Intelligent strength testing system for pavement repair fiber composite

Abstract

The present application relates to the field of fiber composite material strength detection, and more particularly to a road repair fiber composite material strength intelligent testing system, which is characterized by a feature extraction module for obtaining performance data of the fiber composite material to extract corresponding material mixing features; a suitable analysis module for analyzing the quality suitable representation value of the fiber composite material based on the material mixing features to classify the quality suitable categories of the fiber composite material; a selection and calling module for selecting and calling a quality evaluation module or a material labeling module according to the quality suitable categories; the quality evaluation module for evaluating and analyzing the strength quality of the fiber composite material; and the material labeling module for labeling the fiber composite material and performing secondary stirring and dispersion processing. The system realizes efficient, accurate and automatic detection of the strength of the road repair fiber composite material, significantly improves the detection efficiency and quality determination reliability, and reduces the cost of invalid testing and material waste.

Description

Technical Field

[0001] This invention relates to the field of strength testing of fiber composite materials, and in particular to an intelligent strength testing system for fiber composite materials used in road repair. Background Technology

[0002] Fiber-reinforced composite materials, characterized by high tensile strength, good crack resistance, excellent durability, and convenient construction, have become the mainstream material for rapid repair of asphalt pavement defects and are widely used in the repair of pavement potholes, cracks, subsidence, and other structural defects. The mixing uniformity, fiber dispersion state, curing quality, and mechanical strength of fiber-reinforced composite materials used in pavement repair directly determine the load-bearing capacity, deformation resistance, and long-term service life of the pavement repair layer, making them a key aspect of quality control in road engineering construction.

[0003] Therefore, it is necessary to construct an intelligent strength testing system for fiber composite materials used in road repair to accurately determine the strength and uniformity of materials, improve testing efficiency, ensure the quality and service life of road repair, and achieve intelligent quality control.

[0004] Chinese Patent Application Publication No. CN117491170A discloses a fiber composite material strength testing device, including a stamping testing device. This stamping testing device is used to test the impact resistance of fiber composite materials. A fixing clamping device clamps and fixes the fiber composite material to be tested, and a progressive feeding device provides raw materials to the stamping testing device, enabling progressive testing of large batches of materials. The design of the stamping testing device allows for the testing of the impact resistance of fiber composite materials. The worktable provides support for the stamping machine during the fiber composite material testing process and also provides space for the fixing clamping device and the progressive feeding device. The progressive feeding device allows the raw material located between the insert plate and the blocking plate to be fed into the fixing clamping device, while the remaining raw material is divided by the insert plate, achieving the function of progressive feeding.

[0005] However, the following problems still exist in the existing technology.

[0006] Traditional universal testing machines are often used for manual testing, relying on the operator's experience to determine the mixing state and failure result of the material. The testing process is cumbersome and time-consuming, making it difficult to achieve rapid and accurate testing of batch materials. The testing efficiency and the reliability of quality judgment are low. Summary of the Invention

[0007] To address this, the present invention provides an intelligent strength testing system for pavement repair fiber composite materials, which overcomes the problems of existing technologies that rely on traditional universal testing machines for manual testing, depend on the operator's experience to determine the material mixing state and failure results, have a cumbersome testing process, are time-consuming, make it difficult to achieve rapid and accurate testing of batch materials, and have low testing efficiency and reliability of quality judgment.

[0008] To achieve the above objectives, the present invention provides a smart strength testing system for pavement repair fiber composite materials, comprising:

[0009] The feature extraction module is used to obtain the performance data of fiber composite materials in order to extract the corresponding material mixing features, including the thickness of floating fibers and the uniformity of fiber distribution.

[0010] An applicable analysis module, which is connected to the feature extraction module, is used to analyze the quality applicable characterization value of the fiber composite material based on the material mixing characteristics, so as to classify the quality applicable category of the fiber composite material.

[0011] The selectable module is connected to the applicable analysis module and is used to select either the quality assessment module or the material labeling module based on the applicable quality category.

[0012] A quality assessment module, connected to the selection and invocation module, is used to assess and analyze the strength and quality of fiber composite materials, including:

[0013] The fiber composite material after curing is subjected to destructive testing to extract the failure load characteristics and tensile strain of the corresponding material sample, and to calculate the strength load characterization parameters of the material sample to determine whether the corresponding fiber composite material meets the strength load benchmark.

[0014] Obtain a cross-sectional image of the material sample, extract the void concentration area, and based on the void distribution characteristics corresponding to the void concentration area, combine the color deviation of the damaged surface to evaluate the strength quality characterization coefficient and determine the strength quality grade of the corresponding fiber composite material.

[0015] A material labeling module, which is connected to the selection and invocation module, is used to label the fiber composite material and perform secondary stirring and dispersion treatment on the fiber composite material;

[0016] The failure bearing characteristics include the flatness of the failure surface and the bonding strength, and the void distribution characteristics include the variation in void size and the maximum proportion of void depth.

[0017] Furthermore, the applicable analysis module is used to analyze the applicable quality characterization values ​​of the fiber composite material, including:

[0018] The ratio of the floating fiber thickness to the floating fiber thickness threshold is used as the first applicable quality characteristic value;

[0019] The ratio of the fiber distribution uniformity threshold to the fiber distribution uniformity is used as the second applicable quality characteristic value.

[0020] The first quality applicable feature value and the second quality applicable feature value are weighted and summed to determine the quality applicable characterization value.

[0021] Furthermore, the applicable analysis module is used to classify the quality applicable categories of the fiber composite material, including:

[0022] If the quality applicability characterization value of the fiber composite material is greater than or equal to the quality applicability characterization threshold, then the fiber composite material is classified as a low quality applicability category.

[0023] If the quality applicability characterization value of the fiber composite material is less than the quality applicability characterization threshold, then the fiber composite material is classified into the high quality applicability category.

[0024] Furthermore, the selection and invocation module is used to select and invoke the quality assessment module or the material labeling module, including:

[0025] If the fiber composite material is classified as a high-quality applicable category, then select to invoke the quality assessment module;

[0026] If the fiber composite material is in the low-quality applicable category, then select to call the material labeling module.

[0027] Furthermore, the quality assessment module is used to calculate the strength load-bearing characterization parameters of the material sample, including:

[0028] The sum of the ratio of the flatness of the damaged surface to the flatness threshold and the ratio of the bond strength to the bond strength threshold is used as the first strength bearing characteristic value.

[0029] The ratio of the tensile strain of the damaged surface to the tensile strain threshold is used as the second strength bearing characteristic value.

[0030] The first strength bearing characteristic value and the second strength bearing characteristic value are weighted and summed to determine the strength bearing characterization parameter.

[0031] Furthermore, the quality assessment module is used to determine whether the corresponding fiber composite material meets the strength bearing standard, including:

[0032] If the strength bearing capacity characterization parameter of the material sample is greater than or equal to the strength bearing capacity characterization parameter threshold, then the corresponding fiber composite material is determined to meet the strength bearing capacity benchmark.

[0033] Furthermore, the quality assessment module is used to extract areas of concentrated voids, including:

[0034] If there exists an area where the number of voids is greater than a void number threshold, and the uniformity of the spacing between adjacent voids is greater than a spacing uniformity threshold, then the area is defined as the void concentration area.

[0035] Furthermore, the quality assessment module is used to evaluate the strength quality characterization coefficient, including:

[0036] The sum of the ratio of the void size variation to the void size variation threshold and the ratio of the maximum void depth percentage to the maximum void depth percentage threshold is used as the first strength quality characteristic value.

[0037] The ratio of the chromaticity deviation of the damaged surface to the chromaticity deviation threshold is used as the second intensity quality characteristic value.

[0038] The first strength quality characteristic value and the second strength quality characteristic value are weighted and summed to determine the quality strength characterization coefficient.

[0039] Furthermore, the quality assessment module is used to determine the strength quality grade of the corresponding fiber composite material, including:

[0040] The correspondence between the strength quality grade and the predetermined strength characterization coefficient range is preset;

[0041] Determine the range of mass strength characterization coefficients to which the mass strength characterization coefficients of the material sample belong;

[0042] Set the strength quality level corresponding to the range of the quality strength characterization coefficients as the strength quality level of the corresponding fiber composite material;

[0043] Among them, the strength quality grade and the range of quality strength characterization coefficients correspond one-to-one.

[0044] Furthermore, it also includes:

[0045] Increase the monitoring frequency of fiber composite materials undergoing secondary stirring and dispersion treatment, wherein the monitoring frequency is positively correlated with the applicable quality characterization value.

[0046] Compared with existing technologies, this invention acquires performance data of fiber composite materials to extract corresponding material mixing characteristics; based on these characteristics, it analyzes the applicable quality characterization values ​​of the fiber composite materials to classify them into applicable quality categories; and according to these categories, it evaluates and analyzes the strength and quality of the fiber composite materials, or labels them, and then performs secondary mixing and dispersion treatment. This system achieves high efficiency, accuracy, and automation in the strength testing of fiber composite materials for road repair through a closed-loop process of pre-inspection grading, intelligent diversion, comprehensive evaluation, and secondary mixing optimization of unqualified materials. This significantly improves testing efficiency and the reliability of quality judgment, while reducing the cost of invalid tests and material waste.

[0047] In particular, this invention incorporates a pre-screening step, quantifying fiber buoyancy and delamination defects through the thickness of floating fibers. A higher value indicates more severe fiber buoyancy, more pronounced delamination, poorer material mixing uniformity, and lower base molding quality. Quantitatively characterizing the overall dispersion and spatial distribution consistency of fibers in the matrix through fiber distribution uniformity. Lower uniformity indicates more significant fiber agglomeration, greater internal density differences, and a higher risk of weak areas and internal voids. Based on this, this invention calculates quality applicability characterization values ​​to comprehensively reflect the mixing uniformity, fiber dispersion, internal structural density, and pre-molding qualification of the fiber composite material. It accurately identifies typical defects such as fiber buoyancy delamination, uneven distribution, and loose agglomeration, providing reliable data support for subsequent material quality applicability classification. The pre-screening step constructed by this invention can quickly identify unqualified materials before strength testing of fiber composite materials, preventing them from entering subsequent destructive strength testing processes, saving testing resources, reducing the overall investment in road repair projects, and improving the economy and efficiency of the entire testing and construction process.

[0048] In particular, this invention reflects the actual load-bearing capacity of the fiber composite material under ultimate stress by conducting destructive testing on the cured fiber composite material. Simultaneously, it extracts two core characteristics—smoothness and bond strength—from the corresponding fracture surface, reflecting both the density of the bond between the fibers and the matrix within the material and the reliability of the interfacial bonding. Furthermore, in practical applications, pavement repair materials not only require strength but also a certain degree of deformation toughness to resist traffic loads and temperature stresses. Therefore, this invention introduces tensile strain to simultaneously evaluate the material's strength and toughness, making the assessment and analysis more aligned with the long-term durability requirements of pavement. Furthermore, it calculates strength-bearing capacity characterization parameters to comprehensively characterize the overall load-bearing capacity, interfacial bonding reliability, and deformation toughness of the pavement repair fiber composite material under ultimate stress, providing a fundamental basis for further evaluation of whether the fiber composite material meets the strength-bearing capacity benchmark, forming a rigorous evaluation logic of "first judging compliance, then evaluating grade." This invention effectively improves the accuracy and reliability of fiber composite material strength testing, providing a rigorous quality acceptance basis for pavement repair projects and ensuring that the fiber composite materials used in pavement repair meet the mechanical performance requirements for long-term road service.

[0049] In particular, this invention precisely locates internal density defects in materials by acquiring cross-sectional images and extracting concentrated void areas. It analyzes concentrated areas with numerous, uniformly spaced voids, eliminating the non-critical influence of a few scattered voids. This allows the strength evaluation of fiber composite materials to focus on the core defects that truly affect load-bearing safety, improving the accuracy of the assessment and making it more aligned with engineering practice. The variation in void size reflects the uniformity of internal density. Larger void sizes indicate more uneven mixing and curing, and a looser internal structure, reflecting the dispersion of internal defects and serving as a key indicator for judging the stability of material density. The maximum proportion of void depth quantifies the severity of deep internal defects and the overall weakness of the material. Greater depth and higher proportion indicate that internal defects are closer to the core load-bearing layer and have a more significant impact on strength, thus characterizing the severity of deep weak areas within the material. Furthermore, the color deviation of the damaged surface reflects the uniformity of material curing, fiber distribution consistency, and interfacial bonding quality. A greater color deviation indicates inconsistent curing degree, fiber agglomeration or exposure, and uneven matrix bonding, thus reflecting the quality of material molding and overall structural integrity. Therefore, this invention integrates the above characteristics to achieve a dual evaluation of internal defects and surface uniformity, more comprehensively reflecting the strength quality of fiber composite materials. It further evaluates the strength quality characterization coefficient of fiber composite materials, realizing a comprehensive quantitative evaluation and automatic grading of the material's internal density and curing uniformity, thereby improving the comprehensiveness and intelligence level of strength quality assessment for fiber composite materials. Attached Figure Description

[0050] Figure 1 A functional block diagram of the intelligent strength testing system for pavement repair fiber composite materials according to an embodiment of the invention;

[0051] Figure 2 A logic diagram for classifying the applicable quality categories of fiber composite materials in the embodiments of the invention;

[0052] Figure 3 A logic decision diagram for selecting to invoke the quality assessment module or the material labeling module in an embodiment of the invention;

[0053] Figure 4 This is a logic diagram for determining whether a corresponding fiber composite material meets the strength bearing standard in an embodiment of the invention. Detailed Implementation

[0054] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0055] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0056] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0057] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0058] Please see Figure 1 The diagram shown is a functional block diagram of the intelligent strength testing system for pavement repair fiber composite materials according to an embodiment of the present invention. The intelligent strength testing system for pavement repair fiber composite materials according to an embodiment of the present invention includes:

[0059] The feature extraction module is used to obtain the performance data of fiber composite materials in order to extract the corresponding material mixing features, including the thickness of floating fibers and the uniformity of fiber distribution.

[0060] An applicable analysis module, which is connected to the feature extraction module, is used to analyze the quality applicable characterization value of the fiber composite material based on the material mixing characteristics, so as to classify the quality applicable category of the fiber composite material.

[0061] The selectable module is connected to the applicable analysis module and is used to select either the quality assessment module or the material labeling module based on the applicable quality category.

[0062] A quality assessment module, connected to the selection and invocation module, is used to assess and analyze the strength and quality of fiber composite materials, including:

[0063] The fiber composite material after curing is subjected to destructive testing to extract the failure load characteristics and tensile strain of the corresponding material sample, and to calculate the strength load characterization parameters of the material sample to determine whether the corresponding fiber composite material meets the strength load benchmark.

[0064] Obtain a cross-sectional image of the material sample, extract the void concentration area, and based on the void distribution characteristics corresponding to the void concentration area, combine the color deviation of the damaged surface to evaluate the strength quality characterization coefficient and determine the strength quality grade of the corresponding fiber composite material.

[0065] A material labeling module, which is connected to the selection and invocation module, is used to label the fiber composite material and perform secondary stirring and dispersion treatment on the fiber composite material;

[0066] The failure bearing characteristics include the flatness of the failure surface and the bonding strength, and the void distribution characteristics include the variation in void size and the maximum proportion of void depth.

[0067] Specifically, there are no restrictions on the specific structure of the feature extraction module, applicable analysis module, selection and call module, quality assessment module, and material labeling module. Each module or its units can be composed of logical components or combinations of logical components. Logical components include field-programmable processors, computers, or microprocessors in computers.

[0068] Specifically, the performance data includes material mixing characteristics, failure load characteristics, tensile strain of fiber composite materials, number of voids, uniformity of spacing between adjacent voids, void distribution characteristics, and color deviation of the failure surface.

[0069] Specifically, there are no specific limitations on the acquisition method for material mixing characteristics. Laser contour scanners and machine vision imaging systems can be used to acquire surface images of fiber composite materials. The floating fiber boundaries and thickness information can be extracted through image processing, and the fiber distribution uniformity can be calculated through the gray-level co-occurrence matrix algorithm.

[0070] Specifically, there are no specific limitations on the method of acquiring the flatness of the damaged surface. The flatness can be calculated by scanning the damaged surface with a 3D laser scanner and obtaining point cloud data.

[0071] The flatness refers to the root mean square deviation between the actual contour of the damaged surface and the reference plane after the material sample has undergone a destructive test. It is used to quantitatively characterize the degree of unevenness of the damaged surface, the uniformity of the interface fracture, and the density of the material fracture.

[0072] The reference plane can be generated by performing three-dimensional laser scanning on the damaged surface of the material sample to obtain point cloud data, and then fitting it using the least squares method.

[0073] Specifically, there are no specific limitations on the method for collecting the bond strength of the failure surface. It can be tested by loading a universal testing machine, recording the interface failure load and converting it into bond strength.

[0074] Specifically, there are no specific limitations on the method for collecting tensile strain data of fiber composite materials. Electronic extensometers or digital image correlation methods can be used to collect specimen deformation data in real time during tensile failure tests to obtain tensile strain data.

[0075] Specifically, there are no specific limitations on the acquisition methods for the number of cavities and the uniformity of the spacing between adjacent cavities. Internal cross-sectional images can be obtained through industrial CT tomography or high-resolution cross-sectional imaging. Cavities are identified through image segmentation, the number of cavities is counted, and the uniformity of the spacing between adjacent cavities is calculated.

[0076] Specifically, there are no specific limitations on the acquisition method for the distribution characteristics of cavities. It can be based on CT or cross-sectional images to measure the size and depth of each cavity, calculate the absolute deviation of each cavity and take the average value to obtain the cavity size variation; and count the proportion of the maximum cavity depth to the total thickness of the material sample to obtain the maximum proportion of cavity depth.

[0077] Specifically, there are no specific limitations on the method of acquiring the colorimetric deviation of the damaged surface. The colorimetric information of the damaged surface can be acquired by a colorimeter or a color industrial camera. The colorimetric deviation between the colorimetric of the damaged surface and the reference colorimetric is determined by the total color difference calculation formula through RGB / HSV color space analysis.

[0078] Specifically, by acquiring the chromaticity data of material samples corresponding to several qualified fiber composite materials used in road repair, the mean chromaticity value is calculated, and the mean chromaticity value is used as the reference chromaticity.

[0079] Specifically, the applicable analysis module is used to analyze the applicable quality characterization values ​​of the fiber composite material, including:

[0080] The ratio of the floating fiber thickness to the floating fiber thickness threshold is used as the first applicable quality characteristic value;

[0081] The ratio of the fiber distribution uniformity threshold to the fiber distribution uniformity is used as the second applicable quality characteristic value.

[0082] The first quality applicable feature value and the second quality applicable feature value are weighted and summed to determine the quality applicable characterization value.

[0083] Specifically, the thickness of floating fibers reflects the severity of fiber floating and delamination defects in fiber composites during mixing. A greater thickness indicates more pronounced surface fiber aggregation and more severe delamination, a defect that is irreversible. Even with subsequent secondary mixing and dispersion treatment, the already formed severe delamination is difficult to completely eliminate, directly leading to a decrease in the bond strength between the repair layer and the original pavement, and an increased risk of interface peeling, which is extremely critical to the long-term durability of pavement repair. Fiber distribution uniformity reflects the overall uniformity of fiber dispersion in the matrix. A smaller uniformity indicates more significant fiber agglomeration and density differences, but this defect is somewhat improveable. Secondary mixing and dispersion treatment can, to some extent, break up agglomerates and improve uniformity, thereby improving material quality. Therefore, when performing weighted summation, a higher weighting coefficient is assigned to the first applicable quality characteristic value, which can be selected between 0.6 and 0.7; in this embodiment, it is set to 0.7. Correspondingly, the weighting coefficient for the second applicable quality characteristic value is selected between 0.3 and 0.4; in this embodiment, it is set to 0.3. Of course, the specific values ​​can be fine-tuned based on the sensitivity of different fiber composite materials or actual verification results to ensure the accuracy of the evaluation of the applicable quality characterization values ​​under different working conditions.

[0084] In this embodiment, the purpose of setting the floating fiber thickness threshold and the fiber distribution uniformity threshold is to characterize cases where the preforming qualification of the fiber composite material is poor. By acquiring the performance data of several qualified fiber composite materials used in road repair, the floating fiber thickness data and fiber distribution uniformity data of the corresponding fiber composite materials are retrieved, and the average floating fiber thickness and the average fiber distribution uniformity are calculated. Based on the purpose of setting the above two thresholds, the floating fiber thickness threshold is determined as the product of the average floating fiber thickness and the thickness deviation coefficient, and the fiber distribution uniformity threshold is determined as the product of the average fiber distribution uniformity and the uniformity deviation coefficient. The thickness deviation coefficient is selected within the interval [1.05, 1.1], preferably 1.05 in practice, and the uniformity deviation coefficient is selected within the interval [0.95, 0.98], preferably 0.95 in practice.

[0085] Specifically, this invention establishes a pre-screening stage, quantifying fiber buoyancy and delamination defects through the thickness of floating fibers. A higher value indicates more severe fiber buoyancy, more pronounced delamination, poorer material mixing uniformity, and lower base molding quality. Quantifying fiber distribution uniformity characterizes the overall dispersion and spatial distribution consistency of fibers in the matrix. Lower uniformity indicates more significant fiber agglomeration, greater internal density differences, and a higher risk of weak areas and internal voids. Based on this, this invention calculates quality applicability characterization values ​​to comprehensively reflect the mixing uniformity, fiber dispersion, internal structural density, and pre-molding qualification of the fiber composite material. It accurately identifies typical defects such as fiber buoyancy delamination, uneven distribution, and loose agglomeration, providing reliable data support for subsequent material quality applicability classification. The pre-screening stage constructed by this invention can quickly identify unqualified materials before strength testing of fiber composite materials, preventing them from entering subsequent destructive strength testing processes, saving testing resources, reducing the overall investment in road repair projects, and improving the economy and efficiency of the entire testing and construction process.

[0086] Specifically, please refer to Figure 2 As shown, this is a logic diagram for classifying the quality applicability categories of fiber composite materials according to an embodiment of the present invention. The applicability analysis module is used to classify the quality applicability categories of the fiber composite materials, including:

[0087] If the quality applicability characterization value of the fiber composite material is greater than or equal to the quality applicability characterization threshold, then the fiber composite material is classified as a low quality applicability category.

[0088] If the quality applicability characterization value of the fiber composite material is less than the quality applicability characterization threshold, then the fiber composite material is classified into the high quality applicability category.

[0089] To determine the quality suitability characterization threshold, based on a large amount of performance data of qualified fiber composite materials, the corresponding floating fiber thickness and fiber distribution uniformity data are calculated respectively. Then, the quality suitability characterization value of each qualified sample is calculated one by one according to the same weighting formula. The 95th percentile or mean of these values ​​plus 1.5 times the standard deviation is taken as the critical value to distinguish between "high quality suitability" and "low quality suitability," ensuring that the threshold can cover the normal fluctuation range of most qualified materials. Typically, this threshold is set between 1.05 and 1.25, with the specific value fluctuating slightly depending on the material formulation, process stability, and the stringency of quality control. In this embodiment, the quality suitability characterization threshold is set to 1.15, which can effectively separate low-quality materials with excessively thick floating fibers or severely uneven fiber distribution, preventing them from entering the subsequent destructive testing process, while ensuring that qualified materials are not misjudged, thus achieving a reasonable allocation of testing resources.

[0090] Specifically, please refer to Figure 3As shown, this is a logic decision diagram for selecting and calling the quality assessment module or the material labeling module in an embodiment of the present invention. The selection and calling module is used to select and call the quality assessment module or the material labeling module, including:

[0091] If the fiber composite material is classified as a high-quality applicable category, then select to invoke the quality assessment module;

[0092] If the fiber composite material is in the low-quality applicable category, then select to call the material labeling module.

[0093] Specifically, the quality assessment module is used to calculate the strength and load-bearing capacity characterization parameters of the material sample, including:

[0094] The sum of the ratio of the flatness of the damaged surface to the flatness threshold and the ratio of the bond strength to the bond strength threshold is used as the first strength bearing characteristic value.

[0095] The ratio of the tensile strain of the damaged surface to the tensile strain threshold is used as the second strength bearing characteristic value.

[0096] The first strength bearing characteristic value and the second strength bearing characteristic value are weighted and summed to determine the strength bearing characterization parameter.

[0097] Specifically, the first strength bearing capacity characteristic value comprehensively reflects the interfacial bonding reliability and fracture uniformity of fiber composite materials after destructive testing. In road repair engineering, the bonding quality between the repair layer and the original pavement is the primary guarantee to prevent early delamination, shoving, potholes, and other defects. If the bonding strength is insufficient or the fracture surface is uneven, it indicates the existence of weak interfaces. Even if the material has good deformation capacity, it cannot meet the requirements for long-term service. Therefore, the smoothness of the fracture surface and the bonding strength directly determine whether the material is "usable." The tensile strain of the fracture surface reflects the deformation toughness of the material during the stress failure process and is used to evaluate the material's ability to resist traffic loads and temperature stresses. This indicator characteristic is an "optimal option" based on meeting basic strength and bonding requirements. Good toughness can extend service life, but it cannot compensate for fundamental failures caused by bonding or interfacial defects. Therefore, when performing weighted summation, the first strength bearing capacity characteristic value is assigned a higher weight coefficient, which can be selected between 0.6 and 0.7. In this embodiment, it is set to 0.7. Correspondingly, the weight coefficient of the second strength bearing capacity characteristic value is selected between 0.3 and 0.4. In this embodiment, it is set to 0.3. Of course, the specific values ​​can be fine-tuned based on the sensitivity of the road repair project to the reliability of the interfacial bonding or the actual test results, so as to ensure that the strength bearing capacity characterization parameters accurately reflect the basic bearing capacity of the material.

[0098] In this embodiment, the purpose of setting the smoothness threshold, bond strength threshold, and tensile strain threshold is to characterize the situation where the overall load-bearing capacity of the fiber composite material is poor and the interfacial bonding reliability is low. By acquiring the performance data of several qualified fiber composite materials used in road repair, the smoothness data, bond strength data of the damaged surface of the fiber composite material sample, and tensile strain data of the material sample are retrieved to solve for the mean smoothness, mean bond strength, and mean tensile strain. Based on the purpose of setting the above three thresholds, the smoothness threshold is determined to be the product of the mean smoothness and the smoothness deviation coefficient, the bond strength threshold is determined to be the product of the mean bond strength and the bond deviation coefficient, and the tensile strain threshold is determined to be the product of the mean tensile strain and the tensile deviation coefficient. The smoothness deviation coefficient is selected in the interval [1.1, 1.3], preferably 1.1 in practice; the bond deviation coefficient is selected in the interval [1.2, 1.4], preferably 1.2 in practice; and the tensile deviation coefficient is selected in the interval [1.15, 1.25], preferably 1.15 in practice.

[0099] Specifically, this invention uses destructive testing on cured fiber composite materials to reflect their actual load-bearing capacity under extreme stress. Simultaneously, it extracts two core characteristics—smoothness and bond strength—from the corresponding fracture surface, reflecting both the density of the bond between the fibers and the matrix, and the reliability of the interfacial bonding. Furthermore, in practice, pavement repair materials not only require strength but also a certain degree of deformation toughness to resist traffic loads and temperature stress. Therefore, this invention introduces tensile strain to simultaneously evaluate the material's strength and toughness, making the assessment and analysis more aligned with the durability requirements of long-term pavement use. Furthermore, it calculates strength-bearing capacity characterization parameters to comprehensively characterize the overall load-bearing capacity, interfacial bonding reliability, and deformation toughness of the pavement repair fiber composite material under extreme stress, providing a fundamental basis for further evaluation of whether the fiber composite material meets the strength-bearing capacity benchmark, forming a rigorous evaluation logic of "first judging compliance, then evaluating grade." This invention effectively improves the accuracy and reliability of fiber composite material strength testing, providing a rigorous quality acceptance basis for pavement repair projects and ensuring that the fiber composite materials used in pavement repair meet the mechanical performance requirements for long-term road service.

[0100] Specifically, please refer to Figure 4 As shown, this is a logic diagram for determining whether a corresponding fiber composite material meets the strength bearing capacity benchmark in an embodiment of the present invention. The quality assessment module is used to determine whether the corresponding fiber composite material meets the strength bearing capacity benchmark, including:

[0101] If the strength bearing capacity characterization parameter of the material sample is greater than or equal to the strength bearing capacity characterization parameter threshold, then the corresponding fiber composite material is determined to meet the strength bearing capacity benchmark.

[0102] If the strength bearing capacity characteristic parameter of the material sample is less than the strength bearing capacity characteristic parameter threshold, the corresponding fiber composite material is determined to not meet the strength bearing capacity benchmark.

[0103] To determine the threshold for the strength-bearing capacity characterization parameter, a large number of qualified fiber composite material samples meeting the long-term service requirements of pavement repair projects were collected. After curing, destructive tests were conducted on each sample to obtain data on surface smoothness, bond strength, and tensile strain. The strength-bearing capacity characterization parameter for each sample was calculated using the same weighted formula. The 90th percentile or mean of these parameters minus 1.5 times the standard deviation was taken as the lower limit threshold to ensure that the threshold reflects the minimum acceptable bearing capacity of qualified materials under extreme stress. Typically, this threshold is set between 2.4 and 2.8, with the specific value depending on the material design grade and the engineering safety factor. In this embodiment, the strength-bearing capacity characterization parameter threshold is set to 2.6, which effectively identifies unqualified materials with poor smoothness, weak bond, or insufficient deformation toughness, preventing them from entering pavement repair applications, while ensuring that materials meeting mechanical performance standards successfully pass the bearing capacity benchmark assessment.

[0104] Specifically, the quality assessment module is used to extract areas of concentrated voids, including:

[0105] If there exists an area where the number of voids is greater than a void number threshold, and the uniformity of the spacing between adjacent voids is greater than a spacing uniformity threshold, then the area is defined as the void concentration area.

[0106] In this embodiment, the purpose of setting a void quantity threshold is to characterize the situation where the defect density of the material sample is high, and the purpose of setting a spacing uniformity threshold is to characterize the situation where the defect distribution of the material sample is relatively concentrated and contiguous. By acquiring the performance data of several qualified fiber composite materials used in road repair, calling the void quantity data and the spacing uniformity data of several damaged surfaces, the mean void quantity and the mean spacing uniformity are calculated. Based on the purpose of setting the above two thresholds, the void quantity threshold is determined to be the product of the mean void quantity and the quantity deviation coefficient, and the spacing uniformity threshold is determined to be the product of the mean spacing uniformity and the spacing deviation coefficient. The quantity deviation coefficient is selected in the interval [1.2, 1.4], preferably 1.2 in practice, and the spacing deviation coefficient is selected in the interval [0.9, 0.95], preferably 0.9 in practice.

[0107] Specifically, this invention focuses on detecting concentrated defects that truly threaten structural safety, making the analysis of internal defects in fiber composite materials more targeted. First, the number of voids is included to quantify the density and overall compactness of internal defects. A larger number of voids indicates more internal pores and less sufficient mixing and curing, reflecting the overall severity of internal defects and serving as a basis for determining whether weak areas have formed. Furthermore, the uniformity of spacing between adjacent voids reflects the regularity of defect distribution and the uniformity of material forming. Higher spacing uniformity indicates that voids are concentrated in clusters, resulting in poor structural consistency, and can characterize whether internal defects tend to concentrate and form continuous weak zones. In reality, areas with concentrated voids are the weakest parts of the material, having the greatest impact on strength and quality. Extracting these areas for subsequent analysis can significantly improve the accuracy of defect evaluation, making the subsequent assessment of strength and quality characterization coefficients more closely reflect the material's actual stress performance.

[0108] Specifically, the quality assessment module is used to evaluate the strength quality characterization coefficient, including:

[0109] The sum of the ratio of the void size variation to the void size variation threshold and the ratio of the maximum void depth percentage to the maximum void depth percentage threshold is used as the first strength quality characteristic value.

[0110] The ratio of the chromaticity deviation of the damaged surface to the chromaticity deviation threshold is used as the second intensity quality characteristic value.

[0111] The first strength quality characteristic value and the second strength quality characteristic value are weighted and summed to determine the quality strength characterization coefficient.

[0112] Specifically, the first strength quality characteristic value comprehensively reflects the density defects within the fiber composite material, including the dispersion of void size and the severity of deep defects. A larger variation in void size indicates more uneven mixing and curing of the material, resulting in a looser internal structure. A larger proportion of maximum void depth indicates that the defect is closer to the core load-bearing layer, leading to more significant strength degradation. These two characteristics directly determine the material's inherent load-bearing capacity and long-term durability, and these defects are difficult to improve in subsequent use, being the root cause of early pavement damage. The color deviation of the damaged surface reflects the uniformity of material curing and the consistency of fiber distribution. A large color deviation indicates inconsistent curing degree, fiber agglomeration, or uneven matrix bonding, which has a certain impact on molding quality. However, compared to deep internal void defects, its weakening effect on ultimate strength is relatively low and can be improved to some extent through process adjustments. Therefore, when performing weighted summation, a higher weight coefficient is assigned to the first strength quality characteristic value, which can be selected between 0.6 and 0.7; in this embodiment, it is set to 0.7. Accordingly, the weighting coefficient for the second strength quality characteristic value is selected between 0.3 and 0.4, and is set to 0.3 in this embodiment. Of course, the specific value can be fine-tuned according to the sensitivity of the fiber composite material or the actual verification results to ensure the accuracy of the strength quality characterization coefficient under different material types and working conditions.

[0113] In this embodiment, the purpose of setting thresholds for void size variation, maximum void depth ratio, and color deviation is to characterize situations where the overall strength quality of fiber composite materials is low, by obtaining performance data of several qualified fiber composite materials used in road repair. The system retrieves data on the variance in cavity size, the maximum percentage of cavity depth, and the chromaticity deviation of the damaged surface corresponding to the concentrated cavity area. It then calculates the mean variance in cavity size, the mean maximum percentage of cavity depth, and the mean chromaticity deviation. Based on the purpose of setting the above three thresholds, the threshold for variance in cavity size is determined as the product of the mean variance in cavity size and the variance coefficient. The threshold for the maximum percentage of cavity depth is determined as the product of the mean maximum percentage of cavity depth and the depth deviation coefficient. The threshold for chromaticity deviation is determined as the product of the mean chromaticity deviation and the chromaticity deviation coefficient. Specifically, the variance coefficient is selected within the interval [1.1, 1.2], preferably 1.1 in practice; the depth deviation coefficient is selected within the interval [1.15, 1.25], preferably 1.15 in practice; and the chromaticity deviation coefficient is selected within the interval [1.2, 1.3], preferably 1.2 in practice.

[0114] Specifically, this invention acquires cross-sectional images and extracts concentrated void areas to accurately locate internal density defects in materials. It analyzes concentrated areas with numerous, uniformly spaced voids, eliminating the non-critical influence of a few scattered voids. This allows the strength evaluation of fiber composite materials to focus on the core defects that truly affect load-bearing safety, improving the accuracy of the assessment and making it more aligned with engineering practice. The variation in void size reflects the uniformity of internal density. Larger void sizes indicate more uneven mixing and curing, and a looser internal structure, reflecting the dispersion of internal defects and serving as a key indicator of material density stability. The maximum proportion of void depth quantifies the severity of deep internal defects and overall weakness. Greater depth and higher proportion indicate that internal defects are closer to the core load-bearing layer, significantly impacting strength and thus characterizing the severity of deep, weak areas within the material. Furthermore, the color deviation of the damaged surface reflects the uniformity of material curing, fiber distribution consistency, and interfacial bonding quality. A greater color deviation indicates inconsistent curing degree, fiber agglomeration or exposure, and uneven matrix bonding, thus reflecting the quality of material molding and overall structural integrity. Therefore, this invention integrates the above characteristics to achieve a dual evaluation of internal defects and surface uniformity, more comprehensively reflecting the strength quality of fiber composite materials. It further evaluates the strength quality characterization coefficient of fiber composite materials, realizing a comprehensive quantitative evaluation and automatic grading of the material's internal density and curing uniformity, thereby improving the comprehensiveness and intelligence level of strength quality assessment for fiber composite materials.

[0115] Specifically, the quality assessment module is used to determine the strength quality grade of the corresponding fiber composite material, including:

[0116] The correspondence between the strength quality grade and the predetermined strength characterization coefficient range is preset;

[0117] Determine the range of mass strength characterization coefficients to which the mass strength characterization coefficients of the material sample belong;

[0118] Set the strength quality level corresponding to the range of the quality strength characterization coefficients as the strength quality level of the corresponding fiber composite material;

[0119] Among them, the strength quality grade and the range of quality strength characterization coefficients correspond one-to-one.

[0120] In this embodiment, the strength quality grade of the fiber composite material is determined in the following manner:

[0121] The quality strength characterization coefficient is divided into three preset intervals, and three strength quality levels are set simultaneously;

[0122] If the mass strength characterization coefficient corresponding to the fiber composite material is within the first preset range (0, 1.3S0), it is set to the first strength quality level 1.

[0123] If the mass strength characterization coefficient of the fiber composite material is within the second preset range [1.3S0, 1.4S0), then it is set to the second strength mass level 2;

[0124] If the mass strength characterization coefficient corresponding to the fiber composite material is within the third preset range [1.4S0,+∞), then it is set to the third strength quality level 3.

[0125] To determine the threshold S0 for the quality strength characterization coefficient, a large number of fiber composite material samples that have passed long-term pavement service verification were collected. Data on the variation in void size, the maximum proportion of void depth, and the color deviation of the damaged surface within the corresponding void concentration areas were extracted. The quality strength characterization coefficient for each sample was calculated using the same weighting formula. The 85th percentile or mean of these coefficients, plus one standard deviation, was taken as the basic threshold to classify the lowest strength quality grade from higher grades, ensuring that the threshold reflects the normal upper limit of fluctuations in the internal density and curing uniformity of qualified materials. Typically, this threshold is set between 1.2 and 1.5, with the specific value depending on the project's tolerance for internal defects and pavement grade requirements. In this embodiment, the quality strength characterization coefficient threshold is set to 1.35, which allows materials with large differences in void size, a high proportion of deep defects, or significant color deviation to be classified into lower strength quality grades, achieving refined strength quality grading and providing a clear quantitative basis for pavement repair material selection.

[0126] Specifically, this invention constructs a grading and determination logic for fiber composite materials. It eliminates the need for complex manual calculations and can rely on an intelligent system to complete the entire process of coefficient calculation and grade determination in an automated manner, which greatly improves testing efficiency, facilitates rapid on-site screening of qualified materials, and avoids the use of inferior materials in engineering projects.

[0127] Specifically, it also includes:

[0128] Increase the monitoring frequency of fiber composite materials undergoing secondary stirring and dispersion treatment, wherein the monitoring frequency is positively correlated with the applicable quality characterization value.

[0129] In this embodiment, optionally,

[0130] The quality applicability characterization value is compared with the preset first quality applicability characterization comparison threshold and the second quality applicability characterization comparison threshold.

[0131] When the applicable quality characterization value is greater than the second applicable quality characterization comparison threshold, the increase in monitoring frequency is determined as the first increase, which is set to 0.4 times the baseline monitoring frequency.

[0132] When the applicable quality characterization value is greater than or equal to the first applicable quality characterization comparison threshold and less than or equal to the second applicable quality characterization comparison threshold, the increase in monitoring frequency is determined to be the second increase, which is set to 0.3 times the baseline monitoring frequency.

[0133] When the applicable quality characterization value is less than the first applicable quality characterization comparison threshold, the increase in monitoring frequency is determined to be the third increase, which is set to 0.2 times the baseline monitoring frequency.

[0134] The first quality applicability characterization comparison threshold is 1.1 times the quality applicability characterization value threshold, and the second quality applicability characterization comparison threshold is 1.2 times the quality applicability characterization value threshold.

[0135] Understandably, the purpose of increasing the monitoring frequency is to implement differentiated, refined, and adaptive quality monitoring of fiber composite materials undergoing secondary mixing and dispersion treatment, ensuring that reworked materials are fully dispersed, uniformly qualified, and of good quality. Monitoring intensity is matched to the degree of quality defects to achieve precise control. For example, a lower applicable quality characterization value indicates a poorer initial mixing state of the material, making secondary mixing more likely to be insufficient. Therefore, using a higher monitoring frequency and allocating more monitoring resources to materials with lower quality ensures that problems are detected promptly. Simultaneously, it ensures that the effect of secondary mixing and dispersion treatment is stable and controllable. Furthermore, the amount of increase in monitoring frequency under different circumstances can also be adjusted by those skilled in the art.

[0136] Specifically, those skilled in the art can determine the benchmark monitoring frequency based on a large amount of monitoring data corresponding to qualified fiber composite materials in the past. For example, a conventional frequency that can effectively reflect the material state without causing data redundancy can be selected as the benchmark monitoring frequency. Of course, other methods can also be used to determine the benchmark monitoring frequency, which will not be elaborated here.

[0137] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A strength intelligent testing system for a pavement repair fiber composite material, characterized by, include: The feature extraction module is used to obtain the performance data of fiber composite materials in order to extract the corresponding material mixing features, including the thickness of floating fibers and the uniformity of fiber distribution. The applicable analysis module is used to analyze the quality applicable characterization value of the fiber composite material based on the material mixing characteristics, so as to classify the quality applicable category of the fiber composite material; The selectable module is used to select either the quality assessment module or the material labeling module based on the applicable quality category. The quality assessment module is used to evaluate and analyze the strength and quality of fiber composite materials, including: The fiber composite material after curing is subjected to destructive testing to extract the failure load characteristics and tensile strain of the corresponding material sample, and to calculate the strength load characterization parameters of the material sample to determine whether the corresponding fiber composite material meets the strength load benchmark. Obtain a cross-sectional image of the material sample, extract the void concentration area, and based on the void distribution characteristics corresponding to the void concentration area, combine the color deviation of the damaged surface to evaluate the strength quality characterization coefficient and determine the strength quality grade of the corresponding fiber composite material. A material labeling module is used to label the fiber composite material and to perform secondary stirring and dispersion treatment on the fiber composite material; The failure bearing characteristics include the flatness of the failure surface and the bonding strength, and the void distribution characteristics include the variation in void size and the maximum proportion of void depth. The applicable analysis module is used to analyze the applicable quality characterization values ​​of the fiber composite material, including: The ratio of the floating fiber thickness to the floating fiber thickness threshold is used as the first applicable quality characteristic value; The ratio of the fiber distribution uniformity threshold to the fiber distribution uniformity is used as the second applicable quality characteristic value. The first quality applicable feature value and the second quality applicable feature value are weighted and summed to determine the quality applicable characterization value; The applicable analysis module is used to classify the quality applicable categories of the fiber composite material, including: If the quality applicability characterization value of the fiber composite material is greater than or equal to the quality applicability characterization threshold, then the fiber composite material is classified as a low quality applicability category. If the quality applicability characterization value of the fiber composite material is less than the quality applicability characterization threshold, then the fiber composite material is classified into the high quality applicability category. The selection and invocation module is used to select and invoke either the quality assessment module or the material labeling module, including: If the fiber composite material is classified as a high-quality applicable category, then select to invoke the quality assessment module; If the fiber composite material is in the low-quality applicable category, then select to call the material labeling module.

2. The strength intelligent testing system of the pavement repair fiber composite material according to claim 1, characterized in that, The quality assessment module is used to calculate the strength and load-bearing capacity characterization parameters of the material sample, including: The sum of the ratio of the flatness of the damaged surface to the flatness threshold and the ratio of the bond strength to the bond strength threshold is used as the first strength bearing characteristic value. The ratio of the tensile strain of the damaged surface to the tensile strain threshold is used as the second strength bearing characteristic value. The first strength bearing characteristic value and the second strength bearing characteristic value are weighted and summed to determine the strength bearing characterization parameter.

3. The strength intelligent testing system of pavement repair fiber composite materials according to claim 2, characterized in that, The quality assessment module is used to determine whether the corresponding fiber composite material meets the strength bearing standard, including: If the strength bearing capacity characterization parameter of the material sample is greater than or equal to the strength bearing capacity characterization parameter threshold, then the corresponding fiber composite material is determined to meet the strength bearing capacity benchmark.

4. The strength intelligent testing system of pavement repair fiber composite materials of claim 1, wherein, The quality assessment module is used to extract areas of concentrated voids, including: If there exists an area where the number of voids is greater than a void number threshold, and the uniformity of the spacing between adjacent voids is greater than a spacing uniformity threshold, then the area is defined as the void concentration area.

5. The strength intelligent testing system of pavement repair fiber composite materials of claim 1, wherein, The quality assessment module is used to evaluate the strength quality characterization coefficient, including: The sum of the ratio of the void size variation to the void size variation threshold and the ratio of the maximum void depth percentage to the maximum void depth percentage threshold is used as the first strength quality characteristic value. The ratio of the chromaticity deviation of the damaged surface to the chromaticity deviation threshold is used as the second intensity quality characteristic value. The first strength quality characteristic value and the second strength quality characteristic value are weighted and summed to determine the quality strength characterization coefficient.

6. The intelligent strength testing system for pavement repair fiber composite materials according to claim 5, characterized in that, The quality assessment module is used to determine the strength quality grade of the corresponding fiber composite material, including: The correspondence between the strength quality grade and the predetermined strength characterization coefficient range is preset; Determine the range of mass strength characterization coefficients to which the mass strength characterization coefficients of the material sample belong; Set the strength quality level corresponding to the range of the quality strength characterization coefficients as the strength quality level of the corresponding fiber composite material; Among them, the strength quality grade and the range of quality strength characterization coefficients correspond one-to-one.

7. The intelligent strength testing system for pavement repair fiber composite materials according to claim 1, characterized in that, Also includes: Increase the monitoring frequency of fiber composite materials undergoing secondary stirring and dispersion treatment, wherein the monitoring frequency is positively correlated with the applicable quality characterization value.

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