Testing method and system for testing the bending and tensile properties of highway pavement concrete

Abstract

The invention relates to the technical field of concrete detection, and is a detection method and system for detecting the bending and tensile performance of highway pavement concrete. The specific method comprises: constructing a concrete sample detection set, applying gradient loads to the concrete sample detection set respectively, and simulating the actual working condition of the concrete sample detection set; obtaining the surface morphological parameters of the concrete sample detection set after each gradient load application test is completed through an adaptive crack detection algorithm, and classifying the detection stages according to the surface morphological parameters; calculating and obtaining the cumulative deformation coefficient and the instantaneous deformation coefficient; and performing a comprehensive performance evaluation on the bending and tensile performance of the concrete sample according to the cumulative deformation coefficient and the instantaneous deformation coefficient. The invention solves the problem in the prior art that the crack data of the concrete surface in the tensile bending test is large and difficult to be processed one by one in a targeted manner, and the influence of cracks at different positions on the quality of the concrete is difficult to quantify.

Description

Technical Field

[0001] The invention relates to the technical field of concrete detection, and is a detection method and system for detecting the bending and tensile performance of highway pavement concrete. Background Art

[0002] The flexural and tensile properties of concrete used in highway pavements are affected by many factors, including environmental conditions, load changes, etc. Therefore, accurate and effective testing of the flexural and tensile properties of concrete is essential to ensure the normal use and maintenance of roads.

[0003] There are several technical problems in the existing methods for testing the flexural and tensile properties of concrete. First, traditional static testing methods are usually unable to simulate the changes in dynamic loads of concrete under actual working conditions, and the reliability of their test results is limited. At the same time, many testing methods only test under a single load condition and cannot fully reflect the changes in concrete performance under different environmental factors, resulting in test results that are difficult to adapt to complex use conditions. In addition, the existing testing methods lack effective data integration and automated processes, the testing process is relatively cumbersome, and the data obtained from the test are also relatively complex, which increases the complexity and time consumption of the test. Summary of the invention

[0004] The technical problem to be solved by the present invention is that in the prior art, the data of concrete surface cracks is large and difficult to be processed one by one, and the impact of cracks at different positions on the quality of concrete is difficult to quantify. In order to solve the above problems, a detection method and system for detecting the flexural tensile performance of highway pavement concrete are proposed.

[0005] In order to achieve the above-mentioned object, the technical solution of the detection method for detecting the flexural tensile performance of highway pavement concrete provided by the present invention comprises the following steps:

[0006] S1: Take multiple concrete samples and lay them on the soil base to form a concrete sample test set, apply gradient loads to the concrete sample test set, and simulate the actual working conditions of the concrete sample test set;

[0007] S2: Obtain the surface morphological parameters of the concrete sample test set after each gradient load application test through an adaptive crack detection algorithm, and classify the detection stages according to the surface morphological parameters;

[0008] S3: synchronously collecting performance evolution parameters of the concrete sample test set in each gradient load application test, importing the performance evolution parameters into the cumulative deformation coefficient calculation strategy, and calculating and obtaining the cumulative deformation coefficient;

[0009] S4: importing the surface morphological parameters of the concrete sample test set after each gradient load application test into the instantaneous deformation coefficient calculation strategy to calculate and obtain the instantaneous deformation coefficient;

[0010] S5: Comprehensive performance evaluation of the flexural and tensile properties of concrete samples is performed based on the cumulative deformation coefficient and the instantaneous deformation coefficient.

[0011] Specifically, S1 includes the following specific steps:

[0012] S11: taking multiple concrete samples from the same batch and laying them on soil bases with different humidity to form a concrete sample test set;

[0013] Wherein, the concrete sample is a rectangle, the length of the rectangle is a, and the width of the rectangle is b;

[0014] The concrete sample test set includes: 6 groups of concrete sample test combinations, the soil moisture under each concrete sample test combination increases in sequence according to the concrete sample test combination number, the difference between the soil moisture under the concrete sample test combinations with adjacent numbers is a fixed value, and the starting moisture of the soil under the first concrete sample test combination is ;

[0015] The concrete sample detection combination comprises: two concrete samples connected by a rectangular width, an expansion joint is maintained between the two concrete samples, and the width of the expansion joint is a fixed value;

[0016] S12: applying a gradient load to the concrete sample test set through a universal testing machine to simulate the actual working condition of the concrete sample test set;

[0017] Wherein, the gradient load Specifically: , r is a subscript, indicating the rth load application process;

[0018] represents the load applied to the concrete sample for the rth time, , is the initial load, is the unit load increment.

[0019] Specifically, the actual working condition simulation in S12 further includes:

[0020] S121: Preset a load duration item and a load application position item in the universal testing machine, wherein the load duration item includes: a load duration item of 30s and a load duration item of 60s; the load application position item includes: load application position items at a distance of 0.5a, a and 1.5a from the rectangular width of the left concrete sample in the concrete sample detection combination;

[0021] S122: extract the load duration item and the load application position item, and randomly combine them in pairs. The combination sequence of the working condition simulation combination parameters obtained is as follows:

[0022] ;

[0023] S123: applying loads to the six groups of concrete sample test combinations simultaneously by a universal testing machine, wherein before the universal testing machine applies loads to each group of concrete sample test combinations simultaneously, a working condition simulation combination parameter is randomly selected from the combination sequence, and a load is applied to each group of concrete sample test combinations according to the selected working condition simulation combination parameter;

[0024] S124: before the next round of load application, the working condition simulation combination parameters of each group of concrete sample detection combination that have been simulated in the previous round are removed from the combination sequence of the working condition simulation combination parameters of the universal testing machine, so as to ensure that the working condition simulation combination parameters of each group of concrete sample detection combination in each round of load application are not repeated with the working condition simulation combination parameters of the previous completed load application round;

[0025] S125: Perform 6 rounds of load application tests on 6 groups of concrete sample test combinations.

[0026] Specifically, S2 includes the following specific steps:

[0027] S21: obtaining surface morphological parameters of the concrete sample test set after each gradient load application test by an adaptive crack detection algorithm, wherein the surface morphological parameters include: the number of cracks and the maximum crack collapse depth of each crack;

[0028] S22: Classify the detection stage according to the surface morphology parameters, specifically including: when all the concrete sample detection combinations have no cracks or the number of concrete sample detection combinations with cracks is less than 3, it meets the conditions of the first quality detection stage, and executes step S3;

[0029] S23: When the number of cracked concrete sample test combinations is greater than or equal to 3, the conditions of the second quality test stage are met, and the bending and tensile performance test of the concrete sample test set is completed, and step S4 is executed at the same time.

[0030] Specifically, S21 also includes the following specific steps:

[0031] S211: Perform three-dimensional modeling on the concrete sample detection combination in the concrete sample detection set, and divide the load-bearing surface of the concrete sample detection combination in the three-dimensional model into A detection unit grid is formed, and at the same time, a two-dimensional probe cross-section coordinate system is established on the probe cross-section of the spectrum detection probe;

[0032] The cross section of the spectrum detection probe is a square, and the width of each of the four sides is L. Near-infrared light emitters form a detection array, and the probe cross section of the spectrum detection probe has honeycomb holes;

[0033] Each honeycomb hole in the probe cross section of the spectrum detection probe is embedded with a near-infrared light emitter, and the coordinates of each near-infrared light emitter are ,The switching state of each near-infrared light emitter is adaptively controlled by the data acquisition platform, are the horizontal and vertical coordinates of the mth near-infrared light emitter, respectively;

[0034] S212: After each gradient load application test is completed, a two-dimensional load surface coordinate system is established on the load surface of the concrete sample test combination, wherein the coordinates of each test unit grid on the load surface of the concrete sample test combination are , n is the serial number of the detection unit grid, They represent the horizontal and vertical coordinates of the detection unit grid with serial number n, ;

[0035] S213: Adaptively scan the load-bearing surface of the concrete sample test set after each gradient load application test of the concrete sample test set by using a spectral detection probe to obtain surface morphological parameters of the concrete sample test set after each gradient load application test.

[0036] Specifically, in S213, the adaptive scanning specifically includes:

[0037] A1: Place the spectral detection probe on the load-bearing surface of the concrete sample detection assembly at the coordinates The first detection is performed on the detection unit grid of the first detection, wherein the sequence number of the detection unit grid of the first detection is The detection process includes: keeping all near-infrared light emitters on the cross section of the spectrum detection probe in an open state;

[0038] A2: Extract coordinates The detection feedback data obtained on the detection unit grid is used to determine the switching demand of the near-infrared light emitter. The switching demand determination is specifically as follows:

[0039] When the reflectivity is evenly distributed, it means that there is no crack on the detection unit grid. Move the spectrum detection probe to the coordinates On the detection unit grid, keep all near-infrared light emitters on the cross section of the spectrum detection probe turned on and continue detection;

[0040] When a valley reflectivity occurs, the coordinates of the near-infrared light emitter corresponding to each reflectivity fluctuation position detected are extracted, and the coordinates of the near-infrared light emitter corresponding to the reflectivity fluctuation position are connected by a curve on the two-dimensional probe cross-section coordinate system;

[0041] If the curve area obtained after connecting the curves is a closed area, it means that the crack edge shape on this detection unit grid is complete. Move the spectrum detection probe to the coordinate On the detection unit grid, keep all near-infrared light emitters on the cross section of the spectrum detection probe turned on and continue detection;

[0042] If the curve area obtained after the curves are connected is an unclosed area, it means that the edge shape of the crack on this detection unit grid is incomplete, and step A3 is executed.

[0043] Specifically, in S213, the adaptive scanning further includes:

[0044] A3: The near-infrared light emitter that needs to be turned on when the spectral detection probe is to be detected on the next detection unit grid is predicted by the crack collapse prediction algorithm. The crack collapse prediction algorithm specifically includes: the collapse depth of the crack tip corresponding to each reflectivity fluctuation position on the detection unit grid that has completed the first detection ;

[0045] Among them, the total number of crack cusps detected on the detection unit grid that has completed the first detection is I, , Indicates the collapse depth of the i-th crack tip detected on the detection unit grid with serial number n; is the propagation attenuation coefficient of light in concrete sample; is the reflectivity at the tip of the i-th crack;

[0046] is the mean reflectivity at the sharp points without cracks in the concrete sample;

[0047] A4: Based on the collapse depth of the I crack tip detected on the detection unit grid that has completed the first detection in A3, predict the crack depth of the actual detection position of the near-infrared light emitter in the next unit grid to be detected, as follows:

[0048] ;

[0049] in, is the crack collapse expansion factor, is random noise that follows a normal distribution;

[0050] Indicates the serial number is The crack depth of the actual detection position of the mth near-infrared light emitter on the detection unit grid;

[0051] is the crack depth gradient of the i-th crack tip point that has been detected on the detection unit grid with serial number n;

[0052] represents the weight coefficient of the straight-line distance between the actual detection position of the mth near-infrared light emitter and the ith crack tip, The calculation strategy is the inverse of the square of the straight-line distance between the actual detection position of the mth near-infrared light emitter and the i-th crack tip;

[0053] A5: A preset crack collapse depth threshold is set. When the crack collapse depth corresponding to the actual detection position of the m-th near-infrared light emitter on the next detection unit grid is greater than or equal to the crack collapse depth threshold, the m-th acoustic wave emitter is turned on when adaptive crack detection is performed on the next detection unit grid. Otherwise, the m-th acoustic wave emitter is turned off.

[0054] Furthermore, in S213, the adaptive scanning further includes:

[0055] A6: Repeat steps A3-A5, A near-infrared light emitter is used to judge the switching demand, and adaptive crack detection is performed on the concrete sample detection set after each gradient load test based on the judgment result.

[0056] Specifically, S3 includes the following specific steps:

[0057] S31: synchronously collecting performance evolution parameters of the concrete sample test set each time the gradient load application test is completed, wherein the performance evolution parameters specifically include: an increment of the overall length of the concrete sample test set and an increment of the expansion joint;

[0058] S32: Importing the performance evolution parameter into the cumulative deformation coefficient calculation strategy to calculate and obtain the cumulative deformation coefficient su. The cumulative deformation coefficient calculation strategy is specifically:

[0059] ;

[0060] Wherein, z is a subscript, indicating the number of the concrete sample test combination;

[0061] are respectively the increment of the overall length of the concrete sample test combination and the increment of the expansion joint after the gradient load application test of the z-th concrete sample test combination is completed;

[0062] It represents the incremental average value of the overall length and the incremental average value of the expansion joint in the test combination of 6 concrete samples.

[0063] Specifically, in S4, the instantaneous deformation coefficient calculation strategy is as follows:

[0064] ;

[0065] in, is the instantaneous deformation coefficient;

[0066] is a subscript, indicating the number of cracks detected on the z-th concrete sample test combination; represents the number of cracks detected on the z-th concrete sample test combination;

[0067] For the zth concrete sample test combination, The maximum depth of crack collapse of each crack; It represents the average value of the maximum crack collapse depth of all cracks in the z-th concrete sample test combination;

[0068] Indicates The vertical distance between the crack and the rectangular width of the concrete sample on the left; Indicates The vertical distance between the crack and the expansion joint;

[0069] It represents the vertical distance between the load application position and the rectangular width of the left concrete sample in the combined parameters of the working condition simulation where the crack first appears; It represents the vertical distance between the load application position and the expansion joint in the combined parameters of the working condition simulation where the crack first appears.

[0070] Specifically, S5 includes the following specific steps:

[0071] S51: extracting cumulative deformation coefficient and instantaneous deformation coefficient;

[0072] S52: Comprehensive performance evaluation is performed on the flexural and tensile properties of the concrete sample according to the cumulative deformation coefficient and the instantaneous deformation coefficient. The calculation formula of the comprehensive performance index xn of the flexural and tensile properties of the concrete is specifically as follows:

[0073] ;

[0074] in, They are the evaluation proportional coefficients of the cumulative deformation coefficient and the instantaneous deformation coefficient respectively.

[0075] In addition, the detection system for detecting the bending and tensile properties of highway pavement concrete provided by the present invention includes the following modules:

[0076] Gradient load application module, detection stage classification module, cumulative deformation coefficient calculation module, instantaneous deformation coefficient calculation module and comprehensive performance evaluation module;

[0077] The gradient load application module is used to obtain a plurality of concrete samples laid on the soil base respectively to form a concrete sample detection set, apply gradient loads to the concrete sample detection set respectively, and simulate the actual working condition of the concrete sample detection set;

[0078] The detection stage classification module obtains the surface morphological parameters of the concrete sample detection set after each gradient load application test through an adaptive crack detection algorithm, and performs detection stage classification according to the surface morphological parameters;

[0079] The cumulative deformation coefficient calculation module is used to synchronously collect the performance evolution parameters of the concrete sample detection set each time the gradient load application test is completed, import the performance evolution parameters into the cumulative deformation coefficient calculation strategy, and calculate and obtain the cumulative deformation coefficient;

[0080] The instantaneous deformation coefficient calculation module is used to import the surface morphological parameters of the concrete sample detection set after each gradient load application test into the instantaneous deformation coefficient calculation strategy to calculate and obtain the instantaneous deformation coefficient;

[0081] The comprehensive performance evaluation module performs a comprehensive performance evaluation on the flexural tensile performance of the concrete sample according to the cumulative deformation coefficient and the instantaneous deformation coefficient.

[0082] A storage medium stores instructions, and when a computer reads the instructions, the computer executes the detection method for detecting the flexural tensile performance of highway pavement concrete.

[0083] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned detection method for detecting the flexural tensile performance of highway pavement concrete when executing the computer program.

[0084] Compared with the prior art, the technical effects of the present invention are as follows:

[0085] 1. The present invention can more accurately simulate the dynamic loads that concrete is subjected to in actual use, thereby providing more realistic and reliable bending and tensile performance data than traditional static tests. This helps to improve the accuracy of road design and maintenance.

[0086] 2. The present invention reduces manual operations and data processing volume, improves detection efficiency, and reduces the impact of human factors on the results by introducing an adaptive crack detection algorithm and an automated detection process, thereby ensuring the consistency and accuracy of detection.

[0087] 3. The present invention divides the detection process into different stages according to the surface morphology parameters. This classification method can flexibly adjust the detection process according to the actual situation to ensure the high efficiency and accuracy of the detection. Whether there are no cracks in the concrete samples or only a small number of samples have cracks, or when there are a sufficient number of crack samples, it can quickly enter the next detection stage and perform staged detection, thereby improving the detection efficiency and detection accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0089] Figure 1 It is a schematic flow chart of a detection method for detecting the bending and tensile properties of highway pavement concrete of the present invention;

[0090] Figure 2 It is a structural schematic diagram of a detection system for detecting the bending and tensile properties of highway pavement concrete according to the present invention;

[0091] Figure 3 This is an example diagram of sorting the inspection unit grids on the load-bearing surface of the concrete sample inspection combination according to the present invention. DETAILED DESCRIPTION

[0092] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0093] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0094] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0095] Embodiment 1

[0096] like Figure 1 As shown, the detection method for detecting the bending tensile performance of highway pavement concrete in the embodiment of the present invention is as follows: Figure 1 As shown, the specific steps are as follows:

[0097] S1: Take multiple concrete samples and lay them on the soil base to form a concrete sample test set, apply gradient loads to the concrete sample test set, and simulate the actual working conditions of the concrete sample test set;

[0098] S11: taking multiple concrete samples from the same batch and laying them on soil bases with different humidity to form a concrete sample test set;

[0099] Wherein, the concrete sample is a rectangle, the length of the rectangle is a, and the width of the rectangle is b;

[0100] The concrete sample test set includes: 6 groups of concrete sample test combinations, the soil moisture under each concrete sample test combination increases in sequence according to the concrete sample test combination number, the difference between the soil moisture under the concrete sample test combinations with adjacent numbers is a fixed value, and the starting moisture of the soil under the first concrete sample test combination is ;

[0101] The concrete sample detection combination comprises: two concrete samples connected by a rectangular width, an expansion joint is maintained between the two concrete samples, and the width of the expansion joint is a fixed value;

[0102] S12: applying a gradient load to the concrete sample test set through a universal testing machine to simulate the actual working condition of the concrete sample test set;

[0103] Wherein, the gradient load Specifically: , r is a subscript, indicating the rth load application process;

[0104] represents the load applied to the concrete sample for the rth time, , is the initial load, is the unit load increment.

[0105] The actual working condition simulation in S12 also includes:

[0106] S121: Preset a load duration item and a load application position item in the universal testing machine, wherein the load duration item includes: a load duration item of 30s and a load duration item of 60s; the load application position item includes: load application position items at a distance of 0.5a, a and 1.5a from the rectangular width of the left concrete sample in the concrete sample detection combination;

[0107] S122: extract the load duration item and the load application position item, and randomly combine them in pairs. The combination sequence of the working condition simulation combination parameters obtained is as follows:

[0108] ;

[0109] S123: applying loads to the six groups of concrete sample test combinations simultaneously by a universal testing machine, wherein before the universal testing machine applies loads to each group of concrete sample test combinations simultaneously, a working condition simulation combination parameter is randomly selected from the combination sequence, and a load is applied to each group of concrete sample test combinations according to the selected working condition simulation combination parameter;

[0110] S124: before the next round of load application, the working condition simulation combination parameters of each group of concrete sample detection combination that have been simulated in the previous round are removed from the combination sequence of the working condition simulation combination parameters of the universal testing machine, so as to ensure that the working condition simulation combination parameters of each group of concrete sample detection combination in each round of load application are not repeated with the working condition simulation combination parameters of the previous completed load application round;

[0111] S125: Perform 6 rounds of load application tests on 6 groups of concrete sample test combinations.

[0112] S2: Obtain the surface morphological parameters of the concrete sample test set after each gradient load application test through an adaptive crack detection algorithm, and classify the detection stages according to the surface morphological parameters;

[0113] S2 includes the following specific steps:

[0114] S21: obtaining surface morphological parameters of the concrete sample test set after each gradient load application test by an adaptive crack detection algorithm, wherein the surface morphological parameters include: the number of cracks and the maximum crack collapse depth of each crack;

[0115] S22: Classify the detection stage according to the surface morphology parameters, specifically including: when all the concrete sample detection combinations have no cracks or the number of concrete sample detection combinations with cracks is less than 3, it meets the conditions of the first quality detection stage, and executes step S3;

[0116] S23: When the number of cracked concrete sample test combinations is greater than or equal to 3, the conditions of the second quality test stage are met, and the bending and tensile performance test of the concrete sample test set is completed, and step S4 is executed at the same time.

[0117] S21 also includes the following specific steps:

[0118] S211: Perform three-dimensional modeling on the concrete sample detection combination in the concrete sample detection set, and divide the load-bearing surface of the concrete sample detection combination in the three-dimensional model into A detection unit grid is formed, and at the same time, a two-dimensional probe cross-section coordinate system is established on the probe cross-section of the spectrum detection probe;

[0119] like Figure 3 As shown, in this embodiment, The load bearing surface of the concrete sample test combination is evenly divided into A grid of detection units;

[0120] The cross section of the spectrum detection probe is a square, and the width of each of the four sides is L. Near-infrared light emitters form a detection array, and the probe cross section of the spectrum detection probe has honeycomb holes;

[0121] Each honeycomb hole in the probe cross section of the spectrum detection probe is embedded with a near-infrared light emitter, and the coordinates of each near-infrared light emitter are ,The switching state of each near-infrared light emitter is adaptively controlled by the data acquisition platform, are the horizontal and vertical coordinates of the mth near-infrared light emitter, respectively;

[0122] S212: After each gradient load application test is completed, a two-dimensional load surface coordinate system is established on the load surface of the concrete sample test combination, wherein the coordinates of each test unit grid on the load surface of the concrete sample test combination are ;

[0123] like Figure 3 As shown, in this embodiment, the detection unit grids on the load-bearing surface of the concrete sample detection combination are sorted;

[0124] n is the serial number of the detection unit grid, They represent the horizontal and vertical coordinates of the detection unit grid with serial number n, ;

[0125] S213: Adaptively scan the load-bearing surface of the concrete sample test set after each gradient load application test of the concrete sample test set by using a spectral detection probe to obtain surface morphological parameters of the concrete sample test set after each gradient load application test.

[0126] In S213, the adaptive scanning specifically includes:

[0127] A1: Place the spectral detection probe on the load-bearing surface of the concrete sample detection assembly at the coordinates The first detection is performed on the detection unit grid of the first detection, wherein the sequence number of the detection unit grid of the first detection is The detection process includes: keeping all near-infrared light emitters on the cross section of the spectrum detection probe in an open state;

[0128] A2: Extract coordinates The detection feedback data obtained on the detection unit grid is used to determine the switching demand of the near-infrared light emitter. The switching demand determination is specifically as follows:

[0129] When the reflectivity is evenly distributed, it means that there is no crack on the detection unit grid. Move the spectrum detection probe to the coordinates On the detection unit grid, keep all near-infrared light emitters on the cross section of the spectrum detection probe turned on and continue detection;

[0130] When a valley reflectivity occurs, the coordinates of the near-infrared light emitter corresponding to each reflectivity fluctuation position detected are extracted, and the coordinates of the near-infrared light emitter corresponding to the reflectivity fluctuation position are connected by a curve on the two-dimensional probe cross-section coordinate system;

[0131] If the curve area obtained after connecting the curves is a closed area, it means that the crack edge shape on this detection unit grid is complete. Move the spectrum detection probe to the coordinate On the detection unit grid, keep all near-infrared light emitters on the cross section of the spectrum detection probe turned on and continue detection;

[0132] If the curve area obtained after the curves are connected is an unclosed area, it means that the edge shape of the crack on this detection unit grid is incomplete, and step A3 is executed.

[0133] In S213, the adaptive scanning further includes:

[0134] A3: The near-infrared light emitter that needs to be turned on when the spectral detection probe is to be detected on the next detection unit grid is predicted by the crack collapse prediction algorithm. The crack collapse prediction algorithm specifically includes: the collapse depth of the crack tip corresponding to each reflectivity fluctuation position on the detection unit grid that has completed the first detection ;

[0135] Among them, the total number of crack cusps detected on the detection unit grid that has completed the first detection is I, , Indicates the collapse depth of the i-th crack tip detected on the detection unit grid with serial number n; is the propagation attenuation coefficient of light in concrete sample; is the reflectivity at the tip of the i-th crack;

[0136] is the mean reflectivity at the sharp points without cracks in the concrete sample;

[0137] A4: Based on the collapse depth of the I crack tip detected on the detection unit grid that has completed the first detection in A3, predict the crack depth of the actual detection position of the near-infrared light emitter in the next unit grid to be detected, as follows:

[0138] ;

[0139] in, is the crack collapse expansion factor, is random noise that follows a normal distribution;

[0140] Indicates the serial number is The crack depth of the actual detection position of the mth near-infrared light emitter on the detection unit grid;

[0141] is the crack depth gradient of the i-th crack tip point that has been detected on the detection unit grid with serial number n;

[0142] represents the weight coefficient of the straight-line distance between the actual detection position of the mth near-infrared light emitter and the ith crack tip, The calculation strategy is the inverse of the square of the straight-line distance between the actual detection position of the mth near-infrared light emitter and the i-th crack tip;

[0143] For example, in this embodiment, , is the noise variance of random noise;

[0144] For example, in this embodiment, a fracture collapse depth gradient is also provided. The specific calculation formula is:

[0145] ;

[0146] in, represents the coordinates of the crack tip closest to the i-th crack tip to the right of the i-th crack tip;

[0147] represents the coordinates of the crack tip closest to the i-th crack tip on the left side of the i-th crack tip;

[0148] represents the coordinates of the crack tip above the i-th crack tip and closest to the i-th crack tip;

[0149] represents the coordinates of the crack tip below the i-th crack tip and closest to the i-th crack tip;

[0150] A5: A preset crack collapse depth threshold is set. When the crack collapse depth corresponding to the actual detection position of the m-th near-infrared light emitter on the next detection unit grid is greater than or equal to the crack collapse depth threshold, the m-th acoustic wave emitter is turned on when adaptive crack detection is performed on the next detection unit grid. Otherwise, the m-th acoustic wave emitter is turned off.

[0151] Furthermore, in S213, the adaptive scanning further includes:

[0152] A6: Repeat steps A3-A5, A near-infrared light emitter is used to judge the switching demand, and adaptive crack detection is performed on the concrete sample detection set after each gradient load test based on the judgment result.

[0153] S3: synchronously collecting performance evolution parameters of the concrete sample test set in each gradient load application test, importing the performance evolution parameters into the cumulative deformation coefficient calculation strategy, and calculating and obtaining the cumulative deformation coefficient;

[0154] S3 includes the following specific steps:

[0155] S31: synchronously collecting performance evolution parameters of the concrete sample test set each time the gradient load application test is completed, wherein the performance evolution parameters specifically include: an increment of the overall length of the concrete sample test set and an increment of the expansion joint;

[0156] S32: Importing the performance evolution parameter into the cumulative deformation coefficient calculation strategy to calculate and obtain the cumulative deformation coefficient su. The cumulative deformation coefficient calculation strategy is specifically:

[0157] ;

[0158] Wherein, z is a subscript, indicating the number of the concrete sample test combination;

[0159] are respectively the increment of the overall length of the concrete sample test combination and the increment of the expansion joint after the gradient load application test of the z-th concrete sample test combination is completed;

[0160] It represents the incremental average value of the overall length and the incremental average value of the expansion joint in the test combination of 6 concrete samples.

[0161] S4: importing the surface morphological parameters of the concrete sample test set after each gradient load application test into the instantaneous deformation coefficient calculation strategy to calculate and obtain the instantaneous deformation coefficient;

[0162] In S4, the instantaneous deformation coefficient calculation strategy is specifically as follows:

[0163] ;

[0164] in, is the instantaneous deformation coefficient;

[0165] is a subscript, indicating the number of cracks detected on the z-th concrete sample test combination; represents the number of cracks detected on the z-th concrete sample test combination;

[0166] For the zth concrete sample test combination, The maximum depth of crack collapse of each crack; It represents the average value of the maximum crack collapse depth of all cracks in the z-th concrete sample test combination;

[0167] Indicates The vertical distance between the crack and the rectangular width of the concrete sample on the left; Indicates The vertical distance between the crack and the expansion joint;

[0168] It represents the vertical distance between the load application position and the rectangular width of the left concrete sample in the combined parameters of the working condition simulation where the crack first appears; It represents the vertical distance between the load application position and the expansion joint in the combined parameters of the working condition simulation where the crack first appears.

[0169] S5: Comprehensive performance evaluation of the flexural and tensile properties of concrete samples is performed based on the cumulative deformation coefficient and the instantaneous deformation coefficient.

[0170] S5 includes the following specific steps:

[0171] S51: extracting cumulative deformation coefficient and instantaneous deformation coefficient;

[0172] S52: Comprehensive performance evaluation is performed on the flexural and tensile properties of the concrete sample according to the cumulative deformation coefficient and the instantaneous deformation coefficient. The calculation formula of the comprehensive performance index xn of the flexural and tensile properties of the concrete is specifically as follows:

[0173] ;

[0174] in, are the evaluation proportional coefficients of the cumulative deformation coefficient and the instantaneous deformation coefficient, respectively. Further, It is determined by those skilled in the art according to specific technical requirements. In this embodiment, .

[0175] Embodiment 2

[0176] like Figure 2 As shown, the detection system for detecting the bending and tensile properties of highway pavement concrete according to the embodiment of the present invention is as follows: Figure 2 As shown, it includes the following modules:

[0177] Gradient load application module, detection stage classification module, cumulative deformation coefficient calculation module, instantaneous deformation coefficient calculation module and comprehensive performance evaluation module;

[0178] The gradient load application module is used to obtain a plurality of concrete samples laid on the soil base respectively to form a concrete sample detection set, apply gradient loads to the concrete sample detection set respectively, and simulate the actual working condition of the concrete sample detection set;

[0179] The detection stage classification module obtains the surface morphological parameters of the concrete sample detection set after each gradient load application test through an adaptive crack detection algorithm, and performs detection stage classification according to the surface morphological parameters;

[0180] The cumulative deformation coefficient calculation module is used to synchronously collect the performance evolution parameters of the concrete sample detection set each time the gradient load application test is completed, import the performance evolution parameters into the cumulative deformation coefficient calculation strategy, and calculate and obtain the cumulative deformation coefficient;

[0181] The instantaneous deformation coefficient calculation module is used to import the surface morphological parameters of the concrete sample detection set after each gradient load application test into the instantaneous deformation coefficient calculation strategy to calculate and obtain the instantaneous deformation coefficient;

[0182] The comprehensive performance evaluation module performs a comprehensive performance evaluation on the flexural tensile performance of the concrete sample according to the cumulative deformation coefficient and the instantaneous deformation coefficient.

[0183] Embodiment 3

[0184] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0185] The processor executes the above-mentioned detection method for detecting the flexural tensile performance of highway pavement concrete by calling the computer program stored in the memory.

[0186] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, and the computer program is loaded and executed by the processor to implement the detection method for the flexural tensile performance detection of highway pavement concrete provided by the above method embodiment. The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.

[0187] Embodiment 4

[0188] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0189] When the computer program is executed on a computer device, the computer device is caused to execute the above-mentioned detection method for detecting the flexural tensile performance of highway pavement concrete.

[0190] For example, the computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0191] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0192] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0193] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0194] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0195] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0196] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0197] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0198] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0199] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0200] In summary, compared with the prior art, the technical effects of the present invention are as follows:

[0201] 1. The present invention can more accurately simulate the dynamic loads that concrete is subjected to in actual use, thereby providing more realistic and reliable bending and tensile performance data than traditional static tests. This helps to improve the accuracy of road design and maintenance.

[0202] 2. The present invention reduces manual operations and data processing volume, improves detection efficiency, and reduces the impact of human factors on the results by introducing an adaptive crack detection algorithm and an automated detection process, thereby ensuring the consistency and accuracy of detection.

[0203] 3. The present invention divides the detection process into different stages according to the surface morphology parameters. This classification method can flexibly adjust the detection process according to the actual situation to ensure the high efficiency and accuracy of the detection. Whether there are no cracks in the concrete samples or only a small number of samples have cracks, or when there are a sufficient number of crack samples, it can quickly enter the next detection stage and perform staged detection, thereby improving the detection efficiency and detection accuracy.

[0204] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A detection method for detecting the bending and tensile properties of highway pavement concrete, characterized in that: The method comprises the following specific steps: S1: Take multiple concrete samples and lay them on the soil base to form a concrete sample test set, apply gradient loads to the concrete sample test set, and simulate the actual working conditions of the concrete sample test set; S2: Obtain the surface morphological parameters of the concrete sample test set after each gradient load application test through an adaptive crack detection algorithm, and classify the detection stages according to the surface morphological parameters; S3: synchronously collecting performance evolution parameters of the concrete sample test set in each gradient load application test, importing the performance evolution parameters into the cumulative deformation coefficient calculation strategy, and calculating and obtaining the cumulative deformation coefficient; S4: importing the surface morphological parameters of the concrete sample test set after each gradient load application test into the instantaneous deformation coefficient calculation strategy to calculate and obtain the instantaneous deformation coefficient; S5: Comprehensive performance evaluation of the flexural and tensile properties of concrete samples based on the cumulative deformation coefficient and the instantaneous deformation coefficient; S2 includes the following specific steps: S21: obtaining surface morphological parameters of the concrete sample test set after each gradient load application test by an adaptive crack detection algorithm, wherein the surface morphological parameters include: the number of cracks and the maximum crack collapse depth of each crack; S22: Classify the detection stage according to the surface morphology parameters, specifically including: when all the concrete sample detection combinations have no cracks or the number of concrete sample detection combinations with cracks is less than 3, it meets the conditions of the first quality detection stage, and executes step S3; S23: When the number of cracked concrete sample test combinations is greater than or equal to 3, the second quality test stage condition is met, and the bending and tensile performance test of the concrete sample test set is completed, and step S4 is executed at the same time; S21 also includes the following specific steps: S211: Perform three-dimensional modeling on the concrete sample detection combination in the concrete sample detection set, and divide the load-bearing surface of the concrete sample detection combination in the three-dimensional model into A detection unit grid is formed, and at the same time, a two-dimensional probe cross-section coordinate system is established on the probe cross-section of the spectrum detection probe; The cross section of the spectrum detection probe is a square, and the width of each of the four sides is L. Near-infrared light emitters form a detection array, and the probe cross section of the spectrum detection probe has honeycomb holes; Each honeycomb hole in the probe cross section of the spectrum detection probe is embedded with a near-infrared light emitter, and the coordinates of each near-infrared light emitter are ,The switching state of each near-infrared light emitter is adaptively controlled by the data acquisition platform, are the horizontal and vertical coordinates of the mth near-infrared light emitter, respectively; S212: After each gradient load application test is completed, a two-dimensional load surface coordinate system is established on the load surface of the concrete sample test combination, wherein the coordinates of each test unit grid on the load surface of the concrete sample test combination are , n is the serial number of the detection unit grid, They represent the horizontal and vertical coordinates of the detection unit grid with serial number n, ; S213: adaptively scanning the load-bearing surface of the concrete sample test set after each gradient load application test of the concrete sample test set is completed by using a spectral detection probe to obtain surface morphological parameters of the concrete sample test set after each gradient load application test is completed; In S213, the adaptive scanning specifically includes: A1: Place the spectral detection probe on the load-bearing surface of the concrete sample detection assembly at the coordinates The first detection is performed on the detection unit grid of the first detection, wherein the sequence number of the detection unit grid of the first detection is The detection process includes: keeping all near-infrared light emitters on the cross section of the spectrum detection probe in an open state; A2: Extract coordinates The detection feedback data obtained on the detection unit grid is used to determine the switching demand of the near-infrared light emitter. The switching demand determination is specifically as follows: When the reflectivity is evenly distributed, it means that there is no crack on the detection unit grid. Move the spectrum detection probe to the coordinates On the detection unit grid, keep all near-infrared light emitters on the cross section of the spectrum detection probe turned on and continue detection; When a valley reflectivity occurs, the coordinates of the near-infrared light emitter corresponding to each reflectivity fluctuation position detected are extracted, and the coordinates of the near-infrared light emitter corresponding to the reflectivity fluctuation position are connected by a curve on the two-dimensional probe cross-section coordinate system; If the curve area obtained after connecting the curves is a closed area, it means that the crack edge shape on this detection unit grid is complete. Move the spectrum detection probe to the coordinate On the detection unit grid, keep all near-infrared light emitters on the cross section of the spectrum detection probe turned on and continue detection; If the curve area obtained after the curve connection is an unclosed area, it means that the edge shape of the crack on this detection unit grid is incomplete, and step A3 is executed; In S213, the adaptive scanning further includes: A3: The near-infrared light emitter that needs to be turned on when the spectral detection probe is to be detected on the next detection unit grid is predicted by the crack collapse prediction algorithm. The crack collapse prediction algorithm specifically includes: the collapse depth of the crack tip corresponding to each reflectivity fluctuation position on the detection unit grid that has completed the first detection ; Among them, the total number of crack cusps detected on the detection unit grid that has completed the first detection is I, , Indicates the collapse depth of the i-th crack tip detected on the detection unit grid with serial number n; is the propagation attenuation coefficient of light in concrete sample; is the reflectivity at the tip of the i-th crack; is the mean reflectivity at the sharp points without cracks in the concrete sample; A4: Based on the collapse depth of the I crack tip detected on the detection unit grid that has completed the first detection in A3, predict the crack depth of the actual detection position of the near-infrared light emitter in the next unit grid to be detected, as follows: ； in, is the crack collapse expansion factor, is random noise that follows a normal distribution; Indicates the serial number is The crack depth of the actual detection position of the mth near-infrared light emitter on the detection unit grid; is the crack depth gradient of the i-th crack tip point that has been detected on the detection unit grid with serial number n; represents the weight coefficient of the straight-line distance between the actual detection position of the mth near-infrared light emitter and the ith crack tip, The calculation strategy is the inverse of the square of the straight-line distance between the actual detection position of the mth near-infrared light emitter and the i-th crack tip; A5: A preset crack collapse depth threshold is set. When the crack collapse depth corresponding to the actual detection position of the m-th near-infrared light emitter on the next detection unit grid is greater than or equal to the crack collapse depth threshold, the m-th acoustic wave emitter is turned on when adaptive crack detection is performed on the next detection unit grid. Otherwise, the m-th acoustic wave emitter is turned off.

2. The detection method for detecting the flexural tensile performance of highway pavement concrete according to claim 1 is characterized in that: S1 includes the following specific steps: S11: taking multiple concrete samples from the same batch and laying them on soil bases with different humidity to form a concrete sample test set; Wherein, the concrete sample is a rectangle, the length of the rectangle is a, and the width of the rectangle is b; The concrete sample test set includes: 6 groups of concrete sample test combinations, the soil moisture under each concrete sample test combination increases in sequence according to the concrete sample test combination number, the difference between the soil moisture under the concrete sample test combinations with adjacent numbers is a fixed value, and the starting moisture of the soil under the first concrete sample test combination is ; The concrete sample detection combination comprises: two concrete samples connected by a rectangular width, an expansion joint is maintained between the two concrete samples, and the width of the expansion joint is a fixed value; S12: applying a gradient load to the concrete sample test set through a universal testing machine to simulate the actual working condition of the concrete sample test set; Wherein, the gradient load Specifically: , r is a subscript, indicating the rth load application process; represents the load applied to the concrete sample for the rth time, , is the initial load, is the unit load increment.

3. The detection method for detecting the flexural tensile performance of highway pavement concrete according to claim 2 is characterized in that: The actual working condition simulation in S12 also includes: S121: Preset a load duration item and a load application position item in the universal testing machine, wherein the load duration item includes: a load duration item of 30s and a load duration item of 60s; the load application position item includes: load application position items at a distance of 0.5a, a and 1.5a from the rectangular width of the left concrete sample in the concrete sample detection combination; S122: extract the load duration item and the load application position item, and randomly combine them in pairs. The combination sequence of the working condition simulation combination parameters obtained is as follows: ； S123: applying loads to the six groups of concrete sample test combinations simultaneously by a universal testing machine, wherein before the universal testing machine applies loads to each group of concrete sample test combinations simultaneously, a working condition simulation combination parameter is randomly selected from the combination sequence, and a load is applied to each group of concrete sample test combinations according to the selected working condition simulation combination parameter; S124: before the next round of load application, the working condition simulation combination parameters of each group of concrete sample detection combination that have been simulated in the previous round are removed from the combination sequence of the working condition simulation combination parameters of the universal testing machine, so as to ensure that the working condition simulation combination parameters of each group of concrete sample detection combination in each round of load application are not repeated with the working condition simulation combination parameters of the previous completed load application round; S125: Perform 6 rounds of load application tests on 6 groups of concrete sample test combinations.

4. The detection method for detecting the flexural tensile performance of highway pavement concrete according to claim 3 is characterized in that: S3 includes the following specific steps: S31: synchronously collecting performance evolution parameters of the concrete sample test set each time the gradient load application test is completed, wherein the performance evolution parameters specifically include: an increment of the overall length of the concrete sample test set and an increment of the expansion joint; S32: Importing the performance evolution parameter into the cumulative deformation coefficient calculation strategy to calculate and obtain the cumulative deformation coefficient su. The cumulative deformation coefficient calculation strategy is specifically: ； Wherein, z is a subscript, indicating the number of the concrete sample test combination; are respectively the increment of the overall length of the concrete sample test combination and the increment of the expansion joint after the gradient load application test of the z-th concrete sample test combination is completed; It represents the incremental average value of the overall length and the incremental average value of the expansion joint in the test combination of 6 concrete samples.

5. The detection method for detecting the flexural tensile performance of highway pavement concrete according to claim 4 is characterized in that: In S4, the instantaneous deformation coefficient calculation strategy is specifically as follows: ； in, is the instantaneous deformation coefficient; is a subscript, indicating the number of the crack detected on the z-th concrete sample test combination; represents the number of cracks detected on the z-th concrete sample test combination; For the zth concrete sample test combination, The maximum depth of crack collapse of each crack; It represents the average value of the maximum crack collapse depth of all cracks in the z-th concrete sample test combination; Indicates The vertical distance between the crack and the rectangular width of the concrete sample on the left; Indicates The vertical distance between the crack and the expansion joint; It represents the vertical distance between the load application position and the rectangular width of the left concrete sample in the combined parameters of the working condition simulation where the crack first appears; It represents the vertical distance between the load application position and the expansion joint in the combined parameters of the working condition simulation where the crack first appears.

6. The detection method for detecting the flexural tensile performance of highway pavement concrete according to claim 5 is characterized in that: S5 includes the following specific steps: S51: extracting cumulative deformation coefficient and instantaneous deformation coefficient; S52: Comprehensive performance evaluation is performed on the flexural and tensile properties of the concrete sample according to the cumulative deformation coefficient and the instantaneous deformation coefficient. The calculation formula of the comprehensive performance index xn of the flexural and tensile properties of the concrete is specifically as follows: ； in, They are the evaluation proportional coefficients of the cumulative deformation coefficient and the instantaneous deformation coefficient respectively.

7. A detection system for detecting the flexural tensile performance of highway pavement concrete, used to implement the detection method for detecting the flexural tensile performance of highway pavement concrete as claimed in any one of claims 1 to 6, characterized in that: The system includes the following modules: Gradient load application module, detection stage classification module, cumulative deformation coefficient calculation module, instantaneous deformation coefficient calculation module and comprehensive performance evaluation module; The gradient load application module is used to obtain a plurality of concrete samples laid on the soil base respectively to form a concrete sample detection set, apply gradient loads to the concrete sample detection set respectively, and simulate the actual working condition of the concrete sample detection set; The detection stage classification module obtains the surface morphological parameters of the concrete sample detection set after each gradient load application test through an adaptive crack detection algorithm, and performs detection stage classification according to the surface morphological parameters; The cumulative deformation coefficient calculation module is used to synchronously collect the performance evolution parameters of the concrete sample detection set each time the gradient load application test is completed, import the performance evolution parameters into the cumulative deformation coefficient calculation strategy, and calculate and obtain the cumulative deformation coefficient; The instantaneous deformation coefficient calculation module is used to import the surface morphological parameters of the concrete sample detection set after each gradient load application test into the instantaneous deformation coefficient calculation strategy to calculate and obtain the instantaneous deformation coefficient; The comprehensive performance evaluation module performs a comprehensive performance evaluation on the flexural tensile performance of the concrete sample according to the cumulative deformation coefficient and the instantaneous deformation coefficient.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the detection method for detecting the flexural tensile performance of highway pavement concrete as described in any one of claims 1 to 6 is implemented.

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