Asphalt mixture gradation optimization method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of road material engineering, in particular to an asphalt mixture gradation optimization method and device, electronic equipment and a storage medium. According to the gross volume relative density, the apparent relative density and the tamping density of each grade of aggregate, the actually measured stacking density of each grade of aggregate is calculated; on the premise of ensuring that the pre-estimated void ratio, the pre-estimated asphalt saturation and the pre-estimated mineral aggregate void ratio meet the target requirements, in the standard grading range and the parameter value range of the Bailey method, the maximum pre-estimated stacking density, the maximum pre-estimated mineral aggregate void ratio and the minimum pre-estimated asphalt-aggregate ratio are used as target functions, grading iterative optimization is carried out by utilizing a multi-target particle swarm algorithm, and the maximum pre-estimated stacking density, the maximum pre-estimated mineral aggregate void ratio and the minimum pre-estimated asphalt-aggregate ratio are obtained. The Pareto solution of the optimized gradation is obtained; analyzing the Pareto solution of the optimized gradation, and selecting a plurality of groups of optimized gradation according to the principle of minimum asphalt-aggregate ratio. According to the method, the economical efficiency and the success rate of grading optimization of the asphalt mixture are improved.

Description

Technical Field

[0001] This application relates to the technical field of road materials engineering, and in particular, to a method, device, electronic device, and storage medium for optimizing the gradation of asphalt mixtures. Background Art

[0002] The overall mechanical properties of asphalt mixtures stem from the distribution law and mechanical properties of their various components. In addition to the adhesion characteristics of asphalt-aggregate, the gradation of the internal aggregates in asphalt mixtures also has an important impact on their mechanical properties. Theoretically, carrying out gradation optimization is an effective way to improve the mechanical properties of asphalt mixtures. Moreover, compared with asphalt modification schemes, the gradation optimization scheme has better economic benefits and will not significantly affect production costs.

[0003] To achieve the optimized design of the gradation of asphalt mixtures, existing gradation optimization methods first artificially select several groups of gradations within the gradation range of similar projects around the maximum density curve in the Taylor coordinate. Then, according to these gradations and the estimated asphalt dosage, asphalt mixture specimens are prepared and tested to obtain the volume indexes and mechanical properties of the specimens. Finally, the preferred gradation is selected based on the test results. Although the existing gradation methods have achieved the improvement of the performance of asphalt mixtures to a certain extent, there are three problems: First, the existing methods essentially belong to empirical methods, and whether the gradation optimization is successful depends on the engineering experience of the optimizer, which has uncertainty; Second, the asphalt dosage is not considered during gradation optimization. The unit price of asphalt is much higher than that of aggregates. During the gradation optimization process, if the asphalt dosage corresponding to the gradation is not considered, it will be difficult to ensure the economy of asphalt mixtures; Third, it is impossible to judge whether the volume indexes corresponding to the optimized gradation can meet the requirements. The volume indexes of asphalt mixtures include the voids in mineral aggregate VMA , air voids VV , asphalt saturation VFA . These volume indexes are closely related to the mechanical properties of asphalt mixtures, and road workers have set their allowable ranges. Therefore, whether the volume indexes corresponding to the designed gradation are within the allowable range is directly related to the success of the gradation design. That is, during the gradation design process, if the qualification of the volume indexes corresponding to the optimized gradation is not pre-judged, it will be difficult to ensure the success rate of gradation optimization, thereby affecting the time and test volume required for gradation optimization. Summary of the Invention

[0004] To overcome the deficiencies of the prior art, the present invention provides a method, device, electronic device, and storage medium for optimizing the gradation of asphalt mixtures, so as to simultaneously consider the gradation, asphalt dosage, and volume indexes of asphalt mixtures during the optimization process, and improve the success rate and economy of gradation optimization.

[0005] The first aspect of this application provides a method for optimizing the gradation of asphalt mixtures, and the method includes: Obtain the bulk specific gravity, apparent specific gravity, and compacted density of each gradation of aggregates, and determine the measured bulk density of each gradation of aggregates based on the bulk specific gravity and the compacted density; Obtain the asphalt relative density of the asphalt; Determine the target void ratio range, the target minimum difference between the voids in mineral aggregate (VMA) and the void ratio, and the target asphalt saturation range; According to the estimated bulk density PD Maximum, estimated VMA VMA Maximum, estimated asphalt-aggregate ratio OSR Determine the objective function for optimizing the gradation with the minimum value, and according to the estimated void ratio VV 、estimated asphalt saturation VFA and estimated VMA VMA Target requirements, as well as the specified gradation range and the parameter value range of the Bailey method, determine the constraints for gradation optimization, and use the multi-objective particle swarm optimization algorithm to carry out gradation iterative optimization to obtain the Pareto solution of the optimized gradation; the estimated bulk density PD Is determined by the measured bulk density of each gradation of aggregates, and the estimated VMA VMA Is determined by the estimated bulk density PD The estimated asphalt-aggregate ratio OSR Is a parameter participating in the gradation iterative optimization, and the estimated void ratio VV Is determined by the estimated asphalt-aggregate ratio OSR 、the bulk specific gravity, apparent specific gravity, and asphalt relative density of each gradation of aggregates, and the asphalt saturation VFA Is determined by the estimated void ratio VV And the estimated VMA VMA Determined; Determine multiple groups of optimized gradations according to the Pareto solution and the principle of the minimum estimated asphalt-aggregate ratio.

[0006] In an alternative embodiment, the determining the measured bulk density of each gradation of aggregates based on the bulk specific gravity and the compacted density includes: Determine the measured bulk density through the following formula: ; Wherein, Is the measured bulk density, Is the mass of the aggregates when measuring the compacted density of the i -th gradation of aggregates in each gradation of aggregates, Is the i -th gradation of aggregates, V Is the volume of the steel drum used when measuring the compacted density of the i -th gradation of aggregates.

[0007] In an alternative embodiment, said determining the estimated bulk density PD comprises: Determining the percentage of the mass of each size fraction of aggregate in the total aggregate mass according to the asphalt mixture gradation to be analyzed currently; Determining the average value of the upper and lower limits of the particle size of each size fraction of aggregate, and setting the average value as the equivalent diameter of each size fraction of aggregate; Determining the loose packing effect coefficient and the collision effect coefficient between the size fractions of aggregate; Determining the estimated bulk density corresponding to the current gradation when the control particle size is the equivalent diameter according to the loose packing effect coefficient, the collision effect coefficient, and the measured bulk density.

[0008] In an alternative embodiment, said determining the estimated bulk density corresponding to the current gradation when the control particle size is the equivalent diameter according to the loose packing effect coefficient, the collision effect coefficient, and the measured bulk density comprises: Determining, by the following formula, the aggregate bulk density of the asphalt mixture under the current gradation when the control particle size is : : ; wherein, is the aggregate bulk density of the asphalt mixture when the i th size fraction of aggregate is used as the control particle size, is the measured bulk density of the i th size fraction of aggregate, is the loose packing effect coefficient, is the collision effect coefficient, is the percentage of the mass of each size fraction of aggregate in the total aggregate mass, N is the number of size fractions of aggregate corresponding to the asphalt mixture; Determining the estimated bulk density by the following formula: ; wherein, PD is the estimated bulk density, are respectively the aggregate bulk densities corresponding to the current asphalt mixture gradation when the control particle size is the equivalent diameter ; In an alternative embodiment, said determining the estimated voids in mineral aggregate VMA comprises: Determining the estimated voids in mineral aggregate by the following formula: .

[0009] In an alternative embodiment, said determining the estimated air voids VVIncluding: Determine the mass percentage of mineral aggregate in the asphalt mixture according to the estimated asphalt-aggregate ratio; Determine the bulk relative density of the synthetic mineral aggregate corresponding to the gradation to be analyzed according to the bulk relative density of each size aggregate and the percentage of the mass of each size aggregate in the total aggregate mass; Determine the apparent relative density of the synthetic mineral aggregate corresponding to the gradation to be analyzed according to the apparent relative density of each size aggregate and the percentage of the mass of each size aggregate in the total aggregate mass; Determine the effective relative density of the synthetic mineral aggregate corresponding to the gradation to be analyzed according to the bulk relative density and the apparent relative density of the synthetic mineral aggregate corresponding to the gradation to be analyzed; Determine the maximum theoretical density of the asphalt mixture corresponding to the gradation to be analyzed and the asphalt-aggregate ratio according to the gradation to be analyzed, the estimated asphalt-aggregate ratio, the effective relative density of the synthetic mineral aggregate corresponding to the gradation to be analyzed, and the relative density of asphalt; Determine the estimated bulk relative density of the asphalt mixture corresponding to the gradation to be analyzed and the asphalt-aggregate ratio according to the estimated void ratio of mineral aggregate corresponding to the gradation to be analyzed, the bulk relative density of the synthetic mineral aggregate corresponding to the gradation to be analyzed, and the percentage of the mass of each size aggregate in the total aggregate mass; Determine the estimated void ratio of the asphalt mixture corresponding to the gradation to be analyzed and the asphalt-aggregate ratio according to the estimated bulk relative density of the asphalt mixture corresponding to the gradation to be analyzed and the asphalt-aggregate ratio and the maximum theoretical density of the asphalt mixture;

[0010] In an alternative embodiment, the step of determining the estimated void ratio of the asphalt mixture corresponding to the gradation to be analyzed and the asphalt-aggregate ratio according to the estimated bulk relative density of the asphalt mixture corresponding to the gradation to be analyzed and the asphalt-aggregate ratio and the maximum theoretical density of the asphalt mixture includes: Determine the mass percentage of the mineral aggregate in the asphalt mixture by the following formula: ; Wherein, is the mass percentage of the mineral aggregate in the asphalt mixture, OSR is the estimated asphalt-aggregate ratio; Determine the bulk relative density of the synthetic mineral aggregate by the following formula: ; Wherein, is the bulk relative density of the synthetic mineral aggregate, is the bulk relative density of each size aggregate, is the percentage of the mass of each size aggregate in the total aggregate mass; Determine the apparent relative density of the synthetic mineral aggregate by the following formula: ; Wherein, is the apparent relative density of the synthetic aggregate, is the apparent relative density of each size aggregate; The effective relative density of the synthetic aggregate is determined by the following formula: ; Wherein, is the effective relative density of the synthetic aggregate, is the apparent relative density of the synthetic aggregate, C is the asphalt absorption coefficient of the synthetic aggregate; The maximum theoretical density of the asphalt mixture is determined by the following formula: ; Wherein, is the maximum theoretical density of the asphalt mixture, OSR is the estimated asphalt-aggregate ratio, is the effective relative density of the synthetic aggregate, is the relative density of the asphalt; The bulk relative density of the estimated asphalt mixture is determined by the following formula: ; Wherein, is the bulk relative density of the estimated asphalt mixture, is the bulk relative density of the synthetic aggregate, VMA is the estimated void ratio of aggregates, is the mass percentage of aggregates in the asphalt mixture; The estimated void content is determined by the following formula: ; Wherein, VV is the estimated void content, is the bulk relative density of the estimated asphalt mixture, is the maximum theoretical density of the asphalt mixture.

[0011] In an alternative embodiment, determining the estimated asphalt saturation VFA includes: The estimated asphalt saturation is determined by the following formula: .

[0012] A second aspect of the present application provides an asphalt mixture gradation optimization device, the device includes: The first determination module is configured to obtain the bulk specific gravity, apparent specific gravity, and compacted density of each gradation of aggregates, and determine the measured bulk density of each gradation of aggregates according to the bulk specific gravity and the compacted density; The acquisition module is configured to acquire the asphalt relative density of asphalt; The second determination module is configured to determine the target void ratio range, the target minimum difference between the void ratio of the mineral aggregate voids, and the target asphalt saturation range; The optimization module is configured to, according to the estimated bulk density PD maximum, the estimated void ratio of the mineral aggregate voids VMA maximum, the estimated asphalt-aggregate ratio OSR minimum, determine the objective function for optimizing the gradation, and according to the objective requirements of the estimated void ratio VV the estimated asphalt saturation VFA, the estimated void ratio of the mineral aggregate voids VMA and the target requirements of the specification's gradation range and the parameter value range of the Bailey method, determine the constraints for gradation optimization, and use the multi-objective particle swarm optimization algorithm to carry out gradation iterative optimization to obtain the Pareto solution of the optimized gradation; the estimated bulk density PD is determined by the measured bulk density of each gradation of aggregates, and the estimated void ratio of the mineral aggregate voids VMA is determined by the estimated bulk density PD the estimated asphalt-aggregate ratio OSR is a parameter participating in the gradation iterative optimization, and the estimated void ratio VV is determined by the estimated asphalt-aggregate ratio OSR the bulk specific gravity, apparent specific gravity, and asphalt relative density of each gradation of aggregates, and the asphalt saturation VFA is determined by the estimated void ratio VV and the estimated void ratio of the mineral aggregate voids VMA ; The third determination module is configured to determine multiple groups of optimized gradations according to the Pareto solution and the principle of the minimum estimated asphalt-aggregate ratio.

[0013] A third aspect of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the asphalt mixture gradation optimization method are implemented.

[0014] A fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned asphalt mixture gradation optimization method are implemented.

[0015] In summary, the asphalt mixture gradation optimization method, device, electronic device, and storage medium provided by this application consider both gradation and asphalt content during gradation iterative optimization, and use theoretical formulas to control the void ratio of mineral aggregates in asphalt mixtures VMA , air void ratio VV , and asphalt saturation VFA . This makes up for the defect of the prior art relying on engineering experience, overcomes the deficiency of the prior art in being unable to consider asphalt content, and solves the problem that the prior art cannot control the volume indexes of asphalt concrete during optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a schematic flowchart of an asphalt mixture gradation optimization method shown in an embodiment of this application; Figure 2 is a functional module diagram of an asphalt mixture gradation optimization device shown in an embodiment of this application; Figure 3 is a schematic structural diagram of an electronic device shown in an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The present invention will be further described below in conjunction with the drawings and embodiments.

[0018] The concept, specific structure, and technical effects of the present invention will be clearly and completely described below in conjunction with the embodiments and drawings to fully understand the purpose, features, and effects of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention. In addition, all connection / connection relationships involved in the patent do not simply refer to direct connection of components, but refer to more optimal connection structures that can be formed by adding or reducing connection accessories according to specific implementation situations. Each technical feature in the present invention can be combined interactively without conflicting with each other.

[0019] Referring to Figure 1 shown, which is a schematic flowchart of an asphalt mixture gradation optimization method shown in an embodiment of this application, the asphalt mixture gradation optimization method includes the following steps. For the convenience of understanding the inventive concept of this application, the steps of the optimization method are described by taking the gradation optimization of AC-20C asphalt mixture with a nominal maximum particle size of 19 mm as an example.

[0020] S11, obtain the bulk relative density, apparent relative density, and compacted density of each size aggregate, and determine the measured bulk density of each size aggregate according to the bulk relative density and the compacted density.

[0021] In some embodiments, samples can be taken from each batching bin, and after screening and classification, N grading aggregates are obtained. For example, the AC-20C asphalt mixture includes aggregates of 7 gradings, namely aggregates with particle size ranges of 0-2.36 mm, 2.36-4.75 mm, 4.75-9.5 mm, 9.5-13.2 mm, 13.2-16 mm, 16-19 mm, and 19-26.5 mm respectively. Then, according to the "Test Regulations for Aggregates in Highway Engineering (JTG E42-2005)", density tests are carried out on each grading of aggregates, and N the bulk relative density, apparent relative density, and compacted density corresponding to each grading of aggregates in the grading aggregates can be measured. Furthermore, the measured bulk density of each grading of aggregates can be calculated and determined based on the bulk relative density and compacted density data of each grading of aggregates.

[0022] Specifically, the measured bulk density of aggregates is determined through the following formula : ; where is the mass of the aggregates when measuring the compacted density of the i th grading of aggregates in the above-mentioned grading aggregates, which is obtained by an electronic device, is the bulk relative density of the i th grading of aggregates, V is the volume of the steel drum used when measuring the compacted density of the i th grading of aggregates.

[0023] In the embodiments of the present application, the bulk relative density and apparent relative density of each grading of aggregates measured, and the measured bulk density of each grading of aggregates calculated are specifically shown in Table 1 (Density Information of Aggregates).

[0024] Table 1:

[0025] S12. Obtain the relative density of asphalt.

[0026] In some embodiments, samples can be taken from the asphalt tank to obtain the SBS modified asphalt for preparing the asphalt mixture, and the relative density of the SBS modified asphalt is measured. It should be noted that the relative density of asphalt refers to the relative density of asphalt at 25°C.

[0027] In the embodiments of the present application, the relative density of the SBS modified asphalt measured at 25°C is 1.040.

[0028] S13. Determine the target void ratio range, the target minimum difference between the void ratio in mineral aggregate and the void ratio, and the target asphalt saturation range.

[0029] In some embodiments, the electronic device may set the target void ratio of the asphalt mixture according to the specified volume index range VV range, the voids in mineral aggregate ratio VMA and the void ratio VV between the target minimum difference and the target asphalt saturation VFA range. Wherein, the specification is "Technical Specifications for Construction of Highway Asphalt Pavements (JTG F40-2004)", that is, the void ratio range corresponding to the climate zone where the asphalt mixture is located is found from "Technical Specifications for Construction of Highway Asphalt Pavements (JTG F40-2004)" to set the target void ratio VV range; the voids in mineral aggregate ratio corresponding to the nominal maximum particle size of the asphalt mixture are found from "Technical Specifications for Construction of Highway Asphalt Pavements (JTG F40-2004)" VMA and the void ratio VV between the minimum difference is set, and the voids in mineral aggregate ratio VMA and the void ratio VV between the target minimum difference is set; the asphalt saturation range corresponding to the nominal maximum particle size of the asphalt mixture is found from "Technical Specifications for Construction of Highway Asphalt Pavements (JTG F40-2004)" to set the target asphalt saturation VFA range.

[0030] In the embodiments of the present application, the target void ratio of the AC-20C asphalt mixture VV range can be set to 4%-5%, and the target minimum difference between the voids in mineral aggregate ratio of the AC-20C asphalt mixture VMA and the void ratio VV is set to 9.3%. At the same time, the target asphalt saturation of the AC-20C asphalt mixture VFA range is set to 65%-75%.

[0031] S14. Determine the objective function for gradation optimization according to the estimated bulk density PD maximum, the estimated voids in mineral aggregate ratio VMA maximum, the estimated asphalt-aggregate ratio OSR minimum. According to the target requirements of the estimated void ratio VV , the estimated asphalt saturation VFA, , the estimated voids in mineral aggregate ratio VMA , and the specified gradation range and the parameter value range of the Bailey method, determine the constraints for gradation optimization, and use the multi-objective particle swarm optimization algorithm to carry out gradation iterative optimization to obtain the Pareto solution of the optimized gradation.

[0032] Among them, the estimated bulk density PD is determined by the measured bulk density of each size aggregate, and the estimated voids in mineral aggregate ratio VMA is determined by the estimated bulk densityPD It is determined that the estimated asphalt-aggregate ratio OSR is a parameter participating in the gradation iteration optimization, and the estimated void ratio VV is determined by the estimated asphalt-aggregate ratio OSR , the bulk specific gravity, apparent specific gravity of each gradation aggregate, and the relative density of asphalt. The asphalt saturation VFA is determined by the estimated void ratio VV and the estimated void content in mineral aggregate VMA .

[0033] In some embodiments, the electronic device can set the gradation range of the asphalt mixture according to the specified gradation range. Among them, the specified gradation range is the gradation range specified in the "Technical Specification for Construction of Highway Asphalt Pavement (JTG F40-2004)".

[0034] In the embodiments of the present application, the set gradation range of the AC-20C asphalt mixture is specifically shown in Table 2 (Gradation Range of Asphalt Mixture).

[0035] Table 2:

[0036] In some embodiments, the parameter value ranges of the Bailey method are specifically shown in Table 3.

[0037] Table 3:

[0038] In the embodiments of the present application, the parameter value ranges of the Bailey method corresponding to the set AC-20C asphalt mixture are shown in Table 4.

[0039] Table 4:

[0040] In some embodiments, the multi-objective particle swarm optimization algorithm is used to carry out gradation iteration optimization to obtain the Pareto solution of the optimized gradation. Specifically, in each iteration step, the positions of the particles in the particle swarm are updated according to the multi-objective particle swarm optimization algorithm, and the estimated bulk density PD , the estimated void content in mineral aggregate VMA , the estimated void ratio VV and the estimated asphalt saturation VFA of each particle in the particle swarm after the updated position are calculated. Subsequently, according to the estimated results, it is judged whether each particle in the particle swarm after the updated position satisfies the void ratio VV , the asphalt saturation VFA and the void content in mineral aggregate VMAFor the target requirements, if all particles in the particle swarm after updating the position meet the target requirements, the position of the particle swarm is used as the initial particle swarm position for the next iteration step. If there are particles in the particle swarm after updating the position that do not meet the target requirements, continue to update the positions of the particles in the particle swarm according to the multi-objective particle swarm algorithm until all particles in the particle swarm after updating the position meet the target requirements. After the iteration is completed, the Pareto solution of the optimized gradation will be obtained.

[0041] It should be noted that, to ensure the quality of iterative optimization, in some embodiments, the following parameter settings are included for the gradation iterative optimization using the multi-objective particle swarm algorithm: the population size should be no less than 200, the maximum number of iterations should be no less than 500, and the upper limit of the non-dominated solution set size should be no less than 100. For example, in the embodiments of the present application, the population size is set to 200, the maximum number of iterations is set to 1000, and the upper limit of the non-dominated solution set size is set to 200.

[0042] In some embodiments, when it is necessary to calculate the estimated bulk density of each particle in the particle swarm, the estimated bulk density of each particle can be determined according to the measured bulk density of each size of aggregate obtained in step S11.

[0043] Specifically, the electronic device can first determine the percentage of the mass of each size of aggregate in the total aggregate according to the asphalt mixture gradation corresponding to the particle to be analyzed currently. .

[0044] Then, calculate the average value of the upper and lower limits of the particle size of each size of aggregate, and use the average value as the equivalent diameter of each size of aggregate. .

[0045] Subsequently, based on the Compaction Packing Model (CPM model), calculate the loose packing effect coefficient between each size of aggregate. and the collision effect coefficient. .

[0046] The loose packing effect coefficient between each size of aggregate is determined by the following formula. : ; where , are the equivalent diameters of the i th size of aggregate and the j th size of aggregate respectively; The collision effect coefficient between each size of aggregate is determined by the following formula. ; .

[0047] Finally, based on the Compaction Packing Model (CPM model), according to the measured bulk density of each size of aggregate obtained in step S11, the percentage of the mass of each size of aggregate in the total aggregate. The loose packing effect coefficient between each gradation of aggregates and the collision effect coefficient are used to calculate the estimated bulk density corresponding to the current particle, that is, the estimated bulk density corresponding to the asphalt mixture gradation in the current iteration step.

[0048] Through the following formula, the electronic device can determine that when the control particle size is , the estimated bulk density of the asphalt mixture corresponding to the current gradation is ; wherein is the estimated bulk density of the asphalt mixture when the i -th gradation of aggregates is used as the control particle size, , are respectively the measured bulk densities of the i -th and j -th gradations of aggregates, is the proportion of the j -th gradation of aggregates, N is the number of aggregate gradations corresponding to the asphalt mixture.

[0049] After obtaining the estimated bulk densities corresponding to each gradation of aggregates, the estimated bulk density corresponding to the current asphalt mixture gradation can be determined through the following formula PD : ; wherein are respectively the estimated bulk densities of the current asphalt mixture gradation when the control particle size is .

[0050] In the embodiment of the present application, assuming that the asphalt mixture gradation corresponding to the particle to be analyzed currently is specifically shown in Table 5, then the mass percentages of each gradation of aggregates are shown in Table 6.

[0051] Table 5:

[0052] Table 6:

[0053] In the embodiment of the present application, calculate the average value of the upper and lower limits of each gradation of aggregates shown in Table 6, and use the average value as the equivalent diameter of each gradation of aggregates , then the equivalent diameters of each gradation of aggregates are shown in Table 7.

[0054] Table 7:

[0055] Based on the Compaction Packing Model (CPM model), calculate the loose packing effect coefficients between each grade of aggregates shown in Table 7 and the collision effect coefficients , then the loose packing effect coefficients and the collision effect coefficients between each grade of aggregates are shown in Table 8

[0056] Table 8:

[0057] Among them, the upper right part (i.e., the gray part) of Table 8 is the loose packing effect coefficient , and the lower left part is the collision effect coefficient .

[0058] In the embodiments of the present application, based on the Compaction Packing Model (CPM model), according to the measured bulk density of each grade of aggregates obtained in step S11, the percentage of the mass of each grade of aggregates in the total aggregates shown in Table 6 , the loose packing effect coefficients between each grade of aggregates shown in Table 8 and the collision effect coefficients , then the predicted bulk density of the asphalt mixture corresponding to the particles shown in Table 5 PD is 0.864

[0059] In some embodiments, after obtaining the predicted bulk density of the asphalt mixture corresponding to the particles PD , the electronic device can determine the predicted void ratio corresponding to the particles through the following formula VMA : ; In some embodiments, the electronic device can first set a reasonable asphalt-aggregate ratio range according to the asphalt-aggregate ratio of similar projects; then, within the reasonable asphalt-aggregate ratio range, assume a predicted asphalt-aggregate ratio OSR , and participate in iterative optimization together with the predicted asphalt-aggregate ratio OSR and the gradation

[0060] In the embodiments of the present application, the set asphalt-aggregate ratio range for the AC-20C asphalt mixture is 4% - 5.5%

[0061] In some embodiments, according to the predicted asphalt-aggregate ratio OSR , the bulk relative density and apparent relative density of each grade of aggregates, the electronic device can determine the predicted void ratio corresponding to the current analysis gradation VV .

[0062] Specifically, the electronic device first determines the mass percentage of the mineral aggregate in the asphalt mixture through the following formula : ; Then, according to the bulk specific gravity of each size aggregate , and the percentage of the mass of each size aggregate deduced from the gradation to be analyzed in the total aggregate mass , the bulk specific gravity of the combined aggregate corresponding to the gradation to be analyzed is determined by the following formula : ; According to the apparent specific gravity of each size aggregate , and the percentage of the mass of each size aggregate deduced from the gradation to be analyzed in the total aggregate mass , the apparent specific gravity of the combined aggregate corresponding to the gradation to be analyzed is calculated by the following formula ; ; According to the bulk specific gravity of the combined aggregate corresponding to the gradation to be analyzed, and the apparent specific gravity of the combined aggregate , the effective specific gravity of the combined aggregate corresponding to the gradation to be analyzed is determined by the following formula : ; Wherein, C is the asphalt absorption coefficient of the combined aggregate.

[0063] The asphalt absorption coefficient of the combined aggregate is determined by the following formula C : ; Wherein, is the water absorption rate of the combined aggregate.

[0064] The water absorption rate of the combined aggregate is determined by the following formula : ; According to the gradation to be analyzed and its corresponding estimated asphalt-aggregate ratio OSR , the effective specific gravity of the combined aggregate , and the relative density of asphalt , the maximum theoretical density of the asphalt mixture corresponding to the gradation to be analyzed and the asphalt-aggregate ratio is determined by the following formula : ; Subsequently, according to the estimated voids in mineral aggregate VMA corresponding to the gradation to be analyzed, the bulk specific gravity of the combined aggregate , and the percentage of mineral aggregate in the asphalt mixture , the estimated bulk specific gravity of the asphalt mixture is determined by the following formula : ; According to the estimated bulk relative density of the asphalt mixture corresponding to the gradation and asphalt-aggregate ratio to be analyzed , the maximum theoretical density of the asphalt mixture , determine the estimated void ratio through the following formula VV : ; In some embodiments, when determining the estimated void ratio VV and the estimated void content in mineral aggregate VMA , the electronic device can, according to the estimated void ratio VV and the estimated void content in mineral aggregate VMA , determine the estimated asphalt saturation through the following formula VFA : ; Wherein, VFA The unit of

[0065] In the embodiments of the present application, according to the estimated bulk density corresponding to the particles to be analyzed currently PD being 0.864, the estimated void content in mineral aggregate VMA can be calculated to be 13.6%.

[0066] According to the asphalt-aggregate ratio OSR corresponding to the particles to be analyzed currently being 4.744%, the corresponding to the particles to be analyzed currently can be calculated to be 95.47%.

[0067] According to the bulk relative density of each size aggregate, and the percentage of the mass of each size aggregate in the total aggregate mass deduced from the gradation of the particles to be analyzed currently, the bulk relative density of the synthetic mineral aggregate corresponding to the particles to be analyzed currently can be calculated to be 2.662.

[0068] According to the apparent relative density of each size aggregate, and the percentage of the mass of each size aggregate in the total aggregate mass deduced from the gradation of the particles to be analyzed currently, the apparent relative density of the synthetic mineral aggregate corresponding to the particles to be analyzed currently can be calculated to be 2.746.

[0069] According to the bulk relative density of the synthetic mineral aggregate corresponding to the particles to be analyzed, and the apparent relative density of the synthetic mineral aggregate, the effective relative density of the synthetic mineral aggregate corresponding to the particles to be analyzed currently can be calculated to be 2.716.

[0070] According to the gradation and asphalt-aggregate ratio corresponding to the particles to be analyzed, the effective relative density of the synthetic aggregate , the relative density of the asphalt , the maximum theoretical density of the asphalt mixture corresponding to the particles to be analyzed can be calculated is 2.531.

[0071] According to the prediction corresponding to the particles to be analyzed VMA , the bulk relative density of the synthetic aggregate , and the mass percentage of the aggregate corresponding to the particles to be analyzed in the asphalt mixture , the predicted bulk relative density of the asphalt mixture corresponding to the particles to be analyzed can be calculated is 2.410.

[0072] According to the predicted bulk relative density of the asphalt mixture corresponding to the particles to be analyzed , and the maximum theoretical density of the asphalt mixture , the predicted void ratio corresponding to the particles to be analyzed can be calculated VV is 4.78%.

[0073] According to the predicted void ratio VV and the predicted void content in mineral aggregate VMA , the predicted asphalt saturation corresponding to the particles to be analyzed can be calculated VFA is 64.9%.

[0074] S15. Determine multiple groups of optimized gradations according to the Pareto solution and the principle of minimum predicted asphalt-aggregate ratio.

[0075] In some embodiments, when analyzing the Pareto solution of the optimized gradation, screen the Pareto solution of the optimized gradation according to the principle of minimum predicted asphalt-aggregate ratio, and screen out M groups of gradations with the minimum asphalt-aggregate ratio, where M is an integer greater than or equal to 1.

[0076] In the embodiments of the present application, M is set to 3. Among the Pareto solutions of the optimized gradation of the AC-20C asphalt mixture, 3 groups of gradations with the minimum asphalt-aggregate ratio are screened out, as shown in Table 9. In addition, Table 9 also gives the predicted void content in mineral aggregate VMA , the predicted void ratio VV , the predicted asphalt saturation VFA , the predicted void content in mineral aggregate VMA and the difference between the predicted void ratio VV .

[0077] Table 9:

[0078] Prepare Marshall specimens according to the optimized gradation and estimated asphalt-aggregate ratio shown in Table 9, and conduct volume index tests on these specimens. The test results are shown in Table 10.

[0079] Table 10:

[0080] Compare the estimated volume indexes in Table 9 with the measured values in Table 10, and calculate the absolute error values corresponding to each volume index. The details are shown in Table 11.

[0081] Table 11:

[0082] By comparison, it can be seen that the gradation optimization method proposed in this application can accurately predict the volume indexes of asphalt mixtures.

[0083] Compared with the existing technology where the success of gradation optimization depends on the engineering experience of the optimizer, this application provides precise calculations through the compaction accumulation model, solving the problem of the lack of theoretical support in the existing gradation optimization technology; compared with the existing technology that does not consider the asphalt content, the gradation optimization method proposed in this application can consider both gradation and asphalt content during the gradation optimization process, providing support for the economy of gradation optimization; compared with the existing gradation optimization technology that cannot determine whether the volume indexes of the asphalt mixture corresponding to the optimized gradation are qualified and it is difficult to ensure the success rate of gradation optimization, this application proposes a method for predicting the volume indexes of asphalt mixtures, accurately obtaining the predicted values of the volume indexes corresponding to the optimized gradation, and greatly improving the success rate of gradation optimization.

[0084] Refer to Figure 2 As shown, it is the functional module diagram of the asphalt mixture gradation optimization device shown in the embodiments of this application.

[0085] In some embodiments, the asphalt mixture gradation optimization device 20 may include multiple functional modules composed of computer program segments. The computer programs of each program segment of the asphalt mixture gradation optimization device 20 can be stored in the memory of the electronic device and executed by at least one processor to execute (see the details in Figure 1 the description) the functions of asphalt mixture gradation optimization. According to the functions it executes, it can be divided into multiple functional modules. The functional modules may include: a first determination module 201, an acquisition module 202, a second determination module 203, an optimization module 204, and a third determination module 205. The module referred to in this application means a series of computer program segments that can be executed by at least one processor and can complete fixed functions, and are stored in the memory. In this embodiment, the functions of each module will be detailed in subsequent embodiments.

[0086] The first determination module 201 is configured to obtain the bulk specific gravity, apparent specific gravity, and compacted density of each gradation of aggregate, and determine the measured bulk density of each gradation of aggregate according to the bulk specific gravity and the compacted density; The obtaining module 202 is configured to obtain the asphalt relative density of asphalt; The second determination module 203 is configured to determine a target void ratio range, a target minimum difference between the target void ratio of the mineral aggregate and the target void ratio, and a target asphalt saturation range; The optimization module 204 is configured to PD determine the maximum of the estimated bulk density VMA the maximum of the estimated void ratio of the mineral aggregate OSR the minimum of the estimated asphalt-aggregate ratio VV to determine the objective function for optimizing the gradation, and determine the constraint conditions for gradation optimization according to the target requirements of the estimated void ratio VFA, the estimated asphalt saturation VMA the estimated void ratio of the mineral aggregate, and the gradation range specified in the standard and the parameter value range of the Bailey method, and perform gradation iterative optimization using a multi-objective particle swarm algorithm to obtain the Pareto solution of the optimized gradation; the estimated bulk density PD is determined by the measured bulk density of each gradation of aggregate, and the estimated void ratio of the mineral aggregate VMA is determined by the estimated bulk density PD the estimated asphalt-aggregate ratio OSR is a parameter participating in gradation iterative optimization, and the estimated void ratio VV is determined by the estimated asphalt-aggregate ratio OSR the bulk specific gravity, apparent specific gravity of each gradation of aggregate, and the relative density of asphalt, and the asphalt saturation VFA is determined by the estimated void ratio VV and the estimated void ratio of the mineral aggregate VMA ; The third determination module 205 is configured to determine multiple groups of optimized gradations according to the Pareto solution and the principle of the minimum estimated asphalt-aggregate ratio.

[0087] It should be understood that the various change modes and specific embodiments in the asphalt mixture gradation optimization method provided in the above embodiments are equally applicable to the asphalt mixture gradation optimization device in this embodiment. Through the foregoing detailed description of the asphalt mixture gradation optimization method, those skilled in the art can clearly know the implementation method of the asphalt mixture gradation optimization device in this embodiment. For the sake of simplicity of the specification, it will not be elaborated here.

[0088] Refer to Figure 3As shown, it is a schematic structural diagram of an electronic device shown in an embodiment of the present application. In a preferred embodiment of the present application, the electronic device 3 includes a memory 31, at least one processor 32, and at least one communication bus 33.

[0089] Those skilled in the art should understand that Figure 3 the structure of the shown electronic device does not constitute a limitation to the embodiments of the present application. It can be a bus structure or a star structure. The electronic device 3 may further include more or fewer other hardware or software than shown, or different component arrangements.

[0090] In some embodiments, the electronic device 3 is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits, programmable gate arrays, digital signal processors, and embedded devices, etc. The electronic device 3 may further include a user device, and the user device includes, but is not limited to, any electronic product that can perform human-computer interaction with the user through means such as a keyboard, mouse, remote control, touchpad, or voice control device. For example, a personal computer, a tablet computer, a smart phone, a digital camera, etc.

[0091] In the above embodiments provided by the present application, it should be understood that the disclosed methods, devices, computer-readable storage media, and electronic devices can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple components or modules can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or components or modules can be in electrical, mechanical, or other forms.

[0092] The components described as separate components may or may not be physically separated. The components shown as components may or may not be physical modules, that is, they can be located in one place or distributed to multiple network modules. Some or all of the components can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0093] In addition, in each embodiment of the present invention, the various functional modules can be integrated in one processing module, or each component can exist physically alone, or two or more modules can be integrated in one module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules.

[0094] When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0095] It should be noted that, for the foregoing method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily all essential to the present invention.

[0096] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0097] The above is a specific description of the preferred embodiments of the present invention, but the present invention is not limited to the described embodiments. Those skilled in the art can also make various equivalent deformations or substitutions without departing from the spirit of the present invention, and these equivalent deformations or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A method for optimizing asphalt mixture gradation, characterized in that: The method comprises: Obtaining the gross volume relative density, apparent relative density and compacted density of each grade of aggregate, and determining the measured bulk density of each grade of aggregate according to the gross volume relative density and the compacted density; Get the asphalt relative density of asphalt; Determine the target void fraction range, the target minimum difference between the aggregate interstitial fraction and the void fraction, and the target asphalt saturation range; According to the estimated bulk density PD Maximum and estimated mineral void ratio VMA Maximum and estimated oil-to-stone ratio OSR The objective function of minimum gradation optimization is determined based on the estimated void ratio. VV , Estimated asphalt saturation VFA and estimated aggregate void ratio VMA The target requirements, the gradation range of the specification, the parameter value range of the Bailey method, determine the restriction conditions for gradation optimization, use the multi-objective particle swarm algorithm to carry out gradation iterative optimization, and obtain the Pareto solution of the optimized gradation; the estimated bulk density PD The estimated mineral gap ratio is determined by measuring the actual packing density of each aggregate. VMA The estimated bulk density is PD Determine that the estimated oil-rock ratio OSR is the parameter involved in the iterative optimization of gradation, the estimated void ratio VV The estimated oil-stone ratio OSR , the relative density of the gross volume of each aggregate, the apparent relative density, the relative density of asphalt, the asphalt saturation VFA The estimated void fraction VV and the estimated mineral void ratio VMA Sure; Multiple groups of optimized gradations are determined based on the Pareto solution and the estimated minimum oil-stone ratio principle.

2. The asphalt mixture gradation optimization method according to claim 1, characterized in that: Determining the measured bulk density of each aggregate grade according to the bulk volume relative density and the tamping density comprises: The measured bulk density is determined by the following formula: ； in, is the measured bulk density, To measure the first i The aggregate mass at the compacted density of the aggregate, For the i The bulk relative density of the aggregate, V To measure the i The volume of the steel drum used to determine the compacted density of the aggregate.

3. The asphalt mixture gradation optimization method according to claim 1, characterized in that: The method further comprises: According to the asphalt mixture gradation to be analyzed, determine the percentage of each grade of aggregate mass to the total aggregate mass; Determine the average value of the upper and lower limits of the aggregate particle size of each grade, so as to set the average value as the equivalent diameter of each grade of aggregate; Determine the loose effect coefficient and collision effect coefficient between the aggregates of each grade; According to the loose effect coefficient, the collision effect coefficient and the measured bulk density, the estimated bulk density corresponding to the current gradation when the controlled particle size is the equivalent diameter is determined.

4. The asphalt mixture gradation optimization method according to claim 3, characterized in that: Determining the estimated packing density corresponding to the current gradation when the controlled particle size is the equivalent diameter according to the loose packing effect coefficient, the collision effect coefficient and the measured packing density comprises: The controlled particle size is determined by the following formula: Aggregate packing density corresponding to asphalt mixture under current gradation : ； in, For the i The aggregate packing density of asphalt mixture corresponding to the control particle size is For the i The measured bulk density of the aggregate, is the loose effect coefficient, is the collision effect coefficient, is the percentage of each grade of aggregate mass to the total aggregate mass, N is the number of aggregate grades corresponding to the asphalt mixture; The estimated bulk density is determined by the following formula: ； in, PD is the estimated bulk density, The particle size is controlled to be equivalent to the diameter The aggregate packing density corresponding to the current asphalt mixture gradation.

5. The asphalt mixture gradation optimization method according to claim 1, characterized in that: The method further comprises: The estimated mineral material gap ratio is determined by the following formula: 。 in, VMA is the estimated mineral material gap ratio.

6. The asphalt mixture gradation optimization method according to claim 1, characterized in that: The method further comprises: Determine the mass percentage of mineral aggregate in asphalt mixture according to the estimated asphalt-rock ratio; According to the bulk volume relative density of each grade of aggregate and the percentage of the mass of each grade of aggregate in the total aggregate mass, determine the bulk volume relative density of the synthetic mineral material corresponding to the current grading to be analyzed; According to the apparent relative density of each grade of aggregate and the percentage of the mass of each grade of aggregate in the total aggregate mass, the apparent relative density of the synthetic ore corresponding to the current grading to be analyzed is determined; Determine the effective relative density of the synthetic mineral material corresponding to the gradation to be analyzed according to the gross volume relative density of the synthetic mineral material corresponding to the gradation to be analyzed and the apparent relative density of the synthetic mineral material; Determine the maximum theoretical density of the asphalt mixture corresponding to the gradation to be analyzed and the asphalt-stone ratio according to the gradation to be analyzed, the estimated asphalt-stone ratio, the effective relative density of the synthetic mineral material corresponding to the gradation to be analyzed, and the relative density of asphalt; According to the estimated mineral aggregate void ratio corresponding to the gradation to be analyzed, the gross volume relative density of the synthetic mineral aggregate corresponding to the gradation to be analyzed, and the percentage of the mass of each grade of aggregate in the total aggregate mass, the estimated gross volume relative density of the asphalt mixture corresponding to the gradation to be analyzed and the asphalt aggregate ratio is determined; The estimated void fraction corresponding to the gradation to be analyzed and the asphalt-stone ratio is determined based on the estimated asphalt mixture bulk relative density corresponding to the gradation to be analyzed and the asphalt-stone ratio and the maximum theoretical density of the asphalt mixture.

7. The asphalt mixture gradation optimization method according to claim 6, characterized in that: Determining the estimated void ratio corresponding to the gradation to be analyzed and the asphalt-stone ratio according to the estimated asphalt mixture bulk relative density corresponding to the gradation to be analyzed and the asphalt-stone ratio and the maximum theoretical density of the asphalt mixture includes: The mass percentage of the mineral material in the asphalt mixture is determined by the following formula: ； in, is the mass percentage of the mineral aggregate in the asphalt mixture, OSR is the estimated oil-to-stone ratio; The bulk relative density of the synthetic mineral material is determined by the following formula: ； in, is the relative bulk density of the synthetic mineral material, is the relative bulk density of each grade of aggregate, is the percentage of each grade of aggregate mass to the total aggregate mass; The apparent relative density of the synthetic mineral material is determined by the following formula: ； in, is the apparent relative density of the synthetic mineral material, is the apparent relative density of each grade of aggregate; The effective relative density of the synthetic mineral material is determined by the following formula: ； in, is the effective relative density of the synthetic mineral material, is the apparent relative density of the synthetic mineral material, C is the asphalt absorption coefficient of synthetic mineral aggregate; The maximum theoretical density of the asphalt mixture is determined by the following formula: ； in, is the maximum theoretical density of the asphalt mixture, OSR is the estimated oil-to-stone ratio, is the effective relative density of the synthetic mineral material, is the relative density of the asphalt; The estimated asphalt mixture bulk relative density is determined by the following formula: ； in, is the estimated asphalt mixture bulk relative density, is the relative bulk density of the synthetic mineral material, VMA is the estimated mineral material void ratio, is the mass percentage of mineral aggregate in asphalt mixture; The estimated void fraction is determined by the following formula: ； in, VV is the estimated void fraction, is the estimated asphalt mixture bulk relative density, It is the maximum theoretical density of the asphalt mixture.

8. The asphalt mixture gradation optimization method according to claim 1, characterized in that: The method comprises: The estimated asphalt saturation is determined by the following formula: ； in, VFA is the estimated asphalt saturation.

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the asphalt mixture gradation optimization method according to any one of claims 1 to 8 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the asphalt mixture gradation optimization method described in any one of claims 1 to 8 are implemented.

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