Method for rapid evaluation of grouting fullness of large-pore structure asphalt mixture

Abstract

The application provides a rapid evaluation method for grouting fullness of macroporous structure asphalt mixture, relates to the technical field of asphalt detection, and comprises the following steps: obtaining a groutability reference condition of macroporous structure asphalt mixture, triggering a grouting instruction when the groutability reference condition is met; obtaining a target grouting route to perform grouting operation, collecting grouting infiltration time, unit area grouting consumption and surface reverse grouting height in real time; performing overall and independent double grouting state uncertainty joint analysis based on unit grid multidimensional grouting process parameter sequence; performing rapid evaluation of grouting fullness according to a corrected overall grouting state uncertainty coefficient to obtain a target grouting fullness evaluation result. The application solves the technical problem that, in the prior art, due to the uncertainty of the grouting process, the evaluation result of the grouting fullness is inaccurate, which further affects the overall performance and durability of the asphalt mixture, and through accurate evaluation of the grouting fullness, the strength and durability of the road structure are ensured.

Description

Technical Field

[0001] This application relates to the field of asphalt testing technology, specifically to a rapid assessment method for the grout fullness of asphalt mixtures with large pore structures. Background Technology

[0002] In the field of road engineering, grouting-type semi-flexible pavements are widely used in heavy-traffic sections due to their excellent rutting resistance. One of the core quality indicators is the fullness of the filling of the voids inside the large-pore asphalt mixture by the cement-based grout. Currently, existing methods usually rely on simple physical measurement methods. Although they can provide accurate data for local points, they are essentially a post-construction verification method and cannot reflect the actual grouting effect in real time. In particular, due to the complexity of the large-pore structure and the non-uniformity of the grouting process, traditional methods cannot accurately reflect local changes during the grouting process, such as differences in the permeation rate in different areas and changes in the backflow height. As a result, the final fullness test results cannot fully reflect the overall construction quality. Due to the lag and one-sidedness of the assessment, defects in the construction process cannot be detected and remedied in a timely manner, thus affecting the long-term performance and durability of the pavement and creating hidden dangers for the service life of the road.

[0003] In summary, the existing technology suffers from technical problems due to the uncertainty of the grouting process, which leads to inaccurate grout fullness assessment results and further affects the overall performance and durability of asphalt mixtures. Summary of the Invention

[0004] The purpose of this application is to provide a rapid evaluation method for the grout fullness of asphalt mixtures with large pore structures, in order to solve the technical problem in the prior art where the uncertainty of the grouting process leads to inaccurate grout fullness evaluation results, which further affects the overall performance and durability of asphalt mixtures.

[0005] To achieve the above objectives, this application provides a rapid assessment method for the grout fullness of macroporous asphalt mixtures. The method includes: obtaining the groutability benchmark conditions of the macroporous asphalt mixture; triggering a grouting command when the groutability benchmark conditions are met; obtaining the target grouting route; performing grouting operations on the target grouting route based on the grouting command; simultaneously collecting grouting penetration time, grouting consumption per unit area, and surface backflow height in real time using a unit grid as the detection unit to obtain a unit grid multidimensional grouting process parameter sequence and a unit grid sequence; performing a joint analysis of the uncertainty of the overall and independent grouting states based on the unit grid multidimensional grouting process parameter sequence to obtain a corrected overall grouting state uncertainty coefficient; and rapidly assessing the grout fullness based on the corrected overall grouting state uncertainty coefficient to obtain the target grout fullness assessment result.

[0006] Optionally, the groutability benchmark conditions include a porosity of 20% to 28% and a compaction degree of ≥97% for the macroporous asphalt mixture; a flowability of 10 to 14 s for the grouting material; a bleeding rate of ≤1%; and an initial setting time of ≥30 min.

[0007] Optionally, a joint temporal iterative state analysis is performed on the multidimensional grouting process parameter sequence of the unit grid to determine the overall grouting state uncertainty coefficient; the reference grouting full state process parameters of the target unit grid are obtained, and independent grouting state uncertainty analysis is performed on the multidimensional grouting process parameter sequence of the unit grid to obtain a set of unit grid independent grouting state uncertainty coefficients; based on the set of unit grid independent grouting state uncertainty coefficients, the unit grid sequence is subjected to a screening structural continuity constraint evaluation to obtain the screening structural continuity constraint evaluation result; the overall grouting state uncertainty coefficient is corrected according to the screening structural continuity constraint evaluation result to obtain the corrected overall grouting state uncertainty coefficient.

[0008] Optionally, the adjacency parameter consistency identification is performed on the multidimensional grouting process parameter sequence of the unit grid to construct an adjacency parameter consistency state matrix sequence; overall joint multi-scale analysis is performed based on the adjacency parameter consistency state matrix sequence to obtain a multi-scale state fluctuation feature set; time-series iterative state analysis is performed on the multi-scale state fluctuation feature set to obtain the overall grouting state uncertainty coefficient.

[0009] Optionally, the multi-scale state fluctuation feature set is serialized in descending order of scale to obtain a multi-scale state fluctuation feature sequence; the multi-scale state fluctuation feature sequence is iteratively enhanced sequentially until the end of the sequence is reached to obtain the target multi-scale state fluctuation enhancement feature; the overall grouting state uncertainty coefficient is determined based on the target multi-scale state fluctuation enhancement feature.

[0010] Optionally, the set of uncertainties of independent grouting states of the unit grid is filtered according to a preset threshold for the uncertainty coefficient of independent grouting states, and the unit grid sequence is mapped and filtered according to the filtering results to obtain a set of state-credible unit grids; the apparent response of the set of state-credible unit grids is detected 5 to 10 minutes after grouting to obtain a set of apparent response detection results; the set of apparent response detection results is traversed to evaluate the structural continuity constraints, and the set of evaluation results is filtered by mean drift to obtain the filtered structural continuity constraint evaluation results.

[0011] Optionally, within 5 to 10 minutes after grouting is completed, the continuous return grout height of the reliable unit grid set is tested to see if it is greater than or equal to 2 to 3 mm, whether air bubbles are continuously discharged, and whether the surface grout is uniform and free from segregation, thereby obtaining the apparent response test result set.

[0012] Optionally, when the uncertainty coefficient of the corrected overall grouting state is greater than or equal to a preset coefficient threshold, an on-site drilling test is triggered to obtain a random grouting fullness evaluation result; the random grouting fullness evaluation result is used as the target grouting fullness evaluation result.

[0013] Optionally, when the uncertainty coefficient of the corrected overall grouting state is less than the preset coefficient threshold, a field drilling test is conducted at the conventional fullness detection frequency to obtain the conventional grouting fullness assessment result; the conventional grouting fullness assessment result is used as the target grouting fullness assessment result.

[0014] Optionally, fluctuation analysis is performed on the target grout fullness assessment result and a preset number of previous historical target grout fullness assessment results to obtain a fluctuation coefficient; when the fluctuation coefficient is greater than or equal to a preset fluctuation coefficient threshold, the conventional fullness detection frequency is corrected according to the fluctuation coefficient, and conventional grout fullness assessment is performed based on the corrected conventional fullness detection frequency.

[0015] The technical solution provided in this application has at least the following technical effects or advantages: By obtaining the groutability benchmark conditions of the asphalt mixture with large porosity, a grouting command is triggered when the groutability benchmark conditions are met. The target grouting route is obtained, and grouting is performed on the target grouting route based on the grouting command. Simultaneously, using a unit grid as the detection unit, the grouting penetration time, grouting consumption per unit area, and surface backflow height are collected in real time to obtain the unit grid multidimensional grouting process parameter sequence and the unit grid sequence. Based on the unit grid multidimensional grouting process parameter sequence, a joint analysis of the uncertainty of the overall and independent grouting states is performed to obtain a corrected overall grouting state uncertainty coefficient. Based on the corrected overall grouting state uncertainty coefficient, a rapid assessment of grout fullness is performed to obtain the target grout fullness assessment result. In other words, after confirming that the groutability benchmark conditions are met, the specific grouting path is determined, and the grouting operation is carried out. Intelligent sensing elements are used to collect multiple parameters in real time, such as grouting penetration time, grouting consumption per unit area, and surface grouting height. A joint analysis of the uncertainty of the overall and independent grouting states is introduced. By analyzing the parameter sequence of the multidimensional grouting process, the corrected overall grouting state uncertainty coefficient is calculated, anomalies are detected in time and adjustments are made to ensure the uniformity and fullness of the grouting process. This avoids the degradation of the mixture performance caused by grouting problems, ensures the quality and performance of the asphalt mixture, and improves the durability and stability of the project.

[0016] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this application 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 merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the rapid evaluation method for grout fullness of large-pore asphalt mixtures according to this application.

[0019] Figure 2 This is a flowchart illustrating the process for obtaining the corrected overall grouting state uncertainty coefficient in the rapid evaluation method for grouting fullness of large-pore asphalt mixtures in this application. Detailed Implementation

[0020] This application provides a rapid assessment method for the grout fullness of asphalt mixtures with large porosity, solving the technical problem in existing technologies where uncertainties in the grouting process lead to inaccurate grout fullness assessment results, further affecting the overall performance and durability of asphalt mixtures. After confirming that the groutability benchmark conditions are met, the specific grouting path is determined, and the grouting operation is carried out. Intelligent sensing elements are used to collect multiple parameters in real time, such as grout penetration time, grout consumption per unit area, and surface slurry height. A joint analysis of the overall and independent grouting state uncertainties is introduced. Through analysis of the multidimensional grouting process parameter sequence, a corrected overall grouting state uncertainty coefficient is calculated, allowing for timely detection and adjustment of anomalies. This ensures the uniformity and fullness of the grouting process, thereby avoiding performance degradation of the mixture due to grouting problems, ensuring the quality and performance of the asphalt mixture, and improving the durability and stability of the project.

[0021] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0022] For examples, please refer to the appendix. Figure 1 This application provides a rapid evaluation method for the grout fullness of asphalt mixtures with large porosity, wherein the rapid evaluation method for the grout fullness of asphalt mixtures with large porosity specifically includes the following steps: Obtain the groutability benchmark conditions for the asphalt mixture with large porosity. When the groutability benchmark conditions are met, trigger the grouting command.

[0023] Furthermore, this application also includes the following steps: the groutability benchmark conditions include a porosity of 20% to 28% and a compaction degree of ≥97% for the macroporous asphalt mixture; a flowability of 10 to 14 s for the grouting material; a bleeding rate of ≤1% and an initial setting time of ≥30 min.

[0024] Specifically, in road sections where grouting is planned, two tests are conducted in parallel: First, the key indicators of the already formed large-pore asphalt mixture matrix are reviewed. This is usually done by using rapid testing equipment such as a nucleus-free density meter to sample and check the compaction on-site, and by combining the laboratory test report of the same batch of mixture to confirm whether its design void ratio falls within the target range; Second, the on-site rapid performance verification of the grouting material to be used is conducted. Samples are taken at the mixing plant or construction site, and its performance is quickly tested using a flowability meter, a bleeding rate meter, and a setting time meter.

[0025] Groutability benchmark conditions are a series of preliminary, quantifiable technical requirements regarding the matrix and grouting materials that must be met to ensure smooth grouting construction and achieve the expected fullness effect. They serve as the prerequisite for initiating the grouting process. Porosity is the percentage of unfilled pore volume (excluding aggregate and asphalt bulk volume) in an asphalt mixture specimen, a core indicator for measuring the available grouting space in the matrix. Compaction degree is the ratio of the actual density of the asphalt mixture after on-site paving and compaction to the theoretical maximum density obtained by standard laboratory compaction methods, expressed as a percentage. It reflects the density and structural stability of the matrix after construction. Flowability is an indicator of the grouting material's ability to flow under its own weight, typically measured by a specific funnel, indicating the time it takes for a certain volume of grout to completely flow out. A shorter time indicates better flowability. Bleeding rate is the tendency for internal water to precipitate upwards during the static setting and setting process of the grouting material. It is expressed as a percentage of the volume of precipitated water relative to the initial volume of the grout. High bleeding rate leads to unevenness in the grout, with the upper part containing more water and having lower strength, while the lower part has concentrated particles and poor fluidity. Initial setting time is the time from when the grouting material is mixed with water until the grout begins to lose its plasticity and fluidity, providing a safe time window for grouting operations (mixing, transportation, pouring, and finishing).

[0026] All test data is uploaded to the central control system in real time via IoT terminals. The central control system's built-in decision logic automatically compares the real-time data with preset groutability benchmark thresholds: the porosity of the large-pore asphalt mixture is 20%–28%, and the compaction degree is greater than or equal to 97%; the fluidity of the grouting material is 10–14 seconds, the bleeding rate is less than or equal to 1%, and the initial setting time is greater than or equal to 30 minutes. A grouting command is only generated and issued when all measured data meet the groutability benchmarks. If any indicator fails to meet the requirements, the grouting equipment is locked and an alarm is issued, preventing the grouting operation from starting until the material or matrix problem is addressed. This ensures that every grouting operation begins on a qualified matrix using qualified grouting material, fundamentally eliminating inevitable failures due to substandard preconditions. After automatic verification, the screen displays that all groutability benchmarks are met and generates a green grouting permission indicator. Upon receiving the instruction, the on-site commander notifies the grouting truck to begin operation along the predetermined route.

[0027] In addition, it is necessary to monitor the construction temperature of the base asphalt mixture, including the mixture's factory temperature, temperature upon arrival at the site, initial compaction temperature, and final compaction temperature. This monitoring is required for each truckload of material, typically measured with a thermometer. Strict control of the groutability benchmark conditions ensures a good match between the grouting material and the large-pore structure of the asphalt mixture, thereby facilitating the grouting process and guaranteeing the final grout fullness. Meeting the groutability benchmark conditions ensures the permeability and stability of the grouting material, enabling it to efficiently fill the voids in the mixture and improve the pavement's rutting resistance and durability. The ultimate technical effect is a significant improvement in pavement construction quality, ensuring uniformity and stability during the grouting process, reducing construction defects, and enhancing the long-term performance of the road.

[0028] The target grouting route is obtained, and grouting operation is performed on the target grouting route based on the grouting command. At the same time, the grouting infiltration time, grouting consumption per unit area and surface grouting height are collected in real time using a unit grid as the detection unit to obtain the multi-dimensional grouting process parameter sequence of the unit grid and the unit grid sequence.

[0029] Specifically, in grouting construction, the first step is to obtain the target grouting route and determine the flow path of the grouting material. The target grouting route is a pre-planned path that the grouting equipment should follow in the road surface operation area. It is usually obtained from the construction plan to ensure orderly and comprehensive coverage of the entire work surface.

[0030] After receiving the grouting instruction, the grouting truck begins operation along the preset target grouting route. Simultaneously, a series of intelligent sensing elements are activated. The unit grid, serving as the basic detection unit during the grouting process, is used to monitor the grouting progress in real time. The grouting progress of each unit grid area is recorded individually, and the grouting status of that area is reflected through multiple real-time parameters. A high-precision BeiDou / GNSS positioning module acquires the sub-meter-level position coordinates of the grouting truck's nozzles or distribution pipes in real time, matches these coordinates with a pre-digitized road surface unit grid map, determines in real time which grid is being grouted, and generates a unit grid sequence.

[0031] A high-frame-rate visual sensor or laser rangefinder array installed at the grouting nozzle continuously monitors the humidity and height changes of the road surface below. A timer starts when the nozzle begins grouting a new grid based on the positioning information; the timer stops when the sensor detects a continuous and stable grout backflow layer on the grid surface. The recorded time is the grouting penetration time for that grid.

[0032] A high-precision electromagnetic flowmeter installed at the outlet of the grouting pump measures the cumulative volume of the pumped grout in real time. By spatiotemporally correlating the flow rate data with high-precision positioning data, the total volume of grout pumped within a specific grid spatial coordinate range is accurately attributed to the grouting consumption per unit area of ​​that grid.

[0033] Behind the grouting truck, a separate non-contact laser profile scanner scans the area that has just been grouted a few seconds after the grouting is completed. It can quickly obtain the microscopic profile of the road surface, calculate the average surface slurry backflow height by comparing it with the foundation elevation data before grouting, and bind the data to the corresponding grid.

[0034] The multi-source data streams from the positioning module, vision / laser sensor, flow meter and scanner are fused according to a unified timestamp and spatial grid identifier to form a structured data record, namely a multi-dimensional grouting process parameter sequence of unit grid, which corresponds one-to-one with the unit grid sequence, that is, the process parameter data of each unit grid in the unit grid sequence.

[0035] For example, the grouting truck is equipped with an integrated intelligent monitoring system for the grouting process, including a GNSS positioning module, a high frame rate visual sensor, a high-precision electromagnetic flowmeter, and a non-contact laser profiling scanner. The work area is divided into a 1m × 1m virtual grid, with a total area of ​​200m². 2 The grouting truck traveled at a constant speed of 3 meters per minute along the centerline of the lane while grouting. The GNSS module's positioning accuracy was ±2 centimeters, accurately identifying the current grouting grid as B12. An infrared camera installed on the distribution pipe detected a uniform grout film appearing on the surface of grid B12 18 seconds after grouting began, recording the infiltration time as 18 seconds. The flow meter showed that during the approximately 20 seconds of operation covering grid B12, a total of 9.2 L of grout was pumped into B12, recording a grouting consumption of 9.2 L / m². 2 A laser scanner positioned 2 meters behind the vehicle scanned the completed area and calculated the average thickness of the reverse grout layer on the B12 grid surface to be 2.3 mm. As the grouting vehicle moved forward, hundreds of such records were generated sequentially, forming a complete sequence of unit grid multidimensional grouting process parameters and a unit grid sequence.

[0036] By collecting multi-dimensional data in real time, including grout penetration time, grout consumption per unit area, and surface slurry height, and combining this with high-precision measurements from intelligent sensors, the grouting process is comprehensively monitored. This ensures that the grouting material penetrates uniformly and effectively fills the large-pore asphalt mixture, thereby achieving the predetermined grout fullness requirements. The use of intelligent sensors improves the efficiency and accuracy of data acquisition, reduces human intervention and errors, and ensures controllable grouting quality and high efficiency in road construction.

[0037] Based on the parameter sequence of the multidimensional grouting process of the unit grid, a joint analysis of the uncertainty of the overall and independent grouting state is performed to obtain the corrected overall grouting state uncertainty coefficient.

[0038] Further details are attached. Figure 2 As shown, this application further includes the following steps: performing a joint time-series iterative state analysis on the multidimensional grouting process parameter sequence of the unit grid to determine the overall grouting state uncertainty coefficient; obtaining the target unit grid reference grouting full state process parameters, and performing independent grouting state uncertainty analysis on the multidimensional grouting process parameter sequence of the unit grid to obtain a set of unit grid independent grouting state uncertainty coefficients; performing a screening structure continuity constraint evaluation on the unit grid sequence based on the set of unit grid independent grouting state uncertainty coefficients to obtain the screening structure continuity constraint evaluation result; and correcting the overall grouting state uncertainty coefficient according to the screening structure continuity constraint evaluation result to obtain the corrected overall grouting state uncertainty coefficient.

[0039] Specifically, throughout the grouting process, a joint analysis of the uncertainty of the grouting state is conducted on the parameter sequence of the multidimensional grouting process of the unit grid, both globally and independently. First, a global joint time-series iterative state analysis is performed on the parameter sequence of the multidimensional grouting process of the unit grid, focusing on the macroscopic trend, multi-scale fluctuations, and time-series patterns of the data, and outputting a global grouting state uncertainty coefficient that reflects the uncertainty of the entire grouting process.

[0040] The process parameters for the target unit grid's baseline grouting fullness are obtained; that is, under ideal conditions, the parameters that a unit grid with excellent grouting fullness should possess are reference values ​​or reasonable ranges. For each independent unit grid, its actually collected multidimensional process parameters are compared and comprehensively evaluated item by item with the above-mentioned baseline parameters to determine the degree to which the grouting state of the individual grid deviates from the ideal state, and this is quantified into an uncertainty coefficient, reflecting only the local risk at that point. For example, the actual parameters of a certain grid are an infeed time of 32 seconds and a flow rate of 6.5 L / m. 2 The backflow height is 1.2mm, consistent with the reference range of 15-25s and 8.0-9.0L / m. 2 Compared to 2.0-3.0mm, all three parameters deviated significantly. Using a pre-defined scoring model, the independent uncertainty coefficient of this grid was calculated to be 0.85. After independently analyzing each unit grid within the construction area, a set of independent grouting state uncertainty coefficients for each unit grid was generated, forming a point-like distribution map reflecting local quality risks.

[0041] Based on a preset threshold for the uncertainty coefficient of independent grouting states, coefficients smaller than this threshold are selected from the set of uncertainties for independent grouting states within each unit grid. This selection is then used to map and filter the unit grid sequence, resulting in a set of reliable unit grids. Instead of accepting all algorithm results, low-risk grids, such as qualified areas with coefficients <0.35, are first selected. Their apparent state is then rapidly verified on-site within 5-10 minutes after grouting. Spatial clustering algorithms, such as mean-shift filtering, are used to analyze the distribution of all non-compliant points. The core logic is that isolated non-compliant points may be due to random errors, but clusters of continuous non-compliant points are highly likely to indicate real defects. The output is the evaluation result of the continuity constraint of the selected structure, i.e., the areas that need correction.

[0042] Based on the selected structural continuity constraint assessment results, the overall grouting state uncertainty coefficient is corrected. The correction logic is as follows: if empirical evidence confirms the existence of large-area or severe continuity defects, the overall uncertainty coefficient is adjusted upwards, because severe local problems affect global reliability; if only sporadic problems are found, the coefficient may be adjusted downwards or slightly. The final corrected overall grouting state uncertainty coefficient is obtained through this correction. The corrected overall grouting state uncertainty coefficient is achieved by processing the selected defective meshes to adjust the overall grouting state uncertainty coefficient, making it accurately reflect the stability of the entire grouting process.

[0043] For example, overall analysis showed that the infiltration time in the middle section of the bridge was generally about 30% longer than that at both ends, with significant fluctuations, and the initial overall coefficient was determined to be 0.65. Independent analysis revealed a transverse high-risk zone in the middle section with independent coefficients generally higher than 0.78. Spot checks were conducted on the appearance of this high-risk zone and its surrounding low-risk grids. It was found that the backflow height in the high-risk zone was generally only 1.2-1.8 mm, with no air bubbles; while the surrounding checkpoints were in good condition. Spatial cluster analysis confirmed that this high-risk zone was a continuous area. The assessment result was: a continuous high-risk zone of incomplete grouting was confirmed in spans 15-18, approximately 80 meters long, with a confidence level of 95%. Due to the empirical confirmation of a severe continuous defect extending 80 meters, the initial coefficients were significantly revised. The revised overall grouting state uncertainty coefficient was finally determined to be 0.85, triggering the highest level of quality alarm.

[0044] During construction, overall joint temporal iterative state analysis helps identify fluctuations and instabilities throughout the grouting process, while independent grouting state uncertainty analysis further evaluates each unit grid, ensuring the uniformity and stability of grouting quality. Screening structural continuity constraint assessment helps identify areas that do not meet quality requirements in a timely manner, preventing potential grouting defects from affecting the overall construction quality. Finally, by correcting the overall uncertainty coefficient, the stability of the grouting process is accurately assessed, thereby ensuring the quality and long-term durability of the road construction.

[0045] Furthermore, this application also includes the following steps: performing adjacency parameter consistency identification on the unit grid multidimensional grouting process parameter sequence, and constructing an adjacency parameter consistency state matrix sequence; performing overall joint multi-scale analysis based on the adjacency parameter consistency state matrix sequence to obtain a multi-scale state fluctuation feature set; and performing time-series iterative state analysis on the multi-scale state fluctuation feature set to obtain the overall grouting state uncertainty coefficient.

[0046] Furthermore, this application also includes the following steps: serializing the multi-scale state fluctuation feature set in descending order of scale to obtain a multi-scale state fluctuation feature sequence; iteratively enhancing the multi-scale state fluctuation feature sequence until the end of the sequence is reached to obtain a target multi-scale state fluctuation enhancement feature; and determining the overall grouting state uncertainty coefficient based on the target multi-scale state fluctuation enhancement feature.

[0047] Specifically, the consistency of adjacent parameters in the multidimensional grouting process parameter sequence of a unit grid is identified. This involves traversing the data of each pair of adjacent grids in the parameter sequence and calculating their relative changes in each parameter. For example, comparing the energy consumption of grid i and grid i+1, if the former is 8.5 L / m... 2 The latter is 8.7 L / m 2 If the change is gradual, a stable state value is generated; if the latter abruptly changes to 5.2 L / m 2 This generates a state value representing a sudden change. The same operation is performed on infiltration time and backflow height, ultimately organizing the state evaluation values ​​of the parameters into a matrix. The diagonal lines represent the stability of the parameter itself, while the off-diagonal lines may represent the coupling changes between parameters. Generating such a matrix for each pair of adjacent grids yields a sequence of adjacent parameter consistency state matrices, with each matrix corresponding to a pair of adjacent grids. The rows and columns of the matrix typically represent different grouting parameters, such as row 1: infiltration time, row 2: consumption, row 3: backflow height. Each element in the matrix expresses the consistency state of the pair of grids on a specific parameter, such as the rate of change, difference, or statistically based stability score. The entire sequence characterizes the continuity relationship between all adjacent grids along the grouting path.

[0048] A comprehensive, multi-scale analysis is performed based on the consistency state matrix sequence of adjacent parameters. "Comprehensive" refers to all data across the entire construction area; "joint" means considering the correlation between multiple parameters such as infiltration time, grout consumption, and backflow height; and "multi-scale" refers to observing data fluctuation patterns at different scales. Multiple analysis scales are set, and at each scale, a sliding window traverses the entire construction area, performing joint statistical analysis on the original parameters of all grids within the window and their adjacent matrix sequences. This analysis focuses not only on the mean of individual parameters but also on their variance, trends, and the collaborative change patterns between different parameters. For example, within a 10-meter scale window, a coupled trend of gradually increasing infiltration time and gradually decreasing grout consumption is observed. Several such quantitative features are extracted at each scale, and the features extracted from all scales together constitute a multi-scale state fluctuation feature set. This multi-scale state fluctuation feature set is a dataset of fluctuation features obtained through multi-scale analysis, used to describe the fluctuation patterns and laws at various scales during the grouting process, revealing the overall trend and local uncertainties of the grouting state.

[0049] The multi-scale state fluctuation feature set is serialized according to its scale from largest to smallest, forming a multi-scale state fluctuation feature sequence. Simply put, it's a sequence formed by arranging the various state fluctuation features in the multi-scale state fluctuation feature set in descending order of scale.

[0050] The system iteratively enhances multi-scale state fluctuation feature sequences, starting with the first feature (the largest scale feature). It calculates, compares, and fuses this feature with features at the next scale, generating an enhanced feature with richer information. This enhanced feature is then fused with the next scale feature, and so on, until all scales in the sequence have been processed. In other words, the similarity between the first and second features is calculated to obtain a similarity set. This set is then normalized to form a weight vector, which is added to an initially empty matrix. This matrix is ​​then convolved with the second feature to obtain the first enhanced feature. This first enhanced feature is then used to apply the same operation to the third feature to generate the second enhanced feature. This process is repeated until the feature is fused with the last feature in the sequence, ultimately outputting the target multi-scale state fluctuation enhanced feature. Essentially, it allows macroscopic trend information to guide and weightedly fuse microscopic details, ensuring the final feature captures the overall trend without losing crucial local anomaly signals.

[0051] The overall grouting state uncertainty coefficient is determined based on the enhanced characteristics of the target's multi-scale state fluctuations. The central control system maintains a feature-uncertainty coefficient mapping table trained on a large amount of historical data, linking fluctuation characteristics with corresponding grouting state uncertainty coefficients. For example, when feature A is within a certain range and feature B exhibits a certain pattern, the overall uncertainty is usually high. Inputting the enhanced characteristics of the target's multi-scale state fluctuations into the feature-uncertainty coefficient mapping table directly yields the overall grouting state uncertainty coefficient. It is assumed that the enhanced characteristics of the target's multi-scale state fluctuations mainly include the coefficient of variation of the spatial distribution of energy consumption (A), the infiltration time-energy consumption synergistic anomaly index (B), and the density of micro-mutation points (C), all normalized to the range of 0-1; a larger value indicates a more significant fluctuation or anomaly. The coefficient of variation of spatial distribution of grout consumption reflects the degree of unevenness of grout consumption per unit area in the entire road section, with 0 indicating extremely uniformity and 1 indicating extremely unevenness; the infiltration time-consumption synergistic anomaly index reflects the intensity of the abnormal coupling mode of longer infiltration time and reduced grout consumption in the entire road section, with 0 indicating no such abnormal mode and 1 indicating that the mode is very significant; the density of micro-mutation points reflects the proportion of severely inconsistent points detected in the adjacency matrix sequence in the total number of points, with 0 indicating no mutations and 1 indicating many mutation points. Mode 1 represents excellent stability, with a consumption spatial distribution variation coefficient <0.15, a penetration time-consumption synergistic anomaly index <0.1, and a micro-mutation point density <0.05. The uncertainty coefficient is calculated using the logic of (A*0.3+B*0.4+C*0.3)*0.8, resulting in a coefficient range of 0 to 0.2. Mode 2 represents slight fluctuations, with 0.15 ≤ consumption spatial distribution variation coefficient <0.3, 0.1 ≤ penetration time-consumption synergistic anomaly index <0.25, and 0.05 ≤ micro-mutation point density <0.15. The uncertainty coefficient is calculated using the logic of 0.2+(A*0.3+B*0.4+C*0.3)*0.6, resulting in a coefficient range of 0.2 to 0.45. Mode C represents a moderate anomaly, where 0.3 ≤ consumption spatial distribution variation coefficient < 0.5, 0.25 ≤ penetration time-consumption co-anomaly index < 0.4, and 0.15 ≤ micro-mutation point density < 0.25. The uncertainty coefficient is calculated as 0.45 + (A*0.3 + B*0.4 + C*0.3) * 0.5, resulting in a coefficient range of 0.45 to 0.7. Mode 4 represents a severe defect risk, where consumption spatial distribution variation coefficient ≥ 0.5, penetration time-consumption co-anomaly index ≥ 0.4, and micro-mutation point density ≥ 0.25. The uncertainty coefficient is calculated as 0.7 + (A*0.3 + B*0.4 + C*0.3) * 0.3, resulting in a coefficient range of 0.7 to 1.

[0052] By identifying the consistency of adjacent parameters and performing multi-scale analysis, the relationships and fluctuation characteristics between different parameters during grouting are accurately captured, further improving the accuracy and stability of grouting status assessment. The iterative enhancement process ensures that each fluctuation characteristic is effectively improved, making the uncertainty coefficient of the grouting status more accurate. The stability and uncertainty of the entire grouting process can be comprehensively assessed, providing strong support for quality control and real-time adjustments during construction, effectively optimizing grouting quality, and ensuring the long-term durability and performance of pavement engineering.

[0053] Furthermore, this application also includes the following steps: screening the set of uncertainties in the independent grouting state of the unit grid according to a preset threshold for the uncertainty coefficient of the independent grouting state, and mapping and screening the unit grid sequence according to the screening results to obtain a set of state-credible unit grids; detecting the apparent response of the set of state-credible unit grids 5 to 10 minutes after grouting is completed to obtain a set of apparent response detection results; traversing the set of apparent response detection results to evaluate the structural continuity constraints, and performing mean drift screening on the set of evaluation results to obtain screened structural continuity constraint evaluation results.

[0054] Furthermore, this application also includes the following steps: within 5 to 10 minutes after grouting is completed, check whether the continuous return grout height of the reliable unit grid set of the detection state is greater than or equal to 2 to 3 mm, whether air bubbles are continuously discharged, and whether the surface grout is uniform and free from segregation, and obtain the apparent response detection result set.

[0055] Specifically, a preset threshold for the independent grouting state uncertainty coefficient is used as a critical value to determine the reliability of the grouting state of a single grid. When the independent grouting state uncertainty coefficient of a unit grid is less than the preset threshold, the grouting process of that unit grid is considered relatively stable and meets the quality standards. The preset threshold is applied to filter the set of unit grid independent grouting state uncertainty coefficients, selecting all unit grids with independent grouting state uncertainty coefficients less than the preset threshold, and mapping them to their corresponding unit grids to obtain a set of reliable unit grids. Instead of directly checking the most suspicious grids, the algorithm randomly selects those grids considered to be in the best state. By sampling the top performers, if even the top performers show apparent problems, the quality foundation of the entire region is highly questionable; conversely, if the top performers perform well, it provides strong support for overall quality confidence. These selected low-uncertainty grids are extracted from their spatial identifiers (unit grid sequences) through mapping and filtering to form a set of reliable unit grids to be verified.

[0056] Within the critical time window of 5 to 10 minutes after grouting, when the grout has initially stabilized but not yet solidified, a rapid appearance inspection is initiated for the entire assembly. This inspection is conducted by patrol personnel equipped with specialized equipment or an automated inspection vehicle, performing three rapid checks on each grid within the assembly. Using a handheld laser thickness gauge or depth micrometer, the continuous grout return height is measured and recorded to determine if it meets the requirement of ≥2–3 mm. Visual observation or high-definition video recording is used to determine if there are continuous, active fine air bubbles escaping from the grid surface. Visual inspection or image analysis is used to determine if the surface grout is uniform, dense, free of bleeding, and without segregation. A uniform surface without segregation indicates even distribution of grout material, more complete filling of voids, and more stable quality. All inspection results are structured and recorded to form an appearance response inspection result set.

[0057] The system iterates through the apparent response detection results set to perform structural continuity constraint evaluation, identifying all meshes that fail at least one apparent index. The two-dimensional planar coordinates of these non-compliant meshes are extracted and used as input data. Mean shift filtering is applied to identify clusters with high spatial density, ignoring sparse, isolated noise points while highlighting signal points that are close to each other and form meaningful spatial clusters, thus accurately locating the true suspected continuous defect areas. Similar to a density detector, it searches for density peaks in the space formed by all non-compliant points. Isolated meshes far from other non-compliant points have very low point density and are identified as background noise or isolated anomalies. Conversely, if multiple non-compliant points are adjacent or close to each other, forming a locally high-density area, it is identified as a meaningful cluster and judged as a suspected real defect area with spatial continuity. The system outputs the center location, approximate range, and number of meshes contained in these clusters. For example, a total of 12 apparent non-compliant grids were found. After mean shift screening, 8 of them belonged to two spatial clusters: cluster A contained 5 continuous grids and cluster B contained 3 continuous grids. The remaining 4 were isolated points.

[0058] By pre-setting an independent grouting state uncertainty coefficient threshold, stable regions during the grouting process are effectively identified, and high-quality meshes are selected to avoid over- or under-grouting. Apparent response detection and structural continuity constraint assessment further ensure the uniformity and filling degree of the grouting material, promptly identifying areas that do not meet quality requirements. Mean drift screening optimizes the assessment results and improves the accuracy of grouting quality evaluation.

[0059] Based on the modified overall grouting state uncertainty coefficient, the grouting fullness is quickly assessed to obtain the target grouting fullness assessment result.

[0060] Furthermore, this application also includes the following steps: when the uncertainty coefficient of the corrected overall grouting state is greater than or equal to a preset coefficient threshold, a field drilling test is triggered to obtain a random grouting fullness evaluation result; the random grouting fullness evaluation result is used as the target grouting fullness evaluation result.

[0061] Specifically, based on the engineering quality requirements, a preset coefficient threshold is used to determine whether the overall grouting state has reached an acceptable level of stability. If the corrected overall grouting state uncertainty coefficient is higher than the preset coefficient threshold, it means that there may be unevenness or instability during the grouting process, requiring further verification and handling.

[0062] When the overall grouting uncertainty coefficient is greater than or equal to a preset threshold, an on-site drilling test is triggered, requiring quality inspectors to conduct destructive core sampling operations on designated road sections according to a specific plan. An inspection work order is automatically generated, containing not only routine inspection requirements but also clearly marking the road sections requiring focused inspection, i.e., high-risk areas. Based on the risk coefficient and the area of ​​the risk zone, an automatically suggested core drilling frequency significantly higher than the standard frequency is proposed. For example, it might require core drilling locations to be laid out at a density of one point every 10 meters within a 70-meter risk section; simultaneously, within the larger construction area to which this risk section belongs, the overall inspection frequency is increased to one point every 50 meters. The placement of drilling points is determined using grid coordinates, exhibiting a degree of randomness but ensuring coverage of high-risk areas. After receiving the instructions, on-site personnel use a core drilling machine to collect samples at the designated locations and send the core samples to the laboratory. Laboratory personnel cleaned and scanned the cross-sections of the core samples, and used digital image processing software to accurately calculate the ratio of the grout-filled area to the total void area, obtaining specific fullness values ​​for each core sample, thus forming the random grouting fullness assessment results. These random grouting fullness assessment results are obtained after conducting field drilling tests. Typically, this type of assessment involves indicators such as borehole depth, grout filling degree, and porosity to determine whether the grouting has achieved the expected results.

[0063] The results of random grout fullness assessments are used as the final target grout fullness assessment results, recorded and fed back to verify the quality of the overall grouting process and ensure that construction meets the expected standards. If the results show that the grout fullness does not meet the requirements, rectification may be necessary. When the overall grouting state is unstable or uneven, on-site drilling tests are promptly triggered for verification to ensure that the grouting quality meets the requirements. On-site drilling tests can provide direct physical data, making up for the shortcomings of traditional grout fullness assessment methods. In particular, they can detect local problems that may be overlooked by conventional assessment methods, identify potential grouting defects during construction, and make timely corrections. This not only improves the accuracy of construction quality control but also ensures the long-term stability and durability of the pavement.

[0064] Furthermore, this application also includes the following steps: when the uncertainty coefficient of the corrected overall grouting state is less than the preset coefficient threshold, a field drilling test is conducted according to the conventional fullness detection frequency to obtain the conventional grouting fullness evaluation result; the conventional grouting fullness evaluation result is used as the target grouting fullness evaluation result.

[0065] Specifically, the corrected overall grouting state uncertainty coefficient obtained during the grouting process is compared with a preset threshold coefficient. If the corrected overall grouting state uncertainty coefficient is less than the preset threshold coefficient, it indicates that the grouting process is relatively stable and uniform, and the quality control is good. No alarms for intensified or reinforced inspections will be triggered; instead, on-site drilling tests will be automatically selected and conducted according to a predetermined routine fullness testing frequency. The actual fullness of the grouting is evaluated by drilling samples from the asphalt mixture.

[0066] Analysis of borehole test results yields conventional grout fullness assessment results, including the filling degree of different boreholes, indicating whether the grouting is uniform and whether voids exist. If the borehole results show that the grouting is sufficient and uniform, it indicates that the grouting quality meets the standards. The conventional grout fullness assessment results are used as the final target grout fullness assessment results. Since the corrected overall grouting state uncertainty coefficient is below the threshold, indicating that the grouting process is stable, the conventional assessment results are directly used as the basis for quality control to confirm whether the construction quality meets the standards. The conventional grout fullness assessment results represent a standardized and compliant verification of project quality under the premise of controllable risk.

[0067] The target grout fullness assessment result is the final grouting quality assessment result. If the uncertainty coefficient of the corrected overall grouting state is less than the preset threshold, the result of the conventional borehole test is directly used as the target grout fullness assessment result, which serves as the basis for judging the construction quality.

[0068] When the uncertainty coefficient of the overall grouting state is lower than the preset threshold, on-site drilling tests are conducted directly using the conventional fullness detection frequency. This can quickly confirm the quality of the grouting process. By utilizing the results of conventional on-site drilling tests, the stability and uniformity of the grouting quality are ensured, while avoiding unnecessary additional tests, thereby improving construction efficiency and accuracy.

[0069] Furthermore, this application also includes the following steps: performing fluctuation analysis on the target grout fullness assessment result and a preset number of previous historical target grout fullness assessment results to obtain a fluctuation coefficient; when the fluctuation coefficient is greater than or equal to a preset fluctuation coefficient threshold, correcting the conventional fullness detection frequency according to the fluctuation coefficient, and performing conventional grout fullness assessment based on the corrected conventional fullness detection frequency.

[0070] Specifically, during the grouting process, the grout fullness assessment results obtained through on-site drilling tests serve as an evaluation of the current grouting quality, including data such as grout filling degree and porosity. Simultaneously, a predetermined set of historical target grout fullness assessment results is obtained—that is, a collection of multiple grout fullness assessment results gathered over a past period—reflecting the recent stability level of construction quality.

[0071] A fluctuation analysis is performed using the current target grout fullness assessment results and historical data sets. The stability of the grouting process is measured by calculating the fluctuation coefficient, which is the ratio of the standard deviation to the mean. A large fluctuation coefficient indicates significant fluctuations during the grouting process, potentially suggesting problems with quality control and instability. For example, the coefficient of variation, defined as the fullness measurement value (e.g., standard deviation / mean × 100%), indicates greater quality instability as its value increases.

[0072] The preset fluctuation coefficient threshold is a critical value pre-set based on the project's requirements for quality stability, such as 5%. When the fluctuation coefficient exceeds the preset threshold, it is determined that the construction quality stability has declined to a level requiring enhanced monitoring. At this point, the frequency of routine fullness testing needs to be adjusted, increasing the testing frequency to improve the real-time monitoring and feedback speed of grouting quality. If the fluctuation coefficient is small, the routine testing frequency can be maintained. Further grouting fullness assessment is conducted based on the adjusted routine fullness testing frequency. This means that if the grouting process fluctuates significantly, increasing the testing frequency can frequently identify potential problems and allow for timely adjustments. For example, the rule stipulates that the frequency should be increased by 25% when the fluctuation coefficient is between 5% and 7%, and by 50% when it is between 7% and 10%. Assuming the current fluctuation coefficient is 5.8%, the original frequency of one point every 200 meters would be adjusted to one point every 150 meters. Fluctuation analysis and fluctuation coefficient calculation can provide timely feedback on non-uniformity or abnormal fluctuations during the grouting process. When the fluctuation coefficient is large, by adjusting the frequency of routine fullness testing and increasing the frequency of testing, quality data from the construction site can be obtained more frequently, thereby enabling timely detection and adjustment of problems and improving the monitoring capability of grouting quality.

[0073] In summary, the rapid evaluation method for grout fullness of large-pore asphalt mixtures provided in this application has the following technical advantages: By obtaining the groutability benchmark conditions of the asphalt mixture with large porosity, a grouting command is triggered when the groutability benchmark conditions are met. The target grouting route is obtained, and grouting is performed on the target grouting route based on the grouting command. Simultaneously, using a unit grid as the detection unit, the grouting penetration time, grouting consumption per unit area, and surface backflow height are collected in real time to obtain the unit grid multidimensional grouting process parameter sequence and the unit grid sequence. Based on the unit grid multidimensional grouting process parameter sequence, a joint analysis of the uncertainty of the overall and independent grouting states is performed to obtain a corrected overall grouting state uncertainty coefficient. Based on the corrected overall grouting state uncertainty coefficient, a rapid assessment of grout fullness is performed to obtain the target grout fullness assessment result. In other words, after confirming that the groutability benchmark conditions are met, the specific grouting path is determined, and the grouting operation is carried out. Intelligent sensing elements are used to collect multiple parameters in real time, such as grout penetration time, grout consumption per unit area, and surface grouting height. A joint analysis of the uncertainty of the overall and independent grouting states is introduced. Through the analysis of the multidimensional grouting process parameter sequence, the corrected overall grouting state uncertainty coefficient is calculated, anomalies are detected in a timely manner, and adjustments are made to ensure the uniformity and fullness of the grouting process. This avoids the degradation of the mixture performance due to grouting problems, ensuring the quality and performance of the asphalt mixture and improving the durability and stability of the project. The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0074] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A rapid evaluation method for the grout fullness of asphalt mixtures with large porosity, characterized in that, include: Obtain the groutability benchmark conditions for the asphalt mixture with large porosity structure. When the groutability benchmark conditions are met, trigger the grouting command. Obtain the target grouting route, perform grouting operation on the target grouting route based on the grouting command, and at the same time, use the unit grid as the detection unit to collect the grouting infiltration time, grouting consumption per unit area and surface grouting height in real time, and obtain the unit grid multidimensional grouting process parameter sequence and unit grid sequence. Based on the parameter sequence of the multidimensional grouting process of the unit grid, a joint analysis of the uncertainty of the overall and independent grouting state is performed to obtain the corrected overall grouting state uncertainty coefficient. Based on the aforementioned corrected overall grouting state uncertainty coefficient, a rapid assessment of grouting fullness is performed to obtain the target grouting fullness assessment result; Based on the parameter sequence of the multidimensional grouting process of the unit grid, a joint analysis of the uncertainty of the overall and independent grouting states is performed to obtain the corrected overall grouting state uncertainty coefficient, including: A comprehensive joint time-series iterative state analysis was performed on the parameter sequence of the multidimensional grouting process of the unit grid to determine the overall grouting state uncertainty coefficient; Obtain the process parameters of the target unit grid reference grouting full state, and perform independent grouting state uncertainty analysis on the multidimensional grouting process parameter sequence of the unit grid respectively to obtain the set of uncertainty coefficients of the independent grouting state of the unit grid; Based on the set of uncertainty coefficients of the independent grouting state of the unit grid, the structural continuity constraint evaluation of the unit grid sequence is screened to obtain the screening structural continuity constraint evaluation results; The uncertainty coefficient of the overall grouting state is corrected based on the evaluation results of the continuity constraint of the screened structure, and the corrected uncertainty coefficient of the overall grouting state is obtained. A comprehensive joint time-series iterative state analysis is performed on the parameter sequence of the multidimensional grouting process within the unit grid to determine the overall grouting state uncertainty coefficient, including: Adjacent parameter consistency identification is performed on the parameter sequence of the multidimensional grouting process of the unit grid, and an adjacent parameter consistency state matrix sequence is constructed. Based on the adjacency parameter consistent state matrix sequence, a global joint multi-scale analysis is performed to obtain a multi-scale state fluctuation feature set. A time-series iterative state analysis is performed on the multi-scale state fluctuation feature set to obtain the overall grouting state uncertainty coefficient; A time-series iterative state analysis is performed on the multi-scale state fluctuation characteristic set to obtain the overall grouting state uncertainty coefficient, including: The multi-scale state fluctuation feature set is serialized in descending order of scale to obtain a multi-scale state fluctuation feature sequence. The target multi-scale state fluctuation enhancement features are obtained by iteratively enhancing the multi-scale state fluctuation feature sequence until the end of the sequence is reached. The overall grouting state uncertainty coefficient is determined based on the enhanced characteristics of the target multi-scale state fluctuations. Based on the set of uncertainty coefficients for the independent grouting states of the unit grid, the structural continuity constraint assessment of the unit grid sequence is performed to obtain the assessment results, including: The set of uncertainties of independent grouting states of the unit grid is filtered according to a preset threshold for the uncertainty coefficient of independent grouting states, and the unit grid sequence is mapped and filtered according to the filtering results to obtain a set of unit grids with reliable states. The apparent response of the aforementioned state-credible unit grid set was detected 5–10 minutes after grouting was completed, and the apparent response detection result set was obtained. The apparent response detection result set is traversed to evaluate the structural continuity constraint, and the evaluation result set is filtered by mean shift to obtain the filtered structural continuity constraint evaluation results. Based on the aforementioned corrected overall grouting state uncertainty coefficient, a rapid assessment of grouting fullness is performed to obtain the target grouting fullness assessment result, including: When the uncertainty coefficient of the corrected overall grouting state is greater than or equal to the preset coefficient threshold, a field drilling test is triggered to obtain the random grouting fullness evaluation result. The random grout fullness assessment result is used as the target grout fullness assessment result.

2. The rapid evaluation method for grout fullness of asphalt mixtures with large porosity as described in claim 1, characterized in that, The groutability benchmark conditions include a porosity of 20% to 28% and a compaction degree of ≥97% for the macroporous asphalt mixture. The fluidity of the grouting material is 10-14s, the bleeding rate is less than or equal to 1%, and the initial setting time is greater than or equal to 30min.

3. The rapid evaluation method for grout fullness of asphalt mixtures with large porosity as described in claim 1, characterized in that, Within 5 to 10 minutes after grouting is completed, check whether the continuous return grout height of the reliable unit grid set is greater than or equal to 2 to 3 mm, whether air bubbles are continuously discharged, and whether the surface grout is uniform and free from segregation, and obtain the apparent response test result set.

4. The rapid evaluation method for grout fullness of large-pore asphalt mixtures as described in claim 1, characterized in that, When the uncertainty coefficient of the corrected overall grouting state is less than the preset coefficient threshold, on-site drilling tests are conducted at the conventional fullness detection frequency to obtain conventional grouting fullness evaluation results. The conventional grout fullness assessment result is used as the target grout fullness assessment result.

5. The rapid evaluation method for grout fullness of asphalt mixtures with large porosity as described in claim 4, characterized in that, After obtaining the target grout fullness assessment results, the following steps are also included: A fluctuation analysis is performed on the target grout fullness assessment result and a preset number of previous historical target grout fullness assessment results to obtain the fluctuation coefficient. When the fluctuation coefficient is greater than or equal to the preset fluctuation coefficient threshold, the conventional fullness detection frequency is corrected according to the fluctuation coefficient, and the conventional grouting fullness is evaluated based on the corrected conventional fullness detection frequency.

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