Asphalt-aggregate adhesion performance dynamic estimation method, device and application

Abstract

The invention discloses an asphalt-aggregate adhesion performance dynamic estimation method and device and application, and the estimation method comprises the following steps: obtaining a microscopic index and a microcosmic index of an asphalt binder of an asphalt-aggregate system under the action of erosion, and analyzing to obtain a first index; obtaining a microscopic index and a microcosmic index of the aggregate after the surface asphalt of the asphalt-aggregate system is removed, and analyzing to obtain a second index; obtaining the adhesion force between the asphalt and the aggregate of the formed test piece under the erosion action, carrying out principal component analysis according to the correlation between the adhesion force and the first index and the second index, and determining a comprehensive factor for expressing the microscopic index of the adhesion force; the method comprises the following steps: acquiring a correlation between a comprehensive factor of a microscopic index for expressing the adhesion force of asphalt-aggregate system test pieces of different aggregate types and the adhesion force between asphalt and aggregate, constructing an asphalt-aggregate interface adhesion force estimation model, and performing estimation analysis. The method has the advantages of dynamic estimation, wide application range and accurate estimation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of road engineering, in particular to a dynamic prediction method and device for asphalt-aggregate adhesion performance and application. BACKGROUND

[0002] In the technical field of road engineering, the adhesion performance of asphalt and aggregate affects the road performance and long-term durability of asphalt mixture and pavement, and accurate prediction of the adhesion performance of the asphalt-aggregate interface is an important means to understand the performance evolution and service life of asphalt mixture and pavement, and how to accurately predict it has been a key scientific problem faced by scholars in the industry.

[0003] At present, in order to predict the adhesion performance of the asphalt-aggregate interface, researchers usually analyze test parameters under a single scale and a single working condition, and predict by establishing the correlation between the test parameters and the adhesion performance of the interface. However, this method is actually a kind of static characterization, and cannot reflect the long-term evolution of the adhesion performance under different service conditions, so it cannot achieve the prediction goal. In addition, on the one hand, the adhesion performance of the asphalt-aggregate interface is closely related to the test scale, and the existing method of predicting by using test parameters under a single scale ignores the influence of multi-scale characteristics on the adhesion performance of the interface, so that the selection of prediction parameters is not necessarily representative, and the prediction result has a certain degree of deviation, which is difficult to guide actual engineering application. On the other hand, the asphalt pavement will experience multi-field coupling erosion such as water, temperature and load during the service period, and the prediction parameters based on a single working condition cannot fully reflect the actual service conditions of the asphalt pavement, which also causes the technical drawbacks of inaccurate adhesion performance prediction.

[0004] Under this background, there is currently no method for dynamically predicting the adhesion performance of asphalt-aggregate, and the scale characteristics of asphalt-based materials and the actual service conditions of asphalt pavement need to be considered to develop a dynamic prediction method for the adhesion performance of asphalt-aggregate, so as to realize accurate prediction and engineering application of the adhesion performance of the interface. SUMMARY

[0005] The purpose of the present application is to solve the problem that the adhesion performance of asphalt-aggregate is difficult to be dynamically predicted, and to realize accurate prediction and engineering application.

[0006] In order to achieve the above purpose, the present application provides a dynamic prediction method for the adhesion performance of asphalt-aggregate, comprising, obtaining the micro and meso indexes of the asphalt binder of the asphalt-aggregate system under erosion, and analyzing to obtain a first index; obtaining the micro and meso indexes of the aggregate after removing the surface asphalt of the asphalt-aggregate system, and analyzing to obtain a second index; The adhesion between the asphalt and the aggregate under the erosion of the shaped test piece is obtained, the indexes related to the adhesion and having better correlation and weak correlation between the microcosmic indexes are subjected to principal component analysis according to the correlation between the adhesion and the first index and the second index, and the comprehensive factor of the microcosmic index expressing the adhesion is determined; The correlation between the comprehensive factor of the microcosmic index expressing the adhesion and the adhesion between the asphalt and the aggregate of the test piece of different aggregate types is obtained, and a prediction model of the adhesion between the asphalt and the aggregate is constructed and subjected to prediction analysis.

[0007] Further, the microcosmic indexes include the average roughness, the root mean square roughness, the skewness and the kurtosis, and the microcosmic indexes include the polar base component, the polar acid component and the dispersion component.

[0008] Further, the comprehensive factor of the microcosmic index expressing the adhesion determined by the principal component analysis of the indexes having weak correlation includes, The number of principal components is determined according to the principle that the eigenvalue of the principal component is greater than 1, and the expression of the comprehensive factor of the microcosmic index expressing the adhesion is determined, ; wherein, F is the comprehensive factor of the microcosmic index expressing the adhesion, R aa is the average roughness of the asphalt binder, and are the polar acid component of the aggregate and the dispersion component of the aggregate respectively, A 、 B and C are model coefficients.

[0009] Further, the obtaining of the correlation between the comprehensive factor of the microcosmic index expressing the adhesion and the adhesion between the asphalt and the aggregate of the test piece of different aggregate types and the construction of the prediction model of the adhesion between the asphalt and the aggregate include, constructing a relationship between the comprehensive factor and the adhesion between the asphalt and the aggregate of the test piece of different aggregate types under different test conditions, which is referred to as a first relationship; obtaining a second relationship according to the expression of the comprehensive factor of the microcosmic index expressing the adhesion and the first relationship; constructing a change model of the average roughness of the asphalt binder, the polar acid component of the aggregate and the dispersion component of the aggregate of the test piece of different aggregate types under the erosion of the actual environment, and constructing the prediction model of the adhesion between the asphalt and the aggregate in combination with the second relationship.

[0010] Further, the prediction model of the adhesion between the asphalt and the aggregate is, ; wherein,P This refers to the adhesion force between the asphalt and aggregate interface. R aa0 This represents the initial value of the average roughness of the asphalt binder. n For the number of water-temperature coupling cycles, a , b , c and d Represents the model coefficients. e It is the natural base.

[0011] Furthermore, after constructing the prediction model for the asphalt-aggregate interfacial adhesion, the method also includes an evaluation step of the adhesion performance between asphalt and aggregate in the pavement to be predicted at the engineering site. This evaluation step includes... Freeze-thaw splitting tests were conducted on specimens with different aggregate types to obtain the freeze-thaw splitting strength ratio; A model was constructed to establish the relationship between the freeze-thaw splitting strength ratio and the estimated adhesion between asphalt and aggregate of the pavement to be estimated. The standard value of the freeze-thaw splitting strength ratio of the pavement to be estimated was obtained by combining the standard value of the freeze-thaw splitting strength ratio of the pavement to be estimated at the engineering site. Based on the standard and estimated values ​​of the adhesion force between asphalt and aggregate in the pavement to be estimated, evaluate the adhesion performance and water stability of the asphalt-aggregate in the pavement to be estimated at the engineering site.

[0012] Furthermore, the erosion is achieved through a cyclical water bath within a temperature range of -18 to 40°C.

[0013] Furthermore, the microscopic properties of the asphalt binder were determined by atomic force microscopy, and the microscopic properties of the aggregate were determined by laser confocal microscopy. The microscopic indices are obtained using the surface free energy theory.

[0014] This invention also provides a device for dynamically predicting the adhesion properties of asphalt-aggregate, comprising, The first analytical optimization unit is used to obtain the microscopic and mesoscopic indices of the asphalt binder in the asphalt-aggregate system under erosion, and to analyze and obtain the first index. The second analysis and optimization unit is used to obtain the micro and mesoscopic indices of the aggregate after removing the surface asphalt from the asphalt-aggregate system, and to analyze and obtain the second index. The comprehensive factor determination unit is used to obtain the adhesion force between asphalt and aggregate of the molded specimen under erosion. Based on the correlation between adhesion force and the first and second indicators, principal component analysis is performed on the indicators that have a good correlation with adhesion force and a weak correlation with the micro-indices to determine the comprehensive factor of the micro-indices that express adhesion force. The model determines the prediction unit, which is used to obtain the comprehensive factor of the microscopic index of adhesion force of specimens of different aggregate types and the correlation between the adhesion force between asphalt and aggregate. The prediction model of adhesion force at the asphalt-aggregate interface is constructed and the prediction analysis is performed.

[0015] This invention also provides the application of the above-mentioned dynamic prediction method for asphalt-aggregate adhesion properties in predicting the adhesion properties between asphalt and aggregates and the water stability properties of asphalt pavements during their service life.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention can dynamically predict the adhesion performance between asphalt and aggregate and evaluate its quality, providing a methodological reference for the determination and evaluation of the interfacial adhesion performance of asphalt-aggregate system during service; in addition, the present invention can realistically simulate the environmental erosion experienced by asphalt pavement, making the obtained micro-parameters of asphalt and aggregate more objective and realistic.

[0017] (2) When constructing the asphalt-aggregate interface adhesion prediction model, this invention considers the micro and micro parameters of the asphalt binder and the micro and micro parameters of the aggregate. It analyzes the influencing factors of interface adhesion performance from a multi-scale perspective, and the final predicted parameters are more representative and objective.

[0018] (3) When optimizing the micro and fine parameters of asphalt and aggregate, the present invention first analyzes the regularity, sensitivity and repeatability of the characterization results of each parameter under different asphalt binders and different working conditions. The final determined model parameters are more accurate in describing the changes of micro and fine properties under water-temperature coupled working conditions.

[0019] (4) In actual operation, the present invention only requires the old asphalt pavement material on the surface of the road, which causes little damage to the road surface and is simple and efficient. Attached Figure Description

[0020] Figure 1 A flowchart of the dynamic prediction method for asphalt-aggregate adhesion performance of the present invention is shown; Figure 2 This diagram illustrates a single water-temperature coupled erosion process of an asphalt-aggregate system. Figure 3 The results of Pearson correlation analysis between preferred base bitumen, aggregate microstructure parameters, and base bitumen-aggregate interfacial adhesion are shown. Figure 4 This shows the interfacial adhesion between the matrix bitumen and aggregate at a pull-out temperature of 10°C. P -10℃ Combined factors of base asphalt and aggregate F The correlation between them; Figure 5 This shows the interfacial adhesion between the matrix bitumen and aggregate at a pull-out temperature of 25°C. P 25℃ Combined factors of base asphalt and aggregate F The correlation between them; Figure 6 The results of Pearson correlation analysis between the preferred modified asphalt, aggregate microstructure parameters, and modified asphalt-aggregate interfacial adhesion are shown. Figure 7 The adhesion strength of the modified asphalt-aggregate interface at a pull-out temperature of -10°C is shown. P -10℃ Combined factors with modified asphalt and aggregate F The correlation between them; Figure 8 The interfacial adhesion between modified asphalt and aggregate is shown at a pull-out temperature of 25°C. P 25℃ Combined factors with modified asphalt and aggregate F The correlation between them; Figure 9 A schematic diagram of the device for dynamically predicting the adhesion properties of asphalt-aggregate is shown. Detailed Implementation

[0021] The endpoints and any values ​​of the ranges disclosed in this invention are not limited to the precise ranges or values, and these ranges or values ​​should be understood to include values ​​close to these ranges or values. For numerical ranges, the endpoint values ​​of the various ranges, the endpoint values ​​of the various ranges and individual point values, and individual point values ​​can be combined with each other to obtain one or more new numerical ranges, which should be considered as specifically disclosed in this invention.

[0022] The technical solutions of the present invention will be clearly and completely described below with reference to specific embodiments and accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Example 1 like Figure 1 As shown, a method for dynamically predicting the adhesion performance of asphalt-aggregate includes the following steps: Step 1: Heat the base asphalt to a molten state and immerse the aggregate particles in it, so that the base asphalt is evenly coated on the surface of the aggregate, forming a base asphalt-aggregate system.

[0024] Step 2: According to Figure 2As shown, in order to simulate the erosive effect of the actual environment, the matrix asphalt-aggregate system formed in step 1 was subjected to a wide temperature range reciprocating cycle under the action of a water bath environment and temperature gradient. The temperature range was -18℃ to 40℃, and the duration of each cycle was 22 hours. The cycles were repeated 1, 3, 5, 7, 9, 11, 13 and 15 times.

[0025] Step 3: Dissolve the asphalt layer coated on the aggregate surface with trichloroethylene and evaporate it to obtain asphalt binder.

[0026] Step 4: Based on the asphalt binder obtained in Step 3, the microscopic properties after undergoing the number of cycles described in Step 2 are measured using an atomic force microscope. These microscopic properties include average roughness. R aa Root mean square roughness R qa skewness R ska and steepness R kua This embodiment uses two types of 70# base asphalt and two types of 90# base asphalt as examples for illustration. Tables 1 and 2 list the binders of the two types of 70# and two types of 90# base asphalt after water-temperature erosion. R aa , R qa , R ska and R kua .

[0027] Table 1. Microscopic parameters of two types of 70# base asphalt binders (unit: nm)

[0028] Table 2. Microscopic parameters of two types of 90# base asphalt binders (unit: nm)

[0029] Step 5: Analyze the results obtained in Step 4 R aa , R qa , R ska and R kua The regularity and sensitivity of [the data]. Analysis can reveal [these characteristics]. R aa and R qa The number of water-temperature coupled erosion cycles showed a good regularity, and compared to R ska and R kuaAfter undergoing the same number of water-temperature coupled erosion cycles, they showed a large range of changes, indicating that they were highly sensitive to water-temperature coupled erosion.

[0030] Step 6: Analysis R aa , R qa , R ska and R kua Repeatability. This example uses Shell 70# base bitumen as an example to measure its reproducibility after one and 15 cycles of water-temperature coupled erosion. R aa , R qa , R ska and R kua (Multiple repeated measurements) and the coefficients of variation of the four parameters were calculated to evaluate their repeatability. The results are shown in Table 3.

[0031] Table 3. Results of repeated observations and coefficients of variation of microstructure properties of Shell 70# base bitumen binder (Unit for microscopic indicators in the table: nm)

[0032] As shown in Table 3, R aa and R qa The coefficient of variation of the indicator is relatively small, indicating high repeatability. Combining steps 4-6, the optimal result is... R aa and R qa As a characterization index of the microstructure of matrix asphalt binder under water-temperature coupled erosion.

[0033] Step 7: Determine the polar alkali content of the asphalt binder involved in steps 4-6 using the surface free energy theory. polar acid content and dispersion component The study analyzed the regularity of three indicators with the number of water-temperature coupled erosion cycles, as well as the sensitivity and repeatability of the three indicators, to optimize the characterization indicators of the microstructure of asphalt binders. Tables 4 and 5 list the characteristics of two types of 70# and two types of 90# base asphalt binders after water-temperature erosion. , and Table 6 lists the results of Shell 70# base bitumen after one and 15 cycles of water-temperature coupled erosion. , and The results of multiple repeated measurements.

[0034] Table 4. Microstructure properties of two types of 70# base asphalt binders (unit: mJ / m) 2 )

[0035] Table 5. Microstructure properties of two types of 90# base asphalt binders (unit: mJ / m) 2 )

[0036] Table 6. Results of repeated observations and coefficients of variation of the microstructure properties of Shell 70# base bitumen binder (unit: mJ / m) 2 )

[0037] Tables 4 and 5 show that the free energy dispersion component of asphalt binder after undergoing water-temperature coupled erosion is... The erosion frequency gradually decreases with increasing number of erosion cycles, exhibiting a good regularity, as can be seen from Table 6. The coefficient of variation is small, indicating good repeatability. Therefore, the optimal microstructure parameters of asphalt binder are... .

[0038] Select through steps 4-7 R aa and R qa For the microscopic properties of asphalt binder, select These three indicators are the microstructure indicators of asphalt binder, and are taken as the primary indicators.

[0039] Step 8: Completely dissolve the asphalt binder on the surface of the aggregate to obtain aggregate particles.

[0040] Step 9: Use a laser confocal microscope to determine the microscopic properties of the aggregate particles obtained in Step 8, including average roughness. R as Root mean square roughness R qs skewness R sks and steepness R kus And the surface free energy parameters (polar base component) were determined using the vapor adsorption method. polar acid content and dispersion component This embodiment uses four types of aggregates commonly used in road engineering—limestone, basalt, granite, and diabase—as examples for testing. Table 7 shows the index values ​​of the four aggregates after water-temperature erosion, and Table 8 shows the results of repeated measurements of the index values ​​of limestone aggregate after one and 15 water-temperature erosions.

[0041] Table 7. Data on various indicators of aggregates

[0042] Table 8. Results of repeated measurements and coefficients of variation of various parameters of limestone aggregate.

[0043] Step 10: Based on the data of each indicator obtained in Step 9, considering regularity, sensitivity and repeatability, select the best characterization indicators for aggregate properties.

[0044] As can be seen from Tables 7 and 8, the aggregate... R as and R qs compared to R sks and R kus ,as well as and Compared to It exhibits greater discriminative power when distinguishing different aggregates, meaning it is more sensitive to changes in aggregate type, and this sensitivity increases with the number of erosion cycles. R as , R qs , and The four indicators exhibit good regularity, and compared to other indicators, they show relatively small coefficients of variation, indicating high repeatability. Therefore, the preferred method is... R as , R qs , and Four indicators are used to characterize the microstructure of aggregates; these four indicators are the second set of indicators.

[0045] Step 11: Form aggregate-asphalt-aggregate sandwich specimens with an asphalt film thickness of 100 μm and subject them to water-temperature coupled erosion under the same conditions as in Step 2.

[0046] Step 12: Based on the specimens subjected to water-temperature coupled erosion in Step 11, pull-out tests were conducted at a pull-out rate of 5 mm / min and pull-out temperatures of -10℃ and 25℃ to obtain the adhesion force between the asphalt and aggregate interface under the two pull-out temperature conditions. P The results are shown in Tables 9 and 10.

[0047] Table 9. Adhesion between the matrix asphalt and aggregate interface under a pull-out temperature of -10℃. P -10℃

[0048] Table 10 Adhesion between matrix asphalt and aggregate interface at a pull-out temperature of 25℃ P 25℃

[0049] Step 13: As Figure 3 As shown, the Pearson correlation between the indicators selected in steps 6, 7, and 10 and the indicators determined in step 12 is analyzed. It can be seen that... R aa , R qa , , , Adhesion force at -10℃ P -10℃ It has a relatively good Pearson correlation, and R aa , R qa , , , Adhesion at 25°C P 25℃ It exhibits a relatively good Pearson correlation. Furthermore, R aa and R qa Interval and R aa and There is a strong Pearson correlation between them (correlation coefficients reached 0.89 and -0.85, respectively), indicating that there are redundant variables when expressing adhesive force.

[0050] Step 14: Select the indices from Step 13 that have a good correlation with interfacial adhesion and relatively weak correlations among the various microscopic indices for principal component analysis. R aa , and (Selected for principal component analysis), the number of principal components was determined based on the principle that the eigenvalue of the principal components is greater than 1. On this basis, a comprehensive factor expression for the microscopic index of adhesion force was determined, and its form satisfies the following equation. ; in, F A comprehensive factor for expressing the microscopic indices of adhesion. A , B and C Represents the model coefficients.

[0051] The specific fitting results are shown in formula (1).

[0052] (1) In formula (1), F A comprehensive factor for expressing the microscopic indices of adhesion.

[0053] Step 15: Substitute the microscopic index values ​​obtained in Steps 4 and 9 into Formula (1) to obtain... F ,analyze F and P -10℃ and F and P 25℃ The relationship between them, such as Figure 4 and Figure 5 As shown. The obtained relational model is referred to as the first relational formula, as shown in formulas (2)-(9). P -10℃ and P 25℃ The values ​​represent the adhesion force between the asphalt and aggregate interface at -10℃ and 25℃, respectively.

[0054] Limestone: (2) Limestone: (3) Basalt: (4) Basalt: (5) granite: (6) granite: (7) diabase: (8) diabase: (9) Step 16: Obtain the second relation based on the comprehensive factor expression of the microscopic index of adhesion and the first relation, that is, substitute formula (1) into (2)-(9) to obtain P-10℃ and P 25℃ The correlation between the microscopic parameters and the second relationship is shown in formulas (10)-(17).

[0055] Limestone: (10) Limestone: (11) Basalt: (12) Basalt: (13) granite: (14) granite: (15) diabase: (16) diabase: (17) Step 17: Based on the microscopic parameter values ​​listed in Steps 4 and 9, construct... R aa , and The model for the variation of water-temperature erosion cycles is shown in formulas (18)-(26). Among them, R aa0 Indicates asphalt R aa initial value, n This represents the number of water-temperature coupling cycles.

[0056] (18) Limestone: (19) Limestone: (20) Basalt: (twenty one) Basalt: (twenty two) granite: (twenty three) granite: (twenty four) diabase: (25) diabase: (26) Step 18: Based on the variation models of the average roughness of the asphalt binder, the polar acid content of the aggregate, and the dispersion component of the aggregate, and combined with the second relation, construct a prediction model for the asphalt-aggregate interface adhesion force. That is, substitute formulas (18)-(26) into formulas (10)-(17) to obtain the prediction model for the asphalt-aggregate interface adhesion force. The form of the prediction model for the asphalt-aggregate interface adhesion force satisfies the following formula: ; in, P This refers to the adhesion force between the asphalt and aggregate interface. R aa0 This represents the initial value of the average roughness of the aggregate binder. n For the number of water-temperature coupling cycles, a , b , c and d Represents the model coefficients. e It is the natural base.

[0057] The specific fitting results are shown in formulas (27)-(34).

[0058] Limestone: (27) Limestone: (28) Basalt: (29) Basalt: (30) granite: (31) granite: (32) diabase: (33) diabase: (34) Step 19: Calculate the adhesion strength of the asphalt-aggregate interface using the prediction model for the same material. P -10℃ and P 25℃ Marked as P -10℃-预估值 and P 25℃-预估值 The results are shown in Tables 11 and 12.

[0059] Table 11 P -10℃-预估值 Calculation results

[0060] Table 12 P -25℃-预估值 Calculation results

[0061] Based on the calculation results in step 19 and the adhesion between the asphalt and aggregate interface tested in step 12 P This verifies that the model constructed in this embodiment can effectively predict the adhesion force between the asphalt and aggregate interface.

[0062] This embodiment further establishes an evaluation standard for asphalt-aggregate adhesion performance, including the following steps: S1. Marshall specimens of ordinary dense-graded asphalt concrete mixtures and asphalt mastic aggregate (SMA) with the same materials were formed according to the "Test Procedures for Asphalt and Asphalt Mixtures in Highway Engineering" (JTG 3410-2025), and freeze-thaw splitting tests were conducted on the specimens to obtain the freeze-thaw splitting strength ratio. TSR See Table 13.

[0063] Table 13 Freeze-thaw splitting strength ratio of Marshall specimens of base asphalt mixtures TSR

[0064] S2, Construction P -10℃-预估值 and P 25℃-预估值 and TSR The relationship between them, and the matrix asphalt mixture specified in the "Technical Specification for Construction of Highway Asphalt Pavement" (JTG F40-2004) TSR Standard values ​​(for ordinary dense-graded matrix asphalt mixtures and matrix SMA mixtures in wet and humid areas) TSR The standard value is 75%; in semi-arid and arid areas, the standard value for ordinary dense-graded matrix asphalt mixtures and matrix SMA mixtures is 75%. TSR Substituting the standard values ​​of 70% and 75% into the correlation, the adhesion strength of the matrix asphalt-aggregate interface after different water-temperature coupled erosion cycles can be calculated. P 标准值 See Table 14.

[0065] Table 14. Adhesion between the matrix asphalt and aggregate interface after different water-temperature coupled erosion cycles. P 标准值

[0066] like P 预估值 < P 标准值 This indicates that the adhesion performance of the matrix asphalt-aggregate interface does not meet the requirements under the corresponding erosion number and application scenario.

[0067] This embodiment also applies the constructed prediction model of asphalt-aggregate interfacial adhesion to the prediction of asphalt-aggregate adhesion performance in base asphalt pavements during their service life, including the following steps. T1. Select a service-age asphalt pavement, obtain a small amount of asphalt concrete, and extract and separate the old asphalt and aggregates to determine the asphalt binder. R aa0 The measured result was 2.204.

[0068] T2. Determine the climate zone, pavement type, pavement service life and corresponding erosion number (in humid and wet areas, 1 year of service life corresponds to 1 erosion number; in semi-arid and arid areas, 1.5 years of service life corresponds to 1 erosion number), and aggregate type. It was determined that the climate zone of the pavement location is a humid area, the pavement type is SMA pavement, the pavement service life is 9 years, and the aggregate is diabase.

[0069] T3, Calculation P -10℃-预估值 and P 25℃-预估值 The values ​​are 2.51 MPa and 0.61 MPa, respectively. T4, calculate P 预估值 With respect to the corresponding application scenarios and erosion times P 标准值 The asphalt-aggregate interface adhesion performance and the water stability performance of asphalt pavement were compared and evaluated.

[0070] The comparison results showed that P 预估值 >P 标准值 If the asphalt-aggregate interface adhesion performance and water stability performance of the road surface meet the requirements, it indicates that the road surface meets the requirements.

[0071] Example 2 The difference from Example 1 lies in the asphalt materials. The asphalt materials used are SBS-modified asphalt and PE-modified asphalt prepared from Shell 70# (labeled SBSMA-70# and PEMA-70#, respectively), and SBS-modified asphalt and PE-modified asphalt prepared from Shell 90# (labeled SBSMA-90# and PEMA-90#, respectively). The basic analytical procedures and aggregate types remain unchanged.

[0072] from Figure 6 It can be seen from this that R aa , R qa , , , Adhesion force at -10℃ P-10℃ It has a relatively good Pearson correlation, and R aa , R qa , , , Adhesion at 25°C P 25℃ It exhibits a relatively good Pearson correlation. Furthermore, R aa and R qa Interval and R aa and There is a strong Pearson correlation between them (correlation coefficients reached 0.98 and -0.80, respectively), indicating that there are redundant variables when expressing adhesive force.

[0073] Adhesion between modified asphalt and aggregate interface under two pull-out temperature conditions P The results are shown in Tables 15 and 16.

[0074] Table 15 Adhesion between modified asphalt and aggregate interface under pull-out temperature of -10℃ P -10℃

[0075] Table 16 Adhesion between modified asphalt and aggregate interface at a pull-out temperature of 25℃ P 25℃

[0076] The comprehensive factor expression for the microscopic index of adhesion force determined in this embodiment is shown in formula (35).

[0077] (35) F and P -10℃ and F and P 25℃ The relationship between them, such as Figure 7 and Figure 8 As shown. The first relational expression obtained is shown in formulas (36)-(43).

[0078] Limestone: (36) Limestone: (37) Basalt: (38) Basalt: (39) granite: (40) granite: (41) diabase: (42) diabase: (43) The second relation is shown in formulas (44)-(51).

[0079] Limestone: (44) Limestone: (45) Basalt: (46) Basalt: (47) granite: (48) granite: (49) diabase: (50) diabase: (51) Build R aa , and The model of the variation with the number of water-temperature erosions is shown in formulas (52)-(60).

[0080] (52) Limestone: (53) Limestone: (54) Basalt: (55) Basalt: (56) granite: (57) granite: (58) diabase: (59) diabase: (60) Based on the variation model of the average roughness of the asphalt binder, the polar acid component of the aggregate, and the dispersion component of the aggregate, a prediction model of the adhesion force at the asphalt-aggregate interface is constructed in conjunction with the second relation, as shown in formulas (61)-(68).

[0081] Limestone: (61) Limestone: (62) Basalt: (63) Basalt: (64) granite: (65) granite: (66) diabase: (67) diabase: (68) Calculations were performed on the same material based on the prediction model of asphalt-aggregate interfacial adhesion. P -10℃ and P 25℃ Marked as P -10℃-预估值 and P 25℃-预估值 The results are shown in Tables 17 and 18.

[0082] Table 17 P -10℃-预估值 Calculation results

[0083] Table 18 P 25℃-预估值 Calculation results

[0084] An evaluation standard for asphalt-aggregate adhesion performance was established, including the following steps: S1. Marshall specimens of ordinary dense-graded asphalt concrete mixtures and asphalt mastic aggregate (SMA) with the same materials were formed according to the "Test Procedures for Asphalt and Asphalt Mixtures in Highway Engineering" (JTG 3410-2025), and freeze-thaw splitting tests were conducted on the specimens to obtain the freeze-thaw splitting strength ratio. TSR See Table 19.

[0085] Table 19 Freeze-thaw splitting strength ratio of Marshall specimens of modified asphalt mixtures TSR

[0086] S2, Construction P -10℃-预估值 and P 25℃-预估值 and TSRThe relationship between them, and the modified asphalt mixtures specified in the "Technical Specification for Construction of Highway Asphalt Pavement" (JTG F40-2004) TSR Standard values ​​(for ordinary dense-graded modified asphalt mixtures and SMA mixtures in damp and humid areas) TSR The standard value is 80%; in semi-arid and arid areas, the values ​​of ordinary dense-graded modified asphalt mixtures and SMA mixtures are... TSR Substituting the standard values ​​of 75% and 80% into the correlation, the adhesion strength of the modified asphalt-aggregate interface after different water-temperature coupled erosion cycles was calculated. P 标准值 See Table 20.

[0087] Table 20. Adhesion between modified asphalt and aggregate at different water-temperature coupled erosion cycles. P 标准值

[0088] like P 预估值 < P 标准值 This indicates that the adhesion performance of the matrix asphalt-aggregate interface does not meet the requirements under the corresponding erosion number and application scenario.

[0089] The constructed model for predicting the adhesion strength at the asphalt-aggregate interface is applied to predict the adhesion performance between asphalt and aggregate in modified asphalt pavements during their service life. The steps include the following: T1. Select a modified asphalt pavement with a service life, obtain a small amount of asphalt concrete, and extract and separate the old asphalt and aggregates to determine the asphalt binder. R aa0 The measured result was 1.408.

[0090] T2. Determine the climate zone, pavement type, pavement service life and corresponding erosion number (in humid and wet areas, 1 year of service life corresponds to 1 erosion number; in semi-arid and arid areas, 1.5 years of service life corresponds to 1 erosion number), and aggregate type. It was determined that the climate zone of the pavement location is semi-arid, the pavement type is ordinary dense-graded asphalt pavement, the pavement service life is 9 years, and the aggregate is basalt.

[0091] T3, Calculation P -10℃-预估值 and P 25℃-预估值 The values ​​are 3.08 MPa and 1.94 MPa, respectively.

[0092] T4, calculate P 预估值 With respect to the corresponding application scenarios and erosion times P标准值 The asphalt-aggregate interface adhesion performance and the water stability performance of asphalt pavement were compared and evaluated.

[0093] The comparison results showed that P 预估值 >P 标准值 If the asphalt-aggregate interface adhesion performance and water stability performance of the road surface meet the requirements, it indicates that the road surface meets the requirements.

[0094] Example 3 like Figure 9 As shown, a dynamic prediction device for asphalt-aggregate adhesion performance includes, The first analytical optimization unit is used to obtain the microscopic and mesoscopic indices of the asphalt binder in the asphalt-aggregate system under erosion, and to analyze and obtain the first index. The second analysis and optimization unit is used to obtain the micro and mesoscopic indices of the aggregate after removing the surface asphalt from the asphalt-aggregate system, and to analyze and obtain the second index. The comprehensive factor determination unit is used to obtain the adhesion force between asphalt and aggregate of the molded specimen under erosion. Based on the correlation between adhesion force and the first and second indicators, principal component analysis is performed on the indicators that have a good correlation with adhesion force and a weak correlation with the micro-indices to determine the comprehensive factor of the micro-indices that express adhesion force. The prediction unit is determined to obtain the comprehensive factor of the microscopic index of adhesion for specimens of different aggregate types and the correlation between the adhesion between asphalt and aggregate. The prediction model of the adhesion of the asphalt-aggregate interface is constructed and the prediction analysis is performed.

[0095] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for dynamically predicting the adhesion performance of asphalt-aggregate, characterized in that, include, The microscopic and mesoscopic indices of the asphalt binder in the asphalt-aggregate system under erosion were obtained, and the first index was analyzed. The microscopic and mesoscopic indices of the aggregate after surface asphalt removal from the asphalt-aggregate system were obtained, and the second index was analyzed to obtain the results. The adhesion force between asphalt and aggregate in the molded specimen under erosion was obtained. Based on the correlation between adhesion force and the first and second indicators, principal component analysis was performed on the indicators that had a good correlation with adhesion force and a weak correlation with the micro-indices to determine the comprehensive factor of the micro-indices that express adhesion force. The correlation between the comprehensive factor of the microscopic index of adhesion force of specimens of different aggregate types and the adhesion force between asphalt and aggregate was obtained. A prediction model of the adhesion force at the asphalt-aggregate interface was constructed and a prediction analysis was performed.

2. The method for dynamically predicting the adhesion performance of asphalt-aggregate according to claim 1, characterized in that, Microscopic indicators include average roughness, root mean square roughness, skewness, and steepness, while microscopic indicators include polar base component, polar acid component, and dispersion component.

3. The method for dynamically predicting the adhesion performance of asphalt-aggregate according to claim 2, characterized in that, Principal component analysis was performed on indicators with weak correlations to determine the comprehensive factors that express the microscopic indicators of adhesion. The number of principal components was determined based on the principle that the eigenvalues ​​of the principal components were greater than 1, thus obtaining a comprehensive factor expression for the microscopic indices of adhesion. ； in, F A comprehensive factor for expressing the microscopic indices of adhesion. R aa The average roughness of the asphalt binder. and These are the polar acid component and the dispersion component of the aggregate, respectively. A , B and C Represents the model coefficients.

4. The method for dynamically predicting the adhesion performance of asphalt-aggregate according to claim 3, characterized in that, The process of obtaining the comprehensive factor of the microscopic index expressing adhesion of specimens of different aggregate types and the correlation between the adhesion between asphalt and aggregate, and constructing a predictive model for the adhesion at the asphalt-aggregate interface, specifically includes: The relationship between the comprehensive factor and the adhesion force between asphalt and aggregate was constructed for specimens of different aggregate types under different test conditions, referred to as the first relationship; The second relation is obtained based on the comprehensive factor expression of the microscopic index of adhesion and the first relation; Models were constructed to depict the variations in average roughness of asphalt binder, polar acid component of aggregate, and dispersion component of aggregate under actual environmental erosion conditions for specimens of different aggregate types. A predictive model for asphalt-aggregate interfacial adhesion was then constructed in conjunction with the second relational formula.

5. The method for dynamically predicting the adhesion performance of asphalt-aggregate according to claim 3, characterized in that, The prediction model for the adhesion force at the asphalt-aggregate interface is as follows: ； in, P This refers to the adhesion force between the asphalt and aggregate interface. R aa0 This represents the initial value of the average roughness of the asphalt binder. n For the number of water-temperature coupling cycles, a , b , c and d Represents the model coefficients. e It is the natural base.

6. The method for dynamically predicting the adhesion performance of asphalt-aggregate according to claim 1, characterized in that, After constructing the prediction model for the asphalt-aggregate interface adhesion, the process also includes an evaluation step of the adhesion performance between asphalt and aggregate in the pavement to be predicted at the engineering site. This evaluation step includes... Freeze-thaw splitting tests were conducted on specimens with different aggregate types to obtain the freeze-thaw splitting strength ratio; A model was constructed to establish the relationship between the freeze-thaw splitting strength ratio and the estimated adhesion between asphalt and aggregate of the pavement to be estimated. The standard value of the freeze-thaw splitting strength ratio of the pavement to be estimated was obtained by combining the standard value of the freeze-thaw splitting strength ratio of the pavement to be estimated at the engineering site. Based on the standard and estimated values ​​of the adhesion force between asphalt and aggregate in the pavement to be estimated, evaluate the adhesion performance and water stability of the asphalt-aggregate in the pavement to be estimated at the engineering site.

7. The method for dynamically predicting the adhesion performance of asphalt-aggregate according to claim 1, characterized in that, The erosion is achieved through repeated water baths within a temperature range of -18 to 40°C.

8. The method for dynamically predicting the adhesion performance of asphalt-aggregate according to claim 1, characterized in that, The microstructure of the asphalt binder was determined by atomic force microscopy, and the microstructure of the aggregate was determined by laser confocal microscopy. The microscopic indices are obtained using the surface free energy theory.

9. A device for dynamically predicting the adhesion performance of asphalt-aggregate, characterized in that, include, The first analytical optimization unit is used to obtain the microscopic and mesoscopic indices of the asphalt binder in the asphalt-aggregate system under erosion, and to analyze and obtain the first index. The second analysis and optimization unit is used to obtain the micro and mesoscopic indices of the aggregate after removing the surface asphalt from the asphalt-aggregate system, and to analyze and obtain the second index. The comprehensive factor determination unit is used to obtain the adhesion force between asphalt and aggregate of the molded specimen under erosion. Based on the correlation between adhesion force and the first and second indicators, principal component analysis is performed on the indicators that have a good correlation with adhesion force and a weak correlation with the micro-indices to determine the comprehensive factor of the micro-indices that express adhesion force. The model determines the prediction unit, which is used to obtain the comprehensive factor of the microscopic index of adhesion force of specimens of different aggregate types and the correlation between the adhesion force between asphalt and aggregate. The prediction model of adhesion force at the asphalt-aggregate interface is constructed and the prediction analysis is performed.

10. The application of the dynamic prediction method for asphalt-aggregate adhesion performance as described in any one of claims 1-8 in predicting the adhesion performance between asphalt and aggregate and the water stability performance of asphalt pavement during its service life.

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