Evaluation method of additive improving salt and freeze injury resistance of asphalt concrete

Abstract

The evaluation method for improving salt freezing damage resistance of asphalt concrete by admixture belongs to the technical field of asphalt road durability evaluation. The present application aims at the problem that the research on the influence of the existing admixture on the performance of asphalt concrete under salt freezing environment mainly focuses on macro performance, and the improvement effect of the admixture on the performance of asphalt concrete cannot be objectively quantified. Different admixtures are added to asphalt concrete under salt freezing, and each control group is obtained; the microstructure parameters of each control group are obtained; the comprehensive correlation degree of the reference sequence and the comparison sequence in the control group is calculated by using the gray correlation analysis method; combined with the calculation result of the comprehensive correlation degree, the performance damage index of the microstructure parameters on the admixture asphalt concrete is quantitatively evaluated by using the analytic hierarchy process based on the microstructure parameter change rate, so as to determine the improvement effect of the admixture on the performance of asphalt concrete. The present application is used for evaluating the influence of admixture on the performance improvement of asphalt concrete.

Description

Technical Field

[0001] This invention relates to an evaluation method for improving the salt-freezing resistance of asphalt concrete with admixtures, belonging to the technical field of asphalt road durability evaluation. Background Technology

[0002] Asphalt pavement is a commonly used paving material in road engineering, and improving its durability and extending its service life in complex and variable service environments is of paramount importance.

[0003] In coastal / saline-alkali seasonally frozen areas, the effects of salt spray, tides, and rainfall allow salt ions to penetrate into the asphalt pavement through porous matrix and cracks, accumulating and transferring within it. This exacerbates the weakening of the asphalt-aggregate interface bond, potentially inducing spalling, potholes, and other defects. Coupled with seasonal freeze-thaw cycles, early damage to asphalt pavements in these areas is particularly pronounced. Therefore, to improve the service performance of asphalt concrete in coastal / saline-alkali seasonally frozen environments and enhance its resistance to salt-freezing damage, it is necessary to develop performance evaluation technologies for asphalt concrete pavements under salt-freezing conditions. This will provide data support for material selection and optimized design of asphalt concrete in these environments.

[0004] Currently, researchers have conducted extensive studies on the freeze-thaw damage behavior of asphalt concrete, but research on the salt corrosion damage behavior of asphalt pavements is still in its early stages, mainly focusing on the macroscopic evaluation of the impact on the performance degradation of asphalt concrete. Therefore, research on asphalt pavement performance damage based on the coupled environment of salt corrosion and freeze-thaw in coastal seasonally frozen areas is relatively limited. In particular, the extent of damage to asphalt concrete performance caused by salt freezing is unclear, and performance improvement measures and evaluation methods are lacking. Admixtures are currently an effective means of improving the performance of asphalt concrete. For the performance degradation problem of asphalt pavements in coastal / saline-alkali seasonally frozen areas, the addition of admixtures can prevent salt ions from entering the interior of asphalt concrete, improve the structural stability of asphalt concrete, and enhance the adhesion between asphalt and aggregates, thereby mitigating the damage caused by salt freezing, extending pavement service life, and improving maintenance quality.

[0005] Existing studies on the effects of admixtures on the performance of asphalt concrete under salt-freezing conditions mainly focus on macroscopic properties, without considering changes in microstructure. This results in deficiencies in the evaluation methods for salt-freezing damage, a lack of sensitivity quantitative evaluation indicators, unclear specific improvement effects of admixtures, large comparison errors, and insufficient reliability.

[0006] Therefore, there is a need to provide an evaluation method for improving the salt-frost damage resistance of asphalt concrete by admixtures. Summary of the Invention

[0007] To address the problem that existing studies on the impact of admixtures on the performance of asphalt concrete under salt-freezing conditions mainly focus on macroscopic properties and do not consider changes in microstructure, resulting in insufficient evaluation methods for salt-freezing damage resistance and a lack of sensitive quantitative evaluation indicators, thus failing to objectively quantify the effect of admixtures on improving the performance of asphalt concrete, this invention provides an evaluation method for improving the salt-freezing damage resistance of asphalt concrete using admixtures.

[0008] The present invention provides a method for evaluating the performance of admixtures in improving the salt-frost damage resistance of asphalt concrete, comprising:

[0009] Step 1: Add different admixtures to asphalt concrete subjected to salt freezing.

[0010] Asphalt concrete with different admixtures was used as a control group; digital two-dimensional images of each control group were obtained by X-ray scanning; the digital two-dimensional images were processed to obtain three-dimensional reconstructed images, and the microstructure parameters of each control group were obtained through the three-dimensional reconstructed images, including porosity, number of pores, average pore diameter and pore connectivity.

[0011] Step 2: Calculate the overall correlation between the reference sequence and the comparison sequence in each control group using grey relational analysis.

[0012] Step 3: Based on the calculation results of the comprehensive correlation degree, the performance damage index of each microstructural parameter affecting the asphalt concrete by using the analytic hierarchy process (AHP) is used to quantitatively evaluate the performance damage index of the asphalt concrete. The smaller the performance damage index, the better the performance improvement effect of the asphalt concrete using the admixture in the control group.

[0013] According to the evaluation method of the admixture for improving the salt-frost damage resistance of asphalt concrete according to the present invention, in step one, the calculation formulas for porosity, average void diameter, and void connectivity are as follows:

[0014]

[0015]

[0016]

[0017] According to the evaluation method of the admixtures of the present invention for improving the salt-frost resistance of asphalt concrete, the control group in step one includes an anti-stripping agent group, a basalt fiber group, and a crystallization inhibitor group.

[0018] According to the evaluation method of the admixture for improving the salt freeze-thaw resistance of asphalt concrete according to the present invention, in step two, in the process of calculating the comprehensive correlation degree, the comparison sequence and the reference sequence are first determined. In each control group, the porosity, number of voids, average void diameter and void connectivity are used as four comparison sequences, and the freeze-thaw number corresponding to the comparison sequence is used as the reference sequence. The freeze-thaw number is sorted in ascending order to form a reference sequence group.

[0019] The comparison sequences corresponding to each reference sequence group are sequentially dimensionless:

[0020]

[0021] In the formula, k is the sequence number of the reference sequence group, k = 1, 2, ..., n, and n is the total number of reference sequence groups;

[0022] i is the sequence number of the comparison sequence, i = 1, 2, 3, 4, which correspond to the porosity, number of voids, average void diameter and void connectivity, respectively;

[0023] X is the dimensionless numerical value of the comparison sequence i corresponding to the reference sequence group k. i (k) represents the original value of the comparison sequence i corresponding to the reference sequence group k, minX i (n) represents the minimum value of comparison sequence i in the n reference sequence groups, maxX i (n) represents the maximum value of comparison sequence i in the n reference sequence groups.

[0024] According to the evaluation method of the admixture for improving the salt-frost damage resistance of asphalt concrete according to the present invention, step two, the process of calculating the comprehensive correlation degree, further includes calculating... Grey correlation coefficient with reference value:

[0025] n The sum is used as the reference value Y i (n);

[0026] The corresponding grey relational coefficient is represented by ξ. i (k):

[0027]

[0028] Δ min For the minimum difference between two levels, there are n items. The minimum absolute value of the pairwise subtraction; Δ max The maximum difference between two levels, with n values. The maximum absolute value of the pairwise subtraction; Δ i (k) is the reference value Y i (n) and The absolute value of the difference; ρ is the resolution coefficient.

[0029] According to the evaluation method of the admixture for improving the salt-frost damage resistance of asphalt concrete according to the present invention, step two, the process of calculating the comprehensive correlation degree, further includes calculating the grey correlation degree r of the comparison sequence i of the n sets of reference sequence groups. i :

[0030]

[0031] According to the evaluation method of the admixture for improving the salt-frost damage resistance of asphalt concrete according to the present invention, in step two, the comprehensive correlation degree is expressed as r. iC :

[0032]

[0033] Overall correlation r iC Let p be the average grey relational score of the comparison sequence i of the three control groups, where p is the sequence number of the control group and r is the average grey relational score of the comparison sequence i. i(p) Let be the grey relational degree of comparison sequence i of the p-th control group.

[0034] According to the evaluation method of the admixture-enhanced asphalt concrete salt-frost damage resistance of the present invention, in step three, the comprehensive correlation judgment matrix of the microstructure is first determined:

[0035] Based on the comprehensive correlation degree r iC The calculation results determine four comprehensive correlation degree judgment matrices of microstructure. In the comprehensive correlation degree judgment matrix, I is the row variable and J is the column variable, I = 1, 2, 3, 4, J = 1, 2, 3, 4, which correspond to the comprehensive correlation degree of porosity, the comprehensive correlation degree of number of pores, the comprehensive correlation degree of average diameter of pores, and the comprehensive correlation degree of pore connectivity, respectively.

[0036] With a I a represents the comprehensive correlation of the fine structure in row I. J This indicates the overall correlation of the detailed structure in column J.

[0037] With a IJ Indicates a I With a J The results of the importance comparison;

[0038] Based on the comparison rules for judging matrix assignment in existing technologies, we obtain:

[0039]

[0040] a IJ Arranged in order to form the comprehensive correlation judgment matrix R:

[0041]

[0042] According to the evaluation method of the admixture for improving the salt-frost damage resistance of asphalt concrete according to the present invention, in step three, the main eigenvalues ​​of the comprehensive correlation judgment matrix and the comprehensive correlation weights of each microstructure of each control group are calculated based on the comprehensive correlation judgment matrix R.

[0043] According to the evaluation method of the admixture for improving the salt freeze-thaw resistance of asphalt concrete according to the present invention, in step three,

[0044] The performance impairment index is denoted as DI:

[0045]

[0046] w i To compare the overall correlation weights of sequence i;

[0047] A i Weights for the internal structural damage growth rate of comparison sequence i;

[0048] Compare the internal structural damage growth rate weights A of sequence i i The calculation method is as follows:

[0049] The reference sequence group was selected with 0, 7, and 20 freeze-thaw cycles, and the internal structural damage growth rate of each microstructural parameter was calculated. The internal structural damage growth rate of each microstructural parameter is the ratio of the difference between the result of 20 freeze-thaw cycles and the result of 0 freeze-thaw cycles to the result of 0 freeze-thaw cycles.

[0050] Then, based on the internal structural damage growth rate of the mesostructural parameter corresponding to each control group, construct the internal structural damage growth rate judgment matrix for the three control groups according to the method of constructing the comprehensive correlation judgment matrix of mesostructural parameters. Calculate the internal structural damage growth rate judgment matrix for each control group to obtain the weight A of the internal structural damage growth rate for each mesostructural parameter. i ;

[0051] According to w i For and A i The performance damage index (DI) of the three control groups was calculated, and the effect of the admixture on improving the performance of asphalt concrete was determined according to the magnitude of the performance damage index (DI).

[0052] The beneficial effects of this invention are as follows: Starting from the evolution of microstructural deterioration, the method of this invention can quantify the effect of admixtures on improving the performance of asphalt concrete, thereby reducing the error in macroscopic performance comparison, improving the credibility of evaluation results, providing data support for the material selection and optimization design of asphalt concrete in coastal / saline-alkali seasonally frozen environments, and thus extending the service life of pavement and improving maintenance quality.

[0053] This invention addresses the issue of insufficient reliability in existing evaluation methods for improving the performance of asphalt concrete in coastal / saline-alkali seasonally frozen environments. It utilizes X-ray scanning technology to obtain information on changes in the internal structure of asphalt concrete and proposes using parameters such as porosity, number of voids, average void diameter, and void connectivity as evaluation indicators for microstructural degradation. Grey relational analysis is used to determine the order of significance of these microstructural parameters. Finally, based on the rate of change of these microstructural parameters, the analytic hierarchy process (AHP) is used to obtain the weighting coefficients of these parameters. Finally, a performance damage index is used to determine the effectiveness of admixtures in improving asphalt concrete performance, identifying suitable admixture types for asphalt pavement construction in saline-frozen environments. Attached Figure Description

[0054] Figure 1 This is a flowchart of the evaluation method for improving the salt-frost damage resistance of asphalt concrete using admixtures, as described in this invention.

[0055] Figure 2 It is an asphalt concrete gradation curve diagram;

[0056] Figure 3 It is a hierarchical structure model framework diagram. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the 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.

[0058] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0059] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the scope of the invention.

[0060] Specific Implementation Method 1: Combination Figure 1 As shown, this invention provides a method for evaluating the performance of asphalt concrete against salt-frost damage using admixtures, comprising:

[0061] Step 1: Add different admixtures to asphalt concrete subjected to salt freezing.

[0062] Different admixtures of asphalt concrete were used as control groups. Digital two-dimensional images of each control group were obtained using X-ray scanning. The digital two-dimensional images were processed to obtain three-dimensional reconstructed images. The microstructural parameters of each control group were obtained from the three-dimensional reconstructed images, including porosity, number of voids, average void diameter, and void connectivity. X-ray scanning technology was used to capture digital images of the internal structure of asphalt concrete. The captured images were processed using VG Studio Max image processing software to improve image quality, filter noise, and realize three-dimensional reconstruction of the two-dimensional images to obtain microstructural parameters such as the number of voids and void area of ​​the asphalt concrete.

[0063] Step 2: Calculate the comprehensive correlation degree between the reference sequence and the comparison sequence in each control group using grey relational analysis. Grey relational analysis is widely used to evaluate the development trend and correlation between various factors within a system, and can accurately extract the main influencing factors of the system.

[0064] Step 3: Based on the calculation results of the comprehensive correlation degree, the performance damage index of each microstructural parameter affecting the asphalt concrete by using the analytic hierarchy process (AHP) is used to quantitatively evaluate the performance damage index of the asphalt concrete. The smaller the performance damage index, the better the performance improvement effect of the asphalt concrete using the admixture in the control group.

[0065] Furthermore, in step one, the formulas for calculating porosity, average void diameter, and void connectivity are as follows:

[0066]

[0067]

[0068]

[0069] As an example, the control group in step one includes the anti-stripping agent group, the basalt fiber group, and the crystallization inhibitor group. In practical applications, a blank group, i.e., asphalt concrete without admixtures, can be set up to compare the effects of admixtures under salt-freezing conditions, so as to evaluate the damage of different groups of asphalt concrete under salt-freezing conditions.

[0070] Furthermore, in step two, during the calculation of the comprehensive correlation degree, the comparison sequence and the reference sequence are first determined. The comparison sequence is the data of various factors that can affect the system behavior, while the reference sequence is used to characterize the system behavior. In each control group, the porosity, number of voids, average void diameter, and void connectivity are used as four comparison sequences, and the freeze-thaw cycles corresponding to the comparison sequences are used as reference sequences. The freeze-thaw cycles are sorted in ascending order to form a reference sequence group.

[0071] Since the dimensions of the factors in the comparison sequence and the reference sequence are different, and the differences between the data may be large, it is necessary to dimensionless the data to facilitate comparative analysis.

[0072] The comparison sequences corresponding to each reference sequence group are sequentially dimensionless:

[0073]

[0074] In the formula, k is the sequence number of the reference sequence group, k = 1, 2, ..., n, and n is the total number of reference sequence groups;

[0075] i is the sequence number of the comparison sequence, i = 1, 2, 3, 4, which correspond to the porosity, number of voids, average void diameter and void connectivity, respectively;

[0076] X is the dimensionless numerical value of the comparison sequence i corresponding to the reference sequence group k. i (k) represents the original value of the comparison sequence i corresponding to the reference sequence group k, minX i (n) represents the minimum value of comparison sequence i in the n reference sequence groups, maxX i (n) represents the maximum value of comparison sequence i in the n reference sequence groups.

[0077] Step two, the process of calculating the comprehensive correlation degree, also includes calculating... Grey correlation coefficient with reference value:

[0078] n The sum is used as the reference value Y i (n);

[0079] The corresponding grey relational coefficient is represented by ξ. i (k):

[0080]

[0081] Δ min For the minimum difference between two levels, there are n items. The minimum absolute value of the pairwise subtraction; Δ max The maximum difference between two levels, with n values. The maximum absolute value of the pairwise subtraction; Δ i (k) is the reference value Y i (n) and The absolute value of the difference; ρ is the resolution coefficient, which is between 0 and 1, and is usually taken as 0.5.

[0082] Step two, the process of calculating the comprehensive correlation degree also includes calculating the grey correlation degree r of the comparison sequence i in the n reference sequence groups. i :

[0083]

[0084] Because there are many influencing factors, the grey relational coefficient needs to be further integrated, and its average value is usually taken as the grey relational degree. i It is denoted as sequence correlation or linear correlation, r i The closer the value is to 1, the better it indicates that Y... i (n) and The better the correlation between them.

[0085] In step two, in order to comprehensively evaluate the relevant factors of admixture incorporation on salt freeze-thaw damage of asphalt concrete, the comprehensive correlation index method is used to analyze the porosity, number of voids, average void diameter, and void connectivity of asphalt concrete.

[0086] The overall correlation degree is represented by r. iC :

[0087]

[0088] Overall correlation r iC Let p be the average grey relational score of the comparison sequence i of the three control groups, where p is the sequence number of the control group and r is the average grey relational score of the comparison sequence i. i(p) Let be the grey relational degree of comparison sequence i of the p-th control group.

[0089] The order of significance of the microstructural parameters determined through the above process provides a basis for quantitatively evaluating the effect of admixtures using the analytic hierarchy process.

[0090] Furthermore, in step three, the analytic hierarchy process (AHP) is used to quantitatively evaluate the improvement effect of the admixture:

[0091] The main steps of the Analytic Hierarchy Process (AHP) include establishing a hierarchical structure model, constructing a judgment matrix, hierarchical ranking and consistency testing, and comprehensive evaluation. This method combines quantitative and qualitative analysis. Based on the rate of change of microstructural parameters, it uses grey relational analysis to determine the order of significant influence of microstructural parameters, obtains the weight coefficients of microstructural parameters, and finally uses the weight coefficients and consistency testing to quantify the advantages and disadvantages of admixture in improving the performance of asphalt concrete.

[0092] An analytic hierarchy process (AHP) evaluation model is established based on microstructural parameters. A judgment matrix is ​​constructed based on gray-level correlation and the AHP evaluation model, and the weights of each microstructural parameter are calculated. Specifically, the AHP evaluation model is established, including a target layer, a criterion layer, and a scheme layer. The criterion layer consists of microstructural parameters, specifically porosity, number of voids, average void diameter, and void connectivity. The scheme layer represents the number of control groups corresponding to the microstructural parameters in the criterion layer, specifically including anti-stripping agent group, basalt fiber group, and crystallization inhibitor group. The target layer represents the final result of the evaluation of the improvement of asphalt concrete performance by admixture incorporation under salt-freezing conditions.

[0093] First, a hierarchical model is established. Based on the microstructural degradation characteristics, the problem of quantifying the performance improvement effect of admixtures is decomposed into the target layer, the criterion layer, and the scheme layer.

[0094] Then determine the comprehensive correlation judgment matrix of the microstructure:

[0095] The matrix formed by pairwise comparisons is called the judgment matrix. The judgment matrix is ​​a quantification matrix of importance relationships.

[0096] Based on the comprehensive correlation degree r iC The calculation results determine four comprehensive correlation degree judgment matrices of microstructure. In the comprehensive correlation degree judgment matrix, I is the row variable and J is the column variable, I = 1, 2, 3, 4, J = 1, 2, 3, 4, which correspond to the comprehensive correlation degree of porosity, the comprehensive correlation degree of number of pores, the comprehensive correlation degree of average diameter of pores, and the comprehensive correlation degree of pore connectivity, respectively.

[0097] With a I a represents the comprehensive correlation of the fine structure in row I. J This indicates the overall correlation of the detailed structure in column J.

[0098] With a IJ Indicates a I With a J The results of the importance comparison;

[0099] Based on the comparison rules for judging matrix assignment in existing technologies, we obtain:

[0100]

[0101] a IJ Arranged in order to form the comprehensive correlation judgment matrix R:

[0102]

[0103] The comparison rule for determining matrix assignment in the existing technology is the rule defined in the Analytic Hierarchy Process (AHP).

[0104] In step three, based on the comprehensive correlation judgment matrix R, the main eigenvalues ​​of the comprehensive correlation judgment matrix and the comprehensive correlation weights of each microstructure of each control group are calculated.

[0105] Hierarchical sorting and its consistency check. The eigenvector method is used for sorting, and then the consistency ratio is used to check the consistency of the weighted solutions, calculated according to the following formula:

[0106]

[0107] In the formula, CR represents consistency, CI represents consistency index, and RI represents randomness index.

[0108] The formula for calculating CI is as follows:

[0109]

[0110] In the formula λ max The principal eigenvalues ​​are N, and N is the order of the matrix.

[0111] When the CR value is less than 10%, pairwise comparisons are considered consistent. If the CR value is greater than 10%, pairwise comparisons are considered inconsistent, and weights are reallocated in the pairwise comparison matrix. In this embodiment, the calculated CR values ​​are all less than 10%.

[0112] Next, using the damage growth rate of the microstructural parameters of admixture asphalt concrete as the data for the comparison matrix, pairwise comparison matrices were constructed, and hierarchical sorting and consistency testing were performed.

[0113] In step three, based on the established hierarchical structure model of influencing factors, the effect of admixture addition on the performance improvement of asphalt concrete is calculated. The performance impairment index is represented by DI:

[0114]

[0115] w i The comprehensive correlation weight of the criterion layer for comparing sequence i;

[0116] A i To compare the weights of the internal structural damage growth rate of scheme layer i;

[0117] Compare the internal structural damage growth rate weights A of sequence i i The calculation method is as follows:

[0118] The reference sequence group was selected with 0, 7, and 20 freeze-thaw cycles, and the internal structural damage growth rate of each microstructural parameter was calculated. The internal structural damage growth rate of each microstructural parameter is the ratio of the difference between the result of 20 freeze-thaw cycles and the result of 0 freeze-thaw cycles to the result of 0 freeze-thaw cycles.

[0119] Then, based on the internal structural damage growth rate of the mesostructural parameter corresponding to each control group, construct the internal structural damage growth rate judgment matrix for the three control groups according to the method of constructing the comprehensive correlation judgment matrix of mesostructural parameters. Calculate the internal structural damage growth rate judgment matrix for each control group to obtain the weight A of the internal structural damage growth rate for each mesostructural parameter. i ;

[0120] According to w i For and A i The performance damage index (DI) was calculated for three control groups, and the effect of the admixture on improving the performance of asphalt concrete was determined according to the magnitude of the DI. The larger the DI, the worse the performance improvement effect of the admixture; conversely, the smaller the DI, the better the performance improvement effect of the admixture, and the more suitable it is for asphalt pavement paving in coastal / saline-alkali seasonally frozen environments. Specific implementation examples:

[0122] I. Raw Materials

[0123] Asphalt concrete was prepared using 90# asphalt. The 90# asphalt met the requirements of JTG E20-2011, and its main technical indicators are shown in Table 1. The coarse and fine aggregates of the asphalt concrete were andesite from Heilongjiang Province. All technical indicators of the aggregates and mineral powder met the requirements of JTG / F40-2004. The selected gradation was SMA-13, and its composition is as follows: Figure 2 As shown. SMA-13 ​​is a lignin fiber, and its dosage is 0.3% of the mass of asphalt concrete.

[0124] Table 1 Basic Properties of 90# Asphalt

[0125]

[0126] Three admixtures—anti-stripping agent, basalt fiber, and crystallization inhibitor (potassium ferrocyanide)—were used to improve the service performance of asphalt concrete in a salt-freezing environment. The main technical specifications of the admixtures are shown in Table 2.

[0127] Table 2 Main Technical Indicators of Admixtures

[0128]

[0129] The Marshall method was selected for the mix design of SMA-13 ​​asphalt concrete. The optimal asphalt-aggregate ratio for SMA-13 ​​asphalt concrete was 6.3%. The optimal dosage of different admixtures in asphalt concrete was determined by investigating the effect of admixture content on the performance of the asphalt concrete. Specifically, a new type of environmentally friendly "non-amine" anti-stripping agent produced in Italy was used, at a dosage of 5‰ of the asphalt mass, and it could be directly added to hot asphalt and mixed evenly. Basalt fiber was short-diameter, 6 mm long and 5 μm in diameter, at a dosage of 4‰ of the asphalt concrete mass, and was directly mixed with the aggregate. The salt crystallization inhibitor was potassium ferrocyanide (K4Fe(CN)6), at a dosage of 5‰ of the asphalt concrete mass, and was directly mixed with the aggregate. The preparation of asphalt concrete samples followed the requirements of JTG E20-2011, and the molding temperature of the asphalt concrete did not exceed 150℃.

[0130] II. Salt Freeze-Thaw Simulation Environmental Conditions

[0131] Considering the actual conditions in seasonally frozen-thawed areas along the northern coast, taking Dalian as an example, the average minimum winter temperature is -20℃ and the average maximum winter temperature is 25℃. Under the most unfavorable conditions, the temperature and time of the freeze-thaw cycle are redefined. The solutions involved in the freeze-thaw test will be replaced with sodium chloride solutions, such as saturated water or water baths. Each specimen is immersed in a solution at 97.3–98.7 kPa and in a vacuum for 15 minutes, and then in atmospheric pressure for 30 minutes. Then, each cycle involves freezing at -20℃ for 10 hours, followed by immersion in a 5% salt spray at 25℃ for 14 hours, constituting one cycle. A total of 0, 7, and 20 freeze-thaw cycles are performed to simulate the damage changes of asphalt concrete in coastal / saline-alkali seasonally frozen-thawed areas.

[0132] III. Obtaining parameters of microstructural degradation evolution using X-ray scanning technology

[0133] X-ray scanning technology was used to capture digital images of the internal structure of asphalt concrete. The captured images were then processed using VG Studio Max image processing software to improve image quality, filter noise, and achieve three-dimensional reconstruction of the two-dimensional images. This yielded microstructural parameters such as the number of voids, void content, average void diameter, and void connectivity in the asphalt concrete. The microstructural parameters of the blank group, anti-stripping agent group, basalt fiber group, and crystallization inhibitor group are shown in Tables 3 to 6, respectively.

[0134] Table 3. Microstructural parameters of the blank group

[0135]

[0136] Table 4 Microstructural parameters of anti-stripping agents

[0137]

[0138] Table 5. Microstructural parameters of basalt fiber set.

[0139]

[0140]

[0141] Table 6. Microstructural parameters of the crystallization inhibitor group

[0142]

[0143] As shown in Tables 3-6, compared to the control group of asphalt concrete, the incorporation of the three admixtures—anti-stripping agent, basalt fiber, and crystallization inhibitor—can delay the damage to the microstructure of asphalt concrete under salt freeze-thaw action, thus improving its performance. However, due to the large number of influencing parameters and the lack of quantitative methods to evaluate the effect of admixture in improving asphalt concrete performance, a combination of grey relational analysis and analytic hierarchy process (AHP) was used for quantitative analysis.

[0144] IV. Determining the order of significance of microstructural parameters based on grey relational analysis.

[0145] (1) Determine the comparison sequence and reference sequence. The porosity, number of pores, average pore diameter and pore connectivity were used as the comparison sequence, and the salt-freezing time of 0 days, 7 days and 20 days were used as the reference sequence to evaluate the damage of asphalt concrete in the blank group, anti-stripping agent group, basalt fiber group and crystallization inhibitor group under salt-freezing.

[0146] (2) Perform dimensionless processing on the sequence.

[0147] (3) is the grey relational coefficient between the reference sequence and the comparison sequence.

[0148] (4) Calculate the grey relational degree. The grey relational results of the performance damage of asphalt concrete with different admixtures and the microstructural parameters are shown in Table 7.

[0149] Table 7. Grey Relationship between Performance Damage and Microstructural Parameters of Asphalt Concrete with Different Admixtures

[0150]

[0151] As shown in Table 7, the grey relational degree of all admixture-based asphalt concretes is greater than 0.55, indicating a strong correlation between performance degradation and microstructural parameters. Furthermore, the changes in internal structural parameters of admixture-based asphalt concretes under salt freeze-thaw cycles are similar.

[0152] (5) Calculate the comprehensive correlation degree. The comprehensive correlation results of the performance damage and microstructural characteristics of asphalt concrete with different admixtures are shown in Table 8.

[0153] Table 8. Comprehensive Correlation between Performance Damage and Microstructural Characteristics of Asphalt Concrete with Different Admixtures

[0154]

[0155] As shown in Table 8, the significant factors affecting the internal damage of admixture-treated asphalt concrete, from largest to smallest, are: number of voids, void connectivity, porosity, and average void diameter. The determined order of the significant influence of these microstructural parameters provides a basis for quantitatively evaluating the improvement effect of admixtures using the analytic hierarchy process (AHP).

[0156] V. Quantitatively evaluate the effect of admixtures using the analytic hierarchy process (AHP).

[0157] (1) Establish a hierarchical structure model, such as Figure 3 As shown.

[0158] (2) Comprehensive correlation judgment matrix analysis.

[0159] The importance levels of the judgment matrix and their assigned values ​​are shown in Table 9.

[0160] Table 9 Determining Matrix Properties

[0161]

[0162] Grey relational analysis revealed that the significant factors influencing internal damage in admixture asphalt concrete are porosity content, porosity number, average porosity diameter, and porosity connectivity. Combined with judgment matrix analysis, the comprehensive correlation weights of the criterion layer are shown in Table 10.

[0163] Table 10 Comprehensive Relevance Weights of the Criterion Layer

[0164]

[0165] As can be seen from Table 10, the number of voids has the greatest impact on the internal damage of admixture asphalt concrete, with a weight of 0.275 in the judgment matrix; void connectivity is the second largest influencing factor, with a weight of 0.254; the porosity has a weight of 0.247; and the average void diameter is the least important factor, with a weight of 0.223.

[0166] (3) Hierarchical sorting and its consistency test.

[0167] The randomness index (RI) for different matrix orders is shown in Table 11.

[0168] Table 11 Randomness Index

[0169]

[0170] All three admixtures inhibited damage to the internal structure of asphalt concrete. Using the growth rate of the internal structural damage index as the data in the comparison matrix, the analytic hierarchy process (AHP) was employed to calculate the internal structural damage index. A higher index indicates a worse performance improvement effect of the admixture, while a lower index indicates a better performance improvement effect. The growth rates of the pore damage evaluation index for asphalt concrete with different admixtures are shown in Table 12.

[0171] Table 12. Growth rate (%) of internal structural damage parameters in asphalt concrete with different admixtures

[0172]

[0173] Based on the damage growth rate of the internal structure of asphalt concrete with admixtures, a pairwise comparison matrix for the scheme layer was constructed. Table 13 shows the weights and consistency comparison results of the pairwise comparison matrices for each factor in the scheme layer.

[0174] Table 13 Comparison Results of Scheme Layer Weights and Consistency

[0175]

[0176] As shown in Table 13, the CR values ​​are all less than 10%, and the pairwise comparisons are considered consistent.

[0177] (4) Comprehensive evaluation. The performance damage index of asphalt concrete with different admixtures is shown in Table 14.

[0178] Table 14 Performance Damage Index of Asphalt Concrete with Different Admixtures

[0179]

[0180] As shown in Table 14, the crystallization inhibitor has the lowest performance damage index and is suitable for asphalt pavement paving in coastal / saline-alkali frost-prone areas.

[0181] While the invention has been described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the invention. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that different dependent claims and features described herein can be combined in ways different from those described in the original claims. It is also understood that features described in conjunction with individual embodiments can be used in other described embodiments.

Claims

1. A method for evaluating the performance of admixtures in improving the salt-frost damage resistance of asphalt concrete, characterized in that... include, Step 1: Add different admixtures to asphalt concrete subjected to salt freezing. Asphalt concrete with different admixtures was used as a control group; digital two-dimensional images of each control group were obtained by X-ray scanning; the digital two-dimensional images were processed to obtain three-dimensional reconstructed images, and the microstructure parameters of each control group were obtained through the three-dimensional reconstructed images, including porosity, number of pores, average pore diameter and pore connectivity. Step 2: Calculate the overall correlation between the reference sequence and the comparison sequence in each control group using grey relational analysis. Step 3: Based on the calculation results of the comprehensive correlation degree, the performance damage index of each microstructural parameter affecting the asphalt concrete by using the analytic hierarchy process (AHP) is used to quantitatively evaluate the performance damage index of the asphalt concrete. The smaller the performance damage index, the better the performance improvement effect of the asphalt concrete by the admixture used in the control group. In step three, the performance impairment index is denoted as DI: ， To compare the overall correlation weights of sequence i; Weights for the internal structural damage growth rate of comparison sequence i; i is the sequence number of the comparison sequence, i=1, 2, 3, 4, which correspond to the porosity, number of voids, average void diameter and void connectivity, respectively; Compare the internal structural damage growth rate weights of sequence i The calculation method is as follows: The reference sequence group was selected with 0, 7, and 20 freeze-thaw cycles, and the internal structural damage growth rate of each microstructural parameter was calculated. The internal structural damage growth rate of each microstructural parameter is the ratio of the difference between the result of 20 freeze-thaw cycles and the result of 0 freeze-thaw cycles to the result of 0 freeze-thaw cycles. Then, based on the internal structural damage growth rate of the mesostructural parameter corresponding to each control group, construct the internal structural damage growth rate judgment matrix for the three control groups according to the method of constructing the comprehensive correlation judgment matrix of mesostructural parameters. Calculate the internal structural damage growth rate judgment matrix for each control group to obtain the weight of the internal structural damage growth rate for each mesostructural parameter. ; according to For and The performance damage index (DI) of the three control groups was calculated, and the effect of the admixture on improving the performance of asphalt concrete was determined according to the magnitude of the performance damage index (DI).

2. The evaluation method for improving the salt-frost damage resistance of asphalt concrete using admixtures according to claim 1, characterized in that, In step one, the formulas for calculating porosity, average void diameter, and void connectivity are as follows: ， ， 。 3. The evaluation method for improving the salt-frost damage resistance of asphalt concrete using admixtures according to claim 2, characterized in that, The control group in step one includes the anti-stripping agent group, the basalt fiber group, and the crystallization inhibitor group.

4. The evaluation method for improving the salt-frost damage resistance of asphalt concrete using admixtures according to claim 3, characterized in that, In step two, during the calculation of the comprehensive correlation degree, the comparison sequence and the reference sequence are first determined. In each control group, the porosity, number of pores, average diameter of pores and pore connectivity are used as four comparison sequences. The number of freeze-thaw cycles corresponding to the comparison sequences are used as the reference sequences. The number of freeze-thaw cycles are sorted in ascending order to form a reference sequence group. The comparison sequences corresponding to each reference sequence group are sequentially dimensionless: ， In the formula Let k be the sequence number of the reference sequence group, k = 1, 2, ..., n, where n is the total number of reference sequence groups; Reference sequence set The dimensionless value of the corresponding comparison sequence i, Reference sequence set The original value of the corresponding comparison sequence i, Let i be the minimum value of the comparison sequence in the set of n reference sequences. Let i be the maximum value of the comparison sequence i in the n reference sequence groups.

5. The evaluation method for improving the salt-frost damage resistance of asphalt concrete using admixtures according to claim 4, characterized in that, Step two, the process of calculating the comprehensive correlation degree, also includes calculating... Grey correlation coefficient with reference value: n The result of the sum is used as a reference value. ; The corresponding grey relational coefficient is represented by ξ. i (k): ， Δ min For the minimum difference between two levels, there are n items. The minimum absolute value of the pairwise subtraction; Δ max The maximum difference between two levels, with n values. The maximum absolute value of the pairwise subtraction; Δ i (k) is a reference value. and The absolute value of the difference; The resolution coefficient.

6. The evaluation method for improving the salt-frost damage resistance of asphalt concrete using admixtures according to claim 5, characterized in that, Step two, the process of calculating the comprehensive correlation degree also includes calculating the grey correlation degree r of the comparison sequence i in the n reference sequence groups. i : 。 7. The evaluation method for improving the salt-frost damage resistance of asphalt concrete using admixtures according to claim 6, characterized in that, In step two, the overall correlation degree is represented by r. iC : , Overall correlation r iC denoted as the average grey relational score of comparison sequence i for the three control groups, where p is the sequence number of the control group. Let be the grey relational degree of comparison sequence i of the p-th control group.

8. The evaluation method for improving the salt-frost damage resistance of asphalt concrete using admixtures according to claim 7, characterized in that, In step three, the first step is to determine the comprehensive correlation judgment matrix of the microstructure: Based on the comprehensive correlation degree r iC The calculation results determine four comprehensive correlation degree judgment matrices of microstructure. In the comprehensive correlation degree judgment matrix, I is the row variable and J is the column variable, I=1,2,3,4, J=1,2,3,4, which correspond to the comprehensive correlation degree of porosity, the comprehensive correlation degree of number of pores, the comprehensive correlation degree of average diameter of pores, and the comprehensive correlation degree of pore connectivity, respectively. by This indicates the overall correlation of the fine structure in row I. This indicates the overall correlation of the detailed structure in column J. by express and The results of the importance comparison; Based on the comparison rules for judging matrix assignment in existing technologies, we obtain: ， Will Arranged in order to form the comprehensive correlation judgment matrix R: 。 9. The evaluation method for improving the salt-frost damage resistance of asphalt concrete using admixtures according to claim 8, characterized in that, In step three, Based on the comprehensive correlation judgment matrix R, the main eigenvalues ​​of the comprehensive correlation judgment matrix and the comprehensive correlation weights of each microstructure of each control group are calculated.

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