Method and apparatus for evaluating tensile toughness of recycled asphalt mixture
By constructing a multi-parameter coupled method for evaluating the tensile toughness of recycled asphalt mixtures, the problem of lack of scientific evaluation in existing technologies is solved, and the accurate evaluation of the tensile toughness of recycled asphalt mixtures is achieved, thereby improving the scientificity and practicality of road engineering design.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA UNIV OF MINING & TECH
- Filing Date
- 2026-04-30
- Publication Date
- 2026-06-02
AI Technical Summary
The lack of scientific methods for evaluating the tensile toughness of recycled asphalt mixtures in existing technologies leads to reliance on engineering experience for the design and service performance evaluation of cold recycled asphalt mixtures in road engineering. This makes it difficult to meet the refined and standardized requirements of highway design and affects their widespread application.
By acquiring the internal structure and external environmental variables of recycled asphalt mixtures, a tensile toughness assessment equation with multi-parameter coupling relationship is constructed. Combined with experimental calibration and sensitivity analysis, an accurate tensile toughness assessment model is established, taking into account the internal structural state, interface transition zone characteristics, and external loads and environmental parameters.
This enables a refined characterization of the tensile toughness of recycled asphalt mixtures, improving the accuracy and engineering applicability of the evaluation model and meeting the design requirements of road engineering.
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Figure CN122135852A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of analytical technology for recycled asphalt mixtures, specifically a method and equipment for evaluating the tensile toughness of recycled asphalt mixtures. Background Technology
[0002] Cold recycling technology is a technique that involves milling and grading reclaimed asphalt pavement (RAP) from old pavements, then mixing it at room temperature with emulsified asphalt or foamed asphalt, new aggregates, cement, mineral powder, water, recycling agents, and some additives to obtain cold recycled asphalt mixtures that meet the specifications.
[0003] Due to the presence of aged asphalt and cement components, recycled asphalt mixtures exhibit poorer fatigue performance compared to traditional hot-mix asphalt mixtures, leading to insufficient durability and service toughness. However, cold-recycled asphalt mixtures are primarily used in the base or subbase layers of roads, mainly bearing tensile stress or strain under traffic loads. This places corresponding engineering demands on the tensile toughness of cold-recycled materials, which is also a key indicator in the design of cold-recycled asphalt pavements. Cold-recycled mixtures contain multiple material components, involving the fusion of old and new asphalt and the interaction of inorganic and organic materials to form strength. The varied interface forms at close inspection result in a complex strength mechanism for cold-recycled mixtures, making it difficult to effectively and quantitatively analyze their physical and mechanical properties. Specifically, cold-mixed recycled asphalt (RAP) is mixed at room temperature, and its strength increases slowly with moisture evaporation and curing time. The interface between new and old asphalt is less effective than in hot-mix asphalt (HMM) mixtures. Furthermore, the surface layer of RAP is aged asphalt, and cement can improve the early strength of the mixture. The incorporation of RAP and cement causes interfaces with varying strengths between aggregates at different curing stages, both of which contribute to a decrease in the tensile toughness of cold-mixed RAP. In addition, volumetric parameters such as porosity and asphalt content significantly affect the tensile toughness of recycled asphalt mixtures. From a practical application perspective, as a viscoelastic material, the modulus and stiffness of recycled asphalt mixtures are significantly affected by loading frequency and temperature. Under traffic loads, the tensile stress or strain levels experienced by the mixture vary, leading to significant differences in tensile toughness.
[0004] The above analysis shows that the tensile toughness of recycled asphalt mixtures is influenced by complex factors, including both the internal material composition and structure of the mixture, and external environmental factors. However, a standardized assessment method for the tensile toughness of recycled asphalt mixtures has not yet been developed. This leads to a reliance on engineering experience rather than scientific theoretical basis in the design and service performance evaluation of cold recycled asphalt mixtures, making it difficult to meet the refined, standardized, and regulated requirements of asphalt pavement design for highways in my country, and significantly hindering its widespread application in road engineering. Summary of the Invention
[0005] The purpose of this invention is to provide a method and equipment for evaluating the tensile toughness of recycled asphalt mixtures, so as to solve the above-mentioned problems.
[0006] The technical solution of this invention is: A method for evaluating the tensile toughness of recycled asphalt mixtures, comprising: Obtain the characteristic parameters of the recycled asphalt mixture to be evaluated.
[0007] The characteristic parameters include: internally constructed variables and external environmental variables.
[0008] The internally constructed variables include: internal structure state variables. Length of the aggregate-binder two-phase interface transition zone Elastic modulus of the interface transition zone based on indentation test .
[0009] The external environmental variables include: tensile strain ratio. Loading frequency Cold recycled asphalt mixtures at corresponding loading frequencies and dynamic modulus at set temperature Loading frequency The loading frequency at which the cold-regenerated structural layer can withstand vehicle loads.
[0010] The tensile toughness of cold recycled asphalt mixture is calculated based on the internally constructed variables and the external environmental variables, according to a predetermined multi-parameter coupling relationship. The evaluation value.
[0011] Tensile toughness of cold recycled asphalt mixture The evaluation equation is: .
[0012] in, Let be the tensile strain ratio, where For strain level, For the ultimate tensile strain, where , , , and These are the parameters calibrated through experiments.
[0013] By introducing internal structural state variables Interface transition area characteristic parameters and and external loads and environmental parameters , and They constructed a tensile toughness evaluation equation that can accurately reflect the complex mechanical behavior of materials, which solved the limitations of previous evaluations based solely on experience or single-factor experiments, and filled the technological gap in this field.
[0014] Furthermore, through experiments with controlled variables, the parameters were calibrated sequentially. , , , , and Specifically, it includes the following steps: Pick and It is a fixed value.
[0015] By applying different tensile strain ratios Tensile toughness test under fixed conditions , and The value remains unchanged, and we obtain The value of .
[0016] By applying different dynamic moduli The tensile toughness test was conducted, and based on the results... The value is fixed. and The value remains unchanged, determined. The value of .
[0017] By applying different loading frequencies The tensile toughness test was conducted, and based on the results... and The value is fixed. The value remains unchanged, determined. The value of .
[0018] The length of the aggregate-binder two-phase interface transition zone was tested in different specimens. Elastic modulus of the interface transition zone based on indentation test And conduct tensile toughness tests, and based on the results , and The value is determined. The value of .
[0019] The relationship between tensile toughness and various variables was analyzed, and a multivariate nonlinear fitting was performed to optimize the results globally. Perform final calibration and correction. Parameter values.
[0020] Furthermore, this also includes: conducting sensitivity analysis on the calibrated parameters, and based on the analysis results, assessing the tensile toughness of cold recycled asphalt mixtures. Highly sensitive parameters are further expressed as functions of key influencing factors, and these parameters are... Further expressed as cement dosage Linear functions: And determine the linear coefficients through fitting. and ,in Based on the basic cement dosage, the quantitative influence of various factors on tensile toughness was revealed, significantly improving the accuracy and reliability of the evaluation model and achieving a refined characterization of the factors affecting tensile toughness.
[0021] Furthermore, by controlling the number of compaction cycles, different porosity values were obtained in the molded mixture specimens. and asphalt content The recycled asphalt mixture was subjected to tensile toughness tests to assess the tensile toughness of the cold recycled asphalt mixture. With the porosity of the mixture specimen and asphalt content Perform parameter fitting to obtain the internal structural state variables. The calculation formula is as follows: ,in, a 1 and a 2 represents the fitting parameters. The internal structural state variables are obtained by fitting the tensile toughness of the cold recycled asphalt mixture with the porosity and asphalt content of the mixture specimen. The calculation formula makes the model parameters easy to obtain and convenient for practical engineering applications. At the same time, the vehicle load frequency is considered. With dynamic modulus External factors such as road surface conditions make the assessment results more consistent with actual road surface service conditions, thus improving the practicality and engineering applicability of the assessment method.
[0022] Furthermore, the length of the aggregate-binder two-phase interface transition zone... and the elastic modulus of the interface transition zone based on indentation test By scanning along the aggregate, transition zone, and binder using an atomic force microscope and obtaining data based on surface morphology and modulus distribution characteristics, and by combining atomic force microscopy with indentation testing, high-precision, visual, and quantitative characterization of the length of the transition zone and elastic modulus of the aggregate-binder interface was achieved.
[0023] A device for evaluating the tensile toughness of recycled asphalt mixtures includes: a parameter acquisition module for acquiring characteristic parameters of the recycled asphalt mixture to be evaluated; and a processing module for inputting the characteristic parameters into the tensile toughness of cold recycled asphalt mixtures. The calculation is performed in the evaluation equation, and the tensile toughness evaluation value is output.
[0024] Compared with the prior art, the beneficial effects of the present invention are: This invention proposes a multi-parameter coupled evaluation method that comprehensively considers the internal structural characteristics of recycled asphalt mixtures and external environmental factors, introducing internal structural state variables. Interface transition area characteristic parameters and and external loads and environmental parameters , and They constructed a tensile toughness evaluation equation that can accurately reflect the complex mechanical behavior of materials, which solved the limitations of previous evaluations based solely on experience or single-factor experiments, and filled the technological gap in this field.
[0025] This invention calibrates the parameters in the equation by controlling variable experiments, and further performs sensitivity analysis and functional expression on key parameters, revealing the quantitative influence of each factor on tensile toughness, significantly improving the accuracy and reliability of the evaluation model, and realizing a refined characterization of the factors affecting tensile toughness.
[0026] This invention improves the tensile toughness of cold recycled asphalt mixtures. With the porosity of the mixture specimen and asphalt content Perform parameter fitting to obtain the internal structural state variables. The calculation formula makes the model parameters easy to obtain and convenient for practical engineering applications. At the same time, the vehicle load frequency is considered. With dynamic modulus External factors such as road surface conditions make the assessment results more consistent with actual road surface service conditions, thus improving the practicality and engineering applicability of the assessment method. Attached Figure Description
[0027] Figure 1 This is a schematic diagram of the evaluation method of the present invention. Detailed Implementation
[0028] The following is combined with Figure 1 The specific embodiments of the present invention will be described in detail below.
[0029] Example like Figure 1As shown, a method for evaluating the tensile toughness of recycled asphalt mixtures includes: Obtain the characteristic parameters of the recycled asphalt mixture to be evaluated.
[0030] The characteristic parameters include: internally constructed variables and external environment variables.
[0031] Internally constructed variables include: internal structure state variables Length of the aggregate-binder two-phase interface transition zone Elastic modulus of the interface transition zone .
[0032] External environmental variables include: tensile strain ratio Loading frequency Cold recycled asphalt mixtures at corresponding loading frequencies and dynamic modulus at set temperature Loading frequency The loading frequency at which the cold-regenerated structural layer can withstand vehicle loads.
[0033] The tensile toughness of cold recycled asphalt mixtures is calculated based on predetermined multi-parameter coupling relationships using internally constructed variables and external environmental variables. The evaluation value.
[0034] This embodiment constructs an evaluation equation for the tensile toughness of recycled asphalt mixtures, which includes six parameters, based on the significant influence of external environment and internal structure on tensile toughness in real service environments: .in, Tensile toughness of cold recycled asphalt mixture, unit: times. , and Let be the variables that characterize the internal structure, where: The internal structural state variables are dimensionless. The length of the transition zone at the aggregate-binder two-phase interface, in μm. The elastic modulus of the interface transition zone is based on indentation testing, in GPa.
[0035] , and As an external environmental variable, cold recycled asphalt mixture is a viscoelastic material with time-temperature equivalence; therefore, after considering the frequency variable, temperature can be disregarded. Wherein: For strain level, For the ultimate tensile strain, The tensile strain ratio is dimensionless. The loading frequency at which the cold-regenerated structural layer withstands vehicle loads, measured in Hz. For cold recycled asphalt mixtures at the corresponding loading frequency And the dynamic modulus at a set temperature, in MPa.
[0036] By controlling the variables in the experiment, the parameters were calibrated sequentially. , , , , and Specifically, it includes the following steps: Pick and It is a fixed value.
[0037] By applying different tensile strain ratios Tensile toughness test under fixed conditions , and The value remains unchanged, and we obtain The value of .
[0038] By applying different dynamic moduli The tensile toughness test was conducted, and based on the results... The value is fixed. and The value remains unchanged, determined. The value of .
[0039] By applying different loading frequencies The tensile toughness test was conducted, and based on the results... and The value is fixed. The value remains unchanged, determined. The value of .
[0040] The length of the aggregate-binder two-phase interface transition zone was tested in different specimens. Elastic modulus of the interface transition zone based on indentation test And conduct tensile toughness tests, and based on the results , and The value is determined. The value of .
[0041] By testing tensile toughness under different void ratios and asphalt contents, and keeping other factors constant, the internal structural state variables were calibrated. ; The relationship between tensile toughness and various variables was analyzed, and a multivariate nonlinear fitting was performed to optimize the results globally. Perform final calibration and correction. Parameter values.
[0042] Specifically, it includes the following steps: Step 1: Test the tensile toughness of multiple cold recycled asphalt mixtures under different combinations of cement dosage, recycled material content, and new asphalt content, and record the results as follows: Furthermore, tensile toughness testing methods include indirect tensile testing, four-point bending testing, and beam bending testing, which test the number of times cold recycled asphalt mixtures are loaded under repeated loads.
[0043] Step 2 initiates the dynamic modulus test to measure the dynamic modulus of the material under different temperatures and loading frequencies. Multiple sets of cold recycled asphalt mixtures are tested under different combinations of cement dosage, recycled material content, and new asphalt content. Considering that cold recycled asphalt mixtures are generally used in the flexible upper base layer of asphalt pavement structures, the temperature range of this layer is between 5℃ and 25℃, and the frequency range is between 1Hz and 5Hz. Therefore, the selected temperatures for dynamic modulus testing are 5℃, 15℃, and 25℃, and the selected frequencies are 1Hz and 5Hz.
[0044] Step 3: Conduct tensile toughness tests at multiple strain levels, covering the ultimate tensile strain to multiple set strains. Test the tensile toughness of multiple groups of cold recycled asphalt mixtures under different combinations of cement dosage, recycled material content, and new asphalt content at different strain ratios. Perform single-factor analysis on the relationship between specimen tensile toughness and strain ratio: [Further details on fixed strain ratios would be needed for accurate translation.] * The value is 1.5E+5. , and All values are 1, and the change is... The value of tensile toughness was determined by constructing a linear regression model with the theoretical tensile toughness prediction of each mix proportion as the independent variable and the laboratory measured tensile toughness as the dependent variable. The result was determined using weighted least squares estimation, with the minimum sum of squared residuals as the criterion. The optimal estimate.
[0045] Step 4 uses the dynamic modulus as a variable to test the variation of tensile toughness with the modulus. Multiple sets of cold recycled asphalt mixtures with different combinations of cement dosage, recycled material content, and new asphalt content were tested for tensile toughness under different dynamic moduli. A single-factor analysis was performed on the relationship between the tensile toughness and the dynamic modulus of the specimens. The value is the fitting result from step 3. The value can be any single strain ratio. The modulus values were assigned to each group of specimens at loading frequencies of 1Hz and 5Hz, and temperatures of 5℃, 15℃, and 25℃. (Fixed) * The value is 1.5E+5. and All values are 1, and the change is... Using the theoretical tensile toughness prediction as the explanatory variable and the indoor measured tensile toughness as the response variable, a weighted least squares regression model is constructed. The model parameters are estimated by minimizing the sum of squared residuals, and then the model parameters are determined through inversion. The optimal unbiased estimate.
[0046] Step 5: Under the single-factor control framework, analyze the effect of loading frequency on tensile toughness to quantify the sensitivity of the independent variable of frequency to toughness evolution. and The value is the same as in step 4. The value is taken from the fitting result in step 4. The value is taken as the average modulus of each group of specimens. (Fixed) * The value is 1.5E+5. The value is 1, and the change is... Using the theoretical tensile toughness prediction as the explanatory variable and the indoor measured tensile toughness as the response variable, a weighted least squares regression model is constructed. The model parameters are estimated by minimizing the sum of squared residuals, and then the model parameters are determined through inversion. The optimal unbiased estimate.
[0047] Step 6: Conduct microscopic experiments to characterize the interfacial transition zone. Test the microscopic parameters of aggregates, transition zone, and binder in multiple sets of cold recycled asphalt mixtures under different combinations of cement dosage, recycled material content, and new asphalt content. These microscopic parameters include the elastic modulus and transition zone length, used to calibrate the transition zone parameters in the tensile toughness assessment model. Prepare Marshall specimens, and after curing, cut them into 1cm × 1cm × 1cm cubes. Grind the surface of the cubes until the aggregates, transition zone, and binder are clearly visible. After marking the positions, scan along the aggregates, transition zone, and binder using an atomic force microscope to test the surface morphology and elastic modulus of each interval. Obtain the transition zone length based on the surface morphology characteristics. elastic modulus The tensile toughness of the specimen was directly measured by indentation testing. A single-factor analysis was performed to determine the ratio of the tensile toughness to the transition zone width and the elastic modulus. , , and The value is the same as in step 5. The value is taken from the fitting result in step 5. The value is set to a fixed loading frequency. * The value is 1.5E+5, and the change is... Using the theoretical tensile toughness prediction as the explanatory variable and the indoor measured tensile toughness as the response variable, a weighted least squares regression model is constructed. The model parameters are estimated by minimizing the sum of squared residuals, and then the model parameters are determined through inversion. The optimal unbiased estimate.
[0048] In some embodiments, after the above parameters are calibrated, a sensitivity analysis is performed on the calibrated parameters. Based on the analysis results, the tensile toughness of the cold recycled asphalt mixture is assessed. Highly sensitive parameters are further expressed as functions of key influencing factors.
[0049] Specifically, the uncertainty of each model parameter is decomposed, and the contribution of single parameters and interactions to the prediction variance of tensile toughness is quantified, so as to achieve the precise location of key control variables. , , and The corresponding physical quantities all have clear physical meanings. Due to the large number of parameters, principal component analysis is used to evaluate tensile toughness in order to improve the accuracy of the tensile toughness assessment equation. Sensitivity analysis of each parameter. Based on the sensitivity analysis results, the influencing factors are ranked, and the insensitive physical quantities or parameters are constanted. The more sensitive parameters or physical quantities are further analyzed in detail. Based on the essential reasons for the influence of the sensitive factors, they are rewritten as functions of the essential influencing factors and the parameters to be fitted, thereby further improving the accuracy of the tensile toughness model.
[0050] parameters Further expressed as cement dosage Linear functions: And determine the linear coefficients through fitting. and ,in The basic cement dosage.
[0051] For internal structural state variables Perform parameter fitting, asphalt content and porosity It is an effective indicator reflecting the internal structure of cold recycling. By controlling the number of compaction cycles of the specimen, different porosity values of the mixed specimens can be obtained. and asphalt content The recycled asphalt mixture was subjected to tensile toughness tests to assess the tensile toughness of the cold recycled asphalt mixture. With the porosity of the mixture specimen and asphalt content Perform parameter fitting to obtain the internal structural state variables. The calculation formula is as follows: ,in, 1 and 2 represents the fitting parameters.
[0052] Based on a systematic review of the coupling relationship between tensile toughness and various control variables and a summary of all parameter estimation results, a multivariate nonlinear objective function is constructed to optimize the tensile toughness using a global optimization approach. The final correction is performed to obtain the optimal estimate that minimizes the prediction-measurement error norm.
[0053] In this embodiment, Marshall or rotary compaction methods were used to obtain cold recycled asphalt mixture specimens with different cement and RAP content. Cationic slow-setting emulsified asphalt was used, and 42.5 cement was used.
[0054] RAP (Rich Acid Polymer) mixes were used in six grades: 0-3, 3-5, 5-10, 10-15, 15-20, and 20-25. New aggregates were used in six grades: 0-4.75, 4.75-9.5, 9.5-13.2, 13.2-16, 16-19, and 19-26.5. The base RAP content was 60%, and the mineral powder content was 4.5%. The optimum moisture content of the emulsified asphalt cold recycled mixture was determined using the maximum dry density method, and heavy compaction tests were conducted. The optimum moisture content was found to be 4.8%. The optimum emulsified asphalt content was 4.5%, and the optimum cement content was 1.5%. RAP content was selected at 60%, 70%, 80%, and 90%, and cement content was selected at 1%, 1.5%, and 2.5%. Twelve sets of specimens were formed, numbered S1-S2. 12 The RAP and cement content of each group of specimens are shown in Table 1.
[0055] Table 1. RAP and cement content of each group of specimens
[0056] The tensile toughness ultimate tensile strain of 12 groups of specimens was tested using a small beam bending test apparatus. Indirect tensile loading tests and four-point bending loading tests were employed to test the tensile toughness of multiple cold recycled asphalt mixtures with different combinations of cement dosage, recycled aggregate content, and new asphalt content at four strain ratio levels of 0.25, 0.3, 0.4, and 0.5. The results are shown in Table 2. A single-factor analysis was performed to examine the relationship between the tensile toughness and strain ratio of the specimens: [The analysis was conducted with the following parameters fixed]. * The value is 1.5E+5. , , All values are 1, and the change is... Using the theoretical tensile toughness prediction as the explanatory variable and the indoor measured tensile toughness as the response variable, a weighted least squares regression model is constructed. The model parameters are estimated by minimizing the sum of squared residuals, and then the model parameters are determined through inversion. =2.3502.
[0057] Table 2 Tensile toughness of specimens under different strain ratios
[0058] The dynamic modulus test method was used to test the dynamic modulus of 12 groups of specimens at three temperatures (5℃, 15℃, and 25℃) and two frequencies (1Hz and 5Hz). The results are shown in Table 3. Using indirect tensile loading tests and four-point bending loading tests, the tensile toughness of multiple groups of cold recycled asphalt mixtures with different combinations of cement dosage, recycled material content, and new asphalt content was tested under different dynamic moduli. The results are shown in Table 4. A single-factor analysis was performed on the relationship between the tensile toughness and dynamic modulus of the specimens: substituting the fitting results from the previous step... =2.3502, The value is 0.4. For each group of specimens, loading frequencies of 1 Hz and 5 Hz were applied. Modulus values were also calculated for temperatures of 5℃, 15℃, and 25℃. [The following is a separate, unrelated sentence:] Fixed. * The value is 1.5E+5. and The value of is 1, and the change is... Using the theoretical tensile toughness prediction as the explanatory variable and the indoor measured tensile toughness as the response variable, a weighted least squares regression model is constructed. The model parameters are estimated by minimizing the sum of squared residuals, and then the model parameters are determined through inversion. =0.023.
[0059] Table 3 Dynamic modulus of each group of specimens at different temperatures and frequencies
[0060] Table 4 Tensile toughness of specimens at different temperatures and frequencies
[0061] A single-factor analysis was performed to examine the relationship between the tensile toughness of the specimen and the loading frequency. and The values are taken in the same steps as in the dynamic modulus test method, and the fitting results are substituted into this step. =0.023, The value is taken as the average modulus of each group of specimens. (Fixed) * The value is 1.5E+5. The value is 1, and the change is... Using the theoretical tensile toughness prediction as the explanatory variable and the indoor measured tensile toughness as the response variable, a weighted least squares regression model is constructed. The model parameters are estimated by minimizing the sum of squared residuals, and then the model parameters are determined through inversion. =0.232.
[0062] 1cm×1cm×1cm cubes were cut from each of the 12 sets of specimens. The surfaces of the cubes were polished, and the aggregate-transition zone-mortar were clearly visible under a scanning electron microscope. After marking the positions, an atomic force microscope was used to scan along the aggregate-transition zone-mortar to test the surface morphology and elastic modulus of each zone. The lengths of the 12 transition zones were obtained based on the surface morphology characteristics. and elastic modulus The values are shown in Table 5. The elastic modulus of the interface transition zone differs significantly from that of the aggregate and the adhesive; therefore, the length of the interface transition zone is determined by the boundary width, which varies considerably depending on the change in elastic modulus.
[0063] A single-factor analysis was performed on the tensile toughness of the specimen in relation to the ratio of the transition zone length and the elastic modulus. , , and The values are determined using the same steps as in the single-factor analysis of the relationship between the tensile toughness and loading frequency of the specimen. Substituting these values into the fitted results... =0.232, The value is 1Hz. Fixed. * The value is 1.5E+5, and the change is... Using the theoretical tensile toughness prediction as the explanatory variable and the indoor measured tensile toughness as the response variable, a weighted least squares regression model is constructed. The model parameters are estimated by minimizing the sum of squared residuals, and then the model parameters are determined through inversion. α 5 = 0.254.
[0064] Table 5. Width of the transition zone and elastic modulus of each group of specimens
[0065] Parameter sensitivity analysis.
[0066] Tensile toughness is relatively insensitive to dynamic modulus and frequency, while cement dosage has a significant impact on tensile toughness. Different cement admixtures result in varying tensile toughness. The fitting results show significant differences, as shown in Table 6. Fitting the tensile toughness under different cement admixtures with other working conditions simultaneously reduces the accuracy of the tensile toughness formula. Therefore, to improve the accuracy of the tensile toughness model, [the following steps are taken]. Rewrite it in the following form: In the formula, and These are the fitting parameters; For the basic cement dosage, this embodiment uses 1% as the basic cement dosage; This represents the actual cement dosage in the test group. According to... and test cement dosage Relationship fitting obtained and The values are 0.491 and 1.634, respectively.
[0067] Table 6. Different cement admixtures Fitting results
[0068] Fitting of internal structural state variables. By controlling the number of compaction cycles, specimens with porosities of 9.0%, 10.0%, 11.0%, and 12.0% were formed. The porosity, asphalt content, and tensile toughness of different specimens are shown in Table 7. Porosity was determined based on bulk relative density and theoretical maximum density. Bulk relative density was measured using the wax sealing method, and theoretical maximum density was measured using the vacuum method. The formula for calculating porosity is: Porosity = (1 - Bulk Relative Density / Theoretical Maximum Density) × 100%. A 60% RAP content and 4.5% asphalt content were used as the control group. Specimens 1-4 represented cold recycled mixtures with different porosities, while specimens 1 and 5-7 represented cold recycled mixtures with different asphalt contents, all with equal porosities. Internal structural state variables. The formula is: .
[0069] In the formula, and For the fitting parameters, The porosity of the mixture specimen. This refers to the asphalt content. Based on internal structural state variables. and porosity and asphalt content The parameters are obtained by fitting the relationship. and The values are -12.69 and 20.769, respectively.
[0070] Table 7 Tensile toughness of specimens with different internal structural state variables
[0071] The relationship between tensile toughness and various variables was analyzed, multivariate nonlinear fitting was performed, parameter values were corrected, and finally calibration and locking were completed. =0.75.
[0072] In summary, a model for predicting the tensile toughness of cold recycled asphalt mixtures is proposed: .
[0073] A device for evaluating the tensile toughness of recycled asphalt mixtures includes a parameter acquisition module and a processing module. The parameter acquisition module acquires characteristic parameters of the recycled asphalt mixture to be evaluated. The processing module inputs the acquired characteristic parameters into the tensile toughness of the cold recycled asphalt mixture. The calculation is performed in the prediction model, and the tensile toughness assessment value is output.
[0074] The above-disclosed embodiments are merely preferred embodiments of the present invention. However, the embodiments of the present invention are not limited thereto, and any variations that can be conceived by those skilled in the art should fall within the protection scope of the present invention.
Claims
1. A method for evaluating the tensile toughness of recycled asphalt mixtures, characterized in that, include: Obtain the characteristic parameters of the recycled asphalt mixture to be evaluated; The feature parameters include: internally constructed variables and external environmental variables, wherein the internally constructed variables include: , and , These are internal structural state variables. By controlling the number of compaction cycles to form specimens with different void ratios and asphalt contents, the results were obtained by fitting the relationship between measured tensile toughness data and void ratio and asphalt content. The length of the transition zone at the aggregate-binder two-phase interface. The elastic modulus of the interface transition region based on indentation testing was obtained using atomic force microscopy; the external environmental variables were... , and ; The tensile strain ratio. For strain level, For the ultimate tensile strain; Loading frequency; For cold recycled asphalt mixtures at the corresponding loading frequency and the dynamic modulus at a set temperature; Based on the predetermined tensile toughness of cold recycled asphalt mixture, the variables constructed internally and the external environmental variables are used. The tensile toughness of cold recycled asphalt mixture was calculated using the evaluation equation. The evaluation value; The tensile toughness of the cold recycled asphalt mixture The evaluation equation is: ； in, , , , and The calibration was performed sequentially through single-factor control experiments and then calibrated by weighted least squares method.
2. The method for evaluating the tensile toughness of recycled asphalt mixtures according to claim 1, characterized in that, Parameters were calibrated sequentially through a series of single-factor control experiments. , , , and Specifically, it includes the following steps: Pick and It is a fixed value; By applying different tensile strain ratios Tensile toughness test under fixed conditions , and The value remains unchanged, and we obtain The value; By applying different dynamic moduli The tensile toughness test was conducted, and based on the results... The value is fixed. and The value remains unchanged, determined. The value; By applying different loading frequencies The tensile toughness test was conducted, and based on the results... and The value is fixed. The value remains unchanged, determined. The value; The length of the aggregate-binder two-phase interface transition zone was tested in different specimens. Elastic modulus of the interface transition zone based on indentation test And conduct tensile toughness tests, and based on the results , and The value is determined. The value; The relationship between tensile toughness and various variables was analyzed, and a multivariate nonlinear fitting was performed to optimize the results globally. Perform final calibration and correction. Parameter values.
3. The method for evaluating the tensile toughness of recycled asphalt mixtures according to claim 2, characterized in that, Sensitivity analysis was performed on the calibrated parameters. Based on the analysis results, the tensile toughness of cold recycled asphalt mixtures was assessed. Highly sensitive parameters are further expressed as functions of key influencing factors.
4. The method for evaluating the tensile toughness of recycled asphalt mixtures according to claim 3, characterized in that, Based on the results of the sensitivity analysis, Further expressed as cement dosage Linear functions: And determine the linear coefficients through fitting. and ,in The basic cement dosage.
5. The method for evaluating the tensile toughness of recycled asphalt mixtures according to claim 1, characterized in that, The internal structural state variables The calculation formula is as follows: ,in, The porosity of the mixture specimen. The asphalt content of the mixture specimen. a 1 and a 2 represents the fitting parameters.
6. The method for evaluating the tensile toughness of recycled asphalt mixtures according to claim 1, characterized in that, The length of the aggregate-binder two-phase interface transition zone and the elastic modulus of the interface transition zone based on indentation test The data was obtained by scanning along the aggregate, transition zone, and mortar using an atomic force microscope, and based on the surface morphology and modulus distribution characteristics.
7. A device for evaluating the tensile toughness of recycled asphalt mixtures, characterized in that, include: The parameter acquisition module is used to acquire the characteristic parameters of the recycled asphalt mixture to be evaluated in the method for evaluating the tensile toughness of recycled asphalt mixture as described in any one of claims 1 to 6. The processing module is used to input the characteristic parameters into the tensile toughness of cold recycled asphalt mixtures. The calculation is performed in the evaluation equation, and the tensile toughness evaluation value is output.
8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processing module, it implements the method for evaluating the tensile toughness of recycled asphalt mixtures as described in any one of claims 1 to 6.
9. An electronic device comprising a memory, a processing module, and a computer program stored in the memory and executable on the processing module, characterized in that, When the processing module executes the program, it implements the method for evaluating the tensile toughness of recycled asphalt mixtures as described in any one of claims 1 to 6.