity coefficient, dimensionless ρ w The density of water; V max This represents the maximum flow velocity of water in karst-type pipelines. ρ m The density of the coarse-particle sealing material such as sand and gravel being delivered; g is the acceleration due to gravity. d m Characteristic particle size of coarse-particle sealing materials such as sand and gravel; C is the material shape and fit coefficient, which is 0.95-1 for sand and gravel; I q for a 22 =Flow intensity index, dimensionless, where D is the pipe diameter; Q b This is a constant for determining the scale of leakage.

4. The method for quantitative classification of karst-sealed pipeline types and decision-making on sealing measures according to claim 3, characterized in that: The quantitative classification model for the karst-blocked pipeline type is as follows: （3）， In the formula, f(D, K v , I q ) This is a classification function for karst-blocked pipeline types, whose output value is the type of karst-blocked pipeline, respectively. P 11 , P 12 , P 21 and P 22 .

5. The method for quantitative classification of karst-sealed pipeline types and decision-making on sealing measures according to claim 4, characterized in that: The classification model for karst sealing measures is as follows: （4）, In the formula, T This is a classification set of karst sealing measures, comprising three subsets: T 1 =A collection of flow stabilization-type blocking measures, T 2 =A collection of diameter reduction and blocking measures T 3 =A set of closed-off containment measures; T 1 include t 11 , t 11 =Filled with sand and gravel; T 2 include t 21 and t 22 , t 21 =Membrane bag concrete grouting, t 22 =Grouting of expansive concrete; T 3 include t 31 , t 32 and t 33 , t 31 =Cement grouting, t 32 =Clay grouting t 33 =Organic material grouting.

6. The method for quantitative classification of karst-sealed pipeline types and decision-making on sealing measures according to claim 5, characterized in that: The pipeline-type karst plugging decision model is as follows: （5）， In the formula, g(D, K v , I q ) For pipeline-type karst plugging decision function, S 11 、S 12 、S 21 、S 22 Strategies for sealing pipeline-type karst; For P 11 Pipe-type karst, g(D, K v , I q )=S 11 =t 11 →t 21 →t 31 ， That is: first use t 11 Filling the karst conduits with gravel and sand to stabilize the flow and reduce the flow velocity to less than 1 m / s; then employing... t 21 The diameter-reduction sealing measures for membrane bag concrete grouting further reduce the flow velocity to below 0.5 m / s; finally, the following measures are adopted. t 31 Cement grouting is used as a sealing and plugging method to seal karst pipes; For P 12 Pipe-type karst, g(D, K v , I q )=S 12 =t 22 →t 31 ， That is: first adopt t 22 For expansive concrete grouting, diameter reduction sealing measures are used to reduce the flow velocity to below 0.5 m / s; then... t 31 Cement grouting is used as a sealing and plugging method to seal karst pipes; For P 21 Pipe-type karst, g(D, K v , I q )=S 21 =t 11 →t 31 or t 32 ， That is: first use t 11 Filling the karst conduits with gravel and sand to stabilize the flow and reduce the flow velocity to less than 0.5 m / s; then employing... t 31 Cement grouting or t 32 =Clay grouting is used as a sealing and plugging method to seal karst pipes; For P 22 Pipe-type karst, g(D, K v , I q )=S 22 =t 31 or t 32 or t 33 ， That is: directly adopt t 31 Cement grouting or t 32 = Clay grouting or t 33 =The sealing and plugging measures using organic material grouting achieve the sealing of karst pipes.

7. The method for quantitative classification of karst-sealed pipeline types and decision-making on sealing measures according to claim 1, characterized in that: For karst to be sealed, the process of obtaining the type of karst sealing pipeline and the corresponding sealing measures and strategies includes: Based on the karst plugging control element model, basic control elements and dynamic control elements are obtained; Based on the calculation model of dynamic control elements, the dynamic control elements are calculated; Based on the calculation results of the basic control elements and the dynamic control elements, the karst-blocked pipeline type is output using a quantitative classification model based on the karst-blocked pipeline type. Based on the type of karst-sealed pipeline, and using a karst sealing measure classification model and a pipeline-type karst sealing decision model, the corresponding optimal sealing measure strategy is output.

8. A quantified classification system for karst-blocked pipeline types and a decision-making system for blocking measures, applicable to the quantified classification system for karst-blocked pipeline types and the decision-making system for blocking measures described in any one of claims 1-7, characterized in that, include: The karst plugging control element model building module establishes a karst plugging control element model based on the basic and dynamic control elements that determine whether pipeline-type karst plugging can be successful. The module for establishing a dynamic control element calculation model establishes a dynamic control element calculation model based on the hydrodynamic parameters of the karst pipeline, the physical properties of the sealing material, and the geometric features of the pipeline. The module for establishing a quantitative classification model of karst-blocked pipeline types is based on the calculation results of the karst-blocked control element model and the dynamic control element calculation model, combined with engineering practice, to establish a quantitative classification model of karst-blocked pipeline types. The module for establishing a classification model of karst plugging measures establishes a classification model of karst plugging measures based on the flow stabilization, diameter reduction, and closure of karst plugging measures, combined with the requirements of various types of pipeline karst plugging in the quantitative classification model of karst plugging pipeline types. The pipeline-type karst plugging decision-making model establishment module establishes a pipeline-type karst plugging decision-making model based on a quantitative classification model of karst plugging pipeline types and a classification model of karst plugging measures. The optimal closure strategy output module, for karst to be closed, outputs the karst closure pipeline type and the corresponding optimal closure strategy based on the above-mentioned karst closure control element model, dynamic control element calculation model, karst closure measure classification model, karst closure pipeline type quantitative classification model, and pipeline karst closure decision model.

9. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes these computer instructions to implement the method for quantitative classification of karst-blocked pipeline types and decision-making on blocking measures as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, it implements the method for quantitative classification of karst-blocked pipeline types and decision-making on blocking measures as described in any one of claims 1-7.

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