Method for estimating skid resistance of asphalt mixture by fusing aggregate mineral and texture characteristics

Abstract

The invention relates to a method for estimating the skid resistance of an asphalt mixture by fusing aggregate mineral and texture characteristics, aiming at solving the problems that the long-term skid resistance test of the existing asphalt mixture is time-consuming and labor-consuming, is influenced by human and environment and is insufficient in accuracy. The estimation method comprises the following steps: 1, acquiring an average hardness parameter AHP of the aggregate, and measuring a SiO2 / Al2O3 index in mineral components of the aggregate; 2, acquiring aggregate texture parameter root mean square height, kurtosis and fractal dimension; 3, analyzing the correlation of the aggregate characteristic parameters, and selecting parameters without correlation as the aggregate characteristic parameters; and 4, establishing a multivariate linear regression model of the anti-skid performance of the asphalt mixture through the aggregate characteristic parameters, and estimating and calculating the anti-skid performance of the asphalt mixture through the multivariate linear regression model. According to the method, on the basis of considering texture indexes, aggregate mineral chemical parameters are introduced, the multiple linear regression model is established with the help of SPSS software to predict the skid resistance of the asphalt mixture, and the accuracy is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of road materials, and in particular relates to a method for estimating the anti-skid performance of an asphalt mixture that combines aggregate mineral and texture characteristics. Background Art

[0002] As an important component of asphalt mixture, aggregate performance is crucial to the anti-skid performance of asphalt mixture. Compared with short-term performance, the long-term anti-skid performance of asphalt pavement is more critical to driving safety. Although the anti-skid performance of asphalt mixture can generally be characterized by indoor / field tests, the accuracy and repeatability of the test are inevitably affected by changes in operating conditions (human factors, environmental conditions, etc.). Therefore, relying on short-term data to predict the long-term anti-skid performance of asphalt mixtures has received increasing attention. Commonly used anti-skid performance prediction models consist of tire prediction models and pavement prediction models. From the perspective of pavement, this study predicts the anti-skid performance of asphalt pavement based on aggregate mineral, chemical and texture characteristic parameters with the help of a multivariate linear regression model. Summary of the invention

[0003] The present invention aims to solve the problems that the existing long-term anti-skid performance test of asphalt mixture is time-consuming and labor-intensive, is affected by human factors and environmental conditions, and has insufficient accuracy, and provides a method for predicting the anti-skid performance of asphalt mixture that integrates aggregate mineral and texture characteristics.

[0004] The method for estimating the anti-skid performance of asphalt mixture integrating aggregate mineral and texture characteristics of the present invention is implemented by the following steps:

[0005] 1. Obtaining aggregate mineral parameters

[0006] The aggregate mineral properties of single aggregate asphalt mixture are evaluated by the average aggregate hardness parameter AHP. The calculation formula of the average hardness parameter AHP is shown in formulas (1)-(3);

[0007] AHP=dmp+Cd (1)

[0008] dmp=∑dvi*pi (2)

[0009] Cd=dvp / min(dvi) (3)

[0010] Where: AHP is the average hardness parameter of aggregate, dmp is the average hardness of aggregate, Cd is the aggregate hardness ratio, dvi is the Mohs hardness of each mineral constituting the aggregate, pi is the mass percentage of each mineral constituting the aggregate, dvp is the Mohs hardness of the mineral with the largest hardness in the aggregate, and min(dvi) is the smallest Mohs hardness among the minerals constituting the aggregate;

[0011] The SiO2 / Al2O3 index in the mineral composition of aggregates was obtained by X-ray fluorescence spectroscopy (XRF);

[0012] 2. Aggregate texture parameter acquisition

[0013] Aggregate texture parameters include root mean square height R q , Kurtosis R ku and fractal dimension D;

[0014] RMS height R q It represents the root mean square of the average height of all aggregate profiles along the reference length. The root mean square height R q The calculation formula is shown in formula (4);

[0015]

[0016] Where N is the total number of sampling points of the cross-section profile, Z n is the height of the aggregate profile in the reference length direction (μm);

[0017] Kurtosis R ku : Use the fourth power of the RMS height to display the reference length (the reference length is consistent with the reference length in the RMS height) dimensionless fourth power of Z(x), kurtosis R ku The calculation formula is shown in formula (5);

[0018]

[0019] The fractal dimension D is used to quantitatively evaluate the roughness of the aggregate surface. The calculation formula of the fractal dimension D is shown in formula (6);

[0020]

[0021] Where: r is the side length of the cube, N(r) is the number of small cubes required to cover the object;

[0022] 3. Correlation analysis of aggregate characteristic parameters

[0023] The correlation analysis of the average hardness parameter AHP and SiO2 / Al2O3 index of aggregate was carried out, and the root mean square height R q , Kurtosis R ku The correlation analysis between the average hardness parameter AHP, SiO2 / Al2O3 index, root mean square height R q , Kurtosis R ku And select the parameters without correlation in fractal dimension D as aggregate characteristic parameters;

[0024] 4. Establishment of Multiple Linear Regression Model

[0025] Aggregate, mineral powder and asphalt are mixed to prepare asphalt mixture specimens. The dynamic friction coefficient μ of the asphalt mixture specimens is tested and the multivariate linear regression model of the anti-skid performance of the asphalt mixture is established through the aggregate characteristic parameters in step three. The anti-skid performance of the asphalt mixture is then estimated and calculated through the multivariate linear regression model, thereby completing the estimation method of the anti-skid performance of the asphalt mixture that integrates the aggregate mineral and texture characteristics.

[0026] The method for estimating the anti-skid performance of asphalt mixture integrating aggregate mineral and texture characteristics of the present invention mainly includes the process of obtaining aggregate mineral parameters, obtaining aggregate texture parameters, analyzing the correlation of aggregate characteristic parameters and establishing a multivariate linear regression model. q , Kurtosis R ku Parameters with no correlation with fractal dimension D are selected as aggregate characteristic parameters.

[0027] The traditional pavement anti-skid performance prediction model is mainly based on texture, and does not consider the influence of aggregate mineral chemical composition. The present invention introduces aggregate mineral chemical parameters on the basis of considering texture indicators, and uses SPSS software to establish a multivariate linear regression model to predict the anti-skid performance of asphalt mixture. The present invention provides a new way to save manpower and material resources and quickly obtain the anti-skid performance of asphalt pavement. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is the root mean square height R of the four different aggregates in the embodiment q Test chart;

[0029] Figure 2 It is the fractal dimension D test diagram of four different aggregates in the embodiment;

[0030] Figure 3 is the peak state R of four different aggregates in the embodiment ku Test chart;

[0031] Figure 4 This is a test diagram of the anti-skid performance of different types of asphalt mixtures in the examples. DETAILED DESCRIPTION

[0032] Specific implementation method 1: The method for estimating the anti-skid performance of asphalt mixture integrating aggregate mineral and texture characteristics in this implementation method is implemented according to the following steps:

[0033] 1. Obtaining aggregate mineral parameters

[0034] The aggregate mineral properties of single aggregate asphalt mixture are evaluated by the average aggregate hardness parameter AHP. The calculation formula of the average hardness parameter AHP is shown in formulas (1)-(3);

[0035] AHP=dmp+Cd (1)

[0036] dmp=∑dvi*pi (2)

[0037] Cd=dvp / min(dvi) (3)

[0038] Where: AHP is the average hardness parameter of aggregate, dmp is the average hardness of aggregate, Cd is the aggregate hardness ratio, dvi is the Mohs hardness of each mineral constituting the aggregate, pi is the mass percentage of each mineral constituting the aggregate, dvp is the Mohs hardness of the mineral with the largest hardness in the aggregate, and min(dvi) is the smallest Mohs hardness among the minerals constituting the aggregate;

[0039] The SiO2 / Al2O3 index in the mineral composition of aggregates was obtained by X-ray fluorescence spectroscopy (XRF);

[0040] 2. Aggregate texture parameter acquisition

[0041] Aggregate texture parameters include root mean square height R q , Kurtosis R ku and fractal dimension D;

[0042] RMS height R q It represents the root mean square of the average height of all aggregate profiles along the reference length. The root mean square height R q The calculation formula is shown in formula (4);

[0043]

[0044] Where N is the total number of sampling points of the cross-section profile, Z n is the height of the aggregate profile in the reference length direction (μm);

[0045] Kurtosis R ku : Use the fourth power of the RMS height to display the reference length (the reference length is consistent with the reference length in the RMS height) dimensionless fourth power of Z(x), kurtosis R ku The calculation formula is shown in formula (5);

[0046]

[0047] The fractal dimension D is used to quantitatively evaluate the roughness of the aggregate surface. The calculation formula of the fractal dimension D is shown in formula (6);

[0048]

[0049] Where: r is the side length of the cube, N(r) is the number of small cubes required to cover the object;

[0050] 3. Correlation analysis of aggregate characteristic parameters

[0051] The correlation analysis of the average hardness parameter AHP and SiO2 / Al2O3 index of aggregate was carried out, and the root mean square height R q , Kurtosis R ku The correlation analysis between the average hardness parameter AHP, SiO2 / Al2O3 index, root mean square height R q , Kurtosis R ku And select the parameters without correlation in fractal dimension D as aggregate characteristic parameters;

[0052] 4. Establishment of Multiple Linear Regression Model

[0053] Aggregate, mineral powder and asphalt are mixed to prepare asphalt mixture specimens. The dynamic friction coefficient μ of the asphalt mixture specimens is tested and the multivariate linear regression model of the anti-skid performance of the asphalt mixture is established through the aggregate characteristic parameters in step three. The anti-skid performance of the asphalt mixture is then estimated and calculated through the multivariate linear regression model, thereby completing the estimation method of the anti-skid performance of the asphalt mixture that integrates the aggregate mineral and texture characteristics.

[0054] Specific implementation method 2: This implementation method is different from specific implementation method 1 in that the aggregate described in step 1 is artificial aggregate or natural aggregate.

[0055] Specific implementation method three: This implementation method is different from specific implementation method two in that the artificial aggregate is calcined bauxite, and the natural aggregate is basalt or limestone.

[0056] Specific implementation method 4: The difference between this implementation method and any one of specific implementation methods 1 to 3 is that the SiO2 / Al2O3 index in step 2 is obtained by testing with an X-ray fluorescence spectrometer.

[0057] Specific embodiment 5: This embodiment is different from specific embodiments 1 to 4 in that a laser microscope is used in step 2 to obtain a texture image of the aggregate surface to obtain the root mean square height R q , Kurtosis R ku And fractal dimension D.

[0058] Specific implementation method 6: The difference between this implementation method and specific implementation methods 1 to 5 is that in the correlation analysis process in step 3, when the correlation coefficient R 2 >0.7, it means that the two parameters are correlated. 2 <0.7, it means that there is no correlation between the two parameters.

[0059] Specific implementation method seven: This implementation method is different from specific implementation methods one to six in that in step four, the aggregate, mineral powder and asphalt are mixed and compacted to obtain an asphalt mixture test piece.

[0060] Specific implementation eight: The difference between this implementation and any one of specific implementations one to seven is that in step four, a dynamic friction coefficient tester is used to test and obtain the dynamic friction coefficient μ of the asphalt mixture specimen.

[0061] Specific implementation method nine: The difference between this implementation method and any one of specific implementation methods one to eight is that in step four, the multiple linear regression module in SPSS software (statistical products and service solutions software) is used to regress the aggregate characteristic parameters obtained in step three as independent variables.

[0062] Specific implementation method ten: The difference between this implementation method and specific implementation method nine is that in step four, when the statistical value p of the independent variable is less than 0.05, it indicates that the independent variable has a significant effect on the anti-skid performance of the asphalt mixture.

[0063] Embodiment: The method for estimating the anti-skid performance of asphalt mixture by integrating aggregate mineral and texture characteristics in this embodiment is implemented according to the following steps:

[0064] 1. Obtaining aggregate mineral parameters

[0065] In this embodiment, four different aggregates are selected, which are two artificial aggregates, 88# and 75# calcined bauxite, and two natural aggregates, basalt and limestone;

[0066] The aggregate mineral properties of single aggregate asphalt mixture are evaluated by the average aggregate hardness parameter AHP. The calculation formula of the average hardness parameter AHP is shown in formulas (1)-(3);

[0067] AHP=dmp+Cd (1)

[0068] dmp=∑dvi*pi (2)

[0069] Cd=dvp / min(dvi) (3)

[0070] Where: AHP is the average hardness parameter of aggregate, dmp is the average hardness of aggregate, Cd is the aggregate hardness ratio, dvi is the Mohs hardness of each mineral constituting the aggregate, pi is the mass percentage of each mineral constituting the aggregate, dvp is the Mohs hardness of the mineral with the largest hardness in the aggregate, and min(dvi) is the smallest Mohs hardness among the minerals constituting the aggregate;

[0071] The SiO2 / Al2O3 index in the mineral composition of aggregates was obtained by X-ray fluorescence spectroscopy (XRF);

[0072] 2. Aggregate texture parameter acquisition

[0073] Aggregate texture parameters include root mean square height R q , Kurtosis R ku and fractal dimension D;

[0074] RMS height R q It represents the root mean square of the average height of all aggregate profiles along the reference length. The root mean square height R q The calculation formula is shown in formula (4);

[0075]

[0076] Where N is the total number of sampling points of the cross-section profile, Z n is the height of the aggregate profile in the reference length direction;

[0077] Kurtosis R ku : Displays the dimensionless fourth power of the reference length Z(x) using the fourth power of the RMS height, the kurtosis R ku The calculation formula is shown in formula (5);

[0078]

[0079] The fractal dimension D is used to quantitatively evaluate the roughness of the aggregate surface. The calculation formula of the fractal dimension D is shown in formula (6);

[0080]

[0081] Where: r is the side length of the cube, N(r) is the number of small cubes required to cover the object;

[0082] 3. Correlation analysis of aggregate characteristic parameters;

[0083] Origin software was used to conduct correlation analysis on the average hardness parameter AHP and SiO2 / Al2O3 index of aggregates, and the root mean square height R q , Kurtosis R ku and fractal dimension D were analyzed pairwise;

[0084] AHP and SiO2 / Al2O3 indicators are aggregate mineral and chemical parameters. After correlation analysis, the adjusted R 2 is -0.09, indicating that AHP and SiO2 / Al2O3 index have no significant correlation and are two independent variables;

[0085] RMS height R q , Kurtosis R ku and fractal dimension D, are aggregate texture parameters, and their correlation results are shown in Table 3. As can be seen from Table 3, R ku With Rq The correlation coefficient is 0.034, indicating that R ku With R q There is no significant correlation, they are two independent variables; R ku The correlation coefficient with D is 0.234, which means R ku There is no significant correlation between D and R. q The correlation coefficient is 0.894, indicating that D and R q There is a significant correlation between D and R q are the texture characteristic parameters of the same aggregate;

[0086] From the average hardness parameter AHP, SiO2 / Al2O3 index, root mean square height R q and Kurtosis R ku Four parameters are used as aggregate characteristic parameters;

[0087] 4. Establishment of Multiple Linear Regression Model

[0088] Four different aggregates were mixed with mineral powder and asphalt, poured into a mold and compacted with a hand-push roller to obtain asphalt mixture specimens. The controlled oil-stone ratios of the four specimens were 6.51%, 6.58%, 6.02% and 6.04% respectively. Then the dynamic friction coefficient μ of the asphalt mixture specimens was tested, and the multivariate linear regression model of the anti-skid performance of the asphalt mixture was established through the aggregate characteristic parameters in step 3. Then the anti-skid performance of the asphalt mixture was estimated and calculated through the multivariate linear regression model, thus completing the estimation method of the anti-skid performance of the asphalt mixture that integrates the aggregate mineral and texture characteristics.

[0089] Table 1 shows the chemical composition of different types of aggregates. It can be seen from the table that the chemical composition of calcined bauxite is significantly different from that of natural aggregates. Calcined bauxite is mainly composed of SiO2 and Al2O3, while natural aggregates are mainly composed of CaO and SiO2.

[0090] Table 2 shows the main mineral parameters of different aggregates. Similar to the chemical composition, the main mineral compositions of calcined bauxite are corundum and mullite, while natural aggregates contain plagioclase, hornblende, calcite and dolomite.

[0091] Figure 1-3 are the texture parameters of different aggregates. It can be seen from the figure that in most cases, the texture parameters of calcined bauxite are better than those of natural aggregates.

[0092] In this embodiment, the root mean square height Rq, peak state Rku, AHP and SiO2 / Al2O3 are selected as independent variables to characterize aggregate morphology, mineral and chemical properties. The multivariate linear regression module in SPSS software is used to regress the four independent variables affecting the anti-skid performance of asphalt mixture, and the dynamic friction coefficient μ is used to characterize the anti-skid performance of the resulting asphalt mixture, as shown in Table 4. Generally speaking, if the statistical value (p) of the independent variable is less than 0.05, it indicates that the factor has a significant effect on the anti-skid performance of the asphalt mixture. It can be seen from the table that the coefficients of AHP, SiO2 / Al2O3, and Rku are 0.000, 0.001, and 0.000, respectively, which are all less than 0.05, indicating that AHP, SiO2 / Al2O3 and Rku have a significant effect on the μ value. However, as far as Rq is concerned, its coefficient is too large and should be eliminated. Therefore, the multivariate linear regression model is shown in formula (7). The statistical p values ​​of each variable are all less than 0.05, which have a significant effect on the road friction coefficient.

[0093] The multivariate linear regression model established in this embodiment is shown in the following formula (7):

[0094] μ=-0.894+0.004AHP-0.009 SiO2 / Al2O3+0.525 Rku (7)

[0095] Table 1 Main chemical composition of different aggregates

[0096]

[0097] Table 2 Main mineral parameters of different aggregates

[0098]

[0099] Table 3 Pearson correlation coefficient matrix of different texture indicators

[0100]

[0101] Table 4 Multiple linear regression model

[0102]

Claims

1. A method for estimating the anti-skid performance of asphalt mixtures that combines aggregate mineral and texture characteristics, characterized in that The estimation method is implemented in the following steps:

1. Obtaining aggregate mineral parameters The aggregate mineral properties of single aggregate asphalt mixture are evaluated by the average aggregate hardness parameter AHP. The calculation formula of the average hardness parameter AHP is shown in formulas (1)-(3); AHP=dmp+Cd (1) dmp=∑dvi*pi (2) Cd=dvp / min(dvi) (3) Where: AHP is the average hardness parameter of aggregate, dmp is the average hardness of aggregate, Cd is the aggregate hardness ratio, dvi is the Mohs hardness of each mineral constituting the aggregate, pi is the mass percentage of each mineral constituting the aggregate, dvp is the Mohs hardness of the mineral with the largest hardness in the aggregate, and min(dvi) is the smallest Mohs hardness among the minerals constituting the aggregate; The SiO2 / Al2O3 index in the mineral composition of aggregates was obtained by X-ray fluorescence spectrometry; 2. Aggregate texture parameter acquisition Aggregate texture parameters include root mean square height R q , Kurtosis R ku and fractal dimension D; RMS height R q It represents the root mean square of the average height of all aggregate profiles along the reference length. The root mean square height R q The calculation formula is shown in formula (4); Where N is the total number of sampling points of the cross-section profile, Z n is the height of the aggregate profile in the reference length direction; Kurtosis R ku : Displays the dimensionless fourth power of the reference length Z(x) using the fourth power of the root mean square height, the kurtosis R ku The calculation formula is shown in formula (5); The fractal dimension D is used to quantitatively evaluate the roughness of the aggregate surface. The calculation formula of the fractal dimension D is shown in formula (6); Where: r is the side length of the cube, N(r) is the number of small cubes required to cover the object; 3. Correlation analysis of aggregate characteristic parameters The correlation analysis of the average hardness parameter AHP and SiO2 / Al2O3 index of aggregate was carried out, and the root mean square height R q , Kurtosis R ku The correlation analysis between the average hardness parameter AHP, SiO2 / Al2O3 index, root mean square height R q , Kurtosis R ku And select the parameters without correlation in fractal dimension D as aggregate characteristic parameters; 4. Establishment of Multiple Linear Regression Model Aggregate, mineral powder and asphalt are mixed to prepare asphalt mixture specimens. The dynamic friction coefficient μ of the asphalt mixture specimens is tested and obtained. The multivariate linear regression model of the anti-skid performance of the asphalt mixture is established through the aggregate characteristic parameters in step three. The anti-skid performance of the asphalt mixture is then estimated and calculated through the multivariate linear regression model, thereby completing the estimation method of the anti-skid performance of the asphalt mixture that integrates the aggregate mineral and texture characteristics.

2. The method for estimating the anti-skid performance of asphalt mixture integrating aggregate mineral and texture characteristics according to claim 1, characterized in that The aggregate described in step 1 is artificial aggregate or natural aggregate.

3. The method for estimating the anti-skid performance of asphalt mixture integrating aggregate mineral and texture characteristics according to claim 2, characterized in that The artificial aggregate is calcined bauxite, and the natural aggregate is basalt or limestone.

4. The method for estimating the anti-skid performance of asphalt mixture integrating aggregate mineral and texture characteristics according to claim 1, characterized in that In step 2, the SiO2 / Al2O3 index is obtained by testing with an X-ray fluorescence spectrometer.

5. The method for estimating the anti-skid performance of asphalt mixture integrating aggregate mineral and texture characteristics according to claim 1, characterized in that In step 2, a laser microscope is used to obtain a texture image of the aggregate surface to obtain the root mean square height R q , Kurtosis R ku And fractal dimension D.

6. The method for estimating the anti-skid performance of asphalt mixture integrating aggregate mineral and texture characteristics according to claim 1, characterized in that In the correlation analysis process in step 3, when the correlation coefficient R 2 >0.7, it means that the two parameters are correlated. 2 <0.7, it means that there is no correlation between the two parameters.

7. The method for estimating the anti-skid performance of asphalt mixture integrating aggregate mineral and texture characteristics according to claim 1, characterized in that In step 4, the aggregate, mineral powder and asphalt are mixed and compacted to obtain an asphalt mixture specimen.

8. The method for estimating the anti-skid performance of asphalt mixture integrating aggregate mineral and texture characteristics according to claim 1, characterized in that In step 4, a dynamic friction coefficient tester is used to test and obtain the dynamic friction coefficient μ of the asphalt mixture specimen.

9. The method for estimating the anti-skid performance of asphalt mixture integrating aggregate mineral and texture characteristics according to claim 1, characterized in that In step 4, the multiple linear regression module in SPSS software is used to regress the aggregate characteristic parameters obtained in step 3 as independent variables.

10. The method for estimating the anti-skid performance of asphalt mixture integrating aggregate mineral and texture characteristics according to claim 9, characterized in that In step 4, when the statistical value p of the independent variable is less than 0.05, it indicates that the independent variable has a significant effect on the anti-skid performance of asphalt mixture.

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