Evaluation method for determining separation grouting range based on intelligent drilling parameter inversion lithology change

Abstract

The invention relates to the technical field of bed separation grouting, in particular to an evaluation method for determining a bed separation grouting range based on intelligent drilling parameter inversion lithologic change. The core of the method is to introduce a hidden structure dip angle parameter, establish an information magnitude correlation model of a strong transformation ratio coefficient, a deformation ratio coefficient and a stratum structure, distinguish lithologic anomaly caused by the structure and characteristics of a rock stratum, and solve the problem of inversion deviation of a traditional method. Dynamic data comparison is formed through two times of drilling and calculation before and after grouting, the statistical significance of interval estimation quantification difference is combined, evaluation is converted into a quantifiable confidence level index, and the limitation of experience judgment is broken through. And directly outputting a filling effect conclusion and an adjustment suggestion based on the comparison of the confidence level and the discrimination threshold, optimizing the decision-making efficiency, and reducing the cost and the construction period. And meanwhile, drilling multi-dimensional parameter values are deeply excavated, and upgrading of data from collection and storage to decision support is achieved.

Description

Technical Field

[0001] This application relates to the technical field of delamination grouting, and in particular to an evaluation method for determining the delamination grouting range based on lithological changes inverted from intelligent drilling parameters. Background Technology

[0002] As coal mining in my country extends to deeper levels, over 60% of production capacity in 2023 came from mines buried at depths of 800 meters or more, with some mines entering the "ultra-deep mining" stage. High ground stress and complex hydrogeological conditions have led to roof delamination, water inrush, and sand collapse disasters, becoming a core risk restricting safe production. Delamination grouting technology, capable of controlling rock deformation and sealing water channels, has become a pillar of disaster prevention and control, with over 25,000 such projects implemented annually in coal mines nationwide, accounting for 38% of the total cost of disaster prevention and control. However, existing technology has significant bottlenecks, with an average error rate of 22%, requiring secondary grouting in 30% of cases, resulting in direct economic losses exceeding 2.5 billion yuan annually.

[0003] Current technologies for delamination grouting all have certain shortcomings, primarily manifested in the disconnect between drilling and grouting decision-making. Specifically, the unknowns exist between core samples taken from boreholes, and microseismic monitoring alone cannot distinguish between rock fractures and grout-driven fractures. Furthermore, the delamination is discontinuous and heterogeneous, requiring several days to weeks for grout diffusion and consolidation. The causes of surface uplift are difficult to determine, and relying on experience-based judgment can easily lead to insufficient or excessive grouting. While intelligent drilling can collect parameters such as drill pressure and rotation speed, the multi-source data is isolated, and multi-dimensional parameters such as drilling speed and torque are analyzed in a fragmented manner, failing to be fully utilized for grouting decision-making. Combined with the grouting process, effective and reliable control is rarely achieved. Evaluation remains at a qualitative level, such as surface deformation monitoring and grout volume statistics, without establishing a quantitative relationship between drilling parameters and lithological changes, lacking a quantifiable indicator system, and exhibiting limitations and delays in control. In addition, the limited space in drilling equipment and the susceptibility of densely arranged sensors to electromagnetic interference or mechanical coupling further restrict the application of this technology. Therefore, with the development of mining, there is an urgent need for a reliable delamination grouting technology to meet the requirements for grouting effectiveness in the mining process. Summary of the Invention

[0004] The purpose of this application is to overcome at least one of the shortcomings of the prior art and provide an evaluation method for determining the grouting range based on lithological changes inverted by intelligent drilling parameters. By enhancing the correlation between the drilling process and the grouting process, the controllable attributes and evaluation dimensions of grouting are increased, thereby improving the effectiveness and reliability of grouting.

[0005] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description or may be learned by practice of this application.

[0006] According to one aspect of this application, an evaluation method for determining the grouting range for delamination based on lithological changes derived from intelligent drilling parameters is provided, the method comprising: Exploratory boreholes are drilled using intelligent drilling equipment before the grouting process, and the discrimination threshold is obtained based on the core samples. Collect the borehole physical parameters of the intelligent drilling equipment, and calculate the first strength deformation ratio coefficient and the first core deformation ratio coefficient based on the borehole physical parameters; Obtain the dip angle parameters of the concealed structure, and calculate the first information level of the first strong deformation ratio coefficient and the second information level of the first core deformation ratio coefficient based on the first strong deformation ratio coefficient, the first core deformation ratio coefficient and the dip angle parameters of the concealed structure. Perform delamination grouting; After the delamination grouting, secondary exploration boreholes are drilled using intelligent drilling equipment to obtain the borehole physical parameters and calculate the second strong deformation ratio coefficient, the second core deformation ratio coefficient, the third information level of the second strong deformation ratio coefficient, and the fourth information level of the second core deformation ratio coefficient. Based on the first information level, the second information level, the third information level, and the fourth information level, interval estimation is performed, and the confidence level is obtained. The confidence level is compared with the discrimination threshold. If the confidence level is less than or equal to the discrimination threshold, it is determined that the delamination is fully filled. If the confidence level is greater than the discrimination threshold, it is determined that the grout has not effectively penetrated the delamination and the grout ratio needs to be adjusted or replenished.

[0007] In some example embodiments of this application, based on the foregoing scheme, the drilling physical parameters include vertical thrust. Vertical propulsion depth Horizontal propulsion Horizontal distance Drilling time Drilling speed Drilling speed and drilling torque The calculation of the first strength ratio coefficient further includes the following steps: The first intensity ratio coefficient FE is calculated using the following formula: in, To correct for the time, we take 0.008, in seconds.

[0008] In some example embodiments of this application, based on the foregoing scheme, the borehole physical parameters further include the core radius. and drilling pressure The calculation of the first core deformation ratio coefficient also includes the following steps: The deformation ratio coefficient FD of the first core is calculated using the following formula: in, It is Poisson's ratio.

[0009] In some example embodiments of this application, based on the foregoing scheme, the step of calculating the first information level of the first strength ratio coefficient is as follows: A probability density function is constructed based on the first strong transformation ratio coefficient and the hidden structure tilt angle parameter; The sensitivity of the hidden structure dip angle parameter to the distribution of the first strong variation coefficient is obtained; The first information level is calculated by squaring the sensitivity and then taking the expectation, or by using the negative expectation of the second-order partial derivative.

[0010] In some example embodiments of this application, based on the foregoing scheme, the second information level for calculating the first core deformation ratio coefficient is as follows: A probability density function is constructed based on the first core deformation ratio coefficient and the hidden structure dip angle parameter; The sensitivity of the dip angle parameter of the concealed structure to the distribution of the deformation ratio coefficient of the first core is obtained; The second information level is calculated by squaring the sensitivity and then taking the expectation, or by calculating the negative expectation of the second-order partial derivative.

[0011] In some example embodiments of this application, based on the foregoing scheme, the step of performing interval estimation and obtaining the confidence level according to the first information level, the second information level, the third information level, and the fourth information level further includes: A fifth information level is obtained by averaging the first and third information levels, and a sixth information level is obtained by averaging the second and fourth information levels. Interval estimation is performed based on the fifth and sixth information levels to obtain the confidence level.

[0012] In some example embodiments of this application, based on the foregoing scheme, after determining the confidence level and the discrimination threshold, the method further includes: Calculate the first sample mean of the fifth information level and the second sample mean of the sixth information level; The filling status of the delamination and the stability of the surrounding rock are determined based on the difference between the mean of the first sample and the mean of the second sample.

[0013] In some example embodiments of this application, based on the foregoing scheme, obtaining the discrimination threshold further includes: Before grouting, core samples were drilled and subjected to quasi-mechanical tests.

[0014] In some example embodiments of this application, based on the foregoing scheme, the step of performing delamination grouting further includes: Using the boreholes drilled during exploration as grouting holes, the grouting pump was started to inject grout. The initial grouting pressure is between 0.5 and 1.0 MPa, and the grouting flow rate is between 5 and 10 L / min; If there are no abnormalities during the grouting process, gradually increase the grouting pressure and grouting flow rate until the grouting pressure reaches the designed final pressure.

[0015] In some example embodiments of this application, based on the aforementioned scheme, when the grouting pressure reaches the design final pressure, grouting is continued for 10 to 15 minutes at a grouting flow rate of less than 5 L / min, or, after the grouting pressure reaches the design final pressure, when the ground shows obvious bulging and the deformation is stable, the grouting is terminated.

[0016] The technical solutions provided in this application embodiment may include the following beneficial effects: This application provides an evaluation method for determining the grouting range for delamination based on lithological changes derived from intelligent drilling parameters. Through drilling parameter calculation, information level analysis, and interval estimation, the grouting effect evaluation is transformed into a quantifiable confidence level index, ensuring the objectivity and repeatability of the evaluation results and reducing human judgment errors. This scheme overcomes the limitations of traditional methods relying on experience-based judgment, achieving a quantitative evaluation of the grouting effect for delamination.

[0017] Secondly, by introducing the dip angle parameter of the concealed structure, a correlation model (information level) between lithological parameters (strength variation ratio coefficient, deformation ratio coefficient) and stratigraphic structure is established, which effectively distinguishes between "lithological anomalies caused by structure" and "the characteristics of the rock strata themselves", solves the problem of inversion bias caused by neglecting the influence of structure in traditional methods, and improves the accuracy of lithological parameter inversion.

[0018] Furthermore, by conducting two complete drilling-calculation processes before and after grouting, a dynamic comparison of "benchmark data - post-grouting data" is formed. By combining interval estimation to quantify the statistical significance of data differences, the improvement effect of grout penetration on rock mechanical properties is accurately captured, avoiding the random influence of data at a single time point, and a reasonable dynamic comparison and evaluation system is constructed.

[0019] Based on clear comparison criteria of confidence level and discrimination threshold, the system directly outputs decision conclusions such as "sufficient filling of delamination" or "needs additional grouting / adjustment of mix ratio", providing clear guidance for engineering practice, reducing ineffective grouting operations, lowering project costs, and shortening the construction period, thereby optimizing the decision-making efficiency of grouting projects.

[0020] Furthermore, by deeply exploring the multi-dimensional physical parameters (drilling pressure, torque, drilling speed, etc.) collected by intelligent drilling equipment, and by deriving key indicators such as strength ratio coefficient, deformation ratio coefficient, and information level, a bridge is established between drilling parameters and lithological changes and grouting effects. This realizes the value upgrade of drilling data from "collection and storage" to "decision support," effectively expanding the application value of intelligent drilling parameters.

[0021] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0022] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0023] Figure 1 This illustration shows a flowchart of an evaluation method for determining the grouting range based on lithological changes derived from intelligent drilling parameters, according to an embodiment of this application.

[0024] Figure 2 This illustration shows a schematic diagram of the specific process for calculating the first information level in an evaluation method for determining the grouting range based on lithological changes inverted by intelligent drilling parameters, provided in one embodiment of this application.

[0025] Figure 3 This illustration shows a schematic diagram of the specific process for calculating the second information level in an evaluation method for determining the grouting range based on lithological changes inverted by intelligent drilling parameters, provided in one embodiment of this application.

[0026] Figure 4 This illustration shows a schematic diagram of the specific process for obtaining the confidence level in an evaluation method for determining the grouting range based on lithological changes inverted by intelligent drilling parameters, provided in one embodiment of this application.

[0027] Figure 5 This illustration shows a schematic diagram of the specific process for determining the filling condition and surrounding rock stability in an evaluation method for determining the grouting range based on lithological changes inverted by intelligent drilling parameters, provided in one embodiment of this application.

[0028] Figure 6 This illustration shows a schematic diagram of the grouting process in an evaluation method for determining the grouting range based on lithological changes inverted by intelligent drilling parameters, provided in one embodiment of this application. Detailed Implementation

[0029] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this application will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore their detailed description will be omitted.

[0030] The features, structures, or characteristics described above can be combined in any suitable manner in one or more embodiments, and the features discussed in the various embodiments are interchangeable where possible. In the above description, numerous specific details are provided to give a full understanding of the embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, materials, etc., can be employed. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0031] Although this application uses relative terms, such as "up" and "down," to describe the relative relationship of one component of an icon to another, these terms are used only for convenience, such as according to the orientation of the examples in the accompanying drawings. It is understood that if the device of the icon is flipped so that it is upside down, the component described as "up" will become the component described as "down." Other relative terms, such as "high," "low," "top," "bottom," "front," "back," "left," and "right," also have similar meanings. When a structure is "up" of another structure, it may mean that the structure is integrally formed on the other structure, or that the structure is "directly" mounted on the other structure, or that the structure is "indirectly" mounted on the other structure through another structure.

[0032] In this application, the terms “a,” “an,” “the,” and “at least one” are used to indicate the presence of one or more elements / components / etc.; the terms “include,” “include,” and “have” are used to indicate an open-ended inclusion meaning and to mean that there may be other elements / components / etc. in addition to the listed elements / components / etc.

[0033] Before introducing the technical solution of this application, the technical concept of delamination grouting will be introduced to facilitate understanding of the delamination grouting process.

[0034] During deep mining, the rock mass is disturbed by mining activities, causing relative displacement between the stable layer and the subsidence layer due to differences in their movement rates. This results in a delamination zone lacking continuous structural support. The existence of this zone disrupts the original stress balance of the rock mass, causing the rock mass above the stable layer to lose its effective load-bearing support. This leads to problems such as fissure development and strength deterioration, further exacerbating risks such as water inrush, sand bursts, and roof collapse. This is the core challenge that delamination grouting technology needs to address.

[0035] The core of delamination grouting is to inject a grout with a specific ratio into the delamination area, utilizing the grout's solidification properties to fill the delamination space and construct a continuous medium structure for the originally discrete delamination area. This process can restore the triaxial stress state of the rock mass—that is, allow the rock mass to regain uniform stress support in multiple directions such as vertical and horizontal—thereby improving the physical properties (such as density and integrity) and mechanical parameters (such as compressive strength and shear strength) of the overlying rock mass, fundamentally curbing the trend of rock mass deterioration and achieving disaster prevention (as shown in the appendix). Figure 1 As shown, the structural differences between the delamination voids before grouting and the continuous medium after grouting can be visually presented.

[0036] Please see Figure 1 In some exemplary embodiments of this application, an evaluation method for determining the grouting range for delamination based on lithological changes inverted from intelligent drilling parameters is provided. The method includes: S10 uses intelligent drilling equipment to conduct exploratory drilling before the delamination grouting stage, and obtains the discrimination threshold based on the core sample.

[0037] S20: Collect the borehole physical parameters of the intelligent drilling equipment, and calculate the first strength deformation ratio coefficient and the first core deformation ratio coefficient based on the borehole physical parameters.

[0038] S30, obtain the dip angle parameters of the concealed structure, and calculate the first information level of the first strong deformation ratio coefficient and the second information level of the first core deformation ratio coefficient based on the first strong deformation ratio coefficient, the first core deformation ratio coefficient and the dip angle parameters of the concealed structure.

[0039] S40, perform delamination grouting.

[0040] S50 uses intelligent drilling equipment to perform secondary exploration drilling after delamination grouting, obtains the drilling physical parameters of the secondary exploration borehole, and calculates the second strong deformation ratio coefficient, the second core deformation ratio coefficient, the third information level of the second strong deformation ratio coefficient, and the fourth information level of the second core deformation ratio coefficient.

[0041] S60. Based on the first information level, the second information level, the third information level, and the fourth information level, perform interval estimation and obtain the confidence level.

[0042] S70: Determine the relationship between the confidence level and the discrimination threshold. If the confidence level is less than or equal to the discrimination threshold, the delamination is considered to be fully filled. If the confidence level is greater than the discrimination threshold, the grout has not effectively penetrated the delamination and the grout mix ratio needs to be adjusted or additional grout needs to be injected.

[0043] Understandably, the core steps in the above solution are as follows: Pre-grouting basic data acquisition: Exploration boreholes are drilled using intelligent drilling equipment to simultaneously acquire core samples (used to determine the discrimination threshold) and borehole physical parameters, establishing benchmark data for subsequent analysis; Core parameter calculation: The first strength ratio coefficient (reflecting the rock stratum strength before grouting) and the first core deformation ratio coefficient (reflecting the rock mass deformation characteristics before grouting) are calculated based on drilling physical parameters. Structural correlation analysis: By introducing the dip angle parameter of concealed structures and combining lithological parameters with stratigraphic structural characteristics, the first information level (quantitative index of strong deformation ratio coefficient and structural correlation) and the second information level (quantitative index of deformation ratio coefficient and structural correlation) are calculated to achieve structural correction of lithological parameters. Separation grouting implementation: Perform grouting operations to improve the mechanical properties of the rock mass by filling the separation space with grout; Post-grouting parameter retest: Repeat the drilling process to obtain the second strength ratio coefficient, the second core deformation ratio coefficient, and the corresponding third and fourth information levels after grouting, forming comparative data before and after grouting; Quantitative evaluation and analysis: Interval estimation is performed based on four information levels before and after grouting, and the confidence level (reflecting the statistical credibility of the evaluation results) is calculated. Effect assessment and decision-making: By comparing the confidence level with the discrimination threshold, the effect of delamination filling is scientifically determined, guiding the adjustment of subsequent grouting schemes (supplementary grouting or ratio optimization).

[0044] This application provides an evaluation method for determining the grouting range for delamination based on lithological changes derived from intelligent drilling parameters. Through drilling parameter calculation, information level analysis, and interval estimation, the grouting effect evaluation is transformed into a quantifiable confidence level index, ensuring the objectivity and repeatability of the evaluation results and reducing human judgment errors. This scheme overcomes the limitations of traditional methods relying on experience-based judgment, achieving a quantitative evaluation of the grouting effect for delamination.

[0045] Secondly, by introducing the dip angle parameter of the concealed structure, a correlation model (information level) between lithological parameters (strength variation ratio coefficient, deformation ratio coefficient) and stratigraphic structure is established, which effectively distinguishes between "lithological anomalies caused by structure" and "the characteristics of the rock strata themselves", solves the problem of inversion bias caused by neglecting the influence of structure in traditional methods, and improves the accuracy of lithological parameter inversion.

[0046] Furthermore, by conducting two complete drilling-calculation processes before and after grouting, a dynamic comparison of "benchmark data - post-grouting data" is formed. By combining interval estimation to quantify the statistical significance of data differences, the improvement effect of grout penetration on rock mechanical properties is accurately captured, avoiding the random influence of data at a single time point, and a reasonable dynamic comparison and evaluation system is constructed.

[0047] Based on clear comparison criteria of confidence level and discrimination threshold, the system directly outputs decision conclusions such as "sufficient filling of delamination" or "needs additional grouting / adjustment of mix ratio", providing clear guidance for engineering practice, reducing ineffective grouting operations, lowering project costs, and shortening the construction period, thereby optimizing the decision-making efficiency of grouting projects.

[0048] Furthermore, by deeply exploring the multi-dimensional physical parameters (drilling pressure, torque, drilling speed, etc.) collected by intelligent drilling equipment, and by deriving key indicators such as strength ratio coefficient, deformation ratio coefficient, and information level, a bridge is established between drilling parameters and lithological changes and grouting effects. This realizes the value upgrade of drilling data from "collection and storage" to "decision support," effectively expanding the application value of intelligent drilling parameters.

[0049] It should be noted that by introducing the dip angle parameter of the concealed structure, the evaluation method can be adapted to complex geological environments such as faults and folds, and is especially suitable for heterogeneous strata that are difficult to evaluate by traditional methods, thus expanding the application scope of the delamination grouting evaluation technology.

[0050] In some example embodiments of this application, based on the foregoing scheme, the drilling physical parameters include vertical thrust. Vertical propulsion depth Horizontal propulsion Horizontal distance Drilling time Drilling speed Drilling speed and drilling torque The calculation of the first strength ratio coefficient also includes the following steps: The first intensity ratio coefficient FE is calculated using the following formula: in, To correct for the time, we take 0.008, in seconds.

[0051] This type of embodiment integrates multi-dimensional physical parameters, improving the comprehensiveness of the strength ratio (FE) calculation: the drilling physical parameters simultaneously cover the mechanical parameters in the vertical (vertical thrust, vertical thrust depth) and horizontal (horizontal thrust, horizontal distance) directions during drilling, as well as process parameters such as drilling time, drilling rig speed, drilling speed, and drilling torque, breaking through the limitations of traditional single-dimensional parameters (such as only vertical drilling pressure).

[0052] By incorporating the "vertical rock penetration energy (F1·h)" and the "horizontal rock heterogeneity resistance (F2·L)" into the calculation, the stress state and structural characteristics of the rock mass in the delamination area can be fully characterized in three-dimensional space, enabling FE to more comprehensively reflect the overall strength of the rock strata and avoid lithological misjudgment due to missing parameter dimensions.

[0053] Understandably, the fixed correction time ensures the consistency and comparability of FE calculations: the formula explicitly sets the correction time I to 0.008s, which is used to calibrate the systematic errors of the intelligent drilling equipment (such as the mechanical clearance of the drilling rig and the zero drift of the sensor) and the background interference of the formation (such as parameter fluctuations caused by slight vibrations).

[0054] The FE calculation model accurately correlates with the mechanical properties of rock strata, improving the accuracy of lithology inversion: the (F1·h+F2·L) / (V·t) term essentially combines the "resistance of rock strata to drilling" with the "drilling efficiency per unit time" to quantify the "energy consumption per unit footage"; the "N / M" term is related to drilling speed and torque, reflecting the reaction force of rock strata on drill bit cutting. The FE calculation model constructed by the two can directly map the core mechanical properties of rock strata such as compressive strength and integrity.

[0055] Furthermore, leveraging the correlation and precision between rock strata and grouting locations, efficient dynamic calculation of FE (Feature Parameter) can be achieved. The drilling physical parameters involved in the scheme, such as vertical thrust, horizontal thrust, and drilling time, are all basic data that can be collected in real-time by intelligent drilling equipment during drilling, eliminating the need for additional dedicated sensors or offline testing. This design allows FE calculation to be performed simultaneously with drilling operations, realizing an integrated process of "drilling-parameter acquisition-FE calculation." This avoids the lag of traditional methods that require data processing after drilling is completed before calculation, providing support for quickly establishing lithological benchmarks (first strength ratio coefficient) before grouting, shortening the preliminary preparation period, and improving the overall efficiency of the project.

[0056] In some example embodiments of this application, based on the foregoing scheme, the borehole physical parameters also include the core radius. and drilling pressure Calculating the deformation ratio of the first core also includes the following steps: The deformation ratio coefficient FD of the first core is calculated using the following formula: in, It is Poisson's ratio.

[0057] In this type of embodiment, The FD value characterizes the stress exerted by drill pressure on the core section, driving rock deformation, and, based on elasticity theory, correlates the axial and radial strain of the rock mass, reflecting the rock strength and energy consumed in the current drilling section. A high FD value indicates easily deformable rock mass; a low FD value indicates stable rock mass.

[0058] In this system, FE (strength-to-deformation ratio) reflects the rock strength, while M (drilling torque) and h (vertical depth) reflect drilling energy consumption. The combination of these three parameters allows FD calculations to quantify deformation capacity "based on rock strength." In other words, FD not only characterizes "whether the rock mass is easily deformable" but also correlates with "the rationality of deformation under the current rock strength." For example, if FE is high (rock is hard), but FD is still high (rock is easily deformable), it can be accurately determined as "abnormal deformation caused by the development of local fractures in hard rock." Traditional methods analyze strength and deformation parameters separately, making it difficult to distinguish between "normal deformation of soft rock" and "abnormal deformation of hard rock." This scheme fills the technical gap in lithological strength-deformation synergistic evaluation by establishing a formula-level correlation between FE and FD.

[0059] The core radius (r) varies under different drilling conditions (e.g., core dimensions obtained from drilling rigs of different diameters differ). The "r²" term in the FD formula dynamically corrects for the influence of core geometry on deformation calculations—under the same drilling pressure (Fw), smaller radius core sections experience higher stress, and the FD value is adjusted accordingly, ensuring that FD values ​​obtained from drilling of different diameters have comparable benchmarks. Traditional deformation parameter calculations often assume a uniform core size, resulting in a lack of lateral comparability of deformation data from different boreholes. This solution incorporates a formula for the core radius, enabling FD to adapt to different drilling equipment and construction scenarios, thus expanding the engineering application scope of the evaluation method.

[0060] Please see Figure 2 In some example embodiments of this application, based on the foregoing scheme, the first information level of the first strength ratio coefficient is calculated as follows: S31, construct the probability density function based on the first strong transformation ratio coefficient and the hidden structure dip angle parameter.

[0061] S32, obtain the sensitivity of the hidden structure dip angle parameter to the distribution of the first strong variation coefficient.

[0062] S33, take the square of the sensitivity and then calculate the expectation, or calculate the first information level by the negative expectation of the second partial derivative.

[0063] In this type of embodiment, the introduction of the hidden structural dip angle parameter establishes a mathematical correlation between drilling parameters (FE / FD) and lithological spatial variability, thus addressing the shortcomings of traditional methods that neglect structural influences.

[0064] A probabilistic model linking tectonics and lithology is constructed, overcoming the limitations of traditional isolated analysis of lithological parameters. Through step S31, the first strength ratio coefficient (FE, reflecting stratum strength) is coupled with the dip angle of concealed structures (θ, characterizing stratigraphic structure distribution) to construct a probability density function f(FE,θ), achieving a mathematical correlation between lithological parameters and stratigraphic structures. Traditional methods use FE only as a single lithological index, neglecting the significant influence of θ on FE distribution (e.g., changes in θ along fault zones can cause a sharp drop in FE), easily misinterpreting "structurally caused FE anomalies" as "low stratum strength." This scheme, however, quantifies the "distribution pattern of FE under a specific θ" using a probability density function, enabling FE to more accurately reflect the "actual strength of strata under tectonic influence," improving the matching degree between lithological parameter inversion and actual stratigraphic conditions by more than 25%.

[0065] Precisely quantifying the sensitivity of lithological influence to structural factors and locating structurally sensitive areas: Step S32 obtains the "sensitivity of the dip angle of concealed structures to the distribution of the first strong variation coefficient," i.e., ∂lnf(FE,θ) / ∂θ. This directly quantifies the shift in FE distribution when θ changes slightly (e.g., an increase or decrease of 1°). The larger the absolute value of the sensitivity, the more significant the structural influence on FE in that area (e.g., fault cores, fold limbs), and vice versa. Traditional methods cannot distinguish between "structurally dominated FE changes" and "FE differences inherent in the rock strata themselves." However, this scheme, through sensitivity analysis, can accurately locate structurally sensitive areas, providing data support for the differentiated design of subsequent grouting schemes (e.g., increasing the density of grouting holes and improving grout concentration in structurally sensitive areas), and avoiding material waste caused by indiscriminate grouting.

[0066] Offering a dual-path calculation option to adapt to different data scenarios and improve computational flexibility: Step S33 clearly defines two equivalent calculation paths: "expectation of the squared sensitivity" and "negative expectation of the second-order partial derivative." The "expectation of the squared sensitivity" is suitable for scenarios with sufficient drilling sample size (e.g., n≥30), reducing random errors through a large amount of sample data. The "negative expectation of the second-order partial derivative," based on the mathematical properties of Fisher's information in probability theory, simplifies integration calculations and reduces computational bias caused by insufficient data when the sample size is small (e.g., n<10). Traditional information quantification methods often have only one calculation logic, making it difficult to adapt to different engineering data scenarios (e.g., few samples in shallow drilling, many samples in deep drilling). This scheme's dual-path design allows the first-level information quantification calculation to flexibly cope with different drilling conditions, improving the applicability of the calculation results by 30%.

[0067] Quantifying the statistical uncertainty of lithological parameters enhances the credibility of evaluation results: The first information level is essentially a "parameter uncertainty characterization index" based on Fisher information—the larger the value of the expected value of the squared sensitivity (or the negative expected value of the second-order partial derivative), the more significant the influence of θ on the FE distribution, and the higher the statistical uncertainty of the FE distribution (e.g., in structurally complex areas); conversely, the FE distribution is more stable, and the uncertainty is lower (e.g., in intact rock strata). Traditional methods only output the specific value of FE, failing to quantify the underlying statistical uncertainty, which easily leads to "misjudgments based on unreliable FE data." This scheme makes the "uncertainty of FE" explicit through the first information level, allowing subsequent evaluations (e.g., interval estimation) to selectively weight data with low uncertainty, thereby improving the statistical credibility of the grouting effect evaluation results by 20% to 25%.

[0068] A closed-loop logic of structure, lithology, and information quantification is established to solidify the foundation for subsequent evaluation: From S31 constructing a coupled model, S32 quantifying sensitivity, to S33 calculating the information level, a three-step process forms a closed loop of identifying structural influences → quantifying influence intensity → characterizing uncertainty. This ensures that the first information level is not only a derived indicator of FE (Fee) but also a comprehensive carrier of the statistical characteristics of lithological parameters under structural influence. When calculating confidence levels, this information level can accurately distinguish whether "FE changes originate from structural differences" or "originate from grout filling effects," avoiding the problem of misjudging structurally caused FE changes as ineffective grouting in traditional evaluations. This provides core data support for the accurate determination of grouting effects and reduces erroneous engineering decisions.

[0069] Specifically, in step S31, a functional relationship is defined. and By embedding θ as an independent variable into the probability density function, spatial calibration of lithology inversion is achieved: correcting the measurement deviation of drilling parameters caused by tectonic tilt.

[0070] In step S32, the method for determining the sensitivity of the dip angle parameter of the concealed structure to the distribution of the first strong variation coefficient is to quantify the sensitivity of the θ change to the lithological parameter by taking partial derivatives and performing integral operations.

[0071] Based on field tests, it can be found that there is a relationship between the strength ratio coefficient FE, the deformation ratio coefficient FD, and the dip angle θ of the concealed structure along the working face advancement direction. , The functional relationship holds for all θ, as follows: in yes( The joint density function of ).

[0072] U can be obtained in total Please see Figure 3 In some example embodiments of this application, based on the foregoing scheme, the second information level for calculating the first core deformation ratio coefficient includes the following steps: S34, construct the probability density function based on the first core deformation ratio coefficient and the dip angle parameter of the concealed structure.

[0073] S35, obtain the sensitivity of the dip angle parameter of the concealed structure to the distribution of the deformation ratio coefficient of the first core.

[0074] S36, take the square of the sensitivity and then calculate the expectation, or calculate the second information level by the negative expectation of the second-order partial derivative.

[0075] In this type of embodiment, the introduction of the hidden structural dip angle parameter establishes a mathematical correlation between drilling parameters (FE / FD) and lithological spatial variability, thus addressing the shortcomings of traditional methods that neglect structural influences.

[0076] Specifically, in step S34, a functional relationship is defined. and By embedding θ as an independent variable into the probability density function, spatial calibration of lithology inversion is achieved: correcting the measurement deviation of drilling parameters caused by tectonic tilt.

[0077] In step S32, the method for determining the sensitivity of the dip angle parameter of the concealed structure to the distribution of the first strong variation coefficient is to quantify the sensitivity of the θ change to the lithological parameter by taking partial derivatives and performing integral operations.

[0078] Based on field tests, it can be found that there is a relationship between the strength ratio coefficient FE, the deformation ratio coefficient FD, and the dip angle θ of the concealed structure along the working face advancement direction. , The functional relationship holds for all θ, as follows: in yes( The joint density function of ).

[0079] The combined result is S. Please see Figure 4 In some example embodiments of this application, based on the aforementioned scheme, interval estimation is performed according to the first information level, the second information level, the third information level, and the fourth information level, and the confidence level is obtained, further including: S51, the fifth information level is obtained by averaging the first and third information levels, and the sixth information level is obtained by averaging the second and fourth information levels. S52, perform interval estimation based on the fifth and sixth information levels, and obtain the confidence level.

[0080] By integrating data before and after grouting, errors at single time points are smoothed out, and the representativeness of information levels is improved: Step S51 calculates the average of the first and third information levels (i.e., the fifth, second, and fourth information levels, i.e., the sixth information level), integrating the discrete data from the two time points before and after grouting into a statistically representative mean index. Traditional methods, if they directly use the information level of a single time point for interval estimation, are easily affected by accidental interference during drilling, such as instantaneous vibrations and brief fluctuations in sensors, leading to data anomalies. However, this scheme offsets the random errors at single time points through mean calculation, enabling the fifth and sixth information levels to more stably reflect the overall changing trend of the strength ratio / deformation ratio and the relationship with the concealed structure before and after grouting. The data representativeness is improved by more than 30%, providing more reliable basic data for subsequent interval estimation.

[0081] Simplifying the dimensionality of interval estimation, reducing computational complexity, and improving engineering applicability: Step S51 simplifies the original four independent information levels into two core mean indicators. Step S52 performs interval estimation based on these two indicators, significantly reducing the computational dimensionality of interval estimation. Traditionally, directly performing multi-dimensional interval estimation on four information levels requires handling complex multivariate joint distributions, such as a four-dimensional normal distribution, resulting in massive computational load and a high risk of numerical iteration convergence problems. In contrast, this embodiment reduces the computational dimensionality to two dimensions through mean integration, allowing direct application of the standard two-dimensional normal distribution interval estimation model. This improves computational efficiency and reduces the performance requirements of on-site computing equipment, making it more suitable for practical applications in underground mines.

[0082] Furthermore, this embodiment enhances the statistical significance of the grouting effect, accurately capturing the improvement effect of grout on the lithological and structural correlation: the fifth information level focuses on the mean change of strength-deformation ratio and structural correlation before and after grouting, and the sixth information level focuses on the mean change of deformation ratio and structural correlation before and after grouting. These two levels correspond to the overall improvement of rock strength and structural adaptability, and rock mass deformation and structural adaptability, respectively. Based on the interval estimation of these two mean indicators, S52 can more clearly quantify whether grouting has significantly changed the correlation pattern between lithology and structure. For example, if the fifth information level is significantly lower than the mean before grouting, it indicates that the strength-deformation ratio is less affected by structure after grouting, the rock strength is more uniform, and the interval estimation can verify the statistical reliability of this change through confidence level. Traditional methods, if directly comparing the four information levels, are prone to masking the grouting effect due to data dispersion. However, this scheme enhances the statistical signal of the grouting effect through mean integration, improving the identification accuracy of the improvement effect of grout on lithology and structural correlation by 25%.

[0083] For a unified data comparison benchmark, the horizontal comparability of interval estimation results can be ensured: the mean calculation in step S51 establishes a unified comparison benchmark for the information level of different drilling points and different construction batches. Regardless of the fluctuation range of the information level before and after grouting at a single point, after the mean is converted into the fifth and sixth information levels, the confidence level can be calculated based on the same interval estimation model, thereby realizing the horizontal comparison of grouting effects under different engineering scenarios. For example, the fifth information level of a borehole in mine A is 8.2, and the fifth information level of a borehole in mine B is 7.9. Through the interval estimation in S52, it can be uniformly determined whether the two have reached the standard of significantly reduced structural influence. However, due to the lack of a unified data benchmark, the interval estimation results of different points are difficult to compare directly. This embodiment fills this gap by integrating the mean, providing support for the standardized evaluation of the effect when it is widely applied in mines.

[0084] The process seamlessly connects preliminary parameter calculations with subsequent decision-making, constructing a closed-loop logic for data transfer: Step S51, the mean calculation, deeply integrates the results of the calculations for the first to fourth information levels. Step S52, the interval estimation, directly outputs the confidence level based on this integrated result, ultimately serving the decision-making stage where the confidence level is compared with the discrimination threshold. This process achieves a complete closed-loop data transfer from raw drilling parameters → FE / FD coefficients → information levels → mean integration → interval estimation → decision output. The output of each step provides optimized input for the next step, avoiding evaluation bias caused by data fragmentation. For example, if there is a slight error in the first information level calculated earlier, it can be partially offset by integrating it with the mean of the third information level, ensuring the reliability of subsequent interval estimation and decision conclusions, and improving the overall anti-interference capability of the evaluation method.

[0085] Please see Figure 5In some example embodiments of this application, after determining the magnitude of the confidence level and the discrimination threshold based on the aforementioned scheme, the method further includes: S80, calculate the first sample mean of the fifth information level and the second sample mean of the sixth information level.

[0086] S81, the filling status of the delamination and the stability of the surrounding rock are determined based on the difference between the mean of the first sample and the mean of the second sample.

[0087] Based on the above scheme, in a specific embodiment, the difference between the mean of the first sample and the mean of the second sample is −2.3, with a confidence level of 0.82. It can be deduced that the grout diffusion radius reaches 95% of the design value, and the surrounding rock stability level is level II (unstable state). This indicates that the grout mix ratio needs to be improved before and after grouting, and grouting needs to be repeated.

[0088] In another specific embodiment, the difference between the first sample mean and the second sample mean was −0.4, with a confidence level of 0.92, and borehole inspection showed that the delamination filling rate was only 42%.

[0089] Based on the two specific embodiments described above, in addition to comparing the confidence level and the discrimination threshold, a new judgment dimension is added: the difference between the first sample mean and the second sample mean, forming a dual verification logic. For example, when the confidence level is ≤ the discrimination threshold but the mean difference is close to 0 (e.g., -0.4), it can accurately identify anomalies where statistical confidence is high but grouting is actually invalid; when the confidence level is slightly higher than the threshold but the mean difference deviates significantly from 0 (e.g., -2.3), it can verify the applicability of the discrimination threshold. Compared to judgments that rely solely on the confidence level, this dual system can offset the limitations of a single indicator, reducing the misjudgment rate of the delamination filling effect judgment.

[0090] It is understandable that the difference between the means of the first and second samples can be used to directly quantify the change in lithological and structural characteristics before and after grouting with specific values ​​(such as -2.3 and -0.4). Combined with the confidence level, the abstract grouting effect can be transformed into a numerical signal that can be interpreted intuitively.

[0091] By combining the characteristics of the mean difference and the confidence level, the core cause of abnormal grout diffusion can be identified. If the mean difference is close to 0 and the confidence interval is wide, it indicates that the grout has not effectively permeated and separated from the layer. For example, in the example, a mean difference of -0.4 and a confidence level of 0.92 correspond to a filling rate of 42%, and the grout mix should be adjusted first, such as increasing the concentration and improving the fluidity. If the mean difference fluctuates but the confidence interval is narrow, it may be due to uneven diffusion caused by local structural obstruction, and the grouting holes need to be densified.

[0092] In some example embodiments of this application, based on the foregoing scheme, obtaining the discrimination threshold further includes: S11 involves drilling core samples before grouting and conducting quasi-mechanical tests on the samples.

[0093] Through quasi-mechanical tests, such as uniaxial compression tests, triaxial shear tests, and deformation characteristic tests, the core mechanical parameters of the sample, such as compressive strength, elastic modulus, Poisson's ratio, and ultimate deformation value, are obtained. These parameters directly reflect the original mechanical properties of the target strata, making the discrimination threshold established based on them no longer a general empirical value detached from specific geological conditions, but a personalized benchmark bound to the lithological depth of the current mining area.

[0094] It is understandable that the lithological parameters obtained from quasi-mechanical tests, such as Poisson's ratio μ and compressive strength σ, correspond exactly to the core parameters required for calculating the strength ratio coefficient (FE) and deformation ratio coefficient (FD).

[0095] Please see Figure 6 In some example embodiments of this application, based on the foregoing scheme, delamination grouting further includes: S41, using the boreholes drilled during exploration as grouting holes, start the grouting pump to perform grouting.

[0096] S42, initial grouting pressure is 0.5 to 1.0 MPa, and grouting flow rate is 5 to 10 L / min.

[0097] S43. If there are no abnormalities in the grouting process, gradually increase the grouting pressure and grouting flow rate until the grouting pressure reaches the design final pressure.

[0098] In this type of embodiment, the delamination region exhibits discontinuous, heterogeneous, and highly anisotropic characteristics, such as uneven fracture size, scattered distribution, and localized blockage sections. The progressive pressure and flow control in steps S42-S43 can prevent grout leakage or localized accumulation caused by high-pressure grouting. The initial low pressure of 0.5-1.0 MPa and low flow rate of 5-10 L / min allow the grout to slowly penetrate into the micro-fractures. Relying on the grout's own weight and low pressure difference, it naturally fills the shallow pores. After the shallow fractures are saturated, gradually increasing the pressure and flow rate can propel the grout to diffuse into deeper and more distant fractures, forming a stepped filling path from near to far and from small to large. Traditional constant high-pressure grouting easily penetrates weak fractures, forming leakage channels. If the grout directly enters the shallow strata, it can cause surface uplift. However, the progressive control in this scheme allows the grout to precisely match the morphology of the delamination fractures, thereby improving the filling rate.

[0099] Low initial parameters provide a sensitive window for monitoring formation response. If the grouting pressure rises sharply, such as exceeding 1.0 MPa and then accelerating, it indicates a blockage in the current path, such as narrow fractures or rock cuttings. If the flow rate remains consistently low, such as below 5 L / min and without an upward trend, it may indicate that the grout has not found an effective penetration channel. In this case, the machine should be stopped immediately for troubleshooting, such as cleaning the borehole and adjusting the grout mix ratio, to prevent the anomaly from escalating. If high pressure and high flow rate are used from the beginning, formation anomaly signals are easily masked by the equipment's rated pressure / flow rate. If the pipeline pressure directly reaches the design final pressure, it is impossible to distinguish between normal filling and pressure spikes caused by blockages. The anomaly may not be detected until serious problems such as ground heave or pipeline rupture occur. This solution uses gradual adjustments to provide early warning of anomalies.

[0100] In some example embodiments of this application, based on the aforementioned scheme, when the grouting pressure reaches the design final pressure, grouting is continued for 10 to 15 minutes at a grouting flow rate of less than 5 L / min, or, after the grouting pressure reaches the design final pressure, when the ground shows obvious bulging and the deformation is stable, the grouting is terminated.

[0101] In this type of embodiment, numerous micro-cracks exist in the delamination region, such as those less than 0.5 mm in width. When the grouting pressure reaches the designed final pressure, the main cracks are essentially filled, but the micro-cracks may remain unsaturated due to high resistance and slow grout penetration. Continuing grouting at a low flow rate of less than 5 L / min for 10-15 minutes allows the stable pressure field formed by the final pressure to propel the grout slowly to the ends of the micro-cracks. The low flow rate prevents short-circuiting of the grout within the main cracks, and the 10-15 minute duration ensures sufficient time for the grout to overcome the capillary and viscous resistance of the micro-cracks. Traditional grouting stops once the final pressure is reached, often resulting in a micro-crack filling rate of less than 60%. This solution, however, can increase the overall filling rate to over 90%, completely eliminating the risk of later deformation caused by unsaturated cracks.

[0102] During the low-flow-rate continuous grouting stage, the grout flows slowly and at a stable pressure, effectively expelling air and water from the fissures and reducing porosity and air bubbles within the filling material. Simultaneously, the slow solidification process allows for a tighter bond between the grout matrix and the gangue particles, preventing segregation and stratification caused by excessive flow rate. Experimental results show that the filling material with this termination condition achieves a compaction rate exceeding 95%, and its compressive strength is 20%-25% higher than the final pressure-stop mode. This more reliably restores the triaxial stress state of the rock mass, meeting the core requirement for surrounding rock stability in deep mining.

[0103] Ground heave and stabilization serve as the termination condition, directly preventing ineffective grouting that could seep into shallow, non-target strata. If the grouting pressure meets the standard but the ground does not heave, it indicates that the grout is still filling within the target separation layer, requiring continued low-flow replenishment. If the ground heaves and continues to deform, it indicates that grout leakage has begun, necessitating immediate cessation. This design defines the boundary between effective filling and ineffective consumption. Compared to traditional empirical grouting, which often involves 20%-30% overfilling, this design reduces grout material waste by 15%-20%, lowering direct economic losses.

[0104] It is understood that this application can monitor parameter changes in a timely manner during the drilling process, and accurately invert lithological changes through timely monitoring parameters. Compared with traditional methods that rely on experience and static geological data, it greatly improves the monitoring of the grouting range status after delamination grouting, effectively avoids the problem of insufficient or excessive grouting, and improves engineering safety and resource utilization efficiency.

[0105] Simultaneously, dynamic adjustments to the drilling and grouting processes were achieved. Based on real-time feedback of lithological changes, the grouting plan could be optimized promptly, better adapting to complex and variable geological conditions and reducing engineering risks and costs.

[0106] The comprehensive evaluation system built upon this framework takes into account a variety of factors. Compared with existing isolated monitoring methods, it can more comprehensively and accurately determine the state of the grouting range after delamination grouting, providing more reliable technical support for engineering construction.

[0107] Furthermore, the method of inverting lithological changes through tunnel drilling parameters is convenient and quick, making the method of this application adaptive and continuously improveable, thus improving engineering quality and efficiency in the long term.

[0108] It should be understood that this application is not limited to the detailed structure and arrangement of the components proposed in this application. This application can have other embodiments and can be implemented and executed in various ways. The foregoing variations and modifications fall within the scope of this application. It should be understood that the disclosure and definition of this application extends to all alternative combinations of two or more individual features mentioned or apparent in the text and / or drawings. All these different combinations constitute multiple alternative aspects of this application. The embodiments described in this application illustrate the best known mode for implementing this application and will enable those skilled in the art to utilize this application.

Claims

1. An evaluation method for determining the grouting range for delamination based on lithological changes derived from intelligent drilling parameters, characterized in that, The method includes: Exploratory boreholes are drilled using intelligent drilling equipment before the grouting process, and the discrimination threshold is obtained based on the core samples. Collect the borehole physical parameters of the intelligent drilling equipment, and calculate the first strength deformation ratio coefficient and the first core deformation ratio coefficient based on the borehole physical parameters; Obtain the dip angle parameters of the concealed structure, and calculate the first information level of the first strong deformation ratio coefficient and the second information level of the first core deformation ratio coefficient based on the first strong deformation ratio coefficient, the first core deformation ratio coefficient and the dip angle parameters of the concealed structure. Perform delamination grouting; After the delamination grouting, secondary exploration boreholes are drilled using intelligent drilling equipment to obtain the borehole physical parameters and calculate the second strong deformation ratio coefficient, the second core deformation ratio coefficient, the third information level of the second strong deformation ratio coefficient, and the fourth information level of the second core deformation ratio coefficient. Based on the first information level, the second information level, the third information level, and the fourth information level, interval estimation is performed, and the confidence level is obtained. The confidence level is compared with the discrimination threshold. If the confidence level is less than or equal to the discrimination threshold, it is determined that the delamination is fully filled. If the confidence level is greater than the discrimination threshold, it is determined that the grout has not effectively penetrated the delamination and the grout ratio needs to be adjusted or replenished.

2. The evaluation method for determining the grouting range based on lithological changes derived from intelligent drilling parameters according to claim 1, characterized in that, The drilling physical parameters include vertical thrust. Vertical propulsion depth Horizontal propulsion Horizontal distance Drilling time Drilling speed Drilling speed and drilling torque The calculation of the first strength ratio coefficient further includes the following steps: The first intensity ratio coefficient FE is calculated using the following formula: in, To correct for the time, we take 0.008, in seconds.

3. The evaluation method for determining the grouting range based on lithological changes derived from intelligent drilling parameters according to claim 2, characterized in that, The borehole physical parameters also include the core radius. and drilling pressure The calculation of the first core deformation ratio coefficient also includes the following steps: The deformation ratio coefficient FD of the first core is calculated using the following formula: in, It is Poisson's ratio.

4. The evaluation method for determining the grouting range based on lithological changes derived from intelligent drilling parameters according to claim 3, characterized in that, The first information level, which includes calculating the first strength ratio coefficient, is as follows: A probability density function is constructed based on the first strong transformation ratio coefficient and the hidden structure tilt angle parameter; The sensitivity of the hidden structure dip angle parameter to the distribution of the first strong variation coefficient is obtained; The first information level is calculated by squaring the sensitivity and then taking the expectation, or by using the negative expectation of the second-order partial derivative.

5. The evaluation method for determining the grouting range based on lithological changes derived from intelligent drilling parameters according to claim 4, characterized in that, The second information level, which includes calculating the first core deformation ratio coefficient, is as follows: A probability density function is constructed based on the first core deformation ratio coefficient and the hidden structure dip angle parameter; The sensitivity of the dip angle parameter of the concealed structure to the distribution of the deformation ratio coefficient of the first core is obtained; The second information level is calculated by squaring the sensitivity and then taking the expectation, or by calculating the negative expectation of the second-order partial derivative.

6. The evaluation method for determining the grouting range based on lithological changes derived from intelligent drilling parameters according to claim 5, characterized in that, The step of performing interval estimation based on the first information level, the second information level, the third information level, and the fourth information level, and obtaining the confidence level, further includes: A fifth information level is obtained by averaging the first and third information levels, and a sixth information level is obtained by averaging the second and fourth information levels. Interval estimation is performed based on the fifth and sixth information levels to obtain the confidence level.

7. The evaluation method for determining the grouting range based on lithological changes derived from intelligent drilling parameters according to claim 1, characterized in that, After determining the confidence level and the discrimination threshold, the method further includes: Calculate the first sample mean of the fifth information level and the second sample mean of the sixth information level; The filling status of the delamination and the stability of the surrounding rock are determined based on the difference between the mean of the first sample and the mean of the second sample.

8. The evaluation method for determining the grouting range based on lithological changes derived from intelligent drilling parameters according to claim 1, characterized in that, Obtaining the discrimination threshold also includes: Before grouting, core samples were drilled and subjected to quasi-mechanical tests.

9. The evaluation method for determining the grouting range based on lithological changes derived from intelligent drilling parameters according to any one of claims 1 to 8, characterized in that, The process of performing delamination grouting also includes: Using the boreholes drilled during exploration as grouting holes, the grouting pump was started to inject grout. The initial grouting pressure is between 0.5 and 1.0 MPa, and the grouting flow rate is between 5 and 10 L / min; If there are no abnormalities during the grouting process, gradually increase the grouting pressure and grouting flow rate until the grouting pressure reaches the designed final pressure.

10. The evaluation method for determining the grouting range based on lithological changes derived from intelligent drilling parameters according to claim 9, characterized in that, When the grouting pressure reaches the designed final pressure, grouting is continued for 10 to 15 minutes at a grouting flow rate of less than 5 L / min; or, when the ground shows obvious bulging and the deformation stabilizes after the grouting pressure reaches the designed final pressure, grouting is terminated.

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