Testing Method and System for Healing and Cracking Performance of Asphalt Materials Based on Dynamic Surface Energy Parameters

Abstract

The present invention discloses a method and system for detecting the healing and cracking properties of asphalt materials based on dynamic surface energy parameters, specifically relating to the technical field of asphalt material detection. By obtaining the surface energy parameter change curve of the asphalt material under the action of different environmental conditions and its initial surface energy parameters, and combining the calculation of the interfacial energy change rate within the time step, the dynamic healing ability index and the cracking sensitivity index are obtained. In addition, the present invention uses finite element simulation combined with environmental load interaction analysis to calculate the healing and cracking critical values of the material, and further determines the durability prediction parameters. Based on this, the dynamic healing rate and the crack propagation rate are calculated and compared with the measured cracking conditions under different working conditions to optimize the surface energy evolution model. The present invention can accurately evaluate the healing and cracking behavior of asphalt materials in complex environments, optimize the material formula, improve the durability and service life of road structures, and provide a scientific basis for engineering applications.

Description

Technical Field

[0001] The present invention relates to the technical field of asphalt material detection, and particularly to a method and system for detecting the healing and cracking properties of asphalt materials based on dynamic surface energy parameters. Background Art

[0002] Asphalt materials are widely used in the construction of infrastructure such as roads, bridges, and airport runways. Their mechanical properties and durability directly affect the service life of roads and driving safety. Under the action of long-term loads and environmental factors (such as temperature changes, humidity, ultraviolet rays, etc.), microcracks will occur in asphalt materials, and the self-healing ability of microcracks is one of the key factors affecting the durability of asphalt pavements.

[0003] The existing technologies have the following deficiencies:

[0004] The current testing methods for the cracking and healing properties of asphalt materials cannot accurately reflect the dynamic interfacial energy evolution law of materials under complex environmental conditions. Especially under the interactive influence of vehicle load cycles and environmental factors (such as rainfall infiltration), the non-linear change characteristics of the interfacial energy of asphalt materials cannot be effectively measured. For example, under continuous load, asphalt materials may exhibit a short-term stress relaxation phenomenon, causing the interfacial energy of microcracks to decrease and temporarily heal. However, with rainfall infiltration or a sudden drop in temperature, the interfacial energy may rise sharply again, leading to accelerated crack propagation and even irreversible damage. Existing testing methods usually adopt single loading or static measurement under constant environmental conditions, ignoring the influence of dynamic surface energy parameters on the healing and cracking processes, resulting in a large error in the actual durability assessment of asphalt materials. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for detecting the healing and cracking properties of asphalt materials based on dynamic surface energy parameters to solve the deficiencies in the background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions: A method for detecting the healing and cracking properties of asphalt materials based on dynamic surface energy parameters, including:

[0007] Obtaining the surface energy parameter change curve of the asphalt material to be tested under the action of different environmental conditions and the initial surface energy parameter of the material;

[0008] Based on the surface energy parameter change curve, measuring the surface energy of the asphalt material at different time steps and calculating the interfacial energy change rate to obtain an interfacial energy change data sequence table;

[0009] Based on the initial surface energy parameter of the material and the interfacial energy change data sequence table, calculating the dynamic healing ability index and cracking sensitivity index of the asphalt material to be tested at each time step to obtain the healing and cracking state evaluation values of the asphalt material at different time steps.

[0010] Construct a dynamic surface energy evolution model for asphalt materials, and based on finite element simulation and analysis of the interaction of environmental loads, obtain the healing and cracking critical values of asphalt materials under different working conditions to determine the durability prediction parameters of the materials;

[0011] Based on the evaluation values of the healing and cracking states, calculate the predicted dynamic healing rate and the predicted crack propagation rate of the asphalt material, and compare the obtained data with the measured cracking conditions under different working conditions to optimize the dynamic surface energy evolution model of the asphalt material.

[0012] Preferably, the calculation of the interfacial energy change rate adopts a sliding window method, that is, within a set time window, the change rate of the surface energy per unit time step is dynamically calculated to form a data sequence table of the interfacial energy change.

[0013] Preferably, the method for obtaining the dynamic healing ability index is as follows: Let the initial surface energy parameter be , and let the time series surface energy parameter be the surface energy measured at time to form a data sequence ; Calculate the interfacial energy change rate within the unit time step Δt, where reflects the interfacial energy change rate of the asphalt material at time ; Calculate the healing rate , defined as: ; Where: is the surface energy when the asphalt material has initial microcracks, represents the decrease in surface energy after crack generation, represents the degree of surface energy recovery at time , and n is the total number of time steps measured;

[0014] Calculate the healing delay factor , defined as: ; Where: is the starting time of the healing process, represents the time offset, reflects the absolute value of the interfacial energy change rate, indicating the non-uniformity of the healing process;

[0015] Calculate the dynamic healing ability index H, and the dynamic healing ability index H is calculated from the healing rate and the healing delay factor, and the expression is: ; α and β are weight coefficients.

[0016] Preferably, the method for obtaining the cracking sensitivity index is as follows: In the phase field model, the total free energy density function of the asphalt material can be composed of the elastic strain energy and crack dissipation energy Composition: wherein: is the crack degradation function, defined as: wherein, is the crack phase variable, is the elastic strain energy density, is the crack surface energy dissipation term; calculate the crack propagation rate The crack propagation rate is calculated through the spatio-temporal evolution relationship of the phase field variable: wherein: A is the area of the calculation region, and Ω is the crack propagation region; the cracking sensitivity index is obtained by weighted average summation of the crack propagation rate and the interfacial energy change rate.

[0017] Preferably, the dynamic healing ability index and the cracking sensitivity index are converted into a comprehensive feature vector, and the comprehensive feature vector is used as the input of the machine learning model. The machine learning model takes the prediction of the healing and cracking state evaluation value label of the asphalt material at different time steps for each group of comprehensive feature vectors as the prediction target, and takes minimizing the sum of the prediction errors of the healing and cracking state evaluation value labels of all asphalt materials at different time steps as the training target. The machine learning model is trained until the sum of the prediction errors converges, and then the model training is stopped. The healing and cracking state evaluation values of the asphalt material at different time steps are determined according to the model output results, wherein the machine learning model is a polynomial regression model.

[0018] Preferably, the dynamic surface energy evolution model is used to describe the interfacial energy change of the asphalt material under different environmental conditions, expressed as: wherein: is the surface energy at time t, is the initial surface energy, is the dynamic influence factor function, T is the temperature, Q is the humidity, σ is the stress, is the strain rate, and N is the number of load cycles; establish a finite element model, construct the finite element mesh of the asphalt specimen, divide and refine the region to accurately simulate the crack propagation, and input the viscoelastic parameters of the asphalt: apply the temperature field, apply the load and apply the humidity field, and use the extended finite element to solve the crack propagation equation;

[0019] Based on the finite element simulation results, extract the healing and cracking critical values: the healing critical value : when the surface energy of the material recovers to or more, the crack self-heals; the cracking critical value : when the surface energy drops to or less, the crack undergoes irreversible propagation.

[0020] Preferably, the predicted dynamic healing rate is calculated. The dynamic healing rate is used to describe the surface energy recovery ability of asphalt materials within different time steps Δt, and its calculation formula is: ; where: The predicted dynamic healing rate at time t, is the healing state evaluation value at time t, is the time step The healing state evaluation value at, Δt is the time step;

[0021] The predicted crack propagation rate is calculated. The crack propagation rate is used to describe the crack growth trend of asphalt materials within different time steps, and its calculation formula is: ; where: is the predicted crack propagation rate at time t, is the cracking state evaluation value at time t, indicating the degree of crack propagation of the material, is the time step The cracking state evaluation value at;

[0022] Collect the measured cracking data under different working conditions. Under different environmental conditions, long-term monitoring of asphalt specimens is carried out to obtain the measured crack propagation rate ; Compare the calculated crack propagation rate with the measured crack propagation rate, and calculate the prediction error ; If ϵ>15%, it indicates that there is a deviation in the model, and the dynamic surface energy evolution model of the asphalt material needs to be corrected.

[0023] The present invention also provides an asphalt material healing and cracking performance detection system based on dynamic surface energy parameters, including a data acquisition module, an interfacial energy change rate calculation module, an evaluation module, a dynamic surface energy evolution model construction module, and an optimization prediction module;

[0024] Data acquisition module: Obtain the surface energy parameter change curve of the asphalt material to be tested under the action of different environmental conditions and the initial surface energy parameters of the material;

[0025] Interfacial energy change rate calculation module: Based on the surface energy parameter change curve, measure the surface energy of the asphalt material within different time steps, and calculate the interfacial energy change rate to obtain an interfacial energy change data sequence table;

[0026] Evaluation module: Based on the initial surface energy parameters of the material and the interfacial energy change data sequence table, calculate the dynamic healing ability index and cracking sensitivity index of the asphalt material to be tested within each time step to obtain the healing and cracking state evaluation values of the asphalt material at different time steps;

[0027] Dynamic surface energy evolution model construction module: Construct a dynamic surface energy evolution model for asphalt materials, and based on finite element simulation and environmental load interaction analysis, obtain the healing and cracking critical values of asphalt materials under different working conditions to determine the durability prediction parameters of the materials;

[0028] Optimization prediction module: Based on the evaluation values of the healing and cracking states, calculate the predicted dynamic healing rate and predicted crack propagation rate of asphalt materials, and compare the obtained data with the measured cracking conditions under different working conditions to optimize the dynamic surface energy evolution model of asphalt materials.

[0029] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0030] 1. The present invention can accurately evaluate the healing and cracking characteristics of asphalt materials under complex environmental conditions (such as sudden temperature changes, humidity changes, load cycles, and rainfall penetration). Compared with the prior art, this method introduces dynamic surface energy parameters, constructs a surface energy change curve, calculates the interface energy change rate, and quantitatively analyzes the durability of materials based on the dynamic healing ability index and cracking sensitivity index. At the same time, combined with finite element simulation and environmental load interaction analysis, the healing and cracking critical values of asphalt materials are obtained, and a dynamic surface energy evolution model is established to effectively improve the accuracy of material cracking prediction. In addition, the present invention uses a machine learning model to train comprehensive feature vectors through polynomial regression algorithms to predict the healing and cracking states at different time steps, realizing data-driven performance evaluation and optimization.

[0031] 2. The present invention can accurately evaluate the dynamic healing and cracking behaviors of asphalt materials under various complex working conditions (such as high temperature, low temperature, humidity changes, and long-term load effects), thereby optimizing the material formula and improving the durability of road structures. Compared with traditional static measurement methods, this method can not only track the surface energy evolution trend in real time, but also quantify the healing ability and crack propagation rate, optimize the prediction model in combination with measured data, and reduce the durability evaluation error. By optimizing the dynamic surface energy evolution model of asphalt materials, the present invention can effectively improve the anti-cracking performance of road materials, extend the road life, reduce the maintenance cost, and provide scientific and reliable technical support for engineering practice. Description of the Drawings

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0033] Figure 1 It is a flowchart of the method of the present invention.

[0034] Figure 2 This is the system module diagram of the present invention. Detailed implementation manners

[0035] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0036] Embodiment 1. Please refer to Figure 1 As shown in the figure, the method for detecting the healing and cracking performance of asphalt materials based on dynamic surface energy parameters in this embodiment includes:

[0037] Obtain the surface energy parameter change curve of the asphalt material to be tested under the action of different environmental conditions and the initial surface energy parameter of the material;

[0038] Based on the surface energy parameter change curve, measure the surface energy of the asphalt material at different time steps and calculate the interfacial energy change rate to obtain the interfacial energy change data sequence table;

[0039] Based on the initial surface energy parameter of the material and the interfacial energy change data sequence table, calculate the dynamic healing ability index and cracking sensitivity index of the asphalt material to be tested at each time step to obtain the evaluation values of the healing and cracking states of the asphalt material at different time steps;

[0040] Construct a dynamic surface energy evolution model of the asphalt material, and based on finite element simulation and environmental load interaction analysis, obtain the healing and cracking critical values of the asphalt material under different working conditions to determine the durability prediction parameters of the material;

[0041] Based on the evaluation values of the healing and cracking states, calculate the predicted dynamic healing rate and predicted crack propagation rate of the asphalt material, and compare the obtained data with the measured cracking conditions under different working conditions to optimize the dynamic surface energy evolution model of the asphalt material.

[0042] Under the action of different environmental conditions (temperature, humidity, ultraviolet radiation, rain penetration, etc.), the surface energy parameters of the asphalt material to be tested change with time to form time series data. First, the system obtains the original data from the experimental database or real-time sensors and establishes the surface energy parameter change curve: , where: is the environmental variable (such as temperature, humidity) at time step i; is the surface energy parameter of the asphalt material measured at the corresponding time step i. Let is the initial surface energy parameter of the material, serving as the benchmark value for calculating healing and cracking. Through data cleaning and interpolation algorithms (such as spline interpolation, time series regression, etc.), missing or abnormal data are corrected to ensure the smoothness and consistency of the curve.

[0043] To analyze the healing and cracking trends of the material, it is necessary to calculate the rate of change of its interfacial energy, that is, the change in surface energy per unit time step: ; where is the time step, represents the rate of change of interfacial energy at time . This calculation can be implemented based on the sliding window method, that is, within a given time window, the rate of energy change is dynamically calculated to form a data sequence of interfacial energy change: ; where reflects the healing or cracking rate of the asphalt material.

[0044] Based on the initial surface energy parameter of the material and the data sequence table of interfacial energy change, calculate the dynamic healing ability index and cracking sensitivity index of the asphalt material to be tested at each time step;

[0045] The dynamic healing ability index H aims to quantify the self-healing ability of the asphalt material under different environmental conditions, calculated based on the surface energy change data of the time series, combined with the physical properties of the material.

[0046] Among them, the method for obtaining the dynamic healing ability index is: let the initial surface energy parameter be , that is, the surface energy of the asphalt material at the start of the test.

[0047] Let the time series surface energy parameter be the surface energy measured at time to form a data sequence ; calculate the rate of change of interfacial energy per unit time step Δt, where reflects the rate of change of interfacial energy of the asphalt material at time .

[0048] Calculate the healing rate , and the healing rate represents the ability of the asphalt material to recover its surface energy, defined as: ; where: is the surface energy when the asphalt material has initial microcracks (i.e., the critical value for crack propagation), represents the decrease in surface energy after crack generation, represents the degree of surface energy recovery at time , and n is the total number of time steps measured. The greater the healing rate, the stronger the healing ability of the asphalt material within a given time.

[0049] Calculating the healing delay factor , where the healing delay factor describes the time-lag effect of asphalt materials during the healing process and is defined as: ; where: is the starting time of the healing process, represents the time offset, and a larger value indicates that healing occurs later, reflects the absolute value of the rate of change of interfacial energy, indicating the non-uniformity of the healing process. A larger value indicates that the material heals more slowly and there is a more obvious time-lag phenomenon.

[0050] Calculating the dynamic healing ability index H. The dynamic healing ability index H is calculated from the healing rate and the healing delay factor, and the expression is: ; α and β are weighting coefficients determined according to experimental data or material properties. H reflects the overall healing ability of asphalt materials. The higher the H value, the stronger the healing ability of the material and the faster the healing speed.

[0051] The cracking sensitivity index C is used to quantify the crack propagation trend and failure risk of asphalt materials under dynamic environmental conditions (such as sudden temperature drop, humidity change, load cycle, etc.). This index combines the surface energy evolution, crack propagation rate and environmental impact factor of the material to comprehensively evaluate the anti-cracking performance of asphalt materials.

[0052] The method for obtaining the cracking sensitivity index is as follows: In the phase field model, the total free energy density function of asphalt materials can be composed of elastic strain energy and crack dissipation energy : ; where: is the crack degradation function, defined as: ; where, is the crack phase variable (0 represents a complete material, 1 represents complete fracture), is the elastic strain energy density, defined as: ; where, σ is the stress tensor and ε is the strain tensor, is the crack surface energy dissipation term: ; where, is the fracture energy and w is the crack characteristic scale.

[0053] Based on the principle of minimum energy, the crack evolution of the phase field method is described by the following governing equation: , calculating the crack propagation rate , and the crack propagation rate is calculated through the spatio-temporal evolution relationship of the phase field variable: ; where: A is the area (or volume) of the calculation region, used to normalize the calculation results, Ω is the crack propagation region, and the integral calculates the crack propagation rate.

[0054] The cracking sensitivity index C is calculated by weighted average summation of the crack propagation rate and the interfacial energy change rate. C reflects the cracking sensitivity of asphalt materials in complex environments. The larger C is, the higher the cracking risk of the material and the worse the crack resistance performance.

[0055] Convert the dynamic healing ability index and the cracking sensitivity index into a comprehensive feature vector, and use the comprehensive feature vector as the input of the machine learning model. The machine learning model takes the prediction of the healing and cracking state evaluation value label of asphalt materials at different time steps for each set of comprehensive feature vectors as the prediction target, and takes minimizing the sum of the prediction errors of the healing and cracking state evaluation value labels of all asphalt materials at different time steps as the training target to train the machine learning model until the sum of the prediction errors reaches convergence and then stop the model training. Determine the healing and cracking state evaluation values of asphalt materials at different time steps according to the model output results, where the machine learning model is a polynomial regression model.

[0056] The method for obtaining the healing and cracking state evaluation values of asphalt materials at different time steps is as follows: Obtain the corresponding function expression from the comprehensive feature vector training data of the trained machine learning model: ; where is the output function of the model, H is the dynamic healing ability index, C is the cracking sensitivity index, is the healing and cracking state evaluation value of asphalt materials at different time steps.

[0057] In order to accurately predict the healing and cracking behavior of asphalt materials, it is necessary to construct a dynamic surface energy evolution model, and combine finite element simulation (FEM) with the analysis of the interaction of environmental loads to obtain the healing and cracking critical values, and finally determine the durability prediction parameters of the material.

[0058] The dynamic surface energy evolution model is used to describe the interfacial energy change of asphalt materials under different environmental conditions (temperature, humidity, load, etc.), expressed as: ; where: is the surface energy at time t, is the initial surface energy. is the dynamic influence factor function, considering the combined action of the environment and the load, T is the temperature (affecting asphalt viscosity and healing ability). Q is the humidity (affecting water erosion and interfacial adhesion), σ is the stress (deformation caused by external load). is the strain rate (crack propagation rate), N is the number of load cycles (long-term fatigue effect). This model can be solved by data-driven methods (such as LSTM prediction) or physical modeling (such as numerical integration) to obtain the surface energy evolution trend over time.

[0059] A finite element model is established to construct the finite element mesh of the asphalt specimen, and the refinement area is divided to accurately simulate crack propagation. Input the viscoelastic parameters of asphalt, including Young's modulus, Poisson's ratio, viscosity, etc. Boundary conditions: Apply the temperature field (such as day-night temperature difference, high temperature in summer, etc.). Apply loads (such as wheel load impact, fatigue load). Apply the humidity field (considering the influence of rainfall infiltration). Use the extended finite element (XFEM) to solve the crack propagation equation.

[0060] Based on the finite element simulation results, extract the critical values of healing and cracking: Critical value of healing : When the surface energy of the material recovers to or above, the crack self-heals. Critical value of cracking : When the surface energy decreases to or below, the crack undergoes irreversible propagation. Calculation method: , .

[0061] Determine the durability prediction parameters of the material, and define the durability prediction parameter D: ; where: The larger D is, the stronger the healing ability of the material and the better the durability. The smaller D is, the easier the material is to crack and the shorter the service life. Through finite element analysis and dynamic surface energy modeling, it can be used to optimize the asphalt material formula and improve the accuracy of road life prediction.

[0062] The dynamic healing rate and crack propagation rate of the asphalt material can be determined by calculating the change rate of the evaluation values of the healing and cracking states at different time steps, and compared with the measured cracking situation to verify the reliability and prediction accuracy of the model.

[0063] Calculate the predicted dynamic healing rate. The dynamic healing rate is used to describe the surface energy recovery ability of the asphalt material within different time steps Δt, and its calculation formula is: ; where: The predicted dynamic healing rate at time t, is the evaluation value of the healing state at time t, indicating the degree of surface energy recovery of the material, is the evaluation value of the healing state at time step , and Δt is the time step (unit: hour or day).

[0064] Calculate the predicted crack propagation rate. The crack propagation rate is used to describe the crack growth trend of the asphalt material within different time steps, and its calculation formula is: ; where: is the predicted crack propagation rate at time t, is the evaluation value of the cracking state at time t, indicating the degree of crack propagation of the material, is the time step The evaluation value of the cracking state at [location]. Δt is the time step (unit: hours or days).

[0065] Collect the measured cracking data under different working conditions, and conduct long-term monitoring on the asphalt specimens under different environmental conditions (temperature, humidity, load cycle) to obtain the measured crack propagation rate. : ; where: is the crack length at time t (unit: mm), is the time step the crack length at [location].

[0066] Compare the calculated crack propagation rate with the measured crack propagation rate and calculate the prediction error: ; If ϵ < 5%, it indicates that the model prediction accuracy is relatively high and can be used for engineering applications. If 5% ≤ ϵ ≤ 15%5, it indicates that the model needs to adjust parameters, such as optimizing the calculation of the healing and cracking state evaluation values. If ϵ > 15%, it indicates that there is a large deviation in the model and more experimental data need to be introduced or the dynamic surface energy evolution model of the asphalt material needs to be corrected.

[0067] Example 2, please refer to Figure 2 As shown, the asphalt material healing and cracking performance detection system based on dynamic surface energy parameters described in this embodiment includes a data acquisition module, an interfacial energy change rate calculation module, an evaluation module, a dynamic surface energy evolution model construction module, and an optimization prediction module;

[0068] Data acquisition module: Obtain the surface energy parameter change curve of the asphalt material to be tested under the action of different environmental conditions and the initial surface energy parameters of the material;

[0069] Interfacial energy change rate calculation module: Based on the surface energy parameter change curve, measure the surface energy of the asphalt material within different time steps and calculate the interfacial energy change rate to obtain the interfacial energy change data sequence table;

[0070] Evaluation module: Based on the initial surface energy parameters of the material and the interfacial energy change data sequence table, calculate the dynamic healing ability index and cracking sensitivity index of the asphalt material to be tested within each time step to obtain the healing and cracking state evaluation values of the asphalt material at different time steps;

[0071] Dynamic surface energy evolution model construction module: Construct the dynamic surface energy evolution model of the asphalt material, and based on the finite element simulation and the analysis of the interaction between environmental loads, obtain the healing and cracking critical values of the asphalt material under different working conditions to determine the durability prediction parameters of the material;

[0072] Optimization Prediction Module: Based on the evaluation values of the healing and cracking states, calculate the predicted dynamic healing rate and the predicted crack propagation rate of the asphalt material, and compare the obtained data with the measured cracking conditions under different working conditions to optimize the dynamic surface energy evolution model of the asphalt material.

[0073] All the above formulas are dimensionless and take their numerical values for calculation. The formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0074] It should be understood that the term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this article generally represents an "or" relationship between the preceding and following associated objects, but it may also represent an "and / or" relationship. Specifically, it can be understood by referring to the context before and after.

[0075] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0076] As described above, the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered by the protection scope of this application.

Claims

1. A method for detecting the healing and cracking properties of asphalt materials based on dynamic surface energy parameters, characterized in that: Including: Obtaining the surface energy parameter change curve of the asphalt material to be tested under different environmental conditions and the initial surface energy parameters of the material; Based on the surface energy parameter change curve, measuring the surface energy of the asphalt material at different time steps and calculating the interface energy change rate to obtain the interface energy change data sequence table; Based on the initial surface energy parameters of the material and the interface energy change data sequence table, calculating the dynamic healing ability index and cracking sensitivity index of the asphalt material to be tested at each time step to obtain the healing and cracking state evaluation values of the asphalt material at different time steps; Constructing a dynamic surface energy evolution model of the asphalt material and, based on finite element simulation and environmental load interaction analysis, obtaining the healing and cracking critical values of the asphalt material under different working conditions to determine the durability prediction parameters of the material; Based on the healing and cracking state evaluation values, calculating the predicted dynamic healing rate and predicted crack propagation rate of the asphalt material and comparing the obtained data with the measured cracking conditions under different working conditions to optimize the dynamic surface energy evolution model of the asphalt material.

2. The method for detecting the healing and cracking properties of asphalt materials based on dynamic surface energy parameters according to claim 1, wherein: The calculation of the interface energy change rate uses a sliding window method, that is, within a set time window, dynamically calculating the change rate of the surface energy per unit time step to form an interface energy change data sequence table.

3. The method for detecting the healing and cracking properties of asphalt materials based on dynamic surface energy parameters according to claim 1, characterized in that: Wherein, The method for obtaining the dynamic healing ability index is as follows: Set the initial surface energy parameter as , and set the time series surface energy parameter as the surface energy measured at time to form a data sequence ; Calculate the change rate of interfacial energy within a unit time step Δt , where reflects the change rate of interfacial energy of the asphalt material at time ; calculate the healing rate , defined as: ; where: is the surface energy when the initial microcracks occur in the asphalt material, represents the decrease in surface energy after crack generation, represents the degree of recovery of surface energy at time , and n is the total number of time steps measured Calculate the healing delay factor , which is defined as: ; where: is the starting time of the healing process, represents the time offset, reflects the absolute value of the rate of change of the interfacial energy and represents the non-uniformity of the healing process; Calculate the dynamic healing ability index H, which is calculated from the healing rate and the healing delay factor, and the expression is: ; α and β are weighting coefficients.

4. The method for detecting the healing and cracking properties of asphalt materials based on dynamic surface energy parameters according to claim 3, wherein: The method for obtaining the cracking sensitivity index is as follows: In the phase field model, the total free energy density function of the asphalt material consists of elastic strain energy and crack dissipation energy : ; where: is the crack degradation function, defined as: ; where, is the crack phase variable, is the elastic strain energy density, is the crack surface energy dissipation term; calculate the crack propagation rate , and the crack propagation rate is calculated through the spatio-temporal evolution relationship of the phase field variable: ; where: A is the area of the calculation region, Ω is the crack propagation region; the cracking sensitivity index is obtained by weighted average summation of the crack propagation rate and the interface energy change rate.

5. The method for detecting the healing and cracking properties of asphalt materials based on dynamic surface energy parameters according to claim 4, wherein: Converting the dynamic healing ability index and cracking sensitivity index into a comprehensive feature vector, using the comprehensive feature vector as the input of a machine learning model. The machine learning model takes predicting the healing and cracking state evaluation value labels of the asphalt material at different time steps for each group of comprehensive feature vectors as the prediction target, and minimizing the sum of the prediction errors for the healing and cracking state evaluation value labels of all asphalt materials at different time steps as the training target. Training the machine learning model until the sum of the prediction errors reaches convergence and then stopping the model training. Determining the healing and cracking state evaluation values of the asphalt material at different time steps according to the model output results. Among them, the machine learning model is a polynomial regression model.

6. The method for detecting the healing and cracking properties of asphalt materials based on dynamic surface energy parameters according to claim 1, wherein: The dynamic surface energy evolution model is used to describe the interfacial energy change of asphalt materials under different environmental conditions, expressed as: where: is the surface energy at time t, is the initial surface energy, is the dynamic influence factor function, T is the temperature, Q is the humidity, σ is the stress, is the strain rate, N is the number of load cycles; A finite element model is established, the finite element mesh of the asphalt specimen is constructed, the refined area is divided to accurately simulate the crack propagation, and the viscoelastic parameters of the asphalt are input: The temperature field, the load and the humidity field are applied, and the extended finite element is used to solve the crack propagation equation; Based on the finite element simulation results, the critical values for healing and cracking are extracted: the healing critical value : When the surface energy of the material recovers to or above, the crack self-heals; the cracking critical value : When the surface energy decreases to or below, the crack undergoes irreversible propagation.

7. The method for detecting the healing and cracking properties of asphalt materials based on dynamic surface energy parameters according to claim 6, characterized in that: Calculate the predicted dynamic healing rate, which is used to describe the surface energy recovery ability of asphalt materials within different time steps Δt. The calculation formula is as follows: ; where: The predicted dynamic healing rate at time t, is the healing state evaluation value at time t, is the healing state evaluation value at time step , and Δt is the time step; Calculate the predicted crack growth rate, which is used to describe the crack growth trend of asphalt materials at different time steps. The calculation formula is as follows: ; where: is the predicted crack growth rate at time t, is the evaluation value of the cracking state at time t, indicating the degree of crack propagation of the material, is the time step is the evaluation value of the cracking state at; Collect the measured cracking data under different working conditions, and conduct long-term monitoring on asphalt specimens under different environmental conditions to obtain the measured crack propagation rate. Compare the calculated crack propagation rate with the measured crack propagation rate, and calculate the prediction error. If ϵ > 15%, it indicates that there is a deviation in the model, and the dynamic surface energy evolution model of the asphalt material needs to be corrected.

8. An asphalt material healing and cracking performance detection system based on dynamic surface energy parameters, which is used to implement the asphalt material healing and cracking performance detection method based on dynamic surface energy parameters according to any one of claims 1-7, characterized in that: Including a data acquisition module, an interface energy change rate calculation module, an evaluation module, a dynamic surface energy evolution model construction module, and an optimization prediction module; Data acquisition module: Obtaining the surface energy parameter change curve of the asphalt material to be tested under different environmental conditions and the initial surface energy parameters of the material; Interface energy change rate calculation module: Based on the surface energy parameter change curve, measuring the surface energy of the asphalt material at different time steps and calculating the interface energy change rate to obtain the interface energy change data sequence table; Evaluation module: Based on the initial surface energy parameters of the material and the interface energy change data sequence table, calculating the dynamic healing ability index and cracking sensitivity index of the asphalt material to be tested at each time step to obtain the healing and cracking state evaluation values of the asphalt material at different time steps; Dynamic surface energy evolution model construction module: Constructing a dynamic surface energy evolution model of the asphalt material and, based on finite element simulation and environmental load interaction analysis, obtaining the healing and cracking critical values of the asphalt material under different working conditions to determine the durability prediction parameters of the material; Optimization prediction module: Based on the evaluation values of the healing and cracking states, calculate the predicted dynamic healing rate and the predicted crack propagation rate of the asphalt material, and compare the obtained data with the measured cracking conditions under different working conditions to optimize the dynamic surface energy evolution model of the asphalt material.

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