Grouting effect quantitative evaluation method for grouting material

Abstract

This invention relates to a quantitative evaluation method for the grouting effect of grouting materials. The method involves injecting various grouting materials with different flowabilities into a vertical hollow grouting vessel containing uniformly packed aggregate, recording the injection depth data for different injection durations to obtain an injection depth-injection duration curve, and obtaining the overall internal porosity after the grouting material reaches the bottom and stabilizes. Based on limited physical experimental data, a grouting depth guidance function is constructed using flowability and injection duration as independent variables, and a first porosity guidance function is constructed using flowability as an independent variable. This invention utilizes a mathematical model to overcome the reliance on traditional exhaustive physical experiments, significantly shortening the evaluation cycle and cost of grouting effect assessment. Theoretically, it can quantify and deduce the dynamic diffusion depth and static compaction effect of grouting materials with arbitrary flowability, providing a comprehensive, scientific, and efficient evaluation that avoids missing the optimal grouting material.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of civil engineering material testing technology, specifically to a method for quantitatively evaluating the grouting effect of grouting materials. Background Technology

[0002] Grouting technology is a key process in geotechnical engineering, water conservancy and hydropower, transportation infrastructure, and building structure reinforcement. Its core purpose is to improve the integrity, load-bearing capacity, and impermeability of a structure by injecting grout into the strata, cracks, or voids in precast components to fill pores and cement loose media. The quality of grouting directly determines the safety and durability of the project, especially in concealed projects such as dam seepage prevention curtains, post-grouting of pile foundations, tunnel surrounding rock reinforcement, and prestressed duct grouting, where grout density is the lifeline of quality control.

[0003] Therefore, before grouting, it is necessary to conduct a pre-evaluation of the grouting effect of grouting materials with different grouting parameters. This involves injecting grouting materials with different grouting parameters into the grouting environment and using appropriate detection methods (such as core sampling, sonic logging, ground-penetrating radar, etc.) to identify the pore-filling effect of the grouting environment after the injection of grouting materials. This will allow us to obtain the grouting effect of grouting materials with different grouting parameters. Finally, based on the grouting effect and application cost of each grouting material, the optimal grouting material will be determined for subsequent grouting work.

[0004] However, the aforementioned method for pre-evaluating grouting effects requires a complete "material preparation-simulated grouting-effect evaluation" experimental process for each grout with specific grout parameters (such as different flowability). Therefore: First, a large number of experiments are needed to effectively evaluate the grouting effects of various grouts with different parameters; second, even with extensive experiments, it is still impossible to exhaustively obtain the grouting effects of all possible grouts with different parameters, potentially leading to the selection of a grout that is not actually optimal. Thus, this evaluation method is time-consuming, cumbersome, and prone to missing the optimal grout.

[0005] Therefore, there is an urgent need for a method that can evaluate the grouting effect of all possible grouting materials with different grouting parameters, without relying on traditional one-by-one physical tests. Summary of the Invention

[0006] This specification provides a quantitative evaluation method for the grouting effect of grouting materials, which can evaluate the grouting effect of all possible grouting materials with different grouting parameters without relying on traditional one-by-one physical tests.

[0007] The technical solution is as follows:

[0008] Firstly, the embodiments of this specification provide a method for quantitatively evaluating the grouting effect of grouting materials, including:

[0009] Obtain various grouting materials with different fluidity, and obtain multiple vertical hollow grouting vessels in the same quantity as the types of grouting materials. The top of the vertical hollow grouting vessel is open, and the bottom of the vertical hollow grouting vessel has a bottom plate with several through holes.

[0010] Aggregate was stacked in each vertical hollow filling vessel to form multiple stacked specimens with the same or similar internal porosity, and the overall internal porosity of each stacked specimen before filling was obtained.

[0011] Multiple grouting materials with different fluidity were injected into multiple slab specimens, and the injection depth data of each grouting material in the slab specimens at multiple injection times were recorded. Thus, injection depth-injection time curves for each of the multiple grouting materials were obtained.

[0012] For each slab specimen, several through holes on its bottom plate were sealed when the grout was poured to its bottom position, and the overall porosity of the interior was obtained after the grout stabilized inside.

[0013] Based on the flowability and injection depth-injection time curves of each grouting material, a grouting depth guidance function is obtained, which takes the flowability parameter and injection time parameter as independent variables and the injection depth parameter as dependent variable.

[0014] Based on the flowability of each grout and the overall porosity before and after grouting of each slab specimen, a first porosity guiding function is obtained, with the flowability parameter as the independent variable and the overall pore filling rate parameter as the dependent variable.

[0015] As a preferred option, the vertical hollow filling vessel is transparent, and the quantitative evaluation method uses X-ray computed tomography to scan the accumulator specimen to obtain the overall internal porosity of the accumulator specimen before filling and the overall internal porosity after filling.

[0016] As a preferred embodiment, the injection depth data includes the injection depth of the grout on the sidewalls of the accumulator specimen at multiple different azimuth angles.

[0017] Obtain the grouting depth-grouting time curve, including:

[0018] Based on the injection depth data of the grout in the slab specimen at multiple injection times, the average injection depth of the grout in the slab specimen at multiple injection times is obtained, and then the injection depth-injection time curve of the grout is obtained.

[0019] As a preferred embodiment, based on the injection depth data of the grout in the slab specimen at multiple injection durations, the average injection depth of the grout in the slab specimen at multiple injection durations is obtained, thereby obtaining the injection depth-injection duration curve of the grout, including:

[0020] Outlier infusion depths in multiple infusion depth datasets were screened out using the IQR interquartile range method to obtain multiple infusion depth datasets after screening.

[0021] Based on the grouting depth data after screening in the slab specimen at multiple grouting times, the average grouting depth in the slab specimen at multiple grouting times is obtained, and then the grouting depth-grouting time curve is obtained.

[0022] As a preferred approach, the outlier injection depth screening operation for each injection depth data includes:

[0023] The lower and upper quartiles were determined by using the IQR interquartile range method and the injection depth data, which included the injection depths at multiple different azimuth angles on the sidewalls.

[0024] The interquartile range is obtained based on the lower and upper quartiles.

[0025] Based on the interquartile range, lower quartile, and upper quartile, determine the lower and upper outlier limits.

[0026] Based on the lower and upper outlier thresholds, outlier infusion depths are filtered out from the infusion depth data at different azimuth angles on the sidewalls.

[0027] As a preferred embodiment, the step of obtaining a grout injection depth guidance function based on the flowability and injection depth-injection time curves corresponding to each grout material, with flowability and injection time parameters as independent variables and injection depth parameter as dependent variable, includes:

[0028] For each infusion depth-infusion duration curve, several sampling points are determined on the curve, and the infusion depth and infusion duration corresponding to each sampling point are obtained.

[0029] Based on the flowability of each grout and the injection depth and injection time of each sampling point, a grout injection depth guidance function is obtained, which takes the flowability parameter and injection time parameter as independent variables and the injection depth parameter as dependent variable.

[0030] As a preferred approach, the more sampling points are determined on the portion of the curve with greater curvature in the infusion depth-infusion duration curve.

[0031] As a preferred embodiment, the step of obtaining a first porosity guiding function based on the flowability of each grout material and the pre-grouting and post-grouting overall internal porosity of each aggregate specimen, with the flowability parameter as the independent variable and the overall pore filling rate parameter as the dependent variable, includes:

[0032] Based on the overall internal porosity before and after grouting of each pile specimen, the overall pore filling rate of each pile specimen is obtained.

[0033] Based on the flowability of each grout and the overall pore filling rate of each slab specimen, a first porosity guiding function is obtained, with the flowability parameter as the independent variable and the overall pore filling rate parameter as the dependent variable.

[0034] As a preferred approach, the pre-pouring internal layered porosity of each of the multiple stacked specimens at different internal depths is obtained.

[0035] For each slab specimen, the internal layer porosity corresponding to different depths after grouting was obtained after the grout material stabilized inside.

[0036] Based on the flowability of each grout and the internal layered porosity at different depths of multiple slab specimens before and after grouting, a second porosity guiding function is obtained, which uses flowability and depth parameters as independent variables and layered pore filling rate as dependent variable.

[0037] As a preferred embodiment, the step of obtaining a second porosity guidance function based on the flowability of each grout material and the pre-grouting and post-grouting internal layered porosity at different depths of multiple slab specimens includes:

[0038] Based on the internal layered porosity before and after grouting of each of the multiple stacked specimens at different depths, the layered pore filling rate of each of the multiple stacked specimens at different depths is obtained.

[0039] Based on the flowability of each grout and the layered pore filling rate at different depths within each of the multiple slab specimens, a second porosity guidance function is obtained, with flowability and depth parameters as independent variables and layered pore filling rate as dependent variable.

[0040] Secondly, embodiments of this specification provide an electronic device, including a processor and a memory; the processor is connected to the memory; the memory is used to store executable program code; the processor reads the executable program code stored in the memory to run a program corresponding to the executable program code, so as to perform the steps described in the first aspect of the above embodiments.

[0041] Thirdly, embodiments of this specification provide a computer storage medium storing a plurality of instructions adapted for loading by a processor and executing the steps described in the first aspect of the above embodiments.

[0042] The beneficial effects of the technical solutions provided in some embodiments of this specification include at least the following:

[0043] By constructing a mathematical guidance function using a small amount of experimental data, the grouting effect of grout with arbitrary fluidity can be calculated. This eliminates the reliance on traditional "exhaustive" physical tests and greatly shortens the evaluation cycle and cost of grout before grouting.

[0044] This method overcomes the limitation of traditional physical experiments that cannot exhaustively cover all different grouting materials. By using mathematical guidance functions, the grouting effect of each possible grouting material can be theoretically quantified. This can ensure that the final selected grouting material is the optimal choice. (Note: The corresponding grouting material parameters can be substituted into the mathematical guidance function. If the grouting effect data obtained by the mathematical guidance function is better, the grouting material with the specified parameters can be actually prepared and real physical experiments can be conducted. Finally, based on the grouting effect data obtained through real physical experiments and the application cost of the grouting material, it can be determined whether to select the grouting material with the specified parameters for subsequent grouting.)

[0045] It can not only predict the injection depth under different injection times through the "injection depth guidance function", but also predict the final static pore filling effect through the "first porosity guidance function", making the evaluation dimensions of the grouting effect more comprehensive. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 A flowchart illustrating a method for quantitatively evaluating the grouting effect of grouting material according to some embodiments of this disclosure is shown.

[0048] Figure 2 The diagram shows the corresponding injection depths for different injection times after the grout is injected into the slab specimen.

[0049] Figure 3 A schematic diagram of the cross-sectional interconnected pore distribution of the pre-grouting accumulator specimen at different heights is shown.

[0050] Figure 4 The diagram shows the distribution of residual pores in the cross-section of the grouting specimen at different heights.

[0051] Figure 5 A schematic block diagram of an electronic device according to some embodiments of the present disclosure is shown. Detailed Implementation

[0052] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings.

[0053] The terms "first," "second," "third," etc., in the description, claims, and accompanying drawings are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0054] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the described elements without departing from the scope of this specification. Various processes or components may be appropriately omitted, substituted, or added to the examples. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.

[0055] Figure 1 A flowchart illustrating a method for quantitatively evaluating the grouting effect of grouting material, according to some embodiments of this disclosure, is shown. Figure 1 As shown, a quantitative evaluation method for the grouting effect of grouting material may include at least:

[0056] Step 102: Obtain multiple grouting materials with different flowability (Note: Flowability is a technical indicator that measures the ability of grouting materials to flow and deform under their own weight or external force. The flowability data of multiple grouting materials should be obtained using the same test standard). Obtain multiple vertical hollow grouting vessels in the same number as the types of grouting materials. The top of the vertical hollow grouting vessel is open, and the bottom of the vertical hollow grouting vessel has a bottom plate with several through holes (Note: The bottom of the vertical hollow grouting vessel is equipped with a bottom plate with several through holes to ensure that the subsequent grouting material sinks smoothly in the vertical hollow grouting vessel).

[0057] Step 104: Set up aggregate in each vertical hollow filling vessel to form multiple stacked specimens with the same or similar internal porosity (Note: The aggregate can be formed by selecting crushed stone of appropriate particle size and evenly stacking the crushed stone in the vertical hollow filling vessel, or it can be formed in the vertical hollow filling vessel by 3D printing. The former method can form multiple stacked specimens with roughly similar internal porosity, while the latter method can form multiple stacked specimens with the same internal porosity; and the aggregate can be set in the vertical hollow filling vessel according to the actual grouting environment. For example, if the porosity in the actual grouting environment is large, the porosity in the final stacked aggregate will also be large), and obtain the overall internal porosity of each of the multiple stacked specimens before grouting.

[0058] Step 106: Inject various grouting materials with different fluidities into multiple slab specimens, and record the injection depth data of each grouting material in the slab specimens at multiple injection times (Note: Refer to...). Figure 2 As shown, Figure 2 The image shows the injection depth of the sidewall at different injection times from a single perspective. This can be obtained through video recording. With just this one perspective, the injection depth of the sidewall at multiple azimuth angles can be obtained. In actual experiments, it is necessary to record video from multiple perspectives in all directions to obtain the injection depth-injection time curves for various grouting materials.

[0059] Step 108: For each slab specimen, when the grout is poured to its bottom position, several through holes on its bottom plate are sealed, and the overall porosity of the grout is obtained after the grout stabilizes inside (Note: that is, after the grout stops flowing inside the slab specimen).

[0060] Step 110: Based on the flowability and injection depth-injection time curves of each grout, obtain the grout injection depth guidance function with the flowability parameter and injection time parameter as independent variables and the injection depth parameter as dependent variable.

[0061] Step 112: Based on the flowability of each grout and the overall porosity before and after grouting of each slab specimen, obtain the first porosity guiding function with the flowability parameter as the independent variable and the overall pore filling rate parameter as the dependent variable.

[0062] The vertical hollow filling vessel is transparent, and the quantitative evaluation method uses X-ray computed tomography (XCT) to scan the slab specimen to obtain the overall internal porosity before and after filling (Note: the pre-filling internal layered porosity and post-filling internal layered porosity corresponding to different depths mentioned later are also obtained using X-ray computed tomography). X-ray computed tomography (XCT) is an advanced non-destructive testing technique. It uses X-rays to penetrate the specimen, acquiring multi-angle projection data and processing it with computer reconstruction algorithms to obtain a high-resolution three-dimensional digital image of the specimen's internal structure. In this method, based on this three-dimensional image or a two-dimensional cross-sectional image derived from it, the pore size inside the slab specimen can be accurately identified and quantified, thus non-destructively obtaining pre-filling and post-filling overall porosity and layered porosity data.

[0063] In some embodiments of this specification, the quantitative evaluation method further includes:

[0064] Obtain the pre-pouring internal layered porosity of each of the multiple stacked specimens at different internal depths.

[0065] For each slab specimen, the internal layer porosity corresponding to different depths after grouting was obtained after the grout material stabilized inside.

[0066] Based on the flowability of each grout and the internal layered porosity at different depths of multiple slab specimens before and after grouting, a second porosity guiding function is obtained, which uses flowability and depth parameters as independent variables and layered pore filling rate as dependent variable.

[0067] See also Figure 3 , Figure 4 As shown, Figure 3The diagram shows the cross-sectional interconnected pore distribution of the pre-grouting slab specimen at different heights (Note: Based on the cross-sectional interconnected pore distribution diagrams at different heights, the pre-grouting internal layered porosity at different depths can be obtained). Figure 4 The diagram shows the distribution of residual pores in the cross-section of the grouting specimen at different heights (Note: The porosity of the internal stratification at different depths can be obtained based on the distribution of residual pores in the cross-section at different heights).

[0068] It is understandable that this specification provides a quantitative evaluation method for the grouting effect of grouting materials through several embodiments:

[0069] By constructing a mathematical guidance function using a small amount of experimental data, the grouting effect of grout with arbitrary fluidity can be calculated. This eliminates the reliance on traditional "exhaustive" physical tests and greatly shortens the evaluation cycle and cost of grout before grouting.

[0070] This method overcomes the limitation of traditional physical experiments that cannot exhaustively cover all different grouting materials. By using mathematical guidance functions, the grouting effect of each possible grouting material can be theoretically quantified. This can ensure that the final selected grouting material is the optimal choice. (Note: The corresponding grouting material parameters can be substituted into the mathematical guidance function. If the grouting effect data obtained by the mathematical guidance function is better, the grouting material with the specified parameters can be actually prepared and real physical experiments can be conducted. Finally, based on the grouting effect data obtained through real physical experiments and the application cost of the grouting material, it can be determined whether to select the grouting material with the specified parameters for subsequent grouting.)

[0071] It can not only predict the injection depth at different injection times using the "injection depth guidance function", but also predict the final overall static pore filling effect using the "first porosity guidance function" and the layered static pore filling effect at different depths using the "second porosity guidance function", making the evaluation dimensions of the grouting effect more comprehensive.

[0072] It should also be noted that the guiding functions involved in various embodiments of this specification can all be obtained by solving regression functions. A regression function is a statistical model used to describe the relationship between two or more independent variables (explanatory variables) and a dependent variable (response variable). Mathematically, regression function models include linear regression function models and nonlinear regression function models. The linear regression function model can be written in the form of the following equation:

[0073] ;

[0074] in, The dependent variable represents the variable we want to predict or explain. These are independent variables, which are explanatory variables (independent variables) that affect the dependent variable. It is the intercept, which is the expected value of the dependent variable when all independent variables are 0; These are the coefficients of each independent variable, representing the expected change in the dependent variable when the corresponding independent variable changes by one unit. This is the error term, representing the random variation that the model failed to explain.

[0075] In solving the regression function, our goal is to find the optimal coefficients. In order to accurately predict the dependent variable.

[0076] Therefore, the grouting depth guidance function can be expressed as follows:

[0077] ;

[0078] in, Represents the fluidity parameter. This indicates the injection duration parameter. Indicates the injection depth parameter. This represents the intercept in the guide function for grout injection depth. This represents the coefficient corresponding to the flowability parameter in the grout injection depth guide function. This represents the coefficient corresponding to the grouting time parameter in the grouting depth guidance function. This represents the error term in the grouting depth guide function.

[0079] Furthermore, it is understandable that when solving the grouting depth guidance function, it is necessary to obtain multiple sets of data with corresponding flowability data, grouting time data, and grouting depth data in advance (Note: the corresponding grouting time data and grouting depth data in the data set are obtained through the grouting depth-grouting time curve), and solve the grouting depth guidance function based on these data sets. Moreover, multiple sets of data with corresponding flowability data, grouting time data, and grouting depth data can be obtained through real physical experiments.

[0080] The first porosity guide function can be expressed as follows:

[0081] ;

[0082] This represents the overall pore filling rate parameter. This represents the intercept in the first porosity guide function. This represents the coefficient corresponding to the flowability parameter in the first porosity guide function. This represents the error term in the first porosity guide function.

[0083] Furthermore, it is understandable that when solving the first porosity guide function, it is necessary to obtain multiple sets of data with corresponding overall pore filling rate data and flowability data in advance, and solve the first porosity guide function based on these data sets. Moreover, multiple sets of data with corresponding overall pore filling rate data and flowability data can be obtained through real physical experiments.

[0084] The second porosity guide function can be expressed as follows:

[0085] ;

[0086] This represents the stratified pore filling rate parameter. This represents the intercept in the second porosity guide function. This represents the coefficient corresponding to the flowability parameter in the second porosity guide function. This represents the coefficient corresponding to the depth parameter in the second porosity guide function. Indicates depth, This represents the error term in the second porosity guidance function.

[0087] Furthermore, it is understandable that when solving the second porosity guide function, it is necessary to obtain multiple sets of data with corresponding layered pore filling rate data, flowability data, and depth data in advance, and solve the second porosity guide function based on these data sets. Moreover, multiple sets of data with corresponding layered pore filling rate data, flowability data, and depth data can be obtained through real physical experiments.

[0088] It should be noted that when calculating the above-mentioned guiding function, the parameters involved in the guiding function do not include the corresponding units, but only the specific numerical values, in order to ensure the computability of the function.

[0089] In linear regression models, the least squares method is typically used to estimate the coefficients. However, if there is no clear linear relationship between the independent and dependent variables, a nonlinear regression model is required. Similar to the linear regression model, the nonlinear regression model allows the relationship between the independent and dependent variables to be represented by a nonlinear equation. This means that the relationship between one or more independent variables and the dependent variable in the model is not linear, but follows a nonlinear function, which can be, but is not limited to, exponential, logarithmic, or power functions.

[0090] In some embodiments of this specification, the step of obtaining a first porosity guiding function, with the flowability parameter as the independent variable and the overall pore filling rate parameter as the dependent variable, based on the flowability of each grout and the pre-grouting and post-grouting overall porosity of each slab specimen, includes:

[0091] Based on the overall internal porosity before and after grouting of each pile specimen, the overall pore filling rate of each pile specimen is obtained.

[0092] Based on the flowability of each grout and the overall pore filling rate of each slab specimen, a first porosity guiding function is obtained, with the flowability parameter as the independent variable and the overall pore filling rate parameter as the dependent variable.

[0093] The overall pore filling rate (under real physical experiments) can be calculated using the following formula:

[0094] ;

[0095] ;

[0096] ;

[0097] Indicates the overall pore filling rate. Indicates the overall internal porosity before grouting. Indicates the overall porosity of the interior after grouting. Indicates the overall internal pore volume before grouting. Indicates the overall internal pore volume after grouting. This indicates the overall internal volume of a vertically hollow filling vessel.

[0098] In some embodiments of this specification, the process of obtaining a second porosity guidance function, based on the flowability of each grout and the pre-grouting and post-grouting internal layered porosity at different depths of multiple slab specimens, includes:

[0099] Based on the internal layered porosity before and after grouting of each of the multiple stacked specimens at different depths, the layered pore filling rate of each of the multiple stacked specimens at different depths is obtained.

[0100] Based on the flowability of each grout and the layered pore filling rate at different depths within each of the multiple slab specimens, a second porosity guidance function is obtained, with flowability and depth parameters as independent variables and layered pore filling rate as dependent variable.

[0101] The formula for calculating the layered pore filling rate (under real physical experiments) at the corresponding depth can be as follows:

[0102] ;

[0103] ;

[0104] ;

[0105] Indicates depth The layered pore filling rate, Indicates depth The internal layered porosity before grouting, Indicates depth The porosity of the internal stratified layers after grouting Indicates depth The internal layered pore area before irrigation, Indicates depth The internal stratified pore area after grouting Indicates depth The total layered area of ​​the vertical hollow filling vessel (Note: In the embodiments of this specification, a cylindrical vertical hollow filling vessel is used, so the total layered area (i.e., cross-sectional area) of the vertical hollow filling vessel at different depths remains consistent).

[0106] It is understandable that after the grout is injected into the slab specimen, the injection depth at different locations will vary under the same injection time. Therefore, in order to ensure the representativeness of the obtained injection depth data, to ensure the accuracy of the obtained injection depth-injection time curve, and ultimately to ensure the effectiveness of the grout injection depth guidance function, in some embodiments of this specification, the injection depth data includes the injection depth of the grout on the sidewalls of the slab specimen at multiple different azimuth angles.

[0107] Obtain the grouting depth-grouting time curve, including:

[0108] Based on the injection depth data of the grout in the slab specimen at multiple injection times, the average injection depth of the grout in the slab specimen at multiple injection times is obtained, and then the injection depth-injection time curve of the grout is obtained.

[0109] In some embodiments of this specification, based on the injection depth data of the grout in the slab specimen at multiple injection durations, the average injection depth of the grout in the slab specimen at multiple injection durations is obtained, thereby obtaining the injection depth-injection duration curve of the grout, including:

[0110] Outlier infusion depths in multiple infusion depth datasets were screened out using the IQR interquartile range method to obtain multiple infusion depth datasets after screening.

[0111] Based on the grouting depth data after screening in the slab specimen at multiple grouting times, the average grouting depth in the slab specimen at multiple grouting times is obtained, and then the grouting depth-grouting time curve is obtained.

[0112] Understandably, due to the distribution of aggregates, under the same grouting time, there may be situations where the grouting depth at certain azimuth angles differs significantly from the grouting depth at most other azimuth angles. Therefore, it is necessary to first remove these outlier grouting depths from the grouting depth data, and then obtain the average grouting depth based on the grouting depth data after the removal.

[0113] Furthermore, in the embodiments of this specification, the IQR interquartile range method is creatively used to screen outlier injection depths in multiple injection depth datasets. The screening operation for outlier injection depths in each injection depth dataset includes:

[0114] The IQR interquartile range method was used to determine the lower quartile Q1 (note: the quartile at the bottom 25% of all infusion depths, sorted from deep to shallow) and the upper quartile Q3 (note: the quartile at the top 25% of all infusion depths, sorted from deep to shallow) by the infusion depth data at multiple different azimuth angles of the sidewall.

[0115] Based on the lower quartile Q1 and the upper quartile Q3, the interquartile range (IQR) is obtained (the formula is: IQR = Q3 - Q1).

[0116] Based on the interquartile range (IQR), the lower quartile (Q1), and the upper quartile (Q3), determine the lower outlier threshold (calculated as: lower outlier threshold = Q1 - k * IQR, where k is a preset coefficient, usually 1.5, but can be set according to actual needs) and the upper outlier threshold (calculated as: upper outlier threshold = Q3 + k * IQR, where k is a preset coefficient, usually 1.5, but can be set according to actual needs).

[0117] Based on the lower and upper outlier thresholds, outlier infusion depths are filtered out from the infusion depth data at different azimuth angles on the sidewalls.

[0118] Understandably, traditional outlier removal often requires manually setting a fixed threshold (if the value exceeds the threshold, it is considered an anomaly). However, the fluctuation range of grouting depth varies greatly under different flowability grouting materials and different grouting times, making a single fixed threshold insufficient for universal application. The IQR interquartile range (IQR) method, a statistical method based on the data's own distribution, dynamically generates upper and lower limits by calculating the lower quartile, upper quartile, and interquartile range of the actual data. Regardless of overall data fluctuations, the IQR IQR method adaptively delineates the fluctuation boundaries of normal data, exhibiting strong universality and adaptability. This ensures the validity of the obtained average grouting depth, guarantees the accuracy of the grouting depth-grouting time curve, and ultimately ensures the effectiveness of the grouting depth guidance function.

[0119] In some embodiments of this specification, the step of obtaining a grout injection depth guidance function based on the flowability and injection depth-injection time curves corresponding to each grout material, with flowability and injection time parameters as independent variables and injection depth parameter as dependent variable, includes:

[0120] For each infusion depth-infusion duration curve, several sampling points are determined on the curve, and the infusion depth and infusion duration corresponding to each sampling point are obtained.

[0121] Based on the flowability of each grout and the injection depth and injection time of each sampling point, a grout injection depth guidance function is obtained, which takes the flowability parameter and injection time parameter as independent variables and the injection depth parameter as dependent variable.

[0122] Furthermore, the greater the curvature of the infusion depth-infusion duration curve, the more sampling points can be identified on the portion of the curve.

[0123] Understandably, while video recording could directly obtain the injection depth for all injection durations, identifying the injection depth in each frame requires complex image recognition processing. Therefore, obtaining the injection depth for all injection durations solely from the recorded video would significantly increase computational load (note: especially in several embodiments of this specification, it's necessary to obtain the injection depth at multiple different azimuth angles on the sidewalls, and also to use the IQR interquartile range method to filter the injection depth data). Therefore, in this embodiment, the injection depth data of each grout material in the slab specimen at multiple injection durations is recorded. Then, the injection depth-injection duration curves for various grout materials can be obtained through curve fitting. Furthermore, the injection depth for any injection duration can be obtained from the fitted injection depth-injection duration curves. The computational load of curve fitting is relatively less than that of complex image recognition techniques, thus reducing data processing load.

[0124] Furthermore, it is understandable that the greater the curvature of the grouting depth-grouting time curve, the more drastic the grouting speed changes within the grouting time interval corresponding to this part of the curve. Therefore, more sampling points should be set for this part of the curve to ensure that the grouting depth guidance function obtained in the final solution can accurately represent the actual situation of the grouting depth in the grouting time interval, and avoid fitting distortion or smoothing caused by sparse sampling points. This will significantly reduce the computational load of image recognition while ensuring the high-fidelity guidance effect of the grouting depth guidance function on the actual grouting process.

[0125] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this specification are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Versatile Discs (DVDs)), or semiconductor media (e.g., Solid State Disks (SSDs)).

[0126] Figure 5 A block diagram of an electronic device 500 that can implement various embodiments of the present disclosure is shown. For example... Figure 5 As shown, the electronic device 500 includes a processor 510, a disk drive 520, an input / output interface 530, a network interface 540, and a memory 550. The processor 510, disk drive 520, input / output interface 530, network interface 540, and memory 550 can communicate with each other via a communication bus 560.

[0127] The processor 510 can be implemented using a general-purpose CPU, microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits to execute relevant programs and implement the technical solution provided in this application.

[0128] The memory 550 can be implemented in the form of ROM (Read Only Memory), RAM (Read Access Memory), static memory, dynamic storage devices, etc. The memory 550 can store the operating system 551 for controlling the operation of the electronic device 500, and the basic input / output system BIOS 552 for controlling the low-level operations of the electronic device 500. Additionally, it can store a web browser 553, a data storage management system 554, etc. In summary, when implementing the technical solution provided in this application through software or firmware, the relevant program code is stored in the memory 550 and is called and executed by the processor 510.

[0129] The input / output interface 530 is used to connect input / output devices to enable information input and output. Input / output devices can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0130] Network interface 540 is used to connect network devices (not shown in the figure) to enable network communication between the device and other devices. The network devices can communicate via wired means (e.g., USB, Ethernet cable) or wireless means (e.g., mobile network, Wi-Fi, Bluetooth).

[0131] Bus 560 includes a pathway for transmitting information between various components of the device, such as processor 510, disk drive 520, input / output interface 530, network interface 540, and memory 550.

[0132] It should be noted that although the above-described device only shows the processor 510, disk drive 520, input / output interface 530, network interface 540, memory 550, bus 560, etc., in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the method of this application, and does not necessarily include all the components shown in the figures.

[0133] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0134] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. Furthermore, although operations are depicted in a specific order, this should be understood as requiring that such operations be performed in the specific order shown or in sequential order, or requiring that all illustrated operations be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the foregoing discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented individually or in any suitable sub-combination in multiple implementations.

[0135] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for quantitatively evaluating the grouting effect of grouting material, characterized in that, include: Obtain various grouting materials with different fluidity, and obtain multiple vertical hollow grouting vessels in the same quantity as the types of grouting materials. The top of the vertical hollow grouting vessel is open, and the bottom of the vertical hollow grouting vessel has a bottom plate with several through holes. Aggregate was stacked in each vertical hollow filling vessel to form multiple stacked specimens with the same or similar internal porosity, and the overall internal porosity of each stacked specimen before filling was obtained. Multiple grouting materials with different fluidity were injected into multiple slab specimens, and the injection depth data of each grouting material in the slab specimens at multiple injection times were recorded. Thus, injection depth-injection time curves for each of the multiple grouting materials were obtained. For each slab specimen, several through holes on its bottom plate were sealed when the grout was poured to its bottom position, and the overall porosity of the interior was obtained after the grout stabilized inside. Based on the flowability and injection depth-injection time curves of each grouting material, a grouting depth guidance function is obtained, which takes the flowability parameter and injection time parameter as independent variables and the injection depth parameter as dependent variable. Based on the flowability of each grout and the overall porosity before and after grouting of each slab specimen, a first porosity guiding function is obtained, with the flowability parameter as the independent variable and the overall pore filling rate parameter as the dependent variable.

2. The method for quantitatively evaluating the grouting effect of grouting material according to claim 1, characterized in that, The vertical hollow filling vessel is transparent, and the quantitative evaluation method uses X-ray computed tomography to scan the accumulator specimen to obtain the overall internal porosity of the accumulator specimen before filling and the overall internal porosity after filling.

3. The method for quantitatively evaluating the grouting effect of grouting material according to claim 1, characterized in that, The grouting depth data includes the grouting depths at various azimuth angles on the sidewalls of the slab specimen. Obtain the grouting depth-grouting time curve, including: Based on the injection depth data of the grout in the slab specimen at multiple injection times, the average injection depth of the grout in the slab specimen at multiple injection times is obtained, and then the injection depth-injection time curve of the grout is obtained.

4. The method for quantitatively evaluating the grouting effect of grouting material according to claim 3, characterized in that, Based on the injection depth data of the grout in the slab specimen at multiple injection times, the average injection depth of the grout in the slab specimen at multiple injection times is obtained, and then the injection depth-injection time curve of the grout is obtained, including: Outlier infusion depths in multiple infusion depth datasets were screened out using the IQR interquartile range method to obtain multiple infusion depth datasets after screening. Based on the grouting depth data after screening in the slab specimen at multiple grouting times, the average grouting depth in the slab specimen at multiple grouting times is obtained, and then the grouting depth-grouting time curve is obtained.

5. The method for quantitatively evaluating the grouting effect of grouting material according to claim 4, characterized in that, The outlier removal operation for each injection depth data point includes: The lower and upper quartiles were determined by using the IQR interquartile range method and the injection depth data, which included the injection depths at multiple different azimuth angles on the sidewalls. The interquartile range is obtained based on the lower and upper quartiles. Based on the interquartile range, lower quartile, and upper quartile, determine the lower and upper outlier limits. Based on the lower and upper outlier thresholds, outlier infusion depths are filtered out from the infusion depth data at different azimuth angles on the sidewalls.

6. A method for quantitatively evaluating the grouting effect of grouting material according to any one of claims 1 to 5, characterized in that, The process involves obtaining a grouting depth guidance function based on the flowability and grouting depth-grouting time curves corresponding to each grout material, with flowability and grouting time parameters as independent variables and grouting depth parameter as the dependent variable. This function includes: For each infusion depth-infusion duration curve, several sampling points are determined on the curve, and the infusion depth and infusion duration corresponding to each sampling point are obtained. Based on the flowability of each grout and the injection depth and injection time of each sampling point, a grout injection depth guidance function is obtained, which takes the flowability parameter and injection time parameter as independent variables and the injection depth parameter as dependent variable.

7. The method for quantitatively evaluating the grouting effect of grouting material according to claim 6, characterized in that, The greater the curvature of the infusion depth-infusion duration curve, the more sampling points are identified on the portion of the curve.

8. The method for quantitatively evaluating the grouting effect of grouting material according to claim 1, characterized in that, The process involves obtaining a first porosity guiding function based on the flowability of each grout material and the pre-grouting and post-grouting overall porosity of each aggregate specimen, using the flowability parameter as the independent variable and the overall pore filling rate parameter as the dependent variable. This function includes: Based on the overall internal porosity before and after grouting of each pile specimen, the overall pore filling rate of each pile specimen is obtained. Based on the flowability of each grout and the overall pore filling rate of each slab specimen, a first porosity guiding function is obtained, with the flowability parameter as the independent variable and the overall pore filling rate parameter as the dependent variable.

9. The method for quantitatively evaluating the grouting effect of grouting material according to claim 1, characterized in that, Also includes: Obtain the pre-pouring internal layered porosity of each of the multiple stacked specimens at different internal depths. For each slab specimen, the internal layer porosity corresponding to different depths after grouting was obtained after the grout material stabilized inside. Based on the flowability of each grout and the internal layered porosity at different depths of multiple slab specimens before and after grouting, a second porosity guiding function is obtained, which uses flowability and depth parameters as independent variables and layered pore filling rate as dependent variable.

10. The method for quantitatively evaluating the grouting effect of grouting material according to claim 9, characterized in that, The second porosity guidance function is obtained based on the flowability of each grout material and the pre-grouting and post-grouting internal layered porosity at different depths of multiple slab specimens. This function uses flowability and depth parameters as independent variables and layered pore filling rate as a dependent variable. The function includes: Based on the internal layered porosity before and after grouting of each of the multiple stacked specimens at different depths, the layered pore filling rate of each of the multiple stacked specimens at different depths is obtained. Based on the flowability of each grout and the layered pore filling rate at different depths within each of the multiple slab specimens, a second porosity guidance function is obtained, with flowability and depth parameters as independent variables and layered pore filling rate as dependent variable.

Images