An asphalt mixture damage and failure evaluation method and device based on acoustic emission technology

Abstract

The application provides an asphalt mixture damage and destruction evaluation method and device based on acoustic emission technology, and relates to the field of asphalt material analysis. The method comprises the following steps: obtaining a target asphalt mixture sample; obtaining corresponding load-displacement data through a semicircular bending test; obtaining acoustic emission data of the target asphalt mixture sample under the load of the semicircular bending test through 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 characteristics corresponding to the target asphalt mixture sample according to the target image; and inputting the interface damage characteristics into a statistical damage constitutive model and outputting damage and destruction evaluation results corresponding to the target asphalt mixture sample through the statistical damage constitutive model. The application solves the problem that damage detection methods based on visual observation and physical testing cannot accurately detect and evaluate the damage state of asphalt pavement.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of asphalt material analysis, and particularly relates to an asphalt mixture damage and destruction evaluation method and device based on acoustic emission technology. BACKGROUND

[0002] In the construction of transportation infrastructure, asphalt mixture is widely used in road, bridge and other engineering. However, the asphalt mixture will be affected by various factors during use, resulting in gradual damage and destruction.

[0003] In daily use, asphalt pavement is easily affected by vehicle load, climate change and environmental factors, resulting in various damages such as cracks, ruts and peeling. If these damages are not discovered in time, they may lead to more serious structural damage, thus requiring high repair costs. Therefore, early detection and evaluation of pavement damage state is crucial for formulating a reasonable maintenance plan and reducing economic losses.

[0004] At present, the traditional damage detection method mainly relies on visual observation and physical test. This method obtains pavement damage information through visual inspection, manual knocking, core sampling and splitting test, etc. Since the detection results are greatly affected by human factors, and the testing process is destructive and cannot be monitored in real time, the damage state of asphalt pavement cannot be accurately detected and evaluated.

[0005] Therefore, there is an urgent need for an asphalt mixture damage and destruction evaluation method and device based on acoustic emission technology. SUMMARY

[0006] The present application provides an asphalt mixture damage and destruction evaluation method and device based on acoustic emission technology, which solves the problem that the damage detection method based on visual observation and physical test cannot accurately detect and evaluate the damage state of asphalt pavement.

[0007] The first aspect of the application provides a method for evaluating damage and failure of asphalt mixture based on acoustic emission technology, the method comprising: in response to a user's operation of evaluating damage and failure of the asphalt mixture, obtaining a target asphalt mixture sample; performing a semi-circular bending test on the target asphalt mixture sample, and obtaining corresponding load-displacement data through the semi-circular 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 load of the semi-circular 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; inputting the interface damage characteristics into a statistical damage constitutive model, and outputting a damage and failure evaluation result corresponding to the target asphalt mixture sample through the statistical damage constitutive model, the damage and failure evaluation result including the initiation process, the expansion process and the evolution process of the interface cracks in the target asphalt mixture sample.

[0008] Optionally, the acoustic emission data of the target asphalt mixture sample under the load of the semi-circular bending test is obtained through acoustic emission nondestructive testing, specifically comprising: selecting a preset arrangement area on the surface of the target asphalt mixture sample, the preset arrangement area containing the interface damage characteristics; fixing a preset emission sensor on the target asphalt mixture sample according to a preset arrangement mode, the preset emission sensor covering the preset arrangement area according to the preset arrangement mode; performing acoustic emission nondestructive testing on the target asphalt mixture sample through the preset emission sensor; and obtaining the acoustic emission data of the target asphalt mixture sample under the load of the semi-circular bending test through the acoustic emission nondestructive testing.

[0009] Optionally, before the acoustic emission nondestructive testing on the target asphalt mixture sample and the obtaining of the acoustic emission data of the target asphalt mixture sample under the load of the semi-circular bending test through the acoustic emission nondestructive testing, the method further comprises: performing noise evaluation on a 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 value of the 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: according to the load-displacement data, load size change data and displacement amount change data corresponding to the target asphalt mixture sample are obtained; the load size change data and the displacement amount change data are sorted according to a first preset time sequence; according to the acoustic emission data, ringing count data corresponding to the target asphalt mixture sample is obtained, and cumulative ringing count data is calculated according to the ringing count data; the ringing count data and the cumulative ringing count data are sorted according to a second preset time sequence, the first preset time sequence and the second preset time sequence are consistent in the time scale; according to the sorted load size change data, displacement amount change data, ringing count data and cumulative ringing count data, a target image corresponding to the target asphalt mixture sample is constructed.

[0011] Optionally, the damage and failure evaluation result corresponding to the target asphalt mixture sample is output by a statistical damage constitutive model, specifically including: a probability density damage function is constructed according to the interface damage characteristics, the probability density damage function is used to calculate the damage occurrence probability of the target asphalt mixture sample; 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 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 in the damage and failure evaluation operation is reflected through the damage variable; the damage variable is taken as the damage and failure evaluation result.

[0012] Optionally, 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 probability density damage function, specifically including: the number of damaged micro-units is calculated according to the following formula:

[0013] ;

[0014] Wherein, is the number of damaged micro-units at the time of t, is the total number of damaged micro-units in the whole loading process, is the damage occurrence probability, is the distribution characteristics of damaged micro-units, is the environmental temperature at t, is the load condition at t, is the area of the region where the asphalt mixture has been damaged at t, is the temperature influence weight, is the load influence weight.

[0015] ​Optionally, the damage variable corresponding to the target asphalt mixture sample is calculated according to the number of damaged micro-units, and specifically includes: the damage variable is calculated according to the following formula:

[0016] ;

[0017] wherein, is the damage variable, is a scale parameter, is a distribution uniformity, is a temperature coefficient influence function, is a load action influence function, is a damage area influence function.

[0018] In a second aspect of the present application, an asphalt mixture damage and failure evaluation device based on acoustic emission technology is provided, and the device includes an acquisition module and a processing module, wherein,

[0019] The acquisition module is configured to, in response to a user's damage and failure evaluation operation on the asphalt mixture, acquire a target asphalt mixture sample; perform a semi-circular bending test on the target asphalt mixture sample, and acquire corresponding load-displacement data through the semi-circular bending test; and perform acoustic emission nondestructive testing on the target asphalt mixture sample, and acquire acoustic emission data of the target asphalt mixture sample under the semi-circular bending test loading through the acoustic emission nondestructive testing.

[0020] The processing module is configured to, based on the load-displacement data and the acoustic emission data, construct a target image corresponding to the target asphalt mixture sample; acquire an interface damage feature corresponding to the target asphalt mixture sample according to the target image; input the interface damage feature into a statistical damage constitutive model, and output a damage and failure evaluation result corresponding to the target asphalt mixture sample through the statistical damage constitutive model, wherein the damage and failure evaluation result includes a process of initiation, a process of expansion and a process of evolution of an interface crack in the target asphalt mixture sample.

[0021] In a third aspect of the present application, an electronic device is provided, which includes a processor, a memory, a user interface and a network interface, the memory is configured to store instructions, the user interface and the network interface are configured to communicate with other devices, and the processor is configured to execute the instructions stored in the memory to enable the electronic device to perform the method of any one of the above.

[0022] In a fourth aspect of the present application, a computer-readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to perform the method of any one of the above.

[0023] The one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0024] 1, obtain the target asphalt mixture sample, obtain the corresponding load-displacement data through the semi-circular bending test, and obtain the acoustic emission data of the target asphalt mixture sample under the semi-circular bending test loading through acoustic emission nondestructive testing, so as to construct the corresponding target image of 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 failure evaluation result corresponding to the target asphalt mixture sample through the statistical damage constitutive model, thereby improving the precision of the damage and failure evaluation of the asphalt mixture by combining multi-source data fusion analysis, image recognition technology and statistical damage constitutive theory, and solving the problem that the damage detection method based on visual observation and physical test cannot accurately detect and evaluate the damage state of the asphalt pavement.

[0025] 2, noise evaluation is performed on the detection environment of the target asphalt mixture sample within a preset time period, the noise evaluation is used to obtain the background noise signal within the preset time period, and the threshold of the preset emission sensor is adjusted according to the background noise signal, so as to reduce the interference of environmental noise on the detection data, improve the recognition precision of the acoustic emission signal, and ensure the reliability of the acoustic emission data.

[0026] 3, a probability density damage function is constructed according to the interface damage characteristics, and the number of damaged micro-units generated by the target asphalt mixture sample after being damaged under external load is calculated according to the probability density damage function, so as to calculate the damage variable corresponding to the target asphalt mixture sample according to the number of damaged micro-units, and the damage degree change of the target asphalt mixture sample in the damage and failure evaluation operation is reflected through the damage variable, and the damage evolution process of the target asphalt mixture sample is quantified more accurately by calculating the number of micro-units. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 is a flowchart of an asphalt mixture damage and failure evaluation method based on acoustic emission technology provided by the embodiment of the present application;

[0028] Figure 2 is a schematic diagram of an asphalt mixture damage and failure evaluation experimental system based on acoustic emission technology provided by the embodiment of the present application;

[0029] Figure 3 is a schematic diagram of the arrangement of an acoustic emission sensor provided by the embodiment of the present application;

[0030] Figure 4 is a schematic diagram of the change of the acoustic emission ringing count of the asphalt mixture provided by the embodiment of the present application;

[0031] Figure 5is a damage variable diagram in asphalt mixture provided by an embodiment of the present application;

[0032] Figure 6 is a common parameter diagram of an AE test system provided by an embodiment of the present application;

[0033] Figure 7 is a SCB specimen loading device diagram provided by an embodiment of the present application;

[0034] Figure 8 is a module diagram of an asphalt mixture damage and failure evaluation device based on acoustic emission technology provided by an embodiment of the present application;

[0035] Figure 9 is a structural diagram of an electronic device provided by an embodiment of the present application.

[0036] Legend: 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

[0037] In order for those skilled in the art to better understand the technical solutions in the specification, the technical solutions in the specification will be clearly and completely described below in conjunction with the drawings in the embodiments of the specification. Obviously, the described embodiments are only some of the embodiments of the present application, not all.

[0038] 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 limiting to the present application. As used in the specification of the present application, the singular expression "one", "a", "said", "the above", "the", and "this" are intended to also include the plural expression, unless there is clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application means any or all possible combinations of one or more listed items.

[0039] Hereinafter, the terms "first", "second" are only for the purpose of description, and cannot be understood as implying or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, the meaning of "multiple" is two or more.

[0040] In order for those skilled in the art to better understand the technical solutions of the present application, the present application will be further described in detail below with reference to the drawings.

[0041] Please refer to Figure 1 , which shows a flowchart of an asphalt mixture damage and failure evaluation method based on acoustic emission technology provided by the embodiments of the present application. The flowchart mainly includes the following steps: S101 to S106.

[0042] Step S101, in response to the damage and failure evaluation operation of the asphalt mixture, a target asphalt mixture sample is obtained.

[0043] Specifically, before the scheme in the embodiments of the present application is described, first, the sample preparation of the asphalt mixture is carried out: considering the variability of the molded test piece, the molding quality and other problems, the rotary compaction method most suitable for the actual pavement compaction state is selected, the asphalt mixture is molded into a φ150mm×170mm asphalt mixture cylinder by TexTest 5850 rotary compactor, and then the core sample is cut into a 25±2mm SCB asphalt mixture sample by using a high-precision cutting machine, and a small cutting machine is used to cut a pre-cut, and the length of the pre-cut is set to 10mm. A plurality of asphalt mixture samples are prepared by the above method, and the damage and failure evaluation operation is performed on each asphalt mixture sample, so as to increase the universality and reliability of the evaluation results. For the sake of convenience, the damage and failure evaluation of any one of the asphalt mixture samples is taken as an example for description, and the asphalt mixture sample is the target asphalt mixture sample.

[0044] Step S102, a semi-circular bending test is performed on the target asphalt mixture sample, and corresponding load-displacement data is obtained through the semi-circular bending test.

[0045] Specifically, the target asphalt mixture sample is placed in a UTM-100 test instrument, and the pressure rate is controlled by uniform loading at both ends of the sample. At the same time, a sensor is pasted on the surface of the sample, which is used to collect acoustic emission signals generated in 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 subsequent analysis of the damage and failure process of the asphalt mixture.

[0046] Step S103, acoustic emission nondestructive testing is performed on the target asphalt mixture sample, and acoustic emission data of the target asphalt mixture sample under the semi-circular bending test loading is obtained through the acoustic emission nondestructive testing.

[0047] Specifically, please refer to Figure 2 , Figure 2 A schematic diagram of an asphalt mixture damage and failure evaluation experimental system based on acoustic emission technology provided by the embodiments of the present application,Figure 2 The AE sensor 21, the signal preamplifier and filter 22, the AE signal receiver 23, and the AE data processor 24 are included in the AE testing system. The AE sensor 21 is arranged on the surface of the target asphalt mixture specimen. To ensure good coupling effect, a coupling agent is usually applied between the AE sensor 21 and the surface of the specimen, and the AE sensor 21 is ensured to be in close contact with the surface of the specimen. At this time, the vaseline can be used as the coupling agent to ensure the signal transmission quality. The coupling effect of the AE sensor 21 and the surface of the specimen has an important influence on the signal receiving time. If the coupling is not good, the signal receiving will be delayed or the signal strength will be weakened. During the loading process, the AE signal receiver 23 collects the AE data of the AE sensor 21 in real time, and the data analysis and feature extraction are performed by the AE data processor 24, so as to record the occurrence and development of the internal damage of the specimen and provide the basis for the damage failure evaluation. The signal preamplifier and filter 22 are used to enhance the weak AE signal received by the AE sensor 21 and remove the environmental noise, and the enhanced data is sent to the AE signal receiver 23, so as to improve the accuracy and reliability of the data collection.

[0048] In a possible implementation, the step S103 further includes: selecting a preset arrangement area on the surface of the target asphalt mixture specimen, the preset arrangement area containing the interface damage features; fixing the preset AE sensor on the target asphalt mixture specimen according to a preset arrangement mode, the preset AE sensor covering the preset arrangement area according to the preset arrangement mode; performing the AE nondestructive testing on the target asphalt mixture specimen by using the preset AE sensor; and obtaining the AE data of the target asphalt mixture specimen under the semi-circular bending test loading by using the AE nondestructive testing.

[0049] Specifically, the preset arrangement area is selected on the surface of the target asphalt mixture specimen. The preset arrangement area can be set according to the position of the interface damage features on the surface of the target asphalt mixture specimen, and the preset arrangement area needs to contain most of the interface damage features on the surface of the target asphalt mixture specimen. Please refer to Figure 3The applicant shows a schematic diagram of the arrangement of the acoustic emission sensor provided in the embodiment, as shown in the figure, in order to ensure the comprehensiveness of the monitoring, taking the SCB asphalt mixture sample of 25±2 mm in the present application as an example, four sensors can be arranged symmetrically on the back of the target asphalt mixture sample, and the arrangement area between each sensor is a rectangle of 4 cm*3 cm, and the preset emission sensor is the set 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, and the number and setting position of the acoustic emission sensors are not limited in the present application. Break the pencil core at the position between the two sensors. After breaking the pencil core each time, the AE data processor 24 will record the time when the two sensors receive the signal, which are respectively set as the first time and the second time. 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 as to take the calculated propagation wave velocity as one of the acoustic emission data under the loading of the semi-circular bending test. The acoustic emission data also includes ring count, cumulative ring count, etc. The above test method can also be realized by only one acoustic emission sensor, but in order to ensure the accuracy of the data, two acoustic emission sensors are used to acquire the data in the present application. The calculation method of the propagation wave velocity is as follows:

[0050] ;

[0051] wherein, is the propagation wave velocity, is the propagation distance between the preset emission sensor and the two emission sensors, is the first time, is the second time.

[0052] In a possible implementation, step S103 further includes: performing noise evaluation on a detection environment where the target asphalt mixture sample is located within a preset time period, the noise evaluation is used to acquire a background noise signal within the preset time period; and performing threshold adjustment on the preset emission sensor according to the background noise signal.

[0053] Specifically, before starting the formal acoustic emission detection, it is necessary to first perform noise evaluation on the detection environment around the target asphalt mixture sample. The noise evaluation needs to collect the background noise signal of the target asphalt mixture sample within a preset time period under the condition that there is no any acoustic emission source activity (i.e. no external stress is applied to the target asphalt mixture sample, which may cause internal damage to generate acoustic emission). The preset time period can be set to 15-30 minutes, which can be set according to the specific needs of the experiment, and the setting of the preset time period is not limited in the present application. Then, the threshold of the preset emission sensor is adjusted according to the background noise signal, so as to improve the accuracy of data acquisition.

[0054] In step S104, a target image corresponding to the target asphalt mixture sample is constructed based on the load-displacement data and the acoustic emission data.

[0055] Specifically, the load-displacement curve is associated with the acoustic emission data, and the damage propagation is visualized by using the image processing method, so as to intuitively present the damage evolution characteristics of the sample in the loading process.

[0056] In one possible implementation, step S104 further includes: obtaining load size change data and displacement amount change data corresponding to the target asphalt mixture sample according to the load-displacement data; sorting the load size change data and the displacement amount change data according to a first preset time sequence; obtaining ringing count data corresponding to the target asphalt mixture sample according to the acoustic emission data, and calculating cumulative ringing count data according to the ringing count data; sorting the ringing count data and the cumulative ringing count data according to a second preset time sequence, the first preset time sequence and the second preset time sequence being consistent in the time scale; and constructing the target image corresponding to the target asphalt mixture sample according to the sorted load size change data, displacement amount change data, ringing count data and cumulative ringing count data.

[0057] Specifically, first, the load size change data and the displacement amount change data of the target asphalt mixture sample in the test process are obtained from the semi-circular bending test equipment. These data are usually a series of discrete points, which record the change of the load size applied to the target asphalt mixture sample and the corresponding displacement amount change with the increase of the test time (or the loading step number). The load size change data and the displacement amount change data are sorted according to the first preset time sequence, and then the load-displacement curve is drawn, with the horizontal axis representing the sample displacement and the vertical axis representing the load. This curve describes the elastic and plastic deformation behavior of the sample in the loading process. Through the curve, the bending resistance performance of the target asphalt mixture sample and the starting point of crack initiation and propagation can be observed. For the acoustic emission data, including the ringing count data and the cumulative ringing count data, please refer to Figure 4 , which shows a schematic diagram of the acoustic emission ringing count change of the asphalt mixture provided by the embodiment. The ringing count data and the cumulative ringing count data are sorted according to the second preset time sequence, so as to ensure that the time scale of the second preset time sequence is consistent with the first preset time sequence. Then, the ringing count and cumulative ringing count graph is drawn based on the acoustic emission signal. The ringing count graph shows the instantaneous count of the acoustic emission signal generated by the sample in each loading stage under different loads, and the cumulative ringing count graph presents the cumulative effect of the acoustic emission signal in the loading process, which can reflect the gradual evolution of the internal damage of the material. Meanwhile, the image is divided into different regions. The construction of the target image can reflect the interfacial damage of the sample, thereby improving the evaluation ability of the damage evolution process.

[0058] In the embodiments of the present application, after the target image is constructed, the target image can be analyzed, and the image is divided into regions in the following manner. According to the division manner, the process of breaking the target asphalt mixture sample 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 95% before the peak load to 55% after the peak load; and 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 yield, and then to final destruction. The division of the peak load, i.e., the division of the region, is not limited, and can be adjusted dynamically according to different experimental settings, types of asphalt mixture, loading methods, and other factors. For example, in combination with the strength, toughness, and crack propagation characteristics of the material, the division standard of the peak load can be further optimized by 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 and regression analysis. Then, the interface damage of the asphalt mixture sample can be evaluated in the following ways: the starting point of interface damage: when the load reaches a certain value, if the ring count of acoustic emission increases significantly, it usually indicates 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, as the cumulative ring count gradually increases, the damage gradually develops. If the cumulative increase rate of ring count accelerates, it means that the interface damage intensifies, and the sample may be close to breaking. Recognition of the breaking point: when the load-displacement curve shows a significant drop, and the acoustic emission signal reaches the maximum value and starts to stabilize, it usually marks the final fracture or destruction of the asphalt mixture sample.

[0059] In step S105, according to the target image, the interface damage characteristics corresponding to the target asphalt mixture sample are obtained.

[0060] Specifically, the acquisition method of the damage feature can be realized through the following steps: the image region division includes but is not limited to the following steps: determining the image size and the coordinate system: according to the time scale of the load-displacement data and the acoustic emission data, the abscissa of the target image is set as the time axis, and the ordinate is set as the load size or the ringing count data parameter, so as to construct a unified data visualization framework; setting the region division rule: according to the loading stage and the damage evolution law, the whole image is divided into several regions. For example, the image can be divided according to the key inflection points of the load-displacement curve (such as the elastic stage, the yield stage and the failure stage), or according to the sudden increase stage of the acoustic emission signal (such as the initial damage, the crack propagation and the final failure); region mapping and data projection: the load-displacement data and the acoustic emission data are respectively mapped to the corresponding regions. For example, in the crack initiation stage, the initial linear segment of the load-displacement curve and the low-frequency fluctuation of the acoustic emission ringing count are mainly analyzed; in the crack propagation stage, the region of the ringing count is mainly focused on, and the evaluation is carried out in combination with the change of the cumulative ringing count; in the final failure stage, the acoustic emission signal features before and after the failure are extracted, and are corresponded with the load decline trend; region labeling and color layering: different colors or gray levels are used for layering labeling of the image, so as to intuitively distinguish the damage stages. For example, the light color region can represent the micro-damage stage, the dark color region can represent the crack propagation stage, and the red region can represent the final failure stage; image output and evaluation: combined with the visualization results of different regions, the damage evolution process is analyzed, and the key damage features are extracted.

[0061] In step S106, the interface damage feature is input into the statistical damage constitutive model, and the damage failure evaluation result corresponding to the target asphalt mixture sample is output through the statistical damage constitutive model.

[0062] Specifically, based on the Weibull random distribution, a statistical damage constitutive model taking AE ringing count as a parameter is established, the statistical damage constitutive model is input, and the damage failure evaluation result corresponding to the target asphalt mixture sample is output through the statistical damage constitutive model, so as to reflect the initiation process, propagation process and evolution process of the interface crack in the target asphalt mixture sample through the damage failure evaluation result.

[0063] In one possible implementation, step S106 further includes: constructing a probability density damage function according to the interface damage feature, the probability density damage function being used to calculate the damage occurrence probability 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 load according to the probability density damage function; calculating the damage variable corresponding to the target asphalt mixture sample according to the number of damaged micro-units, and reflecting the damage degree change of the target asphalt mixture sample in the damage failure evaluation operation through the damage variable; taking the damage variable as the damage failure evaluation result.

[0064] Specifically, the damage evaluation results include the initiation process, propagation process and evolution process of the interface cracks in the target asphalt mixture specimen. Due to the randomness, non-uniformity and gradualness of the damage process of the target asphalt mixture specimen, a statistical damage constitutive model with AE ring count as a parameter can be established by statistical analysis of AE ring count and Weibull distribution theory to quantify the nonlinear characteristics of the target asphalt mixture specimen during loading process. Weibull distribution is widely used to describe the probability characteristics of material failure, and is particularly suitable for characterizing the damage evolution process of the target asphalt mixture specimen under complex load. The probability density function of Weibull distribution can be expressed as:

[0065]

[0066] wherein, is the damage probability, is the distribution characteristics of the damaged micro-unit, is the ambient temperature at time t, is the load condition at time t, is the area of the damaged region of the asphalt mixture at time t, is the scale parameter, is the distribution uniformity. The ratio of the damaged micro-unit at a certain time point in the loading process to the total damaged micro-unit in the whole loading process is defined as the damage variable.

[0067] As a preferred technical solution, the number of AMV units, i.e. the number of damaged micro-units, is solved as:

[0068] ;

[0069] wherein, is the number of damaged micro-units at time t in the loading process, is the total number of damaged micro-units in the whole loading process, is the temperature influence weight, is the load influence weight. Based on the probability density function of the damaged micro-unit, a conversion formula between AE ring count and AMV unit is constructed to obtain the solving formula of AE ring count:

[0070] wherein,

[0071] is the AE ring count, is the cumulative AE ring count when the asphalt mixture specimen is completely damaged. 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: ​

[0072]

[0073] where, is the damage variable, is the temperature coefficient influence function, is the load action influence function, is the damage area influence function. For , , , the following approaches can be used for calculation: for the temperature coefficient influence function, it can be modeled based on the Arrhenius equation or a polynomial fitting form, with the specific form being:

[0074] ;

[0075] where, is the current temperature, is the reference temperature (e.g., the performance benchmark of asphalt mixture at standard temperature), is the temperature influence index on mechanical behavior, used to describe the sensitivity of temperature change on the stiffness of asphalt material, is the adjustment factor of temperature influence on the microstructure of target asphalt mixture specimen, is the material activation energy, is the order of polynomial, is the coefficient in the th polynomial, used to represent the contribution of temperature to the performance of asphalt mixture, is the temperature in the th polynomial. For the load action influence function, it can be represented by a combination of hyperbolic tangent function and power-law function, considering both the amplification effect and nonlinear characteristics of damage under load:

[0076]

[0077] where, and are fitting parameters, representing the amplification of damage under different load actions, is the power index of load, is the parameter of hyperbolic tangent function, , are used to reflect the nonlinear influence of load size on the target asphalt mixture specimen, is a constant used to adjust the nonlinear behavior of load action, is the decay factor under load action, is the standard value of load, used to normalize the effect of load. The damage area influence function can be represented by a combination of Gaussian function and cubic polynomial, with the specific formula being:

[0078]

[0079] wherein, is the current damage area, is a normalization factor to adjust the influence degree of the damage area, is a reference damage area (for example, the critical damage area of the target asphalt mixture sample) for controlling the starting point of damage propagation, is the standard deviation of the Gaussian distribution for controlling the propagation speed of the damage area, is the coefficient of the term in the cubic polynomial, is the damage area of the term in the cubic polynomial. After multiple comparative verifications, the damage variable in the verification experiment is calculated, and the damage variable according to the conversion formula of the AE ring count and the AMV unit is basically consistent, so the comparative experiment verifies the effectiveness of the damage variable calculation in the present application.

[0080] Through the previous acoustic emission (AE) experiment, we obtained rich AE ring count data. These data reflect the elastic wave signal characteristics generated by the internal micro-crack initiation, propagation and other damage behaviors of the material during the stress process. Based on the conversion relationship between the AE ring count and the AMV unit established in the previous study, further deduction and demonstration were carried out, and according to the conversion formula of the AE ring count and the AMV unit, the calculation formula of the material damage variable can be finally obtained:

[0081] ;

[0082] In the bending test of asphalt mixture, the AE signal ring count data is collected in real time. According to the acoustic emission data, in the loading process, with the increase of load, the AE ring count gradually increases, which shows that the number and expansion rate of micro-cracks in the target asphalt mixture sample gradually increase. At this time, we use Weibull distribution to establish the damage constitutive model. According to the damage constitutive model, the damage variable at a certain time can be calculated. When the damage variable D≤0.3, the material is in the linear elastic stage at this time, and the specimen first experiences the compaction process. At this time, due to the compaction of cracks and voids inside the specimen, damage begins to occur, and the damage variable remains at a very small level. With the continuous increase of load, micro cracks and voids gradually appear inside the specimen, and these micro damages lead to the growth of the damage variable D. Especially when the load is close to the maximum value, the damage value D of the specimen shows a significant accelerated growth, until D=0.8. At this stage, with the increase of external load, the micro cracks 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 specimen, making the damage variable of the specimen show a significant upward trend. When the damage variable D>0.8, it is the terminal performance of the damage accumulation of the steel slag powder modified asphalt mixture, which can be divided into the complete failure stage, and the essence is the result of the synergistic effect of interface debonding and skeleton collapse.

[0083] Referring to Figure 5 , which shows a damage variable diagram in asphalt mixture provided by the embodiment in the present application. With the continuous increase of load and the expansion of cracks, the cracks gradually propagate to the aggregate-asphalt cement interface, and the expansion speed of the cracks will become faster and faster, and the AE signal ring count 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 crack area is increased, and the damage is gradually expanded inside the material.

[0084] At this stage, the interface cracks are not only affected by the load, but also affected by environmental factors such as temperature, humidity and aging, which can cause the cracks to expand more severely. The ring count caused by the increase of external load can better reflect the rapid expansion of cracks at this stage.

[0085] Please refer to Figure 6 , which shows a common parameter diagram of the AE test system provided by the embodiment in the present application. The commonly used parameters in the AE test system include but are not limited to amplitude, energy, ring count, rise time and duration, etc. The AE amplitude corresponds to the peak point of the signal; the energy of the waveform is the part above the threshold, which represents the area under the envelope curve and reflects 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

[0086] Please refer to Figure 7 , which shows a schematic diagram of an SCB specimen loading device provided by the embodiments of the present application, Figure 7 In the present application, a UTM-100 hydraulic servo multifunctional material testing machine is selected to perform a three-point bending loading test on the specimen, and the distance between the two supporting points at the bottom of the specimen is 80 mm. At the beginning of the experiment, a load is applied to the top of the specimen at a constant loading rate of 0.7 mm / min until the crack gradually extends from the pre-cut to the top of the specimen, finally leading to complete fracture of the specimen, and the experiment ends. Record the experimental results and the changes in load and displacement.

[0087] By using the above method, the target asphalt mixture specimen is obtained, the corresponding load-displacement data is obtained through the semi-circular bending test, and the acoustic emission data of the target asphalt mixture specimen under the loading of the semi-circular bending test is obtained through the acoustic emission nondestructive testing, so as to construct the corresponding target image of the target asphalt mixture specimen based on the load-displacement data and the acoustic emission data, and then according to the target image, the interface damage characteristics corresponding to the target asphalt mixture specimen are obtained, the interface damage characteristics are input into the statistical damage constitutive model, and the damage and failure evaluation result corresponding to the target asphalt mixture specimen is output through the statistical damage constitutive model. Therefore, by combining multi-source data fusion analysis, image recognition technology and statistical damage constitutive theory, the precision of the damage and failure evaluation of the asphalt mixture is improved, and the problem that the damage detection method based on visual observation and physical testing cannot accurately detect and evaluate the damage state of the asphalt pavement is solved.

[0088] Please refer to Figure 8 , which shows a module schematic diagram of an asphalt mixture damage and failure evaluation device based on acoustic emission technology provided by the embodiments of the present application, and the device comprises an acquisition module 81 and a processing module 82, wherein,

[0089] The acquisition module 81 is configured to, in response to a user's damage and failure evaluation operation on the asphalt mixture, acquire a target asphalt mixture specimen; perform a semi-circular bending test on the target asphalt mixture specimen, and obtain corresponding load-displacement data through the semi-circular bending test; perform acoustic emission nondestructive testing on the target asphalt mixture specimen, and obtain acoustic emission data of the target asphalt mixture specimen under the loading of the semi-circular bending test through the acoustic emission nondestructive testing.

[0090] The processing module 82 is configured to construct a target image corresponding to the target asphalt mixture sample based on the load-displacement data and the acoustic emission data; acquire 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 damage and failure evaluation results corresponding to the target asphalt mixture sample through the statistical damage constitutive model, wherein the damage and failure evaluation results include the initiation process, the expansion process and the evolution process of the interface cracks in the target asphalt mixture sample.

[0091] In a possible implementation, the acquisition module 81 is configured to acquire acoustic emission data of the target asphalt mixture sample under the semi-circular bending test loading through acoustic emission nondestructive testing, specifically including: selecting a preset arrangement region on the surface of the target asphalt mixture sample, wherein the preset arrangement region contains the interface damage characteristics; fixing a preset emission sensor on the target asphalt mixture sample according to a preset arrangement mode, wherein the preset emission sensor covers the preset arrangement region according to the preset arrangement mode; and performing acoustic emission nondestructive testing on the target asphalt mixture sample through the preset emission sensor; and acquiring the acoustic emission data of the target asphalt mixture sample under the semi-circular bending test loading through the acoustic emission nondestructive testing.

[0092] In a possible implementation, before performing acoustic emission nondestructive testing on the target asphalt mixture sample and acquiring the acoustic emission data of the target asphalt mixture sample under the semi-circular bending test loading through the acoustic emission nondestructive testing, the acquisition module 81 is configured to perform noise evaluation on a detection environment in which the target asphalt mixture sample is located within a preset time period, wherein the noise evaluation is configured to acquire a background noise signal within the preset time period; and perform threshold adjustment on the preset emission sensor according to the background noise signal.

[0093] In a possible implementation, the processing module 82 is configured 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: acquiring load size change data and displacement amount change data corresponding to the target asphalt mixture sample according to the load-displacement data; sorting the load size change data and the displacement amount change data according to a first preset time sequence; acquiring ringing count data corresponding to the target asphalt mixture sample according to the acoustic emission data, and calculating cumulative ringing count data according to the ringing count data; sorting the ringing count data and the cumulative ringing count data according to a second preset time sequence, wherein the first preset time sequence and the second preset time sequence are consistent in the time scale; and constructing the target image corresponding to the target asphalt mixture sample according to the sorted load size change data, displacement amount change data, ringing count data and cumulative ringing count data.

[0094] In a possible implementation, the processing module 82 is configured to output a damage failure evaluation result corresponding to the target asphalt mixture sample by the statistical damage constitutive model, and specifically includes: constructing a probability density damage function according to the interface damage characteristics, the probability density damage function being used to calculate a damage occurrence probability of the target asphalt mixture sample; calculating a number of damaged micro-units generated after the target asphalt mixture sample is damaged under the action of an external load according to the probability density damage function; calculating a damage variable corresponding to the target asphalt mixture sample according to the number of damaged micro-units, and reflecting a damage degree change of the target asphalt mixture sample in the damage failure evaluation operation through the damage variable; and taking the damage variable as the damage failure evaluation result.

[0095] In a possible implementation, the processing module 82 is configured to calculate the number of damaged micro-units generated after the target asphalt mixture sample is damaged under the action of the external load according to the probability density damage function, and specifically includes: calculating the number of damaged micro-units according to the following formula:

[0096] ;

[0097] wherein, is the number of damaged micro-units at a time t in the loading process, is a total number of damaged micro-units in the whole loading process, is the damage occurrence probability, is a distribution characteristic of the damaged micro-units, is an environmental temperature at the time t, is a load condition borne at the time t, is an area of a damaged region of the asphalt mixture at the time t, is a temperature influence weight, is a load influence weight.

[0098] In a possible implementation, the processing module 82 is configured to calculate the damage variable corresponding to the target asphalt mixture sample according to the number of damaged micro-units, and specifically includes: calculating the damage variable according to the following formula:

[0099] ;

[0100] wherein, is the damage variable, is a scale parameter, is a distribution uniformity, is a temperature coefficient influence function, is a load action influence function, is a damage region area influence function.

[0101] ​It should be noted that the apparatus provided in the above examples is only used as an example for the division of the above functional modules in realizing its functions, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above described functions. In addition, the apparatus and method embodiments provided in the above examples belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be described here.

[0102] The present application also provides an electronic device. Referring to Figure 9 , Figure 9 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. The electronic device can 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.

[0103] The communication bus 902 is used to realize the connection and communication between the components.

[0104] The user interface 903 can include a display screen (Display) and a camera (Camera), and the optional user interface 903 can further include a standard wired interface and a wireless interface.

[0105] The network interface 904 can optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0106] The processor 901 can include one or more processing cores. The processor 901 connects various parts within the server through various interfaces and lines, and performs 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. Alternatively, the processor 901 can be implemented in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 901 can integrate a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU is mainly used to process operating systems, user interfaces, and application programs; the GPU is used to render and draw the content to be displayed on the display screen; and the modem is used to process wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 901, but can be realized by a separate chip.

[0107] The memory 905 can include a random access memory (RAM) and a read-only memory (ROM). 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 can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area can store data involved in the above-mentioned various method embodiments, etc. The memory 905 can also be at least one storage device located away from the aforementioned processor 901. Referring to Figure 9 The memory 905 as a computer storage medium can include an operating system, a network communication module, a user interface module, and an asphalt mixture damage and failure evaluation application based on acoustic emission technology.

[0108] In Figure 9In the electronic device shown, the user interface 903 is mainly used to provide an interface for user input, and obtain data input by the user; and the processor 901 can be used to call the asphalt mixture damage and failure evaluation application program based on acoustic emission technology stored in the memory 905, and when executed by one or more processors 901, cause the electronic device to perform the method described in one or more of the above embodiments. It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all expressed as a combination of a series of actions, but those skilled in the art should know that the present application is not limited to the order of the actions described, because according to the present application, certain steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0109] The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors, cause the electronic device to perform the method described in one or more of the above embodiments.

[0110] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0111] In the several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other manners. For example, the division of the units is merely a logical function division, and there can be another division manner in actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0112] The units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. In actual implementation, some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments.

[0113] In addition, each functional unit in the embodiments of the present application can be integrated in one processing unit, or each unit can exist physically as a separate unit, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware, or in the form of software functional units.

[0114] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable memory. Based on such understanding, the technical solutions of the present application essentially or say the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a memory and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned memory includes: a U disk, a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0115] The above is only exemplary embodiments of the present application, and cannot limit the scope of the present application. That is, any equivalent changes and modifications made in accordance with the teachings of the present application are still within the scope of the present application. Other embodiments of the present application will be readily apparent to those skilled in the art upon considering the specification and the practical true disclosure.

[0116] The present application is intended to cover any variations, uses or adaptive changes of the present application, which follow the general principles of the present application and include common knowledge or conventional technical means in the art that are not disclosed in the present application.

Claims

1. A method for evaluating damage and failure of asphalt mixture based on acoustic emission technology, characterized in that, The method comprises: in response to a user's damage and failure evaluation operation on the asphalt mixture, obtaining a target asphalt mixture sample; performing a semi-circular bending test on the target asphalt mixture sample, and obtaining corresponding load-displacement data through the semi-circular 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 semi-circular bending test load 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 an interface damage feature corresponding to the target asphalt mixture sample; inputting the interface damage feature into a statistical damage constitutive model, and outputting a damage and failure evaluation result corresponding to the target asphalt mixture sample through the statistical damage constitutive model, specifically comprising: constructing a probability density damage function according to the interface damage feature, the probability density damage function being used to calculate the damage occurrence probability 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 external load; according to the number of damaged micro-units, calculating a damage variable corresponding to the target asphalt mixture sample, and reflecting the damage degree change of the target asphalt mixture sample in the damage and failure evaluation operation through the damage variable; the damage and failure evaluation result includes the initiation process, expansion process and evolution process of the interface crack in the target asphalt mixture sample; the damage variable is constructed through a temperature coefficient influence function, a load action influence function and a damage area influence function, and the initiation process, expansion process and evolution process of the interface crack in the target asphalt mixture sample are displayed through the damage variable; wherein the temperature coefficient influence function is: ； in, The current temperature. For reference temperature, This is an index representing the effect of temperature on mechanical behavior. This is a moderating factor for the effect of temperature on the microstructure of the target asphalt mixture sample. For material activation energy, Let be the order of the polynomial. For the first The coefficients in the polynomial. For the first Temperature in the polynomial; the load effect function is: ； wherein, and are fitting parameters, respectively representing the increment of damage under different load actions, is the power index of load, is the parameter of hyperbolic tangent function, , is used to reflect the nonlinear influence of load size on the target asphalt mixture sample, is a constant used to adjust the nonlinear behavior of load action, is the decay factor under load action, is the standard value of load; the damage area influence function is: ； wherein, is the current lesion area, is a normalization factor to adjust the degree of influence of the lesion area, is the reference lesion area, is the standard deviation of the Gaussian distribution, is the coefficient of the term of the cubic polynomial, is the lesion area of the term of the cubic polynomial.

2. The method of claim 1, wherein, the acoustic emission data of the target asphalt mixture sample under the semi-circular bending test load is obtained through the acoustic emission nondestructive testing, specifically comprising: selecting a preset arrangement area on the surface of the target asphalt mixture sample, the preset arrangement area containing the interface damage feature; fixing a preset emission sensor on the target asphalt mixture sample according to a preset arrangement mode, the preset emission sensor covering the preset arrangement area according to the preset arrangement mode; performing the 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 semi-circular bending test load through the acoustic emission nondestructive testing.

3. The method of claim 2, wherein, Before performing the acoustic emission nondestructive testing on the target asphalt mixture sample and obtaining the acoustic emission data of the target asphalt mixture sample under the semi-circular bending test load through the acoustic emission nondestructive testing, the method further comprises: performing noise evaluation on a detection environment where the target asphalt mixture sample is located within a preset time period, the noise evaluation being used to obtain background noise signals within the preset time period; The preset emission sensor is threshold adjusted according to the background noise signal.

4. The method of claim 1, wherein, 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: According to the load-displacement data, load size change data and displacement amount change data corresponding to the target asphalt mixture sample are obtained; The load size change data and the displacement amount change data are sorted according to a first preset time sequence; According to the acoustic emission data, ringing count data corresponding to the target asphalt mixture sample is obtained, and cumulative ringing count data is calculated according to the ringing count data; The ringing count data and the cumulative ringing count data are sorted according to a second preset time sequence, and the first preset time sequence and the second preset time sequence are consistent in time scale; According to the sorted load size change data, displacement amount change data, ringing count data and cumulative ringing count data, a target image corresponding to the target asphalt mixture sample is constructed.

5. The method of claim 1, wherein, According to the probability density damage function, the number of damaged micro-units generated after the target asphalt mixture sample is damaged under external load is calculated, specifically including: The number of damaged micro-units is calculated according to the following formula: ； wherein, is the number of damaged micro-units at the loading process, is the number of damaged micro-units at the time t, is the total number of damaged micro-units during the loading process, is the damage occurrence probability, is the distribution characteristic of the damaged micro-units, is the ambient temperature at the time t, is the load condition at the time t, is the area of the damaged region of the asphalt mixture at the time t, is the temperature influence weight, is the load influence weight.

6. The method of claim 5, wherein, According to the number of damaged micro-units, a damage variable corresponding to the target asphalt mixture sample is calculated, specifically including: The damage variable is calculated according to the following formula: ； wherein, is the damage variable, is the scale parameter, is the distribution uniformity, is the temperature coefficient influence function, is the load action influence function, is the damage area influence function.

7. An acoustic emission technology based device for evaluating damage and failure of asphalt mixture, characterized in that, The device includes an acquisition module and a processing module, wherein, The acquisition module is configured to, in response to a user's damage and failure evaluation operation on the asphalt mixture, acquire a target asphalt mixture sample; perform a semi-circular bending test on the target asphalt mixture sample, and obtain corresponding load-displacement data through the semi-circular 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 semi-circular bending test loading through the acoustic emission nondestructive testing; The processing module is configured to, based on the load-displacement data and the acoustic emission data, construct a target image corresponding to the target asphalt mixture sample, specifically including: The processing module is configured to construct a target image corresponding to the target asphalt mixture sample based on the load-displacement data and the acoustic emission data, acquire an interface damage feature corresponding to the target asphalt mixture sample according to the target image, input the interface damage feature into a statistical damage constitutive model, and output a damage and failure evaluation result corresponding to the target asphalt mixture sample by the statistical damage constitutive model. Specifically, the processing module is configured to construct a probability density damage function according to the interface damage feature, wherein the probability density damage function is used to calculate a damage occurrence probability of the target asphalt mixture sample; calculate a number of damaged micro-units generated after the target asphalt mixture sample is damaged under an external load according to the probability density damage function; calculate a damage variable corresponding to the target asphalt mixture sample according to the number of damaged micro-units, and reflect a damage degree change of the target asphalt mixture sample in the damage and failure evaluation operation by the damage variable; and the damage and failure evaluation result includes a crack initiation process, a crack propagation process, and an evolution process of an interface crack in the target asphalt mixture sample. The damage variable is constructed by a temperature coefficient influence function, a load action influence function, and a damage area influence function, and the crack initiation process, the crack propagation process, and the evolution process of the interface crack in the target asphalt mixture sample are displayed by the damage variable. The temperature coefficient influence function is: ； in, The current temperature. For reference temperature, This is an index representing the effect of temperature on mechanical behavior. This is a moderating factor for the effect of temperature on the microstructure of the target asphalt mixture sample. For material activation energy, Let be the order of the polynomial. For the first The coefficients in the polynomial. For the first Temperature in the polynomial; the load effect function is: ； wherein, and are fitting parameters, respectively representing the increment of damage under different load actions, is the power index of load, is the parameter of hyperbolic tangent function, , is used to reflect the nonlinear influence of load size on the target asphalt mixture sample, is a constant used to adjust the nonlinear behavior of load action, is the decay factor under load action, is the standard value of load; the damage area influence function is: ； wherein, is the current lesion area, is a normalization factor to adjust the degree of influence of the lesion area, is the reference lesion area, is the standard deviation of the Gaussian distribution, is the coefficient of the term of the cubic polynomial, is the lesion area of the term of the cubic polynomial.

8. An electronic device, comprising: The electronic device includes a processor, a communication bus, a user interface, a network interface, and a memory. The memory is configured to store instructions. The user interface and the network interface are configured to communicate with other devices. The processor is configured to execute the instructions stored in the memory to enable the electronic device to perform the method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method of any one of claims 1 to 6.

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