Design and performance evaluation method of fatigue-resistant high modulus asphalt mixture

Abstract

The application provides a design and performance evaluation method of fatigue-resistant high-modulus asphalt mixture, relates to the technical field of road engineering, and reduces parameter dependency by constructing a simplified dynamic modulus prediction model by using a historical database through multivariate nonlinear regression analysis, and simplifies the calculation of the equivalent modulus of asphalt mortar on asphalt shear modulus and filler volume fraction, so as to improve the generalization of the model with low cost and high practicability as the goal; the disconnection between gradation design and dynamic modulus optimization is solved by using an intelligent optimization algorithm, an optimization function is constructed in combination with a dynamic modulus target and a gradation constraint, and the optimization is carried out through a cross genetic and particle swarm algorithm, so that the designed optimal mixture can meet the performance indicators; the relative error closed-loop check between actual measurement and the target dynamic modulus is used to enhance the design criterion, and the problem that the prediction deviation leads to multiple design adjustments is solved; and the problems of long cycle and high cost in the traditional method are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of road engineering, in particular to a design and performance evaluation method of fatigue-resistant high-modulus asphalt mixture. BACKGROUND

[0002] Asphalt mixture originated in the late 19th century for road paving, and its design and performance evaluation method has evolved with the increase of traffic load and the impact of climate change. From the experience of proportioning method to the current Marshall method, SMA, Superpave system, emphasizing material structure optimization and functional improvement, focusing on comprehensive performance evaluation of anti-rutting, anti-cracking, durability, etc.

[0003] In the prior art, the patent number CN106938899B, named a design method of modified asphalt mixture suitable for different temperature difference regions; the invention belongs to the technical field of road engineering, and specifically relates to a design method of modified asphalt mixture suitable for different temperature difference regions. It includes determining the annual temperature difference of the region, selecting the type of asphalt mixture, selecting and testing raw materials, designing the gradation of mineral aggregates, determining the oil-stone ratio, preparing modified asphalt mixture, testing the cooling performance and road performance, determining the "hot variation point" and "cold variation point", and evaluating the modified asphalt mixture according to the "hot variation point" and "cold variation point". It effectively solves the difference of pavement temperature diseases in different regions of China, provides quantitative indicators for evaluating high-temperature stability and low-temperature crack resistance, and develops modified asphalt mixture that can improve road performance at high temperature and enhance low-temperature crack resistance, which has high engineering practical value.

[0004] However, the above-mentioned invention has the following technical defects in practical application:

[0005] The dynamic modulus prediction model is too complex: the model depends on multiple parameters, including asphalt mortar modulus and phase angle, and some parameters need to be obtained through complex tests, which increases the design cost and difficulty; nested calculation may cause error accumulation, affecting the prediction accuracy;

[0006] The gradation design and dynamic modulus optimization are disconnected: the initial gradation adjustment is only based on the voids in mineral aggregate (VMA), without considering the dynamic modulus target, which may lead to repeated adjustment of asphalt content and prolong the design cycle; local optimization of asphalt content may mask the potential impact of gradation on modulus, resulting in suboptimal performance of the mixture.

[0007] In addition, the complexity of the model exacerbates the contradiction between gradation and modulus; if the parameters are not accurate, the prediction deviation of dynamic modulus will force multiple adjustments, and even need to backtrack the gradation design, reducing efficiency. This method may not be popularized in engineering with limited test conditions.

[0008] The above information disclosed in the BACKGROUND section is only for enhancing the understanding of the background of the present disclosure, and therefore it can include information that does not constitute the prior art known to those of ordinary skill in the art. SUMMARY

[0009] The purpose of the present application is to provide a design and performance evaluation method of fatigue-resistant high modulus asphalt mixture to solve the problems raised in the background.

[0010] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0011] The design and performance evaluation method of fatigue-resistant high modulus asphalt mixture, the specific steps include:

[0012] Step one, obtain the basic input parameters required for target mixture design, including aggregate gradation range, asphalt type, asphalt dosage range and asphalt performance index, and constraint conditions composed of target dynamic modulus range requirements and volume index requirements;

[0013] Step two, based on the historical database, a simplified dynamic modulus prediction model is constructed, and a multiple nonlinear regression analysis method is used to express the functional relationship between dynamic modulus and basic input parameters by regression relationship;

[0014] Secondly, the equivalent modulus of asphalt mortar is calculated by using the shear modulus of asphalt and the volume fraction of filler ;

[0015] Step three, use intelligent optimization algorithm to generate multiple sets of initial mixture combinations within the aggregate gradation range, and predict the dynamic modulus of each initial mixture combination;

[0016] Screening initial mixture combinations that meet the target dynamic modulus range and volume index requirements at the same time, and taking these combinations as effective combinations;

[0017] Step four, global optimization is performed on the screened effective combinations, and the effective combination whose predicted dynamic modulus is closest to the target dynamic modulus range requirement is taken as the optimal combination;

[0018] Step five, output the optimal combination for specimen preparation and actual measurement verification, and evaluate the actual dynamic modulus BE of the designed mixture, and perform volume performance test at the same time;

[0019] By comparing the actual dynamic modulus BE with the design target dynamic modulus E tar , calculate the relative error ε and evaluate, secondly, adjust and review the parameters until the design requirements are met.

[0020] Further, the basic input parameters required for the target mixture design include:

[0021] The aggregate gradation range includes a distribution ratio of aggregate particles of different particle sizes;

[0022] The asphalt performance indicators include penetration, softening point, ductility, dynamic shear modulus, and phase angle;

[0023] The constraint conditions of the target dynamic modulus range include a target interval of the dynamic modulus under different temperatures and loading frequencies, and stability and limit constraints for ensuring that the dynamic modulus meets the requirements of actual working conditions;

[0024] The constraint conditions of the volume index requirements include a target interval of the air voids, the voids in the mineral aggregate, and the asphalt saturation;

[0025] All the basic input parameters are uniformly formatted and stored as basic input information for subsequent prediction modeling and gradation optimization calculation.

[0026] Further, the simplified dynamic modulus prediction model based on the historical database comprises:

[0027] The dynamic modulus of the known material combination is collected, and the historical database includes the aggregate gradation parameters, the asphalt performance indicators, the aggregate modulus, the test temperature, the frequency, and the dynamic modulus of each group of mixtures;

[0028] A multivariate nonlinear regression analysis method is used, the design parameters of the mixture are used as independent variables, and the dynamic modulus Em of the mixture is used as the dependent variable, to construct a simplified dynamic modulus prediction model;

[0029] The input of the simplified dynamic modulus prediction model includes the voids in the mineral aggregate VMA, the asphalt saturation VFA, the aggregate modulus Eag, the asphalt dynamic shear modulus Elq, and the filler volume fraction Vf of the designed mixture.

[0030] Further, the asphalt shear modulus and the filler volume fraction are used to calculate the asphalt mortar equivalent modulus, which comprises:

[0031] The asphalt mortar equivalent modulus The specific calculation formula is:

[0032]

[0033] In the formula, is the asphalt shear modulus; is the filler volume fraction;

[0034] The constant 2.5 is a mortar enhancement factor based on experimental fitting, which is used to reflect the strengthening effect of the filler in the mortar system on the stiffness contribution.

[0035] Further, the initial mixture combination of the gradation and asphalt content generated in the initial gradation range by the intelligent optimization algorithm comprises:

[0036] Set input parameters, including asphalt mortar equivalent modulus , aggregate gradation range of target mixture, adjustable asphalt content interval, design temperature and loading frequency, target dynamic modulus interval and volume index requirement;

[0037] Based on the particle swarm optimization algorithm, an initial population is constructed, and each individual in the population represents an initial mixture combination of gradation and asphalt content;

[0038] Set the optimization objective function, and the minimum error between the predicted dynamic modulus and the target value is the main optimization objective, and the volume index requirement is set as a hard constraint condition;

[0039] Iterative evolution of the population is performed using the crossover genetic mechanism, and high-quality gradation asphalt content combinations in the design space are gradually selected.

[0040] Further, the dynamic modulus of each initial mixture combination is predicted, and combinations that meet the predicted dynamic modulus and volume index constraint conditions are selected, comprising:

[0041] For each combination of gradation and asphalt content generated by the intelligent optimization algorithm, a simplified dynamic modulus prediction model is called to calculate, and the simplified dynamic modulus prediction model outputs the predicted dynamic modulus AE;

[0042] The predicted volume index parameters corresponding to each combination are also calculated, including the predicted mineral aggregate void ratio A VMA And the predicted asphalt saturation A VFA , and whether it meets the minimum requirement of the design specification is judged;

[0043] The combinations whose predicted dynamic modulus falls within the preset target interval and whose volume parameters meet the specification requirements are selected out of all combination schemes to form a feasible solution set;

[0044] The preset target modulus sorts all combination schemes in the feasible solution set according to the deviation from the target modulus, and the combination scheme with the smallest deviation value is selected as the final recommended design scheme for subsequent test verification and actual application.

[0045] Further, the combination schemes constituting the feasible solution set after screening are globally optimized to determine the optimal combination whose predicted dynamic modulus is closest to the target value, comprising:

[0046] All combinations of gradation and asphalt content in the feasible solution set are used as optimization candidate solutions; set the optimization objective function, and the objective function is the predicted dynamic modulus AE of the combination scheme and the preset target dynamic modulus E tarThe difference between the two is the main evaluation index, and the goal is to minimize the difference;

[0047] In the optimization process, traversal sorting, local fine-tuning or secondary intelligent optimization algorithm is used to finely screen the candidate combinations, including local hill climbing algorithm and mutation adjustment;

[0048] Calculate and compare the target function value of each candidate solution, and select the combination scheme with the minimum target function value as the current optimal solution;

[0049] If there are multiple combinations with approximately the same target function value, further screening is performed according to the secondary optimization index, including the maximum value in the VFA optimal interval, density consistency or economic parameter, to determine that the selected final scheme has engineering applicability in comprehensive performance.

[0050] Further, the volume performance index required by the design specification is considered in the global optimization process to ensure that the determined optimal combination meets the national or industry standards under the premise of meeting the target dynamic modulus, including:

[0051] For each candidate combination, the volume parameters such as predicted aggregate void ratio A VMA , predicted asphalt saturation A VFA and predicted void ratio A Va are tested to see if they are within the permissible range set by the specification;

[0052] Set a hard constraint rule, that is, if any combination does not meet the lower limit of the volume performance parameter or exceeds the recommended upper limit, the combination is automatically excluded from the final evaluation set;

[0053] In the remaining combinations, the scheme with the predicted dynamic modulus closest to the target value is preferentially selected, and if there are equivalent candidates, further comparison of the stability index or parameter adjustment sensitivity of the volume performance is performed, and the design scheme with larger adjustment margin is preferentially selected;

[0054] Output the final optimal gradation ratio and asphalt content combination to provide basic data support for subsequent test ratio verification and engineering practical application.

[0055] Further, the optimal gradation ratio and asphalt content combination is used for specimen preparation and actual measurement verification to evaluate the actual performance of the designed mixture, including:

[0056] According to the corresponding industry standard, the optimal gradation ratio and asphalt content determined is used to configure the mixture and mix, form and compact according to the standard method to prepare standard specimens for dynamic modulus testing;

[0057] Under specified temperature and frequency conditions, the actual dynamic modulus BE of the specimen is measured using a dynamic modulus tester;

[0058] At the same time, the volume performance test is carried out on the test piece to obtain the actual void ratio B Va , the actual mineral aggregate void ratio B VMA and the actual asphalt saturation B VFA .

[0059] Further, the test piece verification process further comprises judging whether the measured performance meets the target design requirement and closed-loop checking, comprising:

[0060] Comparing the actual dynamic modulus BE with the design target dynamic modulus E tar , calculating the relative error ε, and the specific calculation formula is as follows:

[0061]

[0062] The preset relative error threshold M is compared and evaluated with the relative error ε to judge whether it is within the preset tolerance range;

[0063] If the relative error ε is less than or equal to the relative error threshold M, it indicates that the measured result is within the tolerance range, and it is determined to meet the requirement, at this time, the next stage is entered;

[0064] If the relative error ε is greater than the relative error threshold M, it indicates that the measured result exceeds the tolerance range, and it is determined to not meet the requirement, at this time, the following measures are taken, comprising:

[0065] Cause analysis: check the potential problems of materials, processes or test methods;

[0066] Adjustment and optimization: correct the parameters and retest, including the NPCM content and the asphalt modification ratio;

[0067] Threshold review: if the number of overruns is greater than twice, the rationality of the threshold M is re-evaluated at this time, and the equivalent modulus of asphalt mortar is recalculated and adjusted accordingly, and re-evaluated until the measured result is within the tolerance range.

[0068] Compared with the prior art, the beneficial effects of the present application are:

[0069] In the model construction, the present application adopts multivariate nonlinear regression analysis of the historical database to simplify the dynamic modulus prediction model, reduce dependence, and simplify and optimize the calculation of the equivalent modulus of asphalt mortar through the asphalt shear modulus and the filler volume fraction, emphasizes low cost and practicality as the target orientation, and improves the promotion applicability of the model;

[0070] Secondly, the application solves the disconnection of the gradation design and the dynamic modulus optimization process: in the traditional design method, the initial gradation adjustment is usually only based on the numerical value of the gap rate of the mineral aggregate, and the design is constrained by the target dynamic modulus range, ignoring the dynamic modulus target constraint condition, which may lead to repeated adjustment of the asphalt content, prolong the design cycle and complicate the design process; to solve this problem, an intelligent optimization algorithm is used to construct the initial population gradation scheme based on the optimization objective function of the dynamic modulus target and the gradation constraint condition, and the comprehensive optimization of the dynamic modulus and the gradation is realized through crossbreeding and particle swarm optimization, so as to ensure that the designed optimal mixture combination can meet all performance indicators at one time;

[0071] Finally, the application solves the contradiction between the gradation and the modulus caused by the complexity of the model, and the design criteria are strengthened through the closed-loop verification of the actual measurement and the design target; if the preset dynamic modulus prediction parameter is inaccurate and leads to prediction deviation, the traditional method is easy to force multiple adjustments of the design and needs to backtrack to the gradation design, thereby reducing the design efficiency; therefore, the method judges the accuracy of the design through the relative error between the actual dynamic modulus and the design target dynamic modulus, sets a tolerance range, and if there is deviation, the reason is analyzed and the parameter is optimized, so as to ensure the rationality and operability of the design criteria, so that it can be popularized in actual engineering, and the problems of long design cycle and high cost are solved. BRIEF DESCRIPTION OF DRAWINGS

[0072] Figure 1 The figure is a schematic diagram of the overall method flow of the application. DETAILED DESCRIPTION

[0073] In order to make the purpose, technical scheme and advantages of the application clearer and more apparent, the application will be further described in detail below with specific examples.

[0074] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the application should be understood as the usual meaning understood by those skilled in the art to which the application belongs. The "first", "second" and similar words used in the application do not represent any order, quantity or importance, but are only used to distinguish different components. "Include" or "contain" and similar words mean that the elements or objects before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connected" or "connected" and similar words are not limited to physical or mechanical connection, but can include electrical connection, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to represent the relative positional relationship, when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0075] Example one:

[0076] Please refer toFigure 1 The application provides a technical solution: a design and performance evaluation method of fatigue-resistant high-modulus asphalt mixture, and the specific steps include:

[0077] Step one, obtain the basic input parameters required for the target mixture design, including aggregate gradation range, asphalt type, asphalt dosage range and asphalt performance index, and constraint conditions composed of target dynamic modulus range requirements and volume index requirements;

[0078] Step two, construct a simplified dynamic modulus prediction model based on a historical database, and use a multivariate nonlinear regression analysis method to express the functional relationship between dynamic modulus and basic input parameters in a regression relationship;

[0079] Secondly, the equivalent modulus of asphalt mortar is calculated by using the asphalt shear modulus and the filler volume fraction ;

[0080] Step three, use an intelligent optimization algorithm to generate multiple initial mixture combinations within the aggregate gradation range, and predict the dynamic modulus of each initial mixture combination;

[0081] Screen the initial mixture combinations that meet the target dynamic modulus range and volume index requirements at the same time, and use these combinations as effective combinations;

[0082] Step four, perform global optimization on the screened effective combinations, and use the effective combination whose predicted dynamic modulus is closest to the target dynamic modulus range requirement as the optimal combination;

[0083] Step five, output the optimal combination for specimen preparation and actual measurement verification, and evaluate the actual dynamic modulus BE of the designed mixture, and perform volume performance testing;

[0084] By comparing the actual dynamic modulus BE with the design target dynamic modulus E tar , calculate the relative error ε and evaluate it, and then adjust and review the parameters until the design requirements are met.

[0085] In this embodiment, a simplified dynamic modulus prediction model is constructed through multivariate nonlinear regression analysis of a historical database, reducing the dependence on complex parameters, optimizing the combined calculation of asphalt shear modulus and filler volume fraction, and improving the practicability of the model;

[0086] An intelligent optimization algorithm is used to combine the dynamic modulus target and the volume index requirement to generate initial mixture combinations and screen out effective combinations, solving the problem of disconnection between gradation adjustment and dynamic modulus optimization in traditional design;

[0087] Through global optimization, the optimal combination of dynamic modulus prediction value closest to the target modulus is selected to improve design accuracy and efficiency; through the error evaluation of the actual dynamic modulus BE and the target value, the closed-loop check is verified to strengthen the design rationality and avoid multiple adjustments and rework;

[0088] Among them, the basic parameter input is used as the basis for dynamic modulus prediction, the prediction result is input into the optimization algorithm to generate and screen combinations, the screening result is selected by global optimization to select the optimal combination for specimen verification, and the relative error ε is fed back to adjust to ensure that the final design meets the performance requirements;

[0089] This method effectively solves the problems of high prediction complexity, optimization disconnection and long adjustment cycle in traditional design, and improves the accuracy, efficiency and generalization of the design.

[0090] Embodiment 2

[0091] The basic input parameters required for the target mixture design include:

[0092] The aggregate gradation range includes the distribution proportion of different particle size aggregate particles;

[0093] The asphalt performance indicators include penetration, softening point, ductility, dynamic shear modulus and phase angle;

[0094] The constraint conditions of the target dynamic modulus range include the target interval of dynamic modulus under different temperature and loading frequency conditions, and the stability and limit constraints to ensure that the dynamic modulus meets the actual working condition requirements;

[0095] The constraint conditions of the volume index requirements include the target interval of the interstitial porosity, void ratio and asphalt saturation of the mineral aggregate;

[0096] Further, the stability and limit constraints of the actual working condition requirements specifically include determining the applicable aggregate types and their particle size distribution range according to the road grade and climate conditions of the engineering project, and setting the upper and lower limits of the aggregate gradation sieve pass rate; and the specific aggregate types and particle size distribution range are selected according to the actual working condition requirements, including different types of aggregate such as basalt and limestone, and the pore structure and interaction between particles of the mixture are controlled by setting the upper and lower limits of the aggregate gradation range and its sieve pass rate.

[0097] All the basic input parameters are uniformly formatted and stored as the basic input information for subsequent prediction modeling and gradation optimization calculation.

[0098] The simplified dynamic modulus prediction model based on the historical database includes:

[0099] The dynamic modulus measured data of known material combinations are collected, and the historical database contains the aggregate gradation parameters, asphalt performance indicators, aggregate modulus, test temperature, frequency and dynamic modulus measured values corresponding to each group of mixtures;

[0100] A simplified dynamic modulus prediction model is constructed by using multivariate nonlinear regression analysis method, taking the design parameters of the mixture as the independent variables and the dynamic modulus Em of the mixture as the dependent variable.

[0101] The inputs of the simplified dynamic modulus prediction model include the voids in the mineral aggregate VMA, the asphalt saturation VFA, the aggregate modulus Eag, the asphalt dynamic shear modulus Elq and the filler volume fraction Vf of the designed mixture.

[0102] Further, the calculation formula of the predicted dynamic modulus AE is as follows:

[0103]

[0104] Wherein, k, a, b, c, d are empirical coefficients fitted from the historical database by regression analysis; VMA is the voids in the mineral aggregate, VFA is the asphalt saturation, Eag is the aggregate modulus, Elq is the effective modulus of asphalt mortar.

[0105] Further, the asphalt mortar equivalent modulus is a stiffness parameter for evaluating the dynamic performance of asphalt mixture predicted by the regression model, and the asphalt mortar effective modulus is an actual stiffness value calculated based on experimental data and asphalt shear modulus.

[0106] In this embodiment, by collecting historical data including aggregate gradation parameters, asphalt performance indicators, aggregate modulus Eag, test temperature, frequency and dynamic modulus measured values, a simplified dynamic modulus prediction model is constructed by using multivariate nonlinear regression analysis method, which significantly reduces the dependence of dynamic modulus prediction on complex input parameters; the basic design parameters including the voids in the mineral aggregate VMA, the asphalt saturation VFA, the filler volume fraction Vf and the asphalt shear modulus Elq are stored in a unified format, which provides reliable input information for subsequent dynamic modulus prediction and gradation optimization calculation;

[0107] Through the predicted dynamic modulus AE calculation formula, the empirical coefficients k, a, b, c, d are fitted and combined with the functional relationship between the parameters, to ensure that the dynamic modulus prediction model has simplicity and accuracy; the voids in the mineral aggregate VMA reflects the aggregate packing characteristics, the asphalt saturation VFA reflects the distribution performance of asphalt in the mineral aggregate, the filler volume fraction Vf reveals the contribution of filler to the stiffness of the mixture, and the asphalt dynamic shear modulus Elq and the aggregate modulus Eag are the expressions of the key mechanical properties.

[0108] The asphalt mortar effective modulus As the core variable of dynamic modulus prediction, it is directly calculated based on shear modulus, avoiding complex parameter test measurement, significantly improving the practicability and application promotion value of the system, and optimizing the design module to perfect the systematic mechanism of mixture design, effectively balancing the model accuracy and design efficiency.

[0109] Embodiment 3

[0110] The asphalt mortar equivalent modulus is calculated by using the asphalt shear modulus and the filler volume fraction, including:

[0111] The asphalt mortar equivalent modulus The specific calculation formula is:

[0112]

[0113] In the formula, is the asphalt shear modulus; is the filler volume fraction;

[0114] Among them, the constant 2.5 is the mortar enhancement factor based on experimental fitting, which is used to reflect the strengthening effect of the filler in the mortar system on the stiffness contribution.

[0115] According to the type of selected asphalt binder and test temperature, the dynamic shear modulus of the selected asphalt binder is measured by dynamic shear rheometer under standard loading frequency;

[0116] Combined with the filler volume fraction Vf in the design, the asphalt mortar equivalent modulus is calculated by substituting the empirical formula .

[0117] Further, the asphalt mortar equivalent modulus The calculation process does not need to introduce phase angle or other difficult-to-directly-obtain parameters, effectively simplifying the acquisition path of asphalt mortar modulus, improving the practicability and promotion ability of the model, and providing quantifiable and low-cost key input variables for dynamic modulus prediction model.

[0118] The intelligent optimization algorithm is used to generate a plurality of initial mixture combinations of gradation and asphalt content in the initial gradation range, including:

[0119] Set the input parameters, including the asphalt mortar equivalent modulus , the aggregate gradation range of the target mixture, the adjustable asphalt content interval, the design temperature and loading frequency, the target dynamic modulus interval and the volume index requirement;

[0120] Based on the particle swarm optimization algorithm, an initial population is constructed, and each individual in the population represents an initial mixture combination of gradation and asphalt content;

[0121] The optimization objective function is set to minimize the error between the predicted dynamic modulus and the target value as the main optimization objective, and the volume index requirement is set as a hard constraint condition;

[0122] The population is iteratively evolved using the crossover genetic mechanism, and high-quality gradation asphalt dosage combinations in the design space are gradually screened out.

[0123] Further, by gradually screening the gradation asphalt dosage combination, the global optimization efficiency is improved and the diversity and applicability of the design results are ensured.

[0124] The dynamic modulus of each initial mixture combination is predicted, and combinations that meet the predicted dynamic modulus and volume index constraint conditions are screened out, including:

[0125] For each gradation and asphalt dosage combination generated by the intelligent optimization algorithm, a simplified dynamic modulus prediction model is called to calculate the predicted dynamic modulus AE using the simplified dynamic modulus prediction model output.

[0126] The predicted volume index parameters of each combination are also calculated, including the predicted interstitial porosity of the mineral aggregate A VMA And the predicted asphalt saturation A VFA And determine whether it meets the minimum requirements of the design specification.

[0127] Further, the specific calculation formula of the predicted interstitial porosity of the mineral aggregate A VMA

[0128]

[0129] Where Gmb is the measured Marshall density of the mixture, Pb is the asphalt dosage mass percentage, and Gsb is the apparent specific gravity of the aggregate.

[0130] The specific calculation formula of the predicted asphalt saturation A VFA

[0131]

[0132] Where Va is the air void of the mixture, which is input according to the target design value or preliminary estimate. In this embodiment, And After unified dimensionless processing, the output values are in the same range; in this embodiment, the range is set to the interval (0, 1).

[0133] It should be noted that the difference between VMA and Va is always positive, because the total void of the mineral aggregate interstitial space always contains air void, and the value of VMA must be greater than or equal to Va.

[0134] ​​The results in which the predicted dynamic modulus of all combination schemes in all groups falls within the preset target interval and the volume parameter meets the specification requirements are screened out to form a feasible solution set;

[0135] The preset target modulus sorts all combination schemes in the feasible solution set according to the deviation from the target modulus, and the combination scheme with the smallest deviation value is preferentially selected as the final recommended design scheme for subsequent test verification and actual application.

[0136] In this embodiment, the equivalent modulus of asphalt mortar is obtained through the combination calculation of asphalt shear modulus and filler volume fraction, the constant 2.5 is used as the mortar enhancement factor, the measurement path is simplified, the introduction of other complex parameters is avoided, and the practicability and popularization ability of the model are improved;

[0137] The intelligent optimization algorithm generates multiple groups of gradation and asphalt dosage combinations within the initial gradation range, iteratively optimizes through particle swarm optimization and cross genetic mechanism, improves the global optimization efficiency, ensures the diversity and applicability of the design, sets the optimization objective function to minimize the error between the predicted dynamic modulus and the target value, and combines the volume index constraint to ensure the overall performance balance;

[0138] The predicted dynamic modulus AE of each combination is calculated through the simplified dynamic modulus prediction model, and the volume index parameters such as the predicted mineral aggregate interstitial porosity A VMA and the predicted asphalt saturation A VFA are predicted to ensure that the volume performance meets the standard requirements and provides precision assurance for each module in the system;

[0139] This embodiment avoids other complexities while improving the overall design efficiency and experimental feasibility. By combining the measurement and calculation of key parameters, the practicability and popularization are significantly improved, providing an efficient and reliable design scheme in practical applications.

[0140] Embodiment 4

[0141] The combination schemes constituting the feasible solution set after screening are globally optimized to determine the optimal combination whose predicted dynamic modulus is closest to the target value, including:

[0142] All gradation and asphalt dosage combinations in the feasible solution set are used as optimization candidate solutions; the optimization objective function is set, and the difference between the predicted dynamic modulus AE of the combination scheme and the preset target dynamic modulus E tar is used as the main evaluation index, and the goal is to minimize the difference;

[0143] During the optimization process, traversal sorting, local fine-tuning or secondary intelligent optimization algorithm is used to finely screen the candidate combinations, including local hill climbing algorithm and mutation adjustment;

[0144] The target function value of each candidate solution is calculated and compared, and the combination scheme with the minimum target function value is selected as the current optimal solution;

[0145] If there are multiple combinations with approximately the same target function value, further screening is performed according to the secondary preferred index, including the maximum value in the VFA optimal interval, the density consistency or the economic parameter, to determine that the selected final scheme has engineering applicability in comprehensive performance.

[0146] The volume performance index required by the design specification is considered in the global optimization process to ensure that the determined optimal combination meets the national or industry standards under the premise of meeting the target dynamic modulus, including:

[0147] For each candidate combination, the volume parameters, such as the predicted aggregate void ratio A VMA , the predicted asphalt saturation A VFA and the predicted void ratio A Va , are tested to see if they are within the permissible range set by the specification when calculating the dynamic modulus difference;

[0148] A hard constraint rule is set, i.e. if any combination does not meet the lower limit of the volume performance parameter or exceeds the recommended upper limit, the combination is automatically excluded from the final evaluation set;

[0149] Among the remaining combinations, the scheme with the predicted dynamic modulus closest to the target value is preferred, and if there are equivalent candidates, further comparison of the stability index or parameter adjustment sensitivity of the volume performance is performed, and the design scheme with larger adjustment margin is preferred;

[0150] The final optimal proportioning and asphalt dosage combination is output, providing basic data support for subsequent test proportioning verification and engineering practical application.

[0151] Table 1 Optimal combination with predicted dynamic modulus closest to target value:

[0152]

[0153] Based on Table 1, in the global optimization analysis process, first, the initial candidate combinations are screened to exclude schemes whose predicted dynamic modulus does not fall within the target dynamic modulus range, i.e. 3000MPa-5000MPa, among which the predicted dynamic modulus of A3 is 2950MPa, which is lower than the target range, so it is directly excluded;

[0154] For the remaining A1, A2, A4 and A5 combinations, it is calculated whether their volume performance indexes meet the specification requirements, including the aggregate void ratio VMA, the asphalt saturation VFA and the void ratio Va, and the results show that the VFA value of A4 is 75.0%, which just meets the specification, but it is close to the upper limit, indicating that there is a performance stability problem, so it is excluded;

[0155] In the remaining A1, A2 and A5 scheme, the target function value is calculated in turn, that is, the deviation size of the predicted dynamic modulus AE and the target dynamic modulus center value 4000MPa, the results show that A1 is 900MPa, A2 is 800MPa and A5 is 700MPa respectively;

[0156] Under the premise of meeting all the volume performance specifications, the A5 with the smallest error is selected as the current optimal scheme; further comparison of the secondary comprehensive performance indicators, including the volume performance stability and the sensitivity of parameter adjustment, shows that the voids in mineral aggregate VMA and the asphalt saturation VFA of A5 are both in the middle of the target interval, and the air void Va can be flexibly adjusted, so A5 is finally determined as the optimal combination for subsequent test verification and actual engineering application.

[0157] In this embodiment, through the optimization algorithm combined with the global optimization mechanism, the precise matching of the dynamic modulus and the design target is realized, while the strict control of the volume performance indicators is also considered to ensure the comprehensive performance and engineering applicability of the optimized scheme;

[0158] The candidate schemes are screened by using the traversal sorting, local fine-tuning or secondary intelligent optimization algorithm, avoiding the randomness and inefficiency of the tuning process in the traditional method; by minimizing the difference between the predicted dynamic modulus AE and the target dynamic modulus E tar , as the optimization objective function, and combining with the range constraints of the voids in mineral aggregate VMA and the asphalt saturation VFA, the balance between the mechanical properties and the durability of the mixture is further ensured;

[0159] When evaluating the candidate schemes, the standard limits of the predicted parameters and the flexibility of the actual adjustment are fully considered, so that the final optimal combination not only has high prediction accuracy, but also has performance stability; the significance of this module in the system is to greatly improve the reliability and implementation efficiency of the design scheme, reduce the trial and error cost, and at the same time provide a clear direction and optimization basis for subsequent test verification;

[0160] The significance of parameter acquisition and calculation is to express the comprehensive mechanical properties of the mixture by the predicted dynamic modulus AE, to reflect the volume performance and material matching relationship by the voids in mineral aggregate VMA and the asphalt saturation VFA, and to embody the workability and compactness of the mixture by the air void Va, so that the design scheme has theoretical guidance and actual economic value by combining all the calculation and evaluation with the standard constraints.

[0161] Example 5

[0162] The optimal gradation ratio and the asphalt content combination are used for specimen preparation and actual measurement verification to evaluate the actual performance of the designed mixture, including:

[0163] According to the corresponding industry standard, the optimal level of mixture and asphalt content is determined, and the standard specimen for dynamic modulus test is prepared by mixing, molding and compaction according to the standard method;

[0164] Under the specified temperature and frequency conditions, the actual dynamic modulus BE of the specimen is measured by using the dynamic modulus tester;

[0165] At the same time, the volume performance test is carried out on the specimen to obtain the actual air voids B Va , actual mineral aggregate voids B VMA and actual asphalt saturation B VFA .

[0166] The specimen verification process further includes the determination of whether the measured performance meets the target design requirements and the closed-loop verification, including:

[0167] The actual dynamic modulus BE is compared with the design target dynamic modulus E tar , and the relative error ε is calculated, and the specific calculation formula is as follows:

[0168]

[0169] The preset relative error threshold M is compared and evaluated with the relative error ε to determine whether it is within the preset tolerance range;

[0170] If the relative error ε is less than or equal to the relative error threshold M, it means that the measured result is within the tolerance range, and it is determined to meet the requirements, and then the next stage is entered;

[0171] If the relative error ε is greater than the relative error threshold M, it means that the measured result is out of the tolerance range, and it is determined to not meet the requirements, and then the following measures are taken, including:

[0172] Cause analysis: check the potential problems of materials, processes or test methods;

[0173] Adjustment and optimization: correct the parameters and retest, including the NPCM content and the asphalt modification ratio;

[0174] Threshold review: if the number of overruns is greater than twice, the rationality of the threshold M setting is reevaluated, and the equivalent modulus of asphalt mortar is recalculated and adjusted accordingly, and reevaluated until the measured result is within the tolerance range.

[0175] Table 2: Optimal asphalt mixture specimen preparation and performance verification results:

[0176]

[0177] Based on Table 2, the actual performance of the optimal level of mixture and asphalt content combination is evaluated by the specimen preparation and measured verification method;

[0178] First, according to the optimal design scheme determined in advance, the mixture is mixed, formed and compacted according to the industry standard, and the standard test specimen for dynamic modulus test is prepared; then the actual dynamic modulus BE of the specimen is measured by using the dynamic modulus tester under the condition of specified temperature and frequency, and compared with the design target dynamic modulus E tar By comparison, the relative error ε is calculated to evaluate whether it meets the preset tolerance range;

[0179] At the same time, the volume performance of the specimen is tested, including the actual void ratio B Va , the interstitial ratio of mineral aggregate B VMA and the asphalt saturation B VFA and other volume parameters, to comprehensively judge whether it meets the industry standard;

[0180] If the relative error ε meets the preset threshold M requirement, it is considered that the performance of the specimen meets the target requirement and enters the next stage; if it does not meet the requirement, the material, process and test process need to be analyzed, the grading parameters, asphalt content or modification ratio are adjusted, and the rationality of the threshold M is reviewed, through closed loop verification to continuously optimize the design, and finally ensure the good matching of the performance of the mixture and the design target.

[0181] In this embodiment, through the systematic specimen preparation and verification process, a closed loop verification mechanism of design and actual measurement is realized, which ensures the comprehensive compliance of dynamic modulus and volume performance. Compared with the prior art, more attention is paid to the complete and unified standardization verification steps in actual operation, and the precision is improved;

[0182] The collected parameters include actual dynamic modulus BE, actual void ratio B Va , actual interstitial ratio of mineral aggregate B VMA and actual asphalt saturation B VFA , which respectively reflect the mechanical properties and volume stability characteristics of the material. The purpose of calculating the relative error ε is to quantitatively evaluate the deviation between the actual measurement and the target, so as to guide further optimization and adjustment. The significance of this module in the system is to strengthen the consistency between design prediction and actual performance, ensure the reliability and quality control of engineering application, and realize the best allocation and utilization of resources;

[0183] Secondly, when the evaluation of the relative error ε is deviated due to unexpected reasons, if the relative error ε exceeds the preset tolerance threshold M, the influence of material quality, construction process and experimental method needs to be further analyzed. For the instances where the equivalent modulus of asphalt mortar is abnormal or does not meet the standard, the equivalent modulus of mortar is considered to be recalculated , the filler volume fraction Vf or the proportion of modified asphalt is adjusted to optimize the contribution of its enhancement factor;

[0184] At the same time, the adjustment rationality of the error is verified through laboratory small-scale test or simulation calculation;

[0185] If the number of overruns exceeds twice, the applicability of the threshold M is evaluated based on the data of repeated tests combined with actual working conditions, and then the process verification is carried out based on the new parameters after repeated optimization, the temporary deviation of the design scheme is adjusted through test, until the measured results are within the design tolerance range and meet the process requirements;

[0186] This comprehensive scheme emphasizes the adaptability of technical parameters, process implementation and repeated verification, and ensures that the product performance is stable and reliable in dynamic working conditions.

[0187] It should be noted that: all the calculation formulas in the present application file use regression analysis including but not limited to machine learning algorithm to deeply analyze the collected relevant parameters, identify their natural trend and mutual relationship. Professional software such as Python Scikit-learn library or R language is used to automatically generate mathematical models matched with the data. Then, the model performance is objectively evaluated through cross-validation and other methods, and combined with continuous feedback and optimization, to ensure that the created formula truly reflects the inherent law of data, so as to ensure its effectiveness and accuracy. In all the calculation formulas in the present application, the parameters in each formula are processed by consistent range of dimensionless, to ensure that different physical quantities are compared on the same scale; The dimensionless technology means includes but is not limited to Min-Max normalization, Z-Score standardization;

[0188] The technical scheme of the present application can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH, a hard disk or an optical disk, etc., including a number of instructions for making a computer device (which can be a personal computer, a server, or a network device, etc.) execute the method of each embodiment of the present application.

[0189] The logic and / or steps represented in the flow diagrams and / or otherwise described herein, for example, can be considered as a sequence of executable instructions, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0190] It should be noted that the above-mentioned embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, and although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the present application, and all should be covered in the scope of the claims of the present application.

[0191] It should be noted that the above-mentioned embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, and although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the present application, and all should be covered in the scope of the claims of the present application.

Claims

1. A method for designing and evaluating the performance of fatigue resistant high modulus asphalt mixtures, characterized in that, The specific steps include: Step one, obtaining the basic input parameters required for target mixture design, including aggregate gradation range, asphalt type, asphalt dosage range and asphalt performance index, and constraint conditions composed of target dynamic modulus range requirements and volume index requirements; Step two, constructing a simplified dynamic modulus prediction model based on the historical database, using multivariate nonlinear regression analysis method to express the functional relationship between dynamic modulus and basic input parameters by regression relationship; Secondly, the asphalt mortar equivalent modulus is calculated by using the asphalt shear modulus and filler volume fraction ; Step three, generating multiple sets of initial mixture combinations within the aggregate gradation range using intelligent optimization algorithm, and predicting the dynamic modulus of each initial mixture combination through the simplified dynamic modulus prediction model; Screening the initial mixture combinations that meet the target dynamic modulus range and volume index requirements at the same time, and taking these combinations as effective combinations; Step four, performing global optimization on the screened effective combinations, and taking the effective combination with the predicted dynamic modulus closest to the target dynamic modulus range requirement as the optimal combination; Step five, outputting the optimal combination for specimen preparation and actual measurement verification, and evaluating the actual dynamic modulus BE of the designed mixture, while testing the volume performance; The relative error ε is calculated by comparing the actual dynamic modulus BE with the design target dynamic modulus E tar , and is evaluated. Then, parameter adjustment and review are carried out until the design requirements are met.

2. The method for design and performance evaluation of fatigue resistant high modulus asphalt mixture according to claim 1, characterized in that: The basic input parameters required for target mixture design include: The aggregate gradation range includes the distribution proportion of different particle size aggregate particles; The asphalt performance index includes penetration, softening point, ductility, dynamic shear modulus and phase angle; The constraint conditions of the target dynamic modulus range include the target interval of dynamic modulus under different temperature and loading frequency conditions, and the stability and limit constraints to ensure that the dynamic modulus meets the actual working condition requirements; The constraint conditions of the volume index requirements include the target interval of the air voids, the voids in the mineral aggregate and the asphalt saturation.

3. The method for designing and evaluating the performance of fatigue resistant high modulus asphalt mixture according to claim 2, characterized in that: Constructing a simplified dynamic modulus prediction model based on the historical database includes: Collecting the dynamic modulus measurement data of known material combinations, the historical database contains the aggregate gradation parameters, asphalt performance index, aggregate modulus, test temperature, frequency and dynamic modulus measurement value of each mixture; Using multivariate nonlinear regression analysis method, taking the design parameters of the mixture as independent variables and the dynamic modulus Em of the mixture as dependent variables, to construct a simplified dynamic modulus prediction model; The inputs of the simplified dynamic modulus prediction model include: the air voids VMA, the asphalt saturation VFA, the aggregate modulus Eag, the asphalt dynamic shear modulus Elq and the filler volume fraction Vf of the mixture to be designed.

4. The method for designing and evaluating the performance of fatigue resistant high modulus asphalt mixture according to claim 3, characterized in that: The equivalent modulus of asphalt mortar is calculated by using the asphalt shear modulus and the filler volume fraction comprising: Asphalt mortar equivalent modulus The specific calculation formula is: ; wherein G is the asphalt shear modulus; φ is the filler volume fraction; Among them, the constant 2.5 is the mortar enhancement factor based on experimental fitting, which is used to reflect the strengthening effect of filler in the mortar system on the stiffness contribution.

5. The method for designing and evaluating the performance of fatigue resistant high modulus asphalt mixture according to claim 4, characterized in that: Generating multiple sets of initial mixture combinations of gradation and asphalt dosage within the initial gradation range using intelligent optimization algorithm includes: Setting input parameters, including asphalt mortar equivalent modulus , aggregate gradation range of target mixture, adjustable asphalt content interval, design temperature and loading frequency, target dynamic modulus interval and volume index requirement; Based on the particle swarm optimization algorithm, an initial population is constructed, and each individual in the population represents an initial mixture combination of gradation and asphalt dosage; Set the optimization objective function, take the minimum error between the predicted dynamic modulus and the target value as the main optimization objective, and set the volume index requirement as a hard constraint condition; Use the crossover genetic mechanism to iteratively evolve the population, and gradually select high-quality gradation and asphalt dosage combinations in the design space.

6. The method for designing and evaluating the performance of fatigue resistant high modulus asphalt mixture according to claim 5, characterized in that: Predict the dynamic modulus of each initial mixture combination, and select combinations that meet both the predicted dynamic modulus and volume index constraints, including: For each combination of gradation and asphalt content generated by the intelligent optimization algorithm, call the simplified dynamic modulus prediction model for calculation, and use the simplified dynamic modulus prediction model to output the predicted dynamic modulus AE. At the same time, the expected volume index parameters corresponding to each group of combinations are calculated, including the expected aggregate interstitial porosity A VMA and the expected asphalt saturation A VFA and whether it meets the minimum requirements of the design specification is determined. Filter out the results that meet the specification requirements for the volume parameters and have predicted dynamic modulus within the preset target interval from all combination schemes to form a feasible solution set. Sort all combination schemes in the feasible solution set according to the deviation from the target modulus, and select the combination scheme with the smallest deviation value as the final recommended design scheme for subsequent test verification and practical application.

7. The method for designing and evaluating the performance of fatigue resistant high modulus asphalt mixture according to claim 6, characterized in that: Perform global optimization on the combination schemes that constitute the feasible solution set after screening to determine the optimal combination with the predicted dynamic modulus closest to the target value, including: All the gradation and asphalt dosage combinations in the feasible solution set are taken as optimization candidate solutions; an optimization objective function is set, the difference between the predicted dynamic modulus AE of the combination scheme and the preset target dynamic modulus E is taken as the main evaluation index, and the target is to minimize the difference tar ​ Use traversal sorting, local fine-tuning, or secondary intelligent optimization algorithms to finely screen candidate combinations during optimization, including local hill climbing algorithm and mutation adjustment; Calculate and compare the target function values of each candidate solution, and select the combination scheme with the smallest target function value as the current optimal solution; If there are multiple combinations with approximately the same target function value, further filter them according to the secondary selection indicators, including the maximum value within the optimal VFA interval, density consistency, or economic parameters, to determine the final selected scheme in terms of comprehensive performance.

8. The method for designing and evaluating the performance of fatigue resistant high modulus asphalt mixture according to claim 7, characterized in that: Consider the volume performance indicators required by design specifications during global optimization to ensure that the determined optimal combination meets the national or industry standards while satisfying the target dynamic modulus, including: While calculating the dynamic modulus difference for each set of candidate combinations, check that the predicted air voids A VMA , predicted asphalt saturation A VFA , and predicted voids in mineral aggregate A Va volume parameters are within the allowable range set by the specifications. Set hard constraints, i.e., if any combination does not meet the lower limit of the volume performance parameter or exceeds the recommended upper limit, the combination is automatically excluded from the final evaluation set; Prioritize the combination scheme with the predicted dynamic modulus closest to the target value among the remaining combinations, and if there are equivalent candidates, further compare their volume performance stability indicators or parameter adjustment sensitivity to select the design scheme with a larger adjustment margin; Output the optimal gradation and asphalt content combination to provide basic data support for subsequent test proportion verification and practical engineering application.

9. The method for designing and evaluating the performance of fatigue resistant high modulus asphalt mixture according to claim 8, characterized in that: Use the optimal gradation and asphalt content combination for specimen preparation and actual measurement verification to evaluate the actual performance of the designed mixture, including: According to the corresponding industry standards, use the determined optimal gradation and asphalt content to configure the mixture and mix, form, and compact it according to the standard method to prepare standard specimens for dynamic modulus testing; Under specified temperature and frequency conditions, use a dynamic modulus tester to measure the actual dynamic modulus BE of the specimen; At the same time, the volume performance test is carried out on the test piece to obtain the actual void ratio B Va , the actual mineral aggregate gap ratio B VMA and the actual asphalt saturation B VFA .

10. The method for design and performance evaluation of fatigue resistant high modulus asphalt mixture according to claim 9, characterized in that: The specimen verification process also includes determining whether the actual performance meets the target design requirements and closed-loop verification, including: Comparing the actual dynamic modulus BE with the design target dynamic modulus E tar The relative error ε thereof is calculated according to the following formula: ; Compare the relative error threshold M with the relative error ε to evaluate whether it is within the preset tolerance range; If the relative error ε is less than or equal to the relative error threshold M, it means that the measured result is within the tolerance range, and it is determined to meet the requirements, at which point the next phase is entered; If the relative error ε is greater than the relative error threshold M, it means that the measured result exceeds the tolerance range, and it is determined not to meet the requirements, at which point the following measures are taken, including: Root Cause Analysis: Check the potential problems of materials, process or test method; Adjustment and Optimization: Modify parameters and retest, including NPCM content and asphalt modification ratio; Threshold review: if the number of overruns is greater than twice, at this time to assess the rationality of the threshold M set, while re-computed asphalt mortar equivalent modulus And make appropriate adjustments, re-evaluation, until the measured results within the tolerance range.

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