Asphalt mixture damage assessment method and device based on acoustic emission technology

Abstract

The invention provides an asphalt mixture damage assessment method and device based on an acoustic emission technology, and relates to the field of asphalt material analysis. The method comprises the following steps: acquiring a target asphalt mixture sample; obtaining corresponding load-displacement data through a semicircular bending test; acoustic emission data of the target asphalt mixture sample under semicircular bending test loading are obtained through acoustic emission nondestructive testing; based on the load-displacement data and the acoustic emission data, constructing a target image corresponding to the target asphalt mixture sample; according to the target image, obtaining interface damage characteristics corresponding to the target asphalt mixture sample; and inputting the interface damage characteristics into a statistical damage constitutive model, and outputting a damage assessment result corresponding to the target asphalt mixture sample through the statistical damage constitutive model. The problem that a damage detection method based on appearance observation and physical testing cannot accurately detect and evaluate the damage state of the asphalt pavement is solved.

Description

Technical Field

[0001] The present application relates to the field of asphalt material analysis, and in particular to an asphalt mixture damage assessment method and device based on acoustic emission technology. Background Art

[0002] In the construction of transportation infrastructure, asphalt mixtures are widely used in roads, bridges and other projects. However, asphalt mixtures are affected by many factors during use, causing them to gradually become damaged and destroyed.

[0003] Asphalt pavement is easily affected by vehicle loads, climate change and environmental factors in daily use, resulting in various damages such as cracks, rutting and spalling. If these damages are not discovered in time, they may lead to more serious structural damage, which will require high repair costs. Therefore, early detection and assessment of pavement damage status is crucial to formulate reasonable maintenance plans and reduce economic losses.

[0004] At present, traditional damage detection methods mostly rely on appearance observation and physical testing. This method obtains pavement damage information through visual inspection, manual tapping, core sampling and splitting tests. Since the detection results are greatly affected by human factors, and the test process is destructive and cannot be monitored in real time, it is impossible to accurately detect and evaluate the damage status of asphalt pavement.

[0005] Therefore, there is an urgent need for an asphalt mixture damage assessment method and device based on acoustic emission technology. Summary of the invention

[0006] The present application provides an asphalt mixture damage assessment method and device based on acoustic emission technology, which solves the problem that damage detection methods based on appearance observation and physical testing cannot accurately detect and assess the damage status of asphalt pavement.

[0007] In a first aspect of the present application, a method for assessing damage of asphalt mixture based on acoustic emission technology is provided, the method comprising: obtaining a target asphalt mixture sample in response to a user's damage and damage assessment operation on the asphalt mixture; performing a semicircular bending test on the target asphalt mixture sample, and obtaining corresponding load-displacement data through the semicircular bending test; performing acoustic emission nondestructive testing on the target asphalt mixture sample, and obtaining acoustic emission data of the target asphalt mixture sample under the loading of the semicircular bending test through the acoustic emission nondestructive testing; constructing a target image corresponding to the target asphalt mixture sample based on the load-displacement data and the acoustic emission data; obtaining interface damage features corresponding to the target asphalt mixture sample based on the target image; inputting the interface damage features into a statistical damage constitutive model, and outputting damage and damage assessment results corresponding to the target asphalt mixture sample through the statistical damage constitutive model, the damage and damage assessment results including the initiation process, expansion process and evolution process of interface cracks in the target asphalt mixture sample.

[0008] Optionally, the acoustic emission data of the target asphalt mixture sample under the loading of the semicircular bending test is obtained through acoustic emission nondestructive testing, specifically including: selecting a preset layout area on the surface of the target asphalt mixture sample, the preset layout area containing interface damage characteristics; fixing a preset emission sensor on the target asphalt mixture sample according to the preset layout method, and the preset emission sensor covers the preset layout area according to the preset layout method; performing acoustic emission nondestructive testing on the target asphalt mixture sample through the preset emission sensor; obtaining the acoustic emission data of the target asphalt mixture sample under the loading of the semicircular bending test through acoustic emission nondestructive testing.

[0009] Optionally, before performing acoustic emission nondestructive testing on the target asphalt mixture sample and obtaining acoustic emission data of the target asphalt mixture sample under semicircular bending test loading through acoustic emission nondestructive testing, the method also includes: performing noise evaluation on the detection environment where the target asphalt mixture sample is located within a preset time period, the noise evaluation is used to obtain the background noise signal within the preset time period; and adjusting the threshold of a preset emission sensor according to the background noise signal.

[0010] Optionally, based on the load-displacement data and the acoustic emission data, a target image corresponding to the target asphalt mixture sample is constructed, specifically including: obtaining load magnitude change data and displacement change data corresponding to the target asphalt mixture sample according to the load-displacement data; sorting the load magnitude change data and the displacement change data according to a first preset time sequence; obtaining ring count data corresponding to the target asphalt mixture sample according to the acoustic emission data, and calculating cumulative ring count data based on the ring count data; sorting the ring count data and the cumulative ring count data according to a second preset time sequence, the first preset time sequence and the second preset time sequence being consistent in time scale; constructing a target image corresponding to the target asphalt mixture sample according to the sorted load magnitude change data, displacement change data, ring count data and cumulative ring count data.

[0011] Optionally, the damage and destruction assessment results corresponding to the target asphalt mixture sample are output through the statistical damage constitutive model, specifically including: constructing a probability density damage function according to the interface damage characteristics, the probability density damage function is used to calculate the probability of damage occurrence of the target asphalt mixture sample; according to the probability density damage function, the number of damaged micro-units generated after the target asphalt mixture sample is damaged under the action of external load is calculated; according to the number of damaged micro-units, the damage variable corresponding to the target asphalt mixture sample is calculated, and the damage variable is used to reflect the change in the degree of damage of the target asphalt mixture sample during the damage and destruction assessment operation; the damage variable is used as the damage and destruction assessment result.

[0012] Optionally, according to the probability density damage function, the number of damaged micro-units generated after the target asphalt mixture sample is damaged under the external load is calculated, specifically including: calculating the number of damaged micro-units according to the following formula: ; in, During loading The number of damaged microcells at the time, is the total number of damaged microcells during the whole loading process, is the probability of damage occurrence, is the distribution characteristic of the damaged micro-units, is the ambient temperature at time t, is the load at time t, is the area of ​​the asphalt mixture that has been damaged at time t, is the temperature effect weight, is the load impact weight.

[0013] Optionally, calculating the damage variable corresponding to the target asphalt mixture sample according to the number of damaged micro-units specifically includes: calculating the damage variable according to the following formula: ; in, is the damage variable, is the scale parameter, For uniform distribution, is the temperature coefficient influence function, is the load influence function, is the damage area influence function.

[0014] In a second aspect of the present application, a device for assessing damage to asphalt mixture based on acoustic emission technology is provided. The device includes an acquisition module and a processing module, wherein: The acquisition module is used to obtain a target asphalt mixture sample in response to a user's damage and destruction assessment operation on the asphalt mixture; perform a semicircular bending test on the target asphalt mixture sample, and obtain corresponding load-displacement data through the semicircular bending test; perform acoustic emission nondestructive testing on the target asphalt mixture sample, and obtain acoustic emission data of the target asphalt mixture sample under the loading of the semicircular bending test through acoustic emission nondestructive testing.

[0015] The processing module is used to construct a target image corresponding to the target asphalt mixture sample based on the load-displacement data and the acoustic emission data; obtain the interface damage characteristics corresponding to the target asphalt mixture sample according to the target image; input the interface damage characteristics into the statistical damage constitutive model, and output the damage and destruction assessment results corresponding to the target asphalt mixture sample through the statistical damage constitutive model, and the damage and destruction assessment results include the initiation process, expansion process and evolution process of the interface cracks in the target asphalt mixture sample.

[0016] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods described above.

[0017] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to perform any of the above methods.

[0018] One or more technical solutions provided by the embodiments of the present application have at least the following technical effects or advantages: 1. Obtain the target asphalt mixture sample, obtain the corresponding load-displacement data through the semicircular bending test, and obtain the acoustic emission data of the target asphalt mixture sample under the loading of the semicircular bending test through acoustic emission nondestructive testing, so as to construct the target image corresponding to the target asphalt mixture sample based on the load-displacement data and the acoustic emission data, and then obtain the interface damage characteristics corresponding to the target asphalt mixture sample according to the target image, input the interface damage characteristics into the statistical damage constitutive model, and output the damage and destruction assessment result corresponding to the target asphalt mixture sample through the statistical damage constitutive model. Then, by combining multi-source data fusion analysis, image recognition technology and statistical damage constitutive theory, the accuracy of asphalt mixture damage and destruction assessment is improved, and the problem that the damage detection method based on appearance observation and physical testing cannot accurately detect and evaluate the damage state of asphalt pavement is solved.

[0019] 2. Perform noise assessment on the detection environment where the target asphalt mixture sample is located within a preset time period. The noise assessment is used to obtain the background noise signal within the preset time period, and adjust the threshold of the preset emission sensor according to the background noise signal, thereby reducing the interference of environmental noise on the detection data, improving the recognition accuracy of the acoustic emission signal, and ensuring the reliability of the acoustic emission data.

[0020] 3. A probability density damage function is constructed according to the interface damage characteristics, and the number of damaged micro-units generated after the target asphalt mixture sample is damaged under the action of external load is calculated based on the probability density damage function. The damage variable corresponding to the target asphalt mixture sample is calculated according to the number of damaged micro-units, and the damage degree change of the target asphalt mixture sample during the damage assessment operation is reflected by the damage variable. Then, by calculating the number of micro-units, the damage evolution process of the target asphalt mixture sample can be quantified more accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of a method for assessing damage of asphalt mixture based on acoustic emission technology provided in an embodiment of the present application; Figure 2 This is a schematic diagram of an asphalt mixture damage and destruction assessment experimental system based on acoustic emission technology provided in an embodiment of the present application; Figure 3 It is a schematic diagram of an arrangement of an acoustic emission sensor provided in an embodiment of the present application; Figure 4 It is a schematic diagram of the change of the ringing count of acoustic emission of asphalt mixture provided in an embodiment of the present application; Figure 5 is a schematic diagram of damage variables in an asphalt mixture provided in an embodiment of the present application; Figure 6It is a schematic diagram of commonly used parameters of an AE test system provided in an embodiment of the present application; Figure 7 It is a schematic diagram of a SCB specimen loading device provided in an embodiment of the present application; Figure 8 It is a module schematic diagram of an asphalt mixture damage assessment device based on acoustic emission technology provided in an embodiment of the present application; Fig. 9 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0022] Explanation of the accompanying drawings: 21, acoustic emission sensor; 22, signal preamplifier and filter; 23, acoustic emission signal receiver; 24, AE data processor; 81, acquisition module; 82, processing module; 901, processor; 902, communication bus; 903, user interface; 904, network interface; 905, memory. DETAILED DESCRIPTION

[0023] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0024] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more listed items.

[0025] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0026] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0027] Please refer to Figure 1, which shows a flow chart of an asphalt mixture damage assessment method based on acoustic emission technology provided in an embodiment of the present application, and the flow chart mainly includes the following steps: S101 to S106.

[0028] Step S101, in response to a damage assessment operation for an asphalt mixture, a target asphalt mixture sample is obtained.

[0029] Specifically, before describing the scheme in the embodiment of the present application, the asphalt mixture is first prepared as a sample: considering the variability of the molded specimens, the molding quality and other issues, the rotary compaction method that best suits the actual pavement compaction state is selected, and the asphalt mixture is formed into a φ150mm×170mm asphalt mixture cylinder by the Texter 5850 rotary compactor, and then the cored sample is cut into a 25±2mm SCB asphalt mixture sample using a high-precision cutting machine, and a pre-cut is cut using a small cutting machine, and the length of the pre-cut is set to 10mm. The above method is used for multiple preparations to obtain multiple asphalt mixture samples, and a damage assessment operation is performed on each asphalt mixture sample, thereby increasing the universality and reliability of the assessment results. For ease of explanation, the present application takes the damage assessment of any one of the asphalt mixture samples as an example, and the asphalt mixture sample is the target asphalt mixture sample.

[0030] Step S102: Perform a semicircular bending test on the target asphalt mixture sample, and obtain corresponding load-displacement data through the semicircular bending test.

[0031] Specifically, the target asphalt mixture sample is placed in the UTM-100 testing instrument, and the pressure rate is controlled by uniform loading at both ends of the sample. At the same time, sensors are pasted on the surface of the sample to collect the acoustic emission signals generated during the loading process in real time. During the loading process, the size of the applied load and the corresponding displacement data of the sample are recorded to obtain a complete load-displacement curve, which provides data support for the subsequent analysis of the damage and destruction process of the asphalt mixture.

[0032] Step S103, performing acoustic emission nondestructive testing on the target asphalt mixture sample, and obtaining acoustic emission data of the target asphalt mixture sample under the loading of the semicircular bending test through the acoustic emission nondestructive testing.

[0033] Specifically, please refer to Figure 2 , Figure 2 This is a schematic diagram of an asphalt mixture damage assessment experimental system based on acoustic emission technology provided in an embodiment of the present application. Figure 2The acoustic emission sensor 21, signal preamplifier and filter 22, acoustic emission signal receiver 23, and AE data processor 24 are included. The acoustic emission sensor 21 is arranged on the surface of the target asphalt mixture sample, and the acoustic emission sensor 21 is RS-2A. In order to ensure a good coupling effect, a layer of coupling agent is usually applied between the acoustic emission sensor 21 and the surface of the specimen, and it is necessary to ensure that the acoustic emission sensor 21 is in close contact with the surface of the specimen. At this time, vaseline can be used as a coupling agent to ensure the quality of signal transmission. The coupling effect between the acoustic emission sensor 21 and the surface of the specimen has an important influence on the signal reception time. If the coupling is not good, it will cause signal reception delay or signal strength reduction. During the loading process, the acoustic emission signal receiver 23 collects the acoustic emission data of the acoustic emission sensor 21 in real time, and performs data analysis and feature extraction through the AE data processor 24, thereby recording the occurrence and development of internal damage of the sample, and providing a basis for damage assessment. The signal preamplifier and filter 22 are used to enhance the weak acoustic emission signal received by the acoustic emission sensor 21 and remove environmental noise, and send the enhanced data to the acoustic emission signal receiver 23 to improve the accuracy and reliability of data acquisition.

[0034] In a possible implementation, step S103 also includes: selecting a preset layout area on the surface of the target asphalt mixture sample, the preset layout area containing interface damage features; fixing a preset emission sensor on the target asphalt mixture sample according to the preset layout, and the preset emission sensor covers the preset layout area according to the preset layout; performing acoustic emission nondestructive testing on the target asphalt mixture sample through the preset emission sensor; and obtaining acoustic emission data of the target asphalt mixture sample under semicircular bending test loading through acoustic emission nondestructive testing.

[0035] Specifically, a preset layout area is selected on the surface of the target asphalt mixture sample. The preset layout area can be set according to the location of the interface damage characteristics on the surface of the target asphalt mixture sample. The preset layout area needs to include most of the interface damage characteristics on the surface of the target asphalt mixture sample. Please refer to Figure 3, which shows a schematic diagram of the arrangement of an acoustic emission sensor provided in an embodiment of the present application. As shown in the figure, in order to ensure the comprehensiveness of monitoring, taking the 25±2mm SCB asphalt mixture sample in the present application as an example, four sensors can be symmetrically arranged on the back of the target asphalt mixture sample, and the arrangement area between each sensor is a 4cm×3cm rectangle. The preset emission sensor is a collection of all acoustic emission sensors 21 arranged in the target asphalt mixture sample. The number and setting position of the acoustic emission sensors need to be confirmed according to the actual size of the arrangement area. The present application does not limit the number and setting position of the acoustic emission sensors. Break the pencil lead at the position between the two sensors. After each pencil lead is broken, the AE data processor 24 will record the time when the two sensors receive the signal, which is set as the first time and the second time respectively. According to the first time and the second time, and through the basic formula of wave propagation, the propagation wave velocity of the acoustic emission wave in the target asphalt mixture sample can be calculated, so that the calculated propagation wave velocity is used as one of the acoustic emission data under the semicircular bending test loading. The acoustic emission data also includes ring counts, cumulative ring counts, etc. The above test method can also be implemented by only one acoustic emission sensor, but in order to ensure the accuracy of the data, this application uses two acoustic emission sensors to obtain data. The propagation wave velocity is calculated as follows: ; in, is the propagation wave speed, To preset the transmitting sensor and the propagation distance between two transmitting sensors, For the first time, For the second time.

[0036] In a possible implementation, step S103 further includes: performing noise evaluation on the detection environment where the target asphalt mixture sample is located within a preset time period, the noise evaluation being used to obtain a background noise signal within the preset time period; and adjusting a threshold of a preset emission sensor according to the background noise signal.

[0037] Specifically, before starting the formal acoustic emission test, it is necessary to first conduct a noise assessment of the test environment around the target asphalt mixture sample. The noise assessment requires collecting the background noise signal of the target asphalt mixture sample within a preset time period without any acoustic emission source activity (that is, no external stress is applied to the target asphalt mixture sample, which may cause internal damage and produce acoustic emission). The preset time period can be set to 15 minutes to 30 minutes, and can be set according to the specific needs of the experiment. This application does not limit the setting of the preset time period. Afterwards, the threshold of the preset emission sensor is adjusted according to the background noise signal to improve the accuracy of data collection.

[0038] Step S104: constructing a target image corresponding to the target asphalt mixture sample based on the load-displacement data and the acoustic emission data.

[0039] Specifically, the load-displacement curves were correlated with the acoustic emission data, and image processing methods were used to visualize the damage extension in order to intuitively present the damage evolution characteristics of the specimen during the loading process.

[0040] In a possible implementation, step S104 also includes: obtaining load magnitude change data and displacement change data corresponding to the target asphalt mixture sample based on the load-displacement data; sorting the load magnitude change data and displacement change data according to a first preset time sequence; obtaining ring count data corresponding to the target asphalt mixture sample based on the acoustic emission data, and calculating the cumulative ring count data based on the ring count data; sorting the ring count data and the cumulative ring count data according to a second preset time sequence, the first preset time sequence and the second preset time sequence being consistent in time scale; constructing a target image corresponding to the target asphalt mixture sample based on the sorted load magnitude change data, displacement change data, ring count data and cumulative ring count data.

[0041] Specifically, first, the load magnitude change data and displacement change data of the target asphalt mixture sample during the test are obtained from the semicircular bending test equipment. These data are usually a series of discrete points, which record the load magnitude change applied to the target asphalt mixture sample and the corresponding displacement change as the test time (or number of loading steps) increases. The load magnitude change data and displacement change data are sorted according to the first preset time sequence, and then the load-displacement curve is drawn, with the horizontal axis being the sample displacement and the vertical axis being the load. This curve describes the elastic and plastic deformation behavior of the sample during the loading process. Through this curve, the bending resistance of the target asphalt mixture sample and the starting point of crack initiation and expansion can be observed; for acoustic emission data, including ring count data and cumulative ring count data, please refer to Figure 4 , which shows a schematic diagram of the change of acoustic emission ring counts of asphalt mixture provided in an embodiment of the present application. The ring count data and the cumulative ring count data are sorted according to the second preset time sequence to ensure that the time scale of the second preset time sequence is consistent with the first preset time sequence. Then, the ring count and cumulative ring count graphs are drawn based on the acoustic emission signal. The ring count graph shows the instantaneous counts of the acoustic emission signals generated by the specimen at each loading stage under different loads, while the cumulative ring count graph shows the cumulative effect of the acoustic emission signals during the loading process, which can reflect the gradual evolution of internal damage of the material. At the same time, the image is divided into different areas. Constructing a target image can reflect the interface damage of the specimen, thereby improving the ability to evaluate the damage evolution process.

[0042] In an embodiment of the present application, after constructing the target image, the target image can be analyzed, and the image can be divided into regions in the following manner. According to the division method, the process of target asphalt mixture sample destruction is divided into four stages: stage I is the primary stage, which is divided into the first 35% of the peak load; stage II is the development stage, which is divided into 35% to 95% before the peak load; stage III is the destruction stage, which is divided into the first 95% of the peak load to the last 55% of the peak load; stage IV is the failure stage, which is divided into 55% after the peak to the end of the experiment. The above analysis is the process of the target asphalt mixture sample from elastic deformation to plastic yielding and then to final destruction. The present application does not limit the division of peak load, that is, the division of regions, and can be adjusted in a dynamic manner according to different experimental settings, asphalt mixture types, loading methods and other factors. For example, combined with the strength, toughness, crack propagation characteristics of the material, the peak load division criteria can be further optimized through different stress-strain relationships and changes in material constants. In addition, in the division process of each stage, the load distribution can be automatically identified and optimized by combining data mining methods (such as machine learning, regression analysis, etc.). Afterwards, the interface damage of the asphalt mixture specimen can be evaluated in the following ways: Starting point of interface damage: When the load reaches a certain value, if the ring count of the acoustic emission increases significantly, it usually means that the interface of the asphalt mixture begins to be damaged. This moment can be used as the starting point of interface damage. Cumulative development of damage: During the loading process, the damage will gradually develop with the gradual increase of the cumulative ring count. If the cumulative growth rate of the ring count accelerates, it means that the interface damage is aggravated and the specimen may be close to destruction. Identification of the fracture point: When the load-displacement curve shows a significant decline, and the acoustic emission signal reaches a maximum value and begins to stabilize, it usually marks the final fracture or destruction of the asphalt mixture specimen.

[0043] Step S105, obtaining interface damage characteristics corresponding to the target asphalt mixture sample according to the target image.

[0044] Specifically, the acquisition of damage features can be achieved through the following steps: The division of image regions includes but is not limited to the following steps: Determine the image size and coordinate system: According to the time scale of the load-displacement data and the acoustic emission data, set the horizontal axis of the target image to the time axis, and the vertical axis to the load size or ringing count and other data parameters to build a unified data visualization framework; Set the region division rule: According to the loading stage and the law of damage evolution, divide the entire image into several regions. For example, the partition can be made according to the key inflection points of the load-displacement curve (such as the elastic stage, the yield stage, and the destruction stage), or according to the sudden increase stage of the acoustic emission signal (such as initial damage, crack extension, and final destruction); Partition mapping and data projection: Map the load-displacement data and acoustic emission data to the corresponding regions respectively. For example, in the crack initiation stage, the initial linear segment of the load-displacement curve and the low-frequency fluctuations of the acoustic emission ring count are mainly analyzed; in the crack extension stage, the focus is on the area of ​​​​rapid increase in the ring count, and the cumulative ring count changes are combined for evaluation; in the final damage stage, the acoustic emission signal characteristics before and after the damage are extracted and correspond to the load decline trend; Regional annotation and color stratification: Use different colors or gray levels to layer and annotate the image to visually distinguish the various damage stages. For example, light-colored areas can be used to represent the minor damage stage, dark areas can be used to represent the crack extension stage, and red areas can be used to represent the final damage stage; Image output and evaluation: Combine the visualization results of different areas, analyze the damage evolution process, and extract key damage features.

[0045] Step S106: input the interface damage characteristics into the statistical damage constitutive model, and output the damage and failure assessment result corresponding to the target asphalt mixture sample through the statistical damage constitutive model.

[0046] Specifically, based on the Weibull random distribution, a statistical damage constitutive model with AE ringing count as a parameter is established. The statistical damage constitutive model is input and the damage and failure assessment results corresponding to the target asphalt mixture specimen are output through the statistical damage constitutive model. The initiation process, propagation process and evolution process of the interface cracks in the target asphalt mixture specimen can be reflected through the damage and failure assessment results.

[0047] In a possible implementation, step S106 also includes: constructing a probability density damage function based on the interface damage characteristics, the probability density damage function is used to calculate the probability of damage occurrence of the target asphalt mixture sample; calculating the number of damaged micro-units generated after the target asphalt mixture sample is damaged under the action of external loads based on the probability density damage function; calculating the damage variable corresponding to the target asphalt mixture sample based on the number of damaged micro-units, and using the damage variable to reflect the change in the degree of damage of the target asphalt mixture sample during the damage and destruction assessment operation; and using the damage variable as the damage and destruction assessment result.

[0048] Specifically, the damage assessment results include the initiation process, expansion process and evolution process of the interface cracks in the target asphalt mixture specimen. Since the damage process of the target asphalt mixture specimen is random, non-uniform and progressive. Through the statistical analysis of the acoustic emission ring counts, combined with the Weibull distribution theory, a statistical damage constitutive model with AE ring counts as parameters can be established to quantify the nonlinear characteristics of the target asphalt mixture specimen as the damage accumulates during the loading process. Weibull distribution is widely used to describe the probabilistic characteristics of material failure, and is particularly suitable for characterizing the damage evolution process of the target asphalt mixture specimen under complex loads. The probability density function of the Weibull distribution can be expressed as:

[0049] in, is the probability of damage occurrence, is the distribution characteristic of the damaged micro-units, is the ambient temperature at time t, is the load at time t, is the area of ​​the asphalt mixture that has been damaged at time t, is the scale parameter, In the process of loading, countless micro-units of asphalt mixture are damaged. The ratio of the damaged micro-units at a certain time point during the loading process to the total damaged micro-units during the whole loading process is defined as the damage variable.

[0050] As a preferred technical solution, the number of AMV units, that is, the number of damaged micro units, is calculated as: ; in, During loading The number of damaged microcells at the time, is the total number of damaged microcells during the whole loading process, is the temperature effect weight, is the load impact weight. Based on the probability density function of the damaged micro-unit, the conversion formula between AE ringing count and AMV unit is constructed, and the solution formula for AE ringing count is obtained:

[0051] in, is the AE ringing count, It is the accumulated AE ring count when the asphalt mixture specimen is completely destroyed. In order to intuitively and accurately evaluate the damage state of the material under different working conditions, we introduce the key parameter of damage variable:

[0052] in, is the damage variable, is the temperature coefficient influence function, is the load influence function, is the damage area influence function. , , , which can be calculated in the following ways: For the temperature coefficient influence function, modeling can be performed based on the Arrhenius equation or polynomial fitting form, the specific form is: ; in, is the current temperature, is the reference temperature (e.g. the performance benchmark of asphalt mixture at standard temperature), It is the temperature effect index on mechanical behavior, which is used to describe the sensitivity of temperature change to the stiffness of asphalt materials. is the adjustment factor of the temperature effect on the microstructure of the target asphalt mixture sample, is the material activation energy, is the order of the polynomial, For the The coefficients in the polynomial are used to express the contribution of the temperature to the performance of the asphalt mixture. For the The temperature in the polynomial. The load influence function can be expressed by combining the hyperbolic tangent function and the power law function, while considering the load amplification effect and nonlinear characteristics on damage:

[0053] in, and are fitting parameters, representing the increase in damage under different loads. is the power exponent of the load, is the parameter of the hyperbolic tangent function, , It is used to reflect the nonlinear effect of load size on the target asphalt mixture sample. is a constant used to adjust the nonlinear behavior of the load, is the attenuation factor under load, is the standard value of the load, which is used to normalize the effect of the load. The damage area influence function can be expressed by combining the Gaussian function and the cubic polynomial. The specific formula is:

[0054] in, is the current damaged area, is a normalization factor used to adjust the influence of the damaged area. is the reference damage area (e.g., the critical damage area of ​​the target asphalt mixture sample), which is used to control the starting point of damage extension. is the standard deviation of the Gaussian distribution, which is used to control the expansion speed of the damaged area. is the third order polynomial The coefficient of the term, is the third order polynomial After multiple comparisons and verifications, the calculated damage variables in the verification experiment are basically consistent with the damage variables based on the conversion formula of AE ringing count and AMV unit. Therefore, the comparison experiment verifies the validity of the damage variable calculation in this application.

[0055] Through previous acoustic emission (AE) experiments, we have obtained a wealth of AE ring count data. These data reflect the elastic wave signal characteristics generated by the damage behaviors such as the initiation and expansion of internal microcracks in the material during the stress process. Based on the conversion relationship between AE ring counts and AMV units established in previous studies, after further derivation and demonstration, and based on the conversion formula between AE ring counts and AMV units, the calculation formula for material damage variables can be finally obtained: ; During the bending test of asphalt mixture, the ring count data of AE signal was collected in real time. According to the acoustic emission data, during the loading process, as the load increases, the AE ring count gradually increases, indicating that the number and expansion rate of microcracks in the target asphalt mixture specimen gradually accelerate. At this time, we use Weibull distribution to establish a damage constitutive model. According to the damage constitutive model, the damage variable at a certain moment can be calculated. When the damage variable D≤0.3, the material is in the linear elastic stage, and the specimen first undergoes a compaction process. At this time, due to the compaction of cracks and voids inside the specimen, damage begins to initiate, and the damage variable remains at a very small level. As the load continues to increase, tiny cracks and voids gradually begin to appear inside the specimen, and these micro-damages lead to the growth of the damage variable D. Especially when approaching the maximum load, the damage value D of the specimen shows a significant accelerated growth until D=0.8. At this stage, as the external load increases, the microcracks inside the asphalt mixture specimen begin to expand rapidly and gradually form larger cracks. The expansion and development of these cracks directly affect the mechanical properties of the specimens, causing the damage variables of the specimens to show a significant upward trend. When the damage variable D>0.8, it is the final manifestation of the damage accumulation of the steel slag rubber powder modified asphalt mixture, which can be divided into the complete failure stage, which is essentially the result of the synergistic effect of interface debonding and skeleton collapse.

[0056] See also Figure 5, which shows a schematic diagram of damage variables in an asphalt mixture provided in an embodiment of the present application. With the continuous increase of load and the expansion of cracks, the cracks gradually propagate to the aggregate-asphalt binder interface, the crack expansion speed will become faster and faster, and the ringing count of the AE signal will increase significantly. At this time, the damage variable D in the Weibull damage model will show a faster upward trend, indicating that the crack expansion speed is accelerated, the area of ​​the crack region is increased, and the damage is gradually expanding inside the material.

[0057] At this stage, the interface crack is not only affected by the load, but also by environmental factors such as temperature, humidity and aging, which may cause the crack to expand more violently. By increasing the ringing count caused by external load, the rapid expansion of cracks at this stage can be better reflected.

[0058] Please refer to Figure 6 , which shows a schematic diagram of commonly used parameters of an AE test system provided in an embodiment of the present application. Commonly used parameters in the AE test system include but are not limited to amplitude, energy, ring count, rise time and duration. The AE amplitude corresponds to the peak point of the signal; the energy of the waveform is the part above the threshold, representing the area under the envelope curve, reflecting the strength of the signal; the duration is defined as the time interval between the trigger time and the disappearance time of the AE signal; the rise time is the time interval between the trigger time and the peak amplitude time of the AE signal; the ring count represents the number of oscillations of the signal exceeding the threshold. Please refer to Figure 7 , which shows a schematic diagram of a SCB specimen loading device provided in an embodiment of the present application, Figure 7 The UTM-100 hydraulic servo multifunctional material testing machine was selected to conduct a three-point bending loading test on the specimen. The distance between the two fulcrums at the bottom of the specimen was 80 mm. At the beginning of the experiment, the load was applied to the top of the specimen at a constant loading rate of 0.7 mm / min until the crack gradually extended from the pre-cut to the top of the specimen, eventually causing the specimen to completely break, and the experiment ended. The experimental results and load and displacement changes were recorded.

[0059] The present application obtains the target asphalt mixture sample by adopting the above method, obtains the corresponding load-displacement data through a semicircular bending test, and obtains the acoustic emission data of the target asphalt mixture sample under the loading of the semicircular bending test through acoustic emission nondestructive testing, thereby constructing a target image corresponding to the target asphalt mixture sample based on the load-displacement data and the acoustic emission data, and then obtains the interface damage characteristics corresponding to the target asphalt mixture sample based on the target image, inputs the interface damage characteristics into a statistical damage constitutive model, and outputs the damage and destruction assessment result corresponding to the target asphalt mixture sample through the statistical damage constitutive model, and then improves the accuracy of asphalt mixture damage and destruction assessment by combining multi-source data fusion analysis, image recognition technology and statistical damage constitutive theory, and solves the problem that the damage detection method based on appearance observation and physical testing cannot accurately detect and evaluate the damage state of asphalt pavement.

[0060] Please refer to Figure 8 , which shows a module schematic diagram of an asphalt mixture damage assessment device based on acoustic emission technology provided in an embodiment of the present application, the device includes an acquisition module 81 and a processing module 82, wherein: The acquisition module 81 is used to obtain a target asphalt mixture sample in response to a user's damage and destruction assessment operation on the asphalt mixture; perform a semicircular bending test on the target asphalt mixture sample, and obtain corresponding load-displacement data through the semicircular bending test; perform acoustic emission nondestructive testing on the target asphalt mixture sample, and obtain acoustic emission data of the target asphalt mixture sample under the loading of the semicircular bending test through acoustic emission nondestructive testing.

[0061] The processing module 82 is used to construct a target image corresponding to the target asphalt mixture sample based on the load-displacement data and the acoustic emission data; obtain the interface damage characteristics corresponding to the target asphalt mixture sample according to the target image; input the interface damage characteristics into the statistical damage constitutive model, and output the damage and destruction assessment results corresponding to the target asphalt mixture sample through the statistical damage constitutive model, and the damage and destruction assessment results include the initiation process, expansion process and evolution process of the interface cracks in the target asphalt mixture sample.

[0062] In a possible implementation, the acquisition module 81 is used to obtain acoustic emission data of the target asphalt mixture sample under the loading of the semicircular bending test through acoustic emission nondestructive testing, specifically including: selecting a preset layout area on the surface of the target asphalt mixture sample, the preset layout area containing interface damage characteristics; fixing a preset emission sensor on the target asphalt mixture sample according to the preset layout method, and the preset emission sensor covers the preset layout area according to the preset layout method; performing acoustic emission nondestructive testing on the target asphalt mixture sample through the preset emission sensor; obtaining the acoustic emission data of the target asphalt mixture sample under the loading of the semicircular bending test through acoustic emission nondestructive testing.

[0063] In a possible implementation, before performing acoustic emission nondestructive testing on a target asphalt mixture sample and obtaining acoustic emission data of the target asphalt mixture sample under semicircular bending test loading through acoustic emission nondestructive testing, the acquisition module 81 is used to perform noise evaluation on the detection environment where the target asphalt mixture sample is located within a preset time period, and the noise evaluation is used to obtain a background noise signal within a preset time period; and a threshold of a preset emission sensor is adjusted according to the background noise signal.

[0064] In one possible implementation, the processing module 82 is used to construct a target image corresponding to the target asphalt mixture sample based on the load-displacement data and the acoustic emission data, specifically including: obtaining the load magnitude change data and the displacement change data corresponding to the target asphalt mixture sample according to the load-displacement data; sorting the load magnitude change data and the displacement change data according to a first preset time sequence; obtaining the ring count data corresponding to the target asphalt mixture sample according to the acoustic emission data, and calculating the cumulative ring count data based on the ring count data; sorting the ring count data and the cumulative ring count data according to a second preset time sequence, the first preset time sequence and the second preset time sequence being consistent in time scale; constructing a target image corresponding to the target asphalt mixture sample according to the sorted load magnitude change data, displacement change data, ring count data and cumulative ring count data.

[0065] In one possible implementation, the processing module 82 is used to output the damage and destruction assessment results corresponding to the target asphalt mixture sample through a statistical damage constitutive model, specifically including: constructing a probability density damage function based on the interface damage characteristics, the probability density damage function is used to calculate the probability of damage occurrence of the target asphalt mixture sample; according to the probability density damage function, calculating the number of damaged micro-units generated after the target asphalt mixture sample is damaged under the action of external load; calculating the damage variable corresponding to the target asphalt mixture sample based on the number of damaged micro-units, and using the damage variable to reflect the change in the degree of damage of the target asphalt mixture sample during the damage and destruction assessment operation; using the damage variable as the damage and destruction assessment result.

[0066] In a possible implementation, the processing module 82 is used to calculate the number of damaged micro-units generated after the target asphalt mixture sample is damaged under the external load according to the probability density damage function, specifically including: calculating the number of damaged micro-units according to the following formula: ; in, During loading The number of damaged microcells at the time, is the total number of damaged microcells during the whole loading process, is the probability of damage occurrence, is the distribution characteristic of the damaged micro-units, is the ambient temperature at time t, is the load at time t, is the area of ​​the asphalt mixture that has been damaged at time t, is the temperature effect weight, is the load impact weight.

[0067] In a possible implementation, the processing module 82 is used to calculate the damage variable corresponding to the target asphalt mixture sample according to the number of damaged micro-units, specifically including: calculating the damage variable according to the following formula: ; in, is the damage variable, is the scale parameter, For uniform distribution, is the temperature coefficient influence function, is the load influence function, is the damage area influence function.

[0068] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0069] The present application also provides an electronic device. Fig. 9 , Fig. 9 9 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include: at least one processor 901, at least one communication bus 902, a user interface 903, at least one network interface 904, and a memory 905.

[0070] The communication bus 902 is used to realize the connection and communication between these components.

[0071] The user interface 903 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 903 may also include a standard wired interface and a wireless interface.

[0072] The network interface 904 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0073] Among them, the processor 901 may include one or more processing cores. The processor 901 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 905, and calling data stored in the memory 905. Optionally, the processor 901 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 901 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 901, and it can be implemented separately through a chip.

[0074] Among them, the memory 905 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 905 includes a non-transitory computer-readable storage medium. The memory 905 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 905 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 905 may optionally also be at least one storage device located away from the aforementioned processor 901. Refer to Fig. 9 The memory 905 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an asphalt mixture damage assessment application based on acoustic emission technology.

[0075] exist Fig. 9In the electronic device shown, the user interface 903 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 901 can be used to call the asphalt mixture damage assessment application based on acoustic emission technology stored in the memory 905. When executed by one or more processors 901, the electronic device executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0076] The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors, the electronic device executes one or more of the methods described in the above embodiments.

[0077] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0078] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. 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 service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0079] 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.

[0080] In addition, each functional unit in each embodiment of the present application 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. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0081] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0082] The above are only exemplary embodiments disclosed in this application, and the scope of the disclosure of this application cannot be limited by this. That is, any equivalent changes and modifications made according to the teachings disclosed in this application are still within the scope of the disclosure of this application. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other implementation schemes disclosed in this application.

[0083] This application is intended to cover any modifications, uses or adaptive changes disclosed in this application, which follow the general principles disclosed in this application and include common knowledge or customary technical means in the technical field not recorded in this application.

Claims

1. A method for assessing damage to asphalt mixture based on acoustic emission technology, characterized in that: The method comprises: In response to a user's damage assessment operation on asphalt mixture, obtaining a target asphalt mixture sample; Performing a semicircular bending test on the target asphalt mixture sample, and obtaining corresponding load-displacement data through the semicircular bending test; Performing acoustic emission nondestructive testing on the target asphalt mixture sample, and obtaining acoustic emission data of the target asphalt mixture sample under the loading of the semicircular bending test through the acoustic emission nondestructive testing; Based on the load-displacement data and the acoustic emission data, constructing a target image corresponding to the target asphalt mixture sample; According to the target image, obtaining interface damage characteristics corresponding to the target asphalt mixture sample; The interface damage characteristics are input into a statistical damage constitutive model, and the damage and destruction assessment results corresponding to the target asphalt mixture sample are output through the statistical damage constitutive model. The damage and destruction assessment results include the initiation process, expansion process and evolution process of the interface cracks in the target asphalt mixture sample: a temperature coefficient influence function is constructed based on the Arrhenius equation, a load influence function is constructed based on a combination of a hyperbolic tangent function and a power law function, and a damage area influence function is constructed based on a combination of a Gaussian function and a cubic polynomial. Damage variables are constructed through the temperature coefficient influence function, the load influence function and the damage area influence function, and the initiation process, expansion process and evolution process of the interface cracks in the target asphalt mixture sample are displayed through the damage variables.

2. The method according to claim 1, characterized in that Acquiring the acoustic emission data of the target asphalt mixture sample under the loading of the semicircular bending test through the acoustic emission nondestructive testing specifically includes: Selecting a preset layout area on the surface of the target asphalt mixture sample, wherein the preset layout area contains the interface damage feature; According to a preset arrangement, the preset emission sensor is fixed on the target asphalt mixture sample, and according to the preset arrangement, the preset emission sensor covers the preset arrangement area; Performing the acoustic emission nondestructive testing on the target asphalt mixture sample by using a preset emission sensor; Acoustic emission data of the target asphalt mixture sample under the loading of the semicircular bending test is obtained through the acoustic emission nondestructive testing.

3. The method according to claim 2, characterized in that Before performing acoustic emission nondestructive testing on the target asphalt mixture sample and obtaining acoustic emission data of the target asphalt mixture sample under the loading of the semicircular bending test through the acoustic emission nondestructive testing, the method further includes: Performing a noise evaluation on the detection environment where the target asphalt mixture sample is located within a preset time period, wherein the noise evaluation is used to obtain a background noise signal within the preset time period; A threshold value of the preset emission sensor is adjusted according to the background noise signal.

4. The method according to claim 1, characterized in that: Based on the load-displacement data and the acoustic emission data, a target image corresponding to the target asphalt mixture sample is constructed, specifically comprising: According to the load-displacement data, load magnitude change data and displacement change data corresponding to the target asphalt mixture sample are obtained; Sorting the load magnitude change data and the displacement change data according to a first preset time sequence; According to the acoustic emission data, acquiring the ringing count data corresponding to the target asphalt mixture sample, and calculating the cumulative ringing count data according to the ringing count data; Sorting the ring count data and the accumulated ring count data according to a second preset time sequence, wherein the first preset time sequence and the second preset time sequence are consistent in time scale; A target image corresponding to the target asphalt mixture sample is constructed according to the sorted load magnitude change data, the displacement change data, the ringing count data and the cumulative ringing count data.

5. The method according to claim 1, characterized in that Outputting the damage and destruction assessment result corresponding to the target asphalt mixture sample through the statistical damage constitutive model specifically includes: Constructing a probability density damage function according to the interface damage characteristics, wherein the probability density damage function is used to calculate the probability of damage occurrence of the target asphalt mixture sample; According to the probability density damage function, the number of damaged micro-units generated after the target asphalt mixture sample is damaged under the external load is calculated; Calculating the damage variable corresponding to the target asphalt mixture sample according to the number of damaged micro-units, and reflecting the change of the damage degree of the target asphalt mixture sample during the damage and destruction assessment operation through the damage variable; The damage variable is used as the damage failure assessment result.

6. The method according to claim 5, characterized in that According to the probability density damage function, the number of damaged micro-units generated after the target asphalt mixture sample is damaged under the external load is calculated, specifically including: The number of damaged micro-units is calculated according to the following formula: ； in, During loading The number of damaged micro-units at the time, is the total number of damaged microcells during the entire loading process, is the probability of occurrence of the damage, is the distribution characteristic of the damaged microunits, is the ambient temperature at time t, is the load at time t, is the area of ​​the asphalt mixture that has been damaged at time t, is the temperature effect weight, is the load impact weight.

7. The method according to claim 6, characterized in that Calculating the damage variable corresponding to the target asphalt mixture sample according to the number of damaged micro-units specifically includes: The damage variable is calculated according to the following formula: ； in, is the damage variable, is the scale parameter, For uniform distribution, is the temperature coefficient influence function, is the load influence function, is the damage area influence function.

8. An asphalt mixture damage assessment device based on acoustic emission technology, characterized in that: The device comprises an acquisition module and a processing module, wherein: The acquisition module is used to acquire a target asphalt mixture sample in response to a user's damage assessment operation on the asphalt mixture; perform a semicircular bending test on the target asphalt mixture sample, and acquire corresponding load-displacement data through the semicircular bending test; perform acoustic emission nondestructive testing on the target asphalt mixture sample, and acquire acoustic emission data of the target asphalt mixture sample under the loading of the semicircular bending test through the acoustic emission nondestructive testing; The processing module is used to construct a target image corresponding to the target asphalt mixture sample based on the load-displacement data and the acoustic emission data; obtain interface damage characteristics corresponding to the target asphalt mixture sample according to the target image; input the interface damage characteristics into a statistical damage constitutive model, and output a damage and destruction assessment result corresponding to the target asphalt mixture sample through the statistical damage constitutive model, wherein the damage and destruction assessment result includes the initiation process, expansion process and evolution process of interface cracks in the target asphalt mixture sample.

9. An electronic device, characterized in that: It includes a processor, a communication bus, a user interface, a network interface and a memory, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

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