Optimal selection method for recycled asphalt pavement structure of high-grade highway in cold region

Abstract

The invention relates to the technical field of road engineering, and discloses a cold region high-grade highway recycled asphalt pavement structure optimization method which comprises the following steps: constructing an evaluation index system; the fatigue cracking life, the permanent deformation and the low-temperature crack resistance are checked in advance, when a preset threshold value requirement is met, the subjective weight of each evaluation index is determined based on row and column summation relation optimization of the preliminary matrix and a root method, and the objective weight of each evaluation index is determined according to the decision matrix and the risk correction coefficient; dynamically determining a weight fusion coefficient based on the cold region risk sensitivity index; the cold region risk sensitivity index is calculated according to an extreme low temperature coefficient and a freeze-thaw cycle coefficient; determining the comprehensive weight of each evaluation index by adopting a comprehensive weighting method; calculating the relative close degree of each recycled pavement structure scheme; and sorting the schemes according to the relative closeness to determine an optimal recycled pavement structure scheme. According to the method, the optimal recycled pavement structure suitable for the cold region can be accurately screened out.

Description

Technical Field

[0001] This invention relates to the field of road engineering technology, and more specifically, to a preferred method for the structure of recycled asphalt pavement on high-grade highways in cold regions. Background Technology

[0002] Asphalt pavement is widely used in the long-term use and maintenance of highway transportation infrastructure due to its excellent road performance. However, with increasing service life, it is prone to problems such as fatigue cracking, low-temperature cracking, permanent deformation, and decreased load-bearing capacity, requiring reconstruction, expansion, or repaving to restore pavement function. This process generates a large amount of waste asphalt mixture (RAP). If disposed of haphazardly, it not only occupies land resources and pollutes the environment but also wastes high-quality aggregates and asphalt components, contradicting the development concepts of resource conservation and low-carbon environmental protection. Therefore, asphalt pavement recycling technology has become a key research direction in the industry. Especially in some frigid regions where winter temperatures can drop below -20°C, coupled with frequent heavy vehicle traffic, these special climatic and load conditions place higher demands on the structural performance of recycled pavement. However, existing recycled pavement technologies are insufficient to meet actual engineering needs, and many problems urgently need to be solved.

[0003] Existing designs for recycled pavement structures largely rely on experience from regions with normal temperatures, failing to adequately consider the climatic differences in cold regions. This includes not only regional variations but also the frequent occurrence of extreme conditions such as extreme cold, and neglecting to specifically address the fatigue issues of the recycled layer. Previous mechanical analyses of recycled pavements often ignored key indicators such as the tensile strain at the bottom of the recycled layer and the cracking index based on the low-temperature return period, focusing only on conventional parameters like the tensile strain at the bottom of the asphalt layer and the tensile stress at the bottom of the base layer. This leads to structures that are prone to cracking during actual use due to accumulated fatigue damage in the recycled layer, or low-temperature cracking caused by extreme low-temperature values ​​exceeding design expectations. Furthermore, traditional designs lack a systematic quantification of the temperature sensitivity of recycled mixtures, meaning the modulus of recycled mixtures is significantly affected by temperature. They also fail to clearly define the strain response of the recycled layer under different axle loads within the operating temperature range of -20℃ to 40℃, making it difficult to guarantee the stability of the structure throughout its entire life cycle. For example, after many years of operation, the deflection values ​​of some old roads (originally consisting of a 3cm top layer, a 4cm bottom layer, and a 20cm base layer) mostly exceed 20 (0.01mm), and in severe sections even exceed 30 (0.01mm). In addition, there are a large number of transverse cracks, some longitudinal cracks, and local block damage, and the bearing capacity and crack resistance of the road surface are seriously insufficient.

[0004] In the selection of optimal recycled pavement structures, existing evaluation systems suffer from incomplete indicators and unreasonable weight determination methods. Previous evaluations lacked a comprehensive indicator system encompassing mechanical response, structural verification, low-temperature adaptability, and economic efficiency, easily leading to a focus on a single performance characteristic while neglecting overall suitability. Regarding weight determination, the traditional analytic hierarchy process (AHP) involves numerous iterations and is susceptible to human influence, while the simple entropy weight method relies excessively on sample data and easily loses subjective experience information. Both methods suffer from information loss and fail to effectively integrate subjective and objective information. Furthermore, existing technologies, after proposing multiple recycled pavement structure schemes, lack a systematic screening process based on multi-dimensional indicators, often relying on subjective judgment based on experience. This fails to quantify the performance differences of different schemes under cold climate and load conditions, resulting in evaluation results that are disconnected from actual engineering conditions, making it difficult to accurately select the optimal recycled pavement structure suitable for cold regions.

[0005] No effective solutions have yet been proposed to address the problems in the relevant technologies. Summary of the Invention

[0006] In response to the problems in related technologies, this invention proposes a method for optimizing the structure of recycled asphalt pavement for high-grade highways in cold regions, so as to overcome the aforementioned technical problems existing in the existing related technologies.

[0007] Therefore, the specific technical solution adopted by the present invention is as follows:

[0008] A method for optimizing the structure of recycled asphalt pavement for high-grade highways in cold regions includes: constructing an evaluation index system; the evaluation index system includes mechanical response index, structural verification index, low-temperature adaptability index, and economic index; the mechanical response index includes tensile strain at the bottom of the asphalt layer, tensile stress at the bottom of the base layer, vertical compressive strain on the top surface of the old road, and tensile strain at the bottom of the recycled layer; the structural verification index includes fatigue crack life and permanent deformation; the low-temperature adaptability index includes low-temperature crack resistance; the economic index includes structural cost; the fatigue crack life, permanent deformation, and low-temperature crack resistance are pre-checked to determine whether each index meets the preset threshold requirements; when the fatigue crack life... When both permanent deformation and low-temperature crack resistance meet the preset threshold requirements, the subjective weights of each evaluation index are determined based on the optimization of the row and column summation relationship of the preliminary matrix and the square root method. The objective weights of each evaluation index are then determined based on the decision matrix and risk correction coefficient. The weight fusion coefficient is dynamically determined based on the cold region risk sensitivity index, which is calculated using the extreme low temperature coefficient and freeze-thaw cycle coefficient. The comprehensive weight of each evaluation index is determined using a comprehensive weighting method based on the weight fusion coefficient, subjective weights, and objective weights. An evaluation matrix is ​​established, and the relative closeness of each recycled pavement structure scheme is calculated based on the comprehensive weights. The schemes are then ranked according to their relative closeness to determine the optimal recycled pavement structure scheme.

[0009] Furthermore, the preliminary verification of fatigue crack life, permanent deformation, and low-temperature crack resistance includes: determining whether the fatigue crack life meets the cumulative axle load requirement; determining whether the permanent deformation is less than or equal to the preset permanent deformation threshold; determining whether the low-temperature cracking index corresponding to the low-temperature crack resistance is less than or equal to the preset cracking index threshold; if any of the fatigue crack life, permanent deformation, and low-temperature crack resistance indicators fails to meet the corresponding threshold requirements, the recycled pavement structure scheme is deemed unqualified.

[0010] Furthermore, based on the optimization of the row and column summation relationship of the preliminary matrix and the square root method, the subjective weights of each evaluation index are determined as follows: a preliminary matrix is ​​constructed based on each evaluation index; the principles for establishing this preliminary matrix include: if the current index is more important than the next index, it is assigned a preset importance value; if the current index and the next index are equally important, they are assigned a preset equal value; if the current index is less important than the next index, it is assigned a preset minor value; according to the temperature sensitivity characteristics of recycled pavement materials in frigid regions, the importance of the tensile strain at the bottom of the recycled layer relative to the tensile strain at the bottom of the asphalt layer is adjusted in the preliminary matrix; the preliminary matrix is ​​calibrated for cold region risk coefficient based on the climate zoning type of the project location to obtain a calibrated preliminary matrix; the calibrated preliminary matrix is ​​optimized according to the row and column summation relationship to obtain an improved matrix, and the subjective weights of each evaluation index are calculated based on the improved matrix using the square root method.

[0011] Furthermore, the cold region risk coefficient calibration of the preliminary matrix based on the climate zoning type of the project location includes: determining whether the project location belongs to an extreme cold region, a severe cold region, or a cold region; when the project location belongs to an extreme cold region, increasing the weight of low-temperature crack resistance by a first preset ratio; when the project location belongs to a severe cold region, increasing the weight of low-temperature crack resistance by a second preset ratio; when the project location belongs to a cold region, increasing the weight of low-temperature crack resistance by a third preset ratio; wherein the first preset ratio is greater than the second preset ratio, and the second preset ratio is greater than the third preset ratio; matrix normalization processing is performed on the increased weights to ensure the rationality of the weight of low-temperature crack resistance.

[0012] Furthermore, optimizing the calibrated preliminary matrix according to the row-column summation relationship includes: calculating the sum of all elements in each row of the calibrated preliminary matrix; calculating the sum of all elements in each column of the calibrated preliminary matrix; comparing the sum of all elements in the current row with the sum of all elements in the current column of the calibrated preliminary matrix; if the sum of all elements in the current row is greater than or equal to the sum of all elements in the current column, then the difference between the sum of all elements in the current row and the sum of all elements in the current column is added to a preset unit value to obtain the element value at the corresponding position in the improved matrix; if the sum of all elements in the current row is less than the sum of all elements in the current column, then the difference between the sum of all elements in the current row and the sum of all elements in the current column is added to a preset unit value, and then the reciprocal of the result is taken to obtain the element value at the corresponding position in the improved matrix.

[0013] Furthermore, based on the decision matrix and risk correction coefficients, the objective weights of each evaluation indicator are determined as follows: A decision matrix is ​​constructed based on the original indicator data of each recycled pavement structure scheme, and this decision matrix is ​​standardized; the standardized decision matrix is ​​normalized; the risk correction coefficients corresponding to each evaluation indicator are determined based on the correlation between each evaluation indicator and the risk of severe cold; among them, the risk correction coefficient corresponding to low-temperature crack resistance decreases sequentially from extremely cold region to severely cold region to cold region, and the risk correction coefficient corresponding to the tensile strain at the bottom of the recycled layer decreases sequentially from extremely cold region to severely cold region to cold region; the elements of the normalized decision matrix are multiplied by the risk correction coefficients of the corresponding evaluation indicators to obtain the corrected decision matrix; the corrected information entropy is calculated based on this corrected decision matrix, and the objective weights of each evaluation indicator are calculated based on the corrected information entropy.

[0014] Furthermore, the dynamic determination of the weighted fusion coefficient based on the cold region risk sensitivity index includes: determining the extreme low temperature coefficient based on the lowest temperature in the project location within the next preset period to reflect the damage intensity of low temperature to the road surface; determining the freeze-thaw cycle coefficient based on the annual freeze-thaw cycle number in the project location to reflect the degree of damage to the recycled layer caused by freeze-thaw cycles; calculating the cold region risk sensitivity index using the extreme low temperature coefficient and the freeze-thaw cycle coefficient; and calculating the weighted fusion coefficient based on the cold region risk sensitivity index using a nonlinear function, and verifying and constraining the dynamic adjustment of the weighted fusion coefficient.

[0015] Furthermore, the cold region risk sensitivity index is calculated using the extreme low temperature coefficient and the freeze-thaw cycle coefficient by: multiplying the extreme low temperature coefficient by a first preset weight to obtain a first weighted value; multiplying the freeze-thaw cycle coefficient by a second preset weight to obtain a second weighted value; and adding the first weighted value and the second weighted value to obtain the cold region risk sensitivity index.

[0016] Furthermore, based on the cold region risk sensitivity index, the weight fusion coefficient is calculated using a nonlinear function, including: subtracting a preset benchmark value from the cold region risk sensitivity index to obtain a risk offset; taking the negative value of the risk offset as the index, and calculating the natural logarithm of the index to the power of the index to obtain the index decay value; subtracting the index decay value from a preset unit value to obtain the adjustment range; multiplying the adjustment range by a preset volatility coefficient to obtain the weight increment; adding the weight increment to the preset base weight to obtain the weight fusion coefficient; wherein the weight fusion coefficient satisfies the range of a preset lower limit to a preset upper limit.

[0017] Furthermore, establishing an evaluation matrix and calculating the relative closeness of each recycled pavement structure scheme based on comprehensive weights includes: establishing a relevant evaluation matrix based on the original index data of each recycled pavement structure scheme; normalizing the relevant evaluation matrix and establishing a standard matrix considering combined weights based on the comprehensive weights of each evaluation index; determining the optimal value vector and the worst value vector based on the standard matrix; calculating the Euclidean distance between each recycled pavement structure scheme and the optimal value vector, and calculating the Euclidean distance between each recycled pavement structure scheme and the worst value vector; using the Euclidean distance between each recycled pavement structure scheme and the worst value vector as the numerator; using the sum of the Euclidean distances between each recycled pavement structure scheme and the optimal value vector and the worst value vector as the denominator; dividing the numerator by the denominator to obtain the relative closeness of each recycled pavement structure scheme; wherein, the value range of the relative closeness is from a preset minimum value to a preset maximum value, and the larger the value, the better the evaluation object.

[0018] The beneficial effects of this invention are as follows:

[0019] (1) This invention addresses the problem of insufficient design of recycled pavement in frigid regions by establishing a complete technical system from index selection to scheme optimization. Traditional practices often apply the experience of normal temperature regions, ignoring the actual situation in frigid regions where winter temperatures can reach as low as -20 degrees Celsius and heavy vehicles frequently pass through. It also fails to consider the fatigue problem of the recycled layer itself and the cracking risk based on the low temperature return period, resulting in various defects appearing on the pavement within a few years. This invention specifically adds two key indicators: tensile strain at the bottom of the recycled layer and low temperature crack resistance. It takes into account four aspects: mechanical response, structural verification, low temperature adaptability, and economy, forming a relatively comprehensive evaluation index system. The recycled pavement structure designed in this way can not only cope with the low temperature environment in frigid regions but also withstand the long-term effects of heavy traffic. It will not cause early damage due to the lack of consideration of a certain key factor, and the actual engineering application effect is more guaranteed.

[0020] (2) This invention improves the existing evaluation method in terms of scheme selection, making the screening results more scientific and reasonable. In the past, when the weight was determined by the analytic hierarchy process, the calculation process was complicated and easily affected by human factors. The entropy weight method relied too much on the data itself and lost engineering experience. Both methods had obvious defects when used alone. This invention simplifies the judgment matrix construction process of the analytic hierarchy process by adopting a 0-1-2 scale, reducing the number of iterations and calculation errors. At the same time, the entropy weight method was improved for the characteristics of cold regions. Through layered processing, setting threshold constraints and risk correction, the calculated weight can accurately reflect the places where problems are most likely to occur in cold regions, such as low temperature cracking and freeze-thaw damage. At the same time, this invention combines subjective weight and objective weight, and dynamically adjusts the weight coefficient according to the different degrees of cold, so that the evaluation method can adapt to the specific conditions of different cold regions. Thus, the selected recycled pavement structure scheme is not determined by experience or guesswork, but has quantitative basis. It takes into account both the actual needs of the project and respects the objective laws of the data, avoiding blindness in scheme selection. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a grading diagram of evaluation indicators in a preferred method for recycled asphalt pavement structure of high-grade highways in cold regions according to an embodiment of the present invention;

[0023] Figure 2 This is a flowchart illustrating a preferred method for recycled asphalt pavement structure of a high-grade highway in a cold region according to an embodiment of the present invention.

[0024] Figure 3 This is a flowchart illustrating the process of determining the subjective weights of various evaluation indicators in a method for optimizing the structure of recycled asphalt pavement for high-grade highways in cold regions, according to an embodiment of the present invention.

[0025] Figure 4 This is a flowchart illustrating the determination of the objective weights of various evaluation indicators in a method for optimizing the structure of recycled asphalt pavement for high-grade highways in cold regions, according to an embodiment of the present invention.

[0026] Figure 5 This is a schematic diagram of a preferred system for recycled asphalt pavement structure of high-grade highways in cold regions according to an embodiment of the present invention. Detailed Implementation

[0027] To further illustrate the various embodiments, the present invention provides accompanying drawings, which are part of the disclosure of the present invention. These drawings are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these drawings, those skilled in the art should be able to understand other possible implementation methods and the advantages of the present invention. The components in the drawings are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0028] According to an embodiment of the present invention, a preferred method for the structure of recycled asphalt pavement on high-grade highways in cold regions is provided.

[0029] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figure 1 As shown in one embodiment of the present invention, a method for optimizing the structure of recycled asphalt pavement for high-grade highways in cold regions is provided. This method includes: constructing an evaluation index system; the evaluation index system includes mechanical response indexes, structural verification indexes, low-temperature adaptability indexes, and economic indexes; the mechanical response indexes include tensile strain at the bottom of the asphalt layer, tensile stress at the bottom of the base layer, vertical compressive strain on the top surface of the old road, and tensile strain at the bottom of the recycled layer; the structural verification indexes include fatigue crack life and permanent deformation; the low-temperature adaptability indexes include low-temperature crack resistance; and the economic indexes include structural cost; the fatigue crack life, permanent deformation, and low-temperature crack resistance are pre-checked to determine whether each index meets a preset threshold. When fatigue crack life, permanent deformation, and low-temperature crack resistance all meet preset threshold requirements, the subjective weights of each evaluation index are determined based on the optimization of the row and column summation relationship of the preliminary matrix and the square root method. The objective weights of each evaluation index are then determined based on the decision matrix and risk correction coefficient. The weight fusion coefficient is dynamically determined based on the cold-region risk sensitivity index, which is calculated using the extreme low-temperature coefficient and freeze-thaw cycle coefficient. The comprehensive weight of each evaluation index is determined using a comprehensive weighting method based on the weight fusion coefficient, subjective weights, and objective weights. An evaluation matrix is ​​established, and the relative closeness of each recycled pavement structure scheme is calculated based on the comprehensive weights. The schemes are then ranked according to their relative closeness to determine the optimal recycled pavement structure scheme.

[0030] In one embodiment, the preliminary verification of fatigue crack life, permanent deformation, and low-temperature crack resistance includes: determining whether the fatigue crack life meets the cumulative axle load requirement; determining whether the permanent deformation is less than or equal to a preset permanent deformation threshold; determining whether the low-temperature cracking index corresponding to the low-temperature crack resistance is less than or equal to a preset cracking index threshold; if any of the fatigue crack life, permanent deformation, and low-temperature crack resistance indicators fails to meet the corresponding threshold requirements, the recycled pavement structure scheme is deemed unqualified.

[0031] In one embodiment, such as Figure 3As shown, the subjective weights of each evaluation index are determined based on the optimization of the row and column summation relationship of the preliminary matrix and the square root method. This includes: constructing a preliminary matrix based on each evaluation index; the principles for establishing this preliminary matrix include: assigning a preset importance value if the current index is more important than the next index; assigning a preset equal value if the current index and the next index are equally important; and assigning a preset minor value if the current index is less important than the next index. Based on the temperature sensitivity characteristics of recycled pavement materials in frigid regions, the importance of the tensile strain at the bottom of the recycled layer relative to the tensile strain at the bottom of the asphalt layer is adjusted in the preliminary matrix. The preliminary matrix is ​​calibrated for cold region risk coefficients based on the climate zoning type of the project location, resulting in a calibrated preliminary matrix. The calibrated preliminary matrix is ​​then optimized according to the row and column summation relationship to obtain an improved matrix, and the subjective weights of each evaluation index are calculated based on this improved matrix using the square root method.

[0032] In one embodiment, calibrating the cold region risk coefficient of the preliminary matrix based on the climate zoning type of the project location includes: determining whether the project location belongs to an extreme cold region, a severe cold region, or a cold region; when the project location belongs to an extreme cold region, increasing the weight of low-temperature crack resistance by a first preset ratio; when the project location belongs to a severe cold region, increasing the weight of low-temperature crack resistance by a second preset ratio; when the project location belongs to a cold region, increasing the weight of low-temperature crack resistance by a third preset ratio; wherein the first preset ratio is greater than the second preset ratio, and the second preset ratio is greater than the third preset ratio; and performing matrix normalization on the increased weights to ensure the rationality of the weights for low-temperature crack resistance.

[0033] In one embodiment, optimizing the calibrated preliminary matrix according to the row-column summation relationship includes: calculating the sum of all elements in each row of the calibrated preliminary matrix; calculating the sum of all elements in each column of the calibrated preliminary matrix; comparing the sum of all elements in the current row with the sum of all elements in the current column of the calibrated preliminary matrix; if the sum of all elements in the current row is greater than or equal to the sum of all elements in the current column, then the difference between the sum of all elements in the current row and the sum of all elements in the current column is added to a preset unit value to obtain the element value at the corresponding position in the improved matrix; if the sum of all elements in the current row is less than the sum of all elements in the current column, then the difference between the sum of all elements in the current row and the sum of all elements in the current column is added to a preset unit value, and then the reciprocal of the result is taken to obtain the element value at the corresponding position in the improved matrix; wherein, the expression for the element value at the corresponding position in the improved matrix is:

[0034] ;

[0035] ;

[0036] In the formula, To improve the first in the matrix Line number The element values ​​of the column; The first in the calibrated preliminary matrix The sum of all elements in the row; For the first calibrated matrix The sum of all elements in the list.

[0037] In one embodiment, such as Figure 4 As shown, the objective weights of each evaluation indicator are determined based on the decision matrix and risk correction coefficients, including: constructing a decision matrix based on the original indicator data of each recycled pavement structure scheme and standardizing the decision matrix; normalizing the standardized decision matrix; determining the risk correction coefficients corresponding to each evaluation indicator based on the correlation between each evaluation indicator and the risk of severe cold; wherein, the risk correction coefficient corresponding to low-temperature crack resistance decreases sequentially from extreme cold region to severe cold region to cold region, and the risk correction coefficient corresponding to the tensile strain at the bottom of the recycled layer decreases sequentially from extreme cold region to severe cold region to cold region; multiplying the elements of the normalized decision matrix with the risk correction coefficients of the corresponding evaluation indicators to obtain the corrected decision matrix; calculating the corrected information entropy based on the corrected decision matrix, and calculating the objective weights of each evaluation indicator based on the corrected information entropy; wherein, the expression for calculating the objective weights of each evaluation indicator based on the corrected information entropy is:

[0038] ;

[0039] In the formula, For the first The objective weights of each evaluation indicator; For the first The corrected information entropy of each evaluation indicator; This represents the total number of evaluation indicators.

[0040] In one embodiment, dynamically determining the weighted fusion coefficient based on the cold region risk sensitivity index includes: determining an extreme low temperature coefficient based on the lowest temperature in the project location within a preset period to reflect the damage intensity of low temperature to the road surface; determining a freeze-thaw cycle coefficient based on the annual number of freeze-thaw cycles in the project location to reflect the degree of damage to the regenerated layer caused by freeze-thaw cycles; calculating the cold region risk sensitivity index using the extreme low temperature coefficient and the freeze-thaw cycle coefficient; and calculating the weighted fusion coefficient using a nonlinear function based on the cold region risk sensitivity index, and verifying and constraining the dynamic adjustment of the weighted fusion coefficient.

[0041] In one embodiment, calculating the cold region risk sensitivity index using the extreme low temperature coefficient and the freeze-thaw cycle coefficient includes: multiplying the extreme low temperature coefficient by a first preset weight to obtain a first weighted value; multiplying the freeze-thaw cycle coefficient by a second preset weight to obtain a second weighted value; and adding the first weighted value and the second weighted value to obtain the cold region risk sensitivity index; wherein, the expression for calculating the weight fusion coefficient using a nonlinear function is as follows:

[0042] ;

[0043] In the formula, The weighted fusion coefficient; This is a risk sensitivity index for cold regions.

[0044] In one embodiment, calculating the weight fusion coefficient using a nonlinear function based on the cold region risk sensitivity index includes: subtracting a preset benchmark value from the cold region risk sensitivity index to obtain a risk offset; taking the negative value of the risk offset as the index, and calculating the exponent raised to the power of the natural logarithm base to obtain an exponent decay value; subtracting the exponent decay value from a preset unit value to obtain an adjustment range; multiplying the adjustment range by a preset volatility coefficient to obtain a weight increment; and adding the weight increment to a preset base weight to obtain the weight fusion coefficient; wherein the weight fusion coefficient satisfies a value range from a preset lower limit to a preset upper limit.

[0045] In one embodiment, establishing an evaluation matrix and calculating the relative closeness of each recycled pavement structure scheme based on comprehensive weights includes: establishing a relevant evaluation matrix based on the original index data of each recycled pavement structure scheme; normalizing the relevant evaluation matrix and then establishing a standardized matrix considering combined weights based on the comprehensive weights of each evaluation index; determining the optimal value vector and the worst value vector based on the standardized matrix; calculating the Euclidean distance between each recycled pavement structure scheme and the optimal value vector, and calculating the Euclidean distance between each recycled pavement structure scheme and the worst value vector; using the Euclidean distance between each recycled pavement structure scheme and the worst value vector as the numerator; using the sum of the Euclidean distances between each recycled pavement structure scheme and the optimal value vector and the worst value vector as the denominator; dividing the numerator by the denominator to obtain the relative closeness of each recycled pavement structure scheme; wherein the relative closeness ranges from a preset minimum value to a preset maximum value, with a larger value indicating a better evaluation object.

[0046] It should be noted that previous mechanical analyses of recycled pavements often neglected key indicators such as the tensile strain at the bottom of the recycled layer and the cracking index based on the low-temperature return period, focusing only on conventional parameters such as the tensile strain at the bottom of the asphalt layer and the tensile stress at the bottom of the base course. This resulted in the designed structure being prone to cracking in actual use due to the accumulation of fatigue damage in the recycled layer, or low-temperature cracking caused by extreme low-temperature values ​​exceeding design expectations. Furthermore, previous evaluations did not construct a comprehensive indicator system that included mechanical response, structural verification, low-temperature adaptability, and economic efficiency. Mechanical response includes the tensile strain at the bottom of the asphalt layer, the tensile stress at the bottom of the base course, the vertical compressive strain of the old pavement surface, and the tensile strain at the bottom of the recycled layer. Structural verification includes fatigue cracking life and permanent deformation of the asphalt mixture. Low-temperature adaptability corresponds to the low-temperature cracking index, and economic efficiency corresponds to the total life-cycle cost. This evaluation method easily leads to focusing only on a single performance and neglecting overall compatibility. In summary, current recycled pavement technologies applicable to cold regions have significant shortcomings in terms of the specificity of structural design, the completeness of the evaluation system, the exploration of technical benefits, and experimental verification. There is an urgent need to develop a recycled pavement technology that fully integrates the characteristics of cold climate and load, takes into account multi-dimensional performance indicators, can accurately select structural solutions, and has been experimentally verified, in order to solve the pain points of existing technologies and promote the efficient application of recycled pavement in cold regions.

[0047] This invention proposes a method for optimizing the structure of recycled asphalt pavement for high-grade highways in cold regions. Through the collaborative design of an adapted index system, specific thresholds, and improved evaluation methods, it resolves this unique contradiction and overcomes the limitations of current standards. For the first time, it integrates four dimensions: mechanical response, structural verification, low-temperature adaptability, and economic efficiency. It adds two targeted indicators: tensile strain at the bottom of the recycled layer and low-temperature crack resistance, addressing the pain point of incomplete evaluation of recycled pavement in severely cold regions. To address the problems of excessive iterations and susceptibility to computational influence in traditional AHP methods, a 0-1-2 scale matrix is ​​constructed, improving its inherent limitations. Customized design for severely cold regions enhances the stability and accuracy of subjective weights. Through layered processing, threshold constraints, and risk correction, the entropy weight calculation results accurately match the core risks of severely cold regions, including low-temperature cracking and freeze-thaw damage, overcoming the limitation of general normalization methods in distinguishing the priority of severe cold risks. By embedding comprehensive weights into the TOPSIS method and using dynamic α values ​​to adaptively adjust the comprehensive weights according to the severity of cold, it avoids the incompatibility of the same weight scheme in different severely cold regions, making it more suitable for practical engineering applications. By constructing a standardized matrix, determining the optimal and worst vectors, and calculating the relative proximity, a quantitative optimization of recycled pavement structures in frigid regions was achieved, improving the scientific rigor of the selection process. This method comprises the following two main parts:

[0048] I. Establishment of an evaluation index system for recycled asphalt pavement structure in cold regions:

[0049] To address the shortcomings of the current "Specifications for Design of Asphalt Pavement" (JTG D50-2017) in the application of asphalt pavement reconstruction design verification indicators to recycled asphalt pavement structures in frigid regions, this invention proposes an evaluation index system for recycled asphalt pavement structures in frigid regions. This system, while fully utilizing existing indicators in the current specifications, supplements and modifies verification indicators applicable to recycled layers and frigid environments, enabling a comprehensive evaluation of the mechanical response, structural safety, low-temperature adaptability, and economy of recycled pavement structures.

[0050] Specifically, the evaluation index system consists of the following four primary indicators and their corresponding secondary indicators: ① Mechanical response index A1, including A 11 That is, the tensile strain at the bottom of the asphalt layer (measured value); A 12 That is, the tensile stress at the bottom of the base layer (measured value); A 13 That is, the vertical compressive strain on the top surface of the old road (measured value); A 14 ① The tensile strain at the bottom of the regenerated layer (measured value); ② Structural verification index A2, including A 21 That is, fatigue crack life (cumulative axle load); A 22 ③ Permanent deformation (≤15mm); ③ Low temperature adaptability index A3, including A 31 ④ Low-temperature crack resistance (low-temperature cracking index CI≤3); ④ Economic indicators A4, including A 41 This refers to the structural cost (actual value). The threshold values ​​for the evaluation indicators of high-grade highways in cold regions are shown in Table 1.

[0051] Table 1 - Thresholds for Evaluation Indicators of High-Grade Highways in Cold Regions

[0052] The following three secondary indicators from structural verification index A2 and low-temperature adaptability index A3 are checked first. If any one of them fails to meet the requirements, the proposed structure is deemed unqualified. Only when all three indicators are met can the final scheme be ranked and selected according to the second part of the optimization method.

[0053] II. Comprehensive Evaluation System for Regenerated Pavement Structures Based on the Integrated Weighting-TOPSIS Method:

[0054] Based on a defined evaluation index system for recycled asphalt pavement structures in cold regions, determining the weights of each index is crucial. Current methods for determining index weights have limitations, leading to a disconnect between evaluation results and actual engineering conditions, and hindering the accurate selection of the optimal recycled pavement structure for severely cold regions. This invention provides a comprehensive evaluation system for recycled pavement structures based on the integrated weighting-TOPSIS method, deeply integrating it with the failure mechanisms in cold regions. It forms a dedicated scheme from risk identification and index assignment to weight fusion, achieving the fusion of subjective and objective information and quantitative evaluation of multi-dimensional performance, thereby improving the scientific rigor and accuracy of structure selection. Specifically, it includes:

[0055] 1) Subjective weight determination based on improved analytic hierarchy process (AHP): In this invention, considering the shortcomings of using the traditional analytic hierarchy process to determine the subjective weights of each evaluation index, such as the large number of iterations and the large influence of the calculation process on the results, a cubic scale improved analytic hierarchy process is adopted to determine the subjective weights of each evaluation index.

[0056] Specifically, ① construct an initial matrix A. The principle for constructing the initial matrix is ​​as follows: Indicator Ratio The indicator of importance is marked as 2, and the indicator of equal importance is marked as 1. No indicators The importance of the indicator is recorded as 0, and and The values ​​of are all in the range of 1, 2, 3…n; the expression for the preliminary matrix is:

[0057] (Equation 1)

[0058] Under low-temperature conditions in frigid regions, test data shows that at -20℃, the tensile strain increase at the bottom of the recycled layer reaches 35%, while the increase is only 12%. This indicates that the temperature sensitivity of the tensile strain at the bottom of the recycled layer is significantly higher than that of the asphalt layer. Therefore, in the judgment matrix of this invention, based on the innovative design of the characteristics of recycled pavement materials, the importance assignment of the tensile strain at the bottom of the recycled layer relative to that of the asphalt layer is adjusted from the conventional 1 (equally important) to 2 (more important). Based on the three-stage scaling assignment, a cold-region risk coefficient calibration step is added. That is, the scaling matrix elements are adjusted according to the extreme low temperatures of the project location. The extreme low temperatures of the project location are classified into extremely cold, severely cold, or cold regions according to the severe cold region classification in GB / T4754-2017 "National Economic Industry Classification". The extreme low temperature regions corresponding to the extremely cold, severely cold, or cold regions are assigned to A. 31 The assignment weights are then increased by 30%, 20%, and 10% respectively. For example, in the frigid zone A... 31 vsA 14 The value was adjusted from 2 to 2.4, and matrix normalization was used to ensure its rationality.

[0059] Specifically, ② improve the initial matrix to obtain the improved matrix B. Further improve the initial matrix A to eliminate its inferiority, and construct the improved matrix B; the expression is:

[0060] (Equation 2)

[0061] (Equation 3)

[0062] In the formula, To improve the first... Line number List; and These are the first and second digits of the calibrated initial matrix A. line, number Add the elements of the column.

[0063] Specifically, ③ Determining subjective weights. Subjective weights are determined using the root method, and the calculation process is as follows:

[0064] (Equation 4)

[0065] (Equation 5)

[0066] (Equation 6)

[0067] In the formula, As the indicator weight; For process parameters; This refers to the number of indicators.

[0068] 2) Objective weight determination based on the Entropy Weight Method (EWM), specifically including: ① Constructing a matrix based on the original data and performing standardization processing to obtain matrix C, with the expression:

[0069] (Equation 7)

[0070] In the formula, Based on the original indicator data OK ② Normalize matrix C to obtain matrix D, and calculate the information entropy of each evaluation index according to entropy theory. The expression is:

[0071] (Equation 8)

[0072] In the formula, 0≤ ≤1; It is the information entropy coefficient; The number of evaluation indicators. ③ Determine the risk adjustment coefficient k. jThat is, based on the correlation between the indicator and the risk of severe cold, different correction coefficients are assigned (the higher the correlation, the higher the correction coefficient). j (The larger the value), as shown in Table 2.

[0073] Table 2 - Correction Coefficient Assignment Table

[0074] Specifically, the corrected entropy value is calculated, and the normalized matrix C is... ij Multiply by the correction factor k j The correction matrix C'=C is obtained. ij ×k j Then, based on C', the corrected information entropy is calculated. ④ The formula for determining objective weights using the entropy weight method is shown in equation (9):

[0075] (Equation 9)

[0076] In the formula, This is the corrected information entropy; For objective weighting.

[0077] 3) Comprehensive weighting method for determining comprehensive weights. Considering that subjective weight determination, while simple, is heavily influenced by human factors, and objective weight determination is affected by the sample, both methods suffer from information loss. Therefore, using comprehensive weighting to determine comprehensive weights can minimize information loss and make the weighting results as close to reality as possible. Commonly used comprehensive weighting methods include ensemble analysis and linear weighting methods. This invention constructs a risk-oriented weight fusion coefficient for cold regions. The overall weight is determined by dynamically adjusting the system.

[0078] Specifically, ① Clearly The dynamic adjustment of the value is based on the cold region risk sensitivity index S. First, the cold region risk sensitivity index S is defined. S is an indicator that quantifies the intensity of severe cold risk in the environment where the evaluation scheme is located. In this invention, it is calculated based on two core parameters, including parameter 1 and parameter 2.

[0079] Among them, parameter 1, namely the extreme low temperature coefficient T, is determined based on the lowest temperature in the project location over the past 10 years, reflecting the intensity of damage to the road surface caused by low temperatures, as shown in Table 3. Parameter 2, namely the freeze-thaw cycle coefficient F, is determined based on the number of freeze-thaw cycles per year in the project location, reflecting the degree of damage to the regenerated layer caused by freeze-thaw cycles, as shown in Table 4.

[0080] Table 3 - Extreme Low Temperature Coefficient

[0081] Table 4 - Freeze-thaw cycle coefficients

[0082] Specifically, the risk sensitivity index S is then calculated. Since extreme low temperatures and freeze-thaw cycles contribute equally to pavement failure in frigid regions, T and F are each weighted 50% when calculating the risk sensitivity index S, as shown in the following formula:

[0083] S = 0.5 × T + 0.5 × F (Equation 10)

[0084] For example, in an extremely cold region (T=1.2) with 5 freeze-thaw cycles per year (F=1.2), then S=0.5×1.2+0.5×1.2=1.2; in a cold region (T=0.8) with 2 freeze-thaw cycles per year (F=0.8), then S=0.8.

[0085] Specifically, ② establish The value is dynamically correlated with S to achieve non-linear adjustment. This dynamic correlation includes: when S ≥ 1.1, it is judged as an extremely cold region with extremely high risk. The value is set between 0.5 and 0.6, giving it a higher weight to subjective judgment and ensuring low-temperature crack resistance A. 31 Tensile strain A at the bottom of the regenerated layer 14 The weights of core indicators are not diluted; when 0.9 < S < 1.1, it is judged as a severely cold region with moderate risk. The value is set between 0.45 and 0.5 to balance subjective and objective weights, taking into account both risk priority and data authenticity; when S ≤ 0.9, it is judged as a cold region with relatively weak risk. The value is set to 0.4~0.45, and the subjective weight is appropriately reduced to make the influence of objective data such as permanent deformation and fatigue life more significant.

[0086] Specifically, this invention employs a nonlinear function to calculate the weight fusion coefficients in order to achieve... Smooth adjustment of values ​​to avoid abrupt changes:

[0087] (Equation 11)

[0088] When S=0.8 (lower limit of cold region), =0.4, which meets the criteria of low risk. Value; when S=1.0 (middle of the frigid zone), =0.4+0.3×(1-e -0.2 )≈0.4+0.3×0.18=0.45, which meets the criteria for medium risk. Value; when S=1.2 (upper limit for extremely cold regions). =0.4+0.3×(1-e -0.4 )≈0.4+0.3×0.33=0.49, corrected to 0.5, which meets the criteria of high risk. value.

[0089] Specifically, ③ The value is dynamically adjusted through verification and constraints to ensure its reasonableness. Verification of dynamic value adjustment, that is, each time a value is determined... After setting the value, its rationality needs to be verified through core indicator weighting: for extremely cold regions, low-temperature crack resistance A is required. 31 The overall weight is ≥28%, and the tensile strain A at the bottom of the regenerated layer is... 14 The overall weight should be ≥20%; otherwise, it will be fine-tuned. Value, such as The value increases by 0.05; for frigid regions, A is required to... 31 The overall weight is ≥25%, A 14 The overall weight is ≥18%; for cold regions, A is required. 31 The overall weight is ≥22%, A 14 The overall weight is ≥16%.

[0090] Specifically, the Constraints for dynamic value adjustment, i.e. The value must satisfy 0.4 ≤ ≤0.6, to avoid extreme values ​​that could lead to weight imbalance. Among them, ≤0.6 is used to prevent excessive subjective weighting and neglect of the actual engineering problems reflected by objective data, such as scheme A. 31 Subjective weighting is high, but actual data shows that its fracture energy drops sharply after freeze-thaw cycles. A value of ≥0.4 is used to prevent excessively high objective weighting, which could lead to core risk indicators being overshadowed by non-critical indicators (such as economic performance). 41 )suppress.

[0091] Specifically, the formula for calculating the overall weight is as follows:

[0092] (Equation 12)

[0093] (Equation 13)

[0094] 4) Overall evaluation model of pavement structure based on the comprehensive weighting-TOPSIS method. It should be noted that the TOPSIS method mainly uses the mathematical concept of vectors to analyze the differences between various schemes, evaluate the optimal scheme among various schemes, and expresses it by the closeness between each evaluation index and the optimal solution. The smaller the closeness, the optimal scheme.

[0095] Specifically, ① a relevant evaluation matrix Y is established based on the original data. After normalization, a normalized matrix X considering the combined weights is established, as shown in equation (14).

[0096] (Equation 14)

[0097] In the formula, ; For the first The overall weight of each indicator.

[0098] Specifically, ② determine the optimal value vector and worst-case vector As shown in equation (15):

[0099] (Equation 15)

[0100] In the formula, .

[0101] Specifically, ③ determine the Euclidean distance between each scheme and the optimal and worst solution vectors, as shown in equation (16):

[0102] (Equation 16)

[0103] Specifically, ④ calculate the relative similarity of each scheme, as shown in equation (17):

[0104] (Equation 17)

[0105] In the formula, The value represents the degree of closeness, ranging from 0 to 1. The higher the value, the better the evaluation object.

[0106] To facilitate understanding of the above-mentioned technical solution of the present invention, the following is a specific description using a road project in a frigid northern region as an example:

[0107] First, determine the overall weights. To simplify the calculation, the eight evaluation indicators of pavement structure are divided into four primary indicators: mechanical response index A1, structural verification index A2, low temperature index A3, and economic index A4. Among them, mechanical response index A1 includes tensile strain at the bottom of the asphalt layer and tensile stress at the bottom of the base layer A4. 12 Vertical compressive strain A on the top surface of the old road 13 Tensile strain A at the bottom of the regenerated layer 14 The four secondary indicators, structural verification indicator A2 includes fatigue crack life A. 21 Permanent deformation of asphalt mixture A 22 The judgment matrices for the two secondary indicators, determined through analysis, are shown in Tables 5 and 6.

[0108] Table 5 - Evaluation Index Judgment Matrix

[0109] Table 6 - Judgment Matrix of Revised Evaluation Indicators

[0110] The subjective weights of each indicator were obtained through analysis and calculation. The weights of each indicator after normalization are shown in Table 7.

[0111] Table 7 - Subjective Weights of Primary Evaluation Indicators

[0112] Using the same method, the relative weights of the secondary indicators within each evaluation index were determined, and the judgment matrices of each evaluation index were obtained as shown in Tables 8 and 9.

[0113] Table 8 - Evaluation Index Judgment Matrix

[0114] Table 9 - Judgment Matrix of Revised Evaluation Indicators

[0115] Table 10 - Subjective Weights of Each Evaluation Indicator

[0116] The objective weights of each indicator are determined according to equations (7) to (9), as shown in Table 11.

[0117] Table 11 - Calculation Results of Objective Weights

[0118] The subjective weight Q and objective weight M were calculated separately. Since the project is located in a frigid region, the comprehensive weight coefficient was calculated according to equations (10) and (11). Therefore, the calculated AHP-EWM comprehensive weights are shown in Table 12.

[0119] Table 12 - Comprehensive Weight Results Based on AHP-EWM

[0120] Next, a comprehensive evaluation of the proposed pavement structure scheme is conducted. The results of the calculation of various indicators based on the proposed scheme are shown in Table 13.

[0121] Table 13 - Calculation Results of Various Indicators for Proposed Road Surface Result

[0122] The normalized matrix Y is obtained by normalizing the original matrix obtained from the calculation results:

[0123]

[0124] The normalized matrix X after weighting is as follows:

[0125]

[0126] Optimal value vector and worst-case vector as follows:

[0127]

[0128]

[0129] The Euclidean distances between each solution and the optimal and worst solution vectors are:

[0130] =[0.16 0.15 0.27 0.33 0.32 0.32 0.36 0.35 0.35 0.34 0.35 0.36 0.34 0.35];

[0131] =[0.410.370.260.150.160.150.090.100.100.120.100.100.100.09];

[0132] The final calculation shows the relative similarity of each typical recycled pavement scheme to the following:

[0133] [0.71510.71470.49120.31020.32640.32030.19360.22800.21500.25590.21750.21250.22220.2030].

[0134] Based on the relative similarity of different recycled pavement structure schemes, the comprehensive evaluation ranking of each pavement structure scheme in the frigid region is determined as follows: L-3 > L-5 > L-6 > L-4 > R-3 > R-1 > R-6 > R-4 > R-2 > R-5 > R-7 > L-7 (the fatigue life of the inorganic binder layers in L-1 and L-2 does not meet the requirements after calculation). Based on the calculation results, the typical recycled structure schemes L-3 (cold recycling) and R-3 (hot recycling) are selected. This process can be used as a reference for determining the comprehensive evaluation of other typical recycled pavement structures in frigid regions.

[0135] like Figure 5 As shown, according to another embodiment of the present invention, a preferred system for recycled asphalt pavement structure of high-grade highways in cold regions is also provided. This preferred system includes:

[0136] The indicator construction module is used to construct the evaluation indicator system. The evaluation indicator system includes mechanical response indicators, structural verification indicators, low-temperature adaptability indicators, and economic indicators. The mechanical response indicators include tensile strain at the bottom of the asphalt layer, tensile stress at the bottom of the base layer, vertical compressive strain on the top surface of the old road, and tensile strain at the bottom of the recycled layer. The structural verification indicators include fatigue crack life and permanent deformation. The low-temperature adaptability indicators include low-temperature crack resistance. The economic indicators include structural cost.

[0137] The preliminary verification module is used to perform preliminary verification of fatigue crack life, permanent deformation and low temperature crack resistance to determine whether each indicator meets the preset threshold requirements.

[0138] The weighting analysis module is used to determine the subjective weight of each evaluation index based on the optimization of the row and column summation relationship of the preliminary matrix and the square root method when fatigue crack life, permanent deformation and low temperature crack resistance all meet the preset threshold requirements. It also determines the objective weight of each evaluation index based on the decision matrix and risk correction coefficient.

[0139] The dynamic coefficient module is used to dynamically determine the weighted fusion coefficients based on the cold region risk sensitivity index; this cold region risk sensitivity index is calculated based on the extreme low temperature coefficient and the freeze-thaw cycle coefficient.

[0140] The comprehensive weighting module is used to determine the comprehensive weight of each evaluation indicator based on the weight fusion coefficient, subjective weight, and objective weight using the comprehensive weighting method.

[0141] The scheme selection module is used to establish an evaluation matrix and calculate the relative similarity of each recycled pavement structure scheme based on comprehensive weights; the schemes are ranked according to the relative similarity to determine the optimal recycled pavement structure scheme.

[0142] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. 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 optimizing the structure of recycled asphalt pavement for high-grade highways in cold regions, characterized in that, include: An evaluation index system is constructed, comprising mechanical response index, structural verification index, low-temperature adaptability index, and economic index. The mechanical response index includes tensile strain at the bottom of the asphalt layer, tensile stress at the bottom of the base layer, vertical compressive strain on the top surface of the old road, and tensile strain at the bottom of the recycled layer. The structural verification index includes fatigue crack life and permanent deformation. The low-temperature adaptability index includes low-temperature crack resistance. The economic index includes structural cost. The fatigue crack life, permanent deformation and low temperature crack resistance are checked in advance to determine whether each index meets the preset threshold requirements. When fatigue crack life, permanent deformation and low temperature crack resistance all meet the preset threshold requirements, the subjective weight of each evaluation index is determined based on the optimization of the row and column summation relationship of the preliminary matrix and the square root method, and the objective weight of each evaluation index is determined according to the decision matrix and risk correction coefficient. The weighting fusion coefficient is dynamically determined based on the cold region risk sensitivity index; this cold region risk sensitivity index is calculated based on the extreme low temperature coefficient and the freeze-thaw cycle coefficient. Based on the weight fusion coefficient, subjective weight, and objective weight, the comprehensive weight of each evaluation indicator is determined by the comprehensive weighting method. An evaluation matrix is ​​established, and the relative similarity of each recycled pavement structure scheme is calculated based on the comprehensive weight. The schemes are ranked according to the relative similarity to determine the optimal recycled pavement structure scheme.

2. The preferred method for recycled asphalt pavement structure of high-grade highways in cold regions according to claim 1, characterized in that, The preliminary verification of fatigue crack life, permanent deformation, and low-temperature crack resistance includes: Determine whether the fatigue crack life meets the cumulative axle load requirement; Determine whether the amount of permanent deformation is less than or equal to a preset permanent deformation threshold; Determine whether the low-temperature cracking index corresponding to the low-temperature crack resistance is less than or equal to a preset cracking index threshold; If any of the fatigue crack life, permanent deformation, and low-temperature crack resistance indicators fail to meet the corresponding threshold requirements, the recycled pavement structure scheme is deemed unqualified.

3. The preferred method for recycled asphalt pavement structure of high-grade highways in cold regions according to claim 1, characterized in that, The optimization of the row and column summation relationship based on the preliminary matrix and the square root method, which determines the subjective weights of each evaluation index, includes: A preliminary matrix is ​​constructed based on each evaluation indicator. The principles for establishing this preliminary matrix include: if the current indicator is more important than the next indicator, it is assigned a preset importance value; if the current indicator is equally important as the next indicator, it is assigned a preset equal value; if the current indicator is less important than the next indicator, it is assigned a preset minor value. Based on the temperature sensitivity characteristics of recycled pavement materials in frigid regions, the importance of the tensile strain at the bottom of the recycled layer relative to the tensile strain at the bottom of the asphalt layer is assigned in the preliminary matrix. The preliminary matrix is ​​calibrated for cold region risk coefficient based on the climate zone type of the project location to obtain the calibrated preliminary matrix; The calibrated initial matrix is ​​optimized according to the row and column summation relationship to obtain the improved matrix, and the subjective weight of each evaluation index is calculated based on the improved matrix using the square root method.

4. The preferred method for recycled asphalt pavement structure of high-grade highways in cold regions according to claim 3, characterized in that, The calibration of the cold region risk coefficient of the preliminary matrix based on the climate zoning type of the project location includes: Determine whether the project location is in an extremely cold, severely cold, or cold region; When the project site is located in an extremely cold region, the weight of the low-temperature crack resistance is increased by a first preset ratio; When the project site is located in a frigid region, the weighting of the low-temperature crack resistance is increased by a second preset ratio; When the project site is located in a cold region, the weight of the low-temperature crack resistance is increased by a third preset ratio; wherein the first preset ratio is greater than the second preset ratio, and the second preset ratio is greater than the third preset ratio; The improved assignment weights are subjected to matrix normalization to ensure the rationality of the assignment weights for low-temperature crack resistance.

5. The preferred method for the recycled asphalt pavement structure of a high-grade highway in a cold region according to claim 3, characterized in that, The optimization of the calibrated preliminary matrix according to the row and column summation relationship includes: Calculate the sum of all elements in each row of the calibrated preliminary matrix; Calculate the sum of all elements in each column of the calibrated preliminary matrix; Compare the sum of all elements in the current row with the sum of all elements in the current column in the calibrated preliminary matrix; If the sum of all elements in the current row is greater than or equal to the sum of all elements in the current column, then the difference between the sum of all elements in the current row and the sum of all elements in the current column is added to a preset unit value to obtain the element value at the corresponding position in the improved matrix. If the sum of all elements in the current row is less than the sum of all elements in the current column, then the difference between the sum of all elements in the current row and the sum of all elements in the current column is added to a preset unit value, and then the reciprocal of the result is taken to obtain the element value at the corresponding position in the improved matrix. The expression for the element value at the corresponding position in the improved matrix is: ； ； In the formula, To improve the first in the matrix Line number The element values ​​of the column; The first in the calibrated preliminary matrix The sum of all elements in the row; For the first calibrated matrix The sum of all elements in the list.

6. The preferred method for recycled asphalt pavement structure of high-grade highways in cold regions according to claim 1, characterized in that, The determination of the objective weights of each evaluation indicator based on the decision matrix and risk correction coefficient includes: A decision matrix is ​​constructed based on the original index data of each recycled pavement structure scheme, and the decision matrix is ​​then standardized. The standardized decision matrix is ​​then normalized. Based on the correlation between each evaluation index and the risk of severe cold, the risk correction coefficient corresponding to each evaluation index is determined; among them, the risk correction coefficient corresponding to the low temperature crack resistance decreases sequentially from the extreme cold region, the severe cold region and the cold region, and the risk correction coefficient corresponding to the tensile strain at the bottom of the regenerated layer decreases sequentially from the extreme cold region, the severe cold region and the cold region. The elements of the normalized decision matrix are multiplied by the risk correction coefficients of the corresponding evaluation indicators to obtain the corrected decision matrix. The corrected information entropy is calculated based on the corrected decision matrix, and the objective weights of each evaluation index are calculated based on the corrected information entropy. The expression for calculating the objective weights of each evaluation index based on the corrected information entropy is as follows: ； In the formula, For the first The objective weight of each evaluation indicator; For the first The corrected information entropy of each evaluation indicator; This represents the total number of evaluation indicators.

7. The preferred method for recycled asphalt pavement structure of high-grade highways in cold regions according to claim 1, characterized in that, The dynamic determination of weighted fusion coefficients based on the cold region risk sensitivity index includes: The extreme low temperature coefficient is determined based on the lowest temperature in the project location within the next preset period to reflect the intensity of damage to the road surface caused by low temperatures. The freeze-thaw cycle coefficient is determined based on the annual number of freeze-thaw cycles at the project site to reflect the degree of damage to the regenerated layer caused by freeze-thaw cycles. The extreme low temperature coefficient and freeze-thaw cycle coefficient are used to calculate the risk sensitivity index of cold regions; Based on the cold region risk sensitivity index, a nonlinear function is used to calculate the weight fusion coefficient, and the dynamic adjustment of the weight fusion coefficient is verified and constrained.

8. The preferred method for recycled asphalt pavement structure of a high-grade highway in a cold region according to claim 7, characterized in that, The calculation of the cold region risk sensitivity index using the extreme low temperature coefficient and freeze-thaw cycle coefficient includes: The extreme low temperature coefficient is multiplied by the first preset weight to obtain the first weighted value; The freeze-thaw cycle coefficient is multiplied by the second preset weight to obtain the second weighted value; Add the first weighted value to the second weighted value to obtain the cold region risk sensitivity index; The expression for calculating the weight fusion coefficient using a nonlinear function is as follows: ； In the formula, The weighted fusion coefficient; This is a risk sensitivity index for cold regions.

9. The preferred method for the structure of recycled asphalt pavement for high-grade highways in cold regions according to claim 7, characterized in that, The weighted fusion coefficients calculated using a nonlinear function based on the cold region risk sensitivity index include: The risk offset is obtained by subtracting the preset benchmark value from the cold region risk sensitivity index. The risk offset is negatively represented as an exponent, and the exponent is raised to the power of the natural logarithm base to obtain the exponential decay value. Subtract the exponential decay value from the preset unit value to obtain the adjustment range; Multiply the adjustment amount by the preset volatility coefficient to obtain the weight increment; The weight increment is added to the preset base weight to obtain the weight fusion coefficient; The weighted fusion coefficient satisfies a value range from a preset lower limit to a preset upper limit.

10. The preferred method for recycled asphalt pavement structure of a high-grade highway in a cold region according to claim 1, characterized in that, The establishment of the evaluation matrix and the calculation of the relative similarity of each recycled pavement structure scheme based on comprehensive weights include: Establish relevant evaluation matrices based on the original index data of each recycled pavement structure scheme; After normalizing the relevant evaluation matrix, a standardized matrix considering combined weights is established by combining the comprehensive weights of each evaluation indicator. The optimal and worst value vectors are determined based on the standardization matrix. Calculate the Euclidean distance between each recycled pavement structure scheme and the optimal value vector, and calculate the Euclidean distance between each recycled pavement structure scheme and the worst value vector; Use the Euclidean distance between each recycled pavement structure scheme and the worst value vector as the numerator; use the sum of the Euclidean distances between each recycled pavement structure scheme and the best value vector and the worst value vector as the denominator. Dividing the numerator by the denominator yields the relative similarity of each recycled pavement structure scheme; The relative proximity value ranges from a preset minimum to a preset maximum value, with a larger value indicating a better evaluation object.

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