CN-122024940-A - Asphalt mixture mineral aggregate gap rate prediction method and computer equipment

CN122024940ACN 122024940 ACN122024940 ACN 122024940ACN-122024940-A

Abstract

The invention discloses a method for predicting the gap rate of asphalt mixture mineral aggregate and computer equipment, and relates to the field of asphalt pavement materials; the method comprises the steps of calculating the basic total specific volume of the asphalt mixture according to particle size distribution of the particles, calculating the basic total specific volume of the asphalt mixture according to the basic total specific volume of all the asphalt mixture, calculating the mineral aggregate clearance rate of the asphalt mixture according to the basic total specific volume, calculating the main control particle size of the asphalt mixture according to particle size distribution of the asphalt mixture, calculating the main particle size range according to the main control particle size, dividing the asphalt mixture into framework particles, filling particles and suspension particles according to the main particle size range, calculating the optimal distribution function by utilizing an optimization algorithm, calculating the specific volumes of the framework particles, the filling particles and the suspension particles according to the main particle size range and the optimal distribution function, and calculating the basic total specific volume of the asphalt mixture according to the specific volumes of the framework particles, the filling particles and the suspension particles.

Inventors

LI HAO
LIN YONGKANG
Tan Youwei
ZHU ZHENTAO
LI YUHUAN
ZHAN BINGYANG
YANG HAOLONG

Assignees

佛交科天诺(广东)材料有限公司

Dates

Publication Date: 20260512
Application Date: 20251204

Claims (11)

1. A method for predicting the gap rate of asphalt mixture mineral aggregate is characterized by comprising the following steps of Respectively obtaining particle size distribution of asphalt mixtures with different proportions; calculating the standard total specific volume of asphalt mixtures with different proportions according to the particle size distribution of the particles; calculating the comprehensive total specific volume of the asphalt mixture according to the standard total specific volume of all the asphalt mixtures; calculating the mineral aggregate clearance rate of the asphalt mixture according to the comprehensive total specific volume; the step of calculating the reference total specific volume of the asphalt mixture comprises the following steps: Calculating the main control particle size of the asphalt mixture according to the particle size distribution of the asphalt mixture; Calculating a primary particle size range based on the primary control particle size; dividing the asphalt mixture into framework particles, filling particles and suspended particles according to the main particle size range; Calculating by using an optimization algorithm to obtain an optimal distribution function; calculating specific volumes of the framework particles, the filling particles and the suspended particles based on the main particle size range and the optimal distribution function respectively; And calculating the reference total specific volume of the asphalt mixture according to the specific volumes of the framework particles, the filling particles and the suspended particles.
2. A method for predicting asphalt mixture mineral aggregate gap ratio as defined in claim 1, wherein said method for calculating a primary control particle size of said asphalt mixture from a particle size distribution of said asphalt mixture comprises: determining the nominal maximum particle size of the asphalt mixture according to the particle size distribution of the asphalt mixture; Calculating an initial control particle size from the nominal maximum particle size; verifying the initial control particle size using coarse aggregate proportions; And if the coarse aggregate proportion is in a preset range, taking the initial control particle size as a main control particle size.
3. The method for predicting asphalt mixture mineral aggregate gap rates as defined in claim 1, wherein said step of calculating an optimal distribution function using an optimization algorithm comprises: constructing a quantization difference objective function according to the particle size distribution and the distribution function of the particles; according to the particle size distribution of the particles, solving the minimum value of the quantitative difference objective function by using a numerical optimization algorithm; And taking the parameter corresponding to the minimum value of the quantization difference objective function as an optimal parameter, and bringing the parameter into the distribution function to obtain an optimal distribution function.
4. The method for predicting asphalt mixture mineral aggregate gap rates according to claim 1, wherein the method for calculating specific volumes of the framework particles, the filler particles and the suspended particles based on the main particle diameter range and the optimal distribution function, respectively, comprises: calculating the total specific volume of the framework particles based on the mixed accumulation model; calculating the total specific volume of the filler particles based on a linear stacking model; based on the linear stacking model, the total specific volume of the suspended particles is calculated.
5. A method for predicting asphalt mixture mineral aggregate gap rates as defined in claim 1 or 4, wherein said method for calculating the total specific volume of said skeletal particles comprises: The total specific volume of the skeletal particles was calculated according to the following formula: Wherein, the Representing the total initial specific volume of the skeletal particles, Representing the total initial bulk density of the skeletal particles, Representing the interaction function of the hybrid stack model, The upper limit of the primary particle diameter is indicated, The lower limit of the primary particle diameter is indicated, Indicating the mixture ratio number as The total specific volume of the framework particles a of (c), The optimum distribution function is represented by a graph, Representing framework particles.
6. A method for predicting asphalt mixture mineral aggregate gap rates as defined in claim 1 or 4, wherein said method for calculating the total specific volume of said filler particles comprises: Calculating the total specific volume of the filling particles according to the formula: Wherein, the The reference specific volume of the particles is indicated, Indicating the particle size of the smallest particles in the asphalt mix, The lower limit of the primary particle diameter is indicated, The optimum distribution function is represented by a graph, Representing the composition And components The function of the interaction between them, Indicating the mixture ratio number as The total specific volume of the filler particles B of (c), Representing filler particles.
7. A method for predicting asphalt mixture mineral aggregate gap rates as defined in claim 1 or 4, wherein said method for calculating the total specific volume of suspended particles comprises: the total specific volume of the suspended particles was calculated according to the following formula: Wherein, the The reference specific volume of the particles is indicated, Indicating the particle size of the largest particles in the asphalt mix, The upper limit of the primary particle diameter is indicated, The optimum distribution function is represented by a graph, Representing the composition And components The function of the interaction between them, Indicating the mixture ratio number as The total specific volume of the suspended particles C of (C), Representing suspended particles.
8. A method for predicting asphalt mixture mineral aggregate gap rates as defined in claim 1, wherein said method for calculating a reference total specific volume of said asphalt mixture based on specific volumes of said skeletal particles, filler particles and suspended particles comprises: Calculating the reference total specific volume of the asphalt mixture according to the following formula: Wherein, the Indicating the mixture ratio number as The total specific volume of the framework particles a of (c), Indicating the mixture ratio number as The total specific volume of the filler particles B of (c), Indicating the mixture ratio number as The total specific volume of the suspended particles C of (C), The upper limit of the primary particle diameter is indicated, The lower limit of the primary particle diameter is indicated, Indicating the mixture ratio number as The reference total specific volume of the asphalt mixture.
9. A method for predicting asphalt mixture mineral aggregate gap ratio as defined in claim 1, wherein said method for calculating the integrated total specific volume of asphalt mixture based on the reference total specific volume of all asphalt mixtures comprises: The overall total specific volume of the asphalt mixture was calculated according to the following formula: Wherein, the Indicating the total specific volume of the asphalt mixture, Indicating the mixture ratio number as The reference total specific volume of the asphalt mixture.
10. A method of asphalt mixture mineral aggregate gap ratio prediction as defined in claim 1, wherein said method of calculating the mineral aggregate gap ratio of asphalt mixtures from said integrated total specific volume comprises: According to the formula Calculating the mineral aggregate clearance rate of the asphalt mixture; Wherein, the Represents the total specific volume of the combination, The mineral aggregate gap ratio of the asphalt mixture is shown.
11. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor, when executing the computer program, implements a method for predicting asphalt mixture mineral aggregate gap rates according to any one of claims 1 to 10.

Description

Asphalt mixture mineral aggregate gap rate prediction method and computer equipment Technical Field The invention relates to the field of asphalt pavement materials, in particular to a method for predicting the gap rate of asphalt mixture mineral aggregate and computer equipment. Background Mineral void fraction (VMA), which is an important volume parameter in asphalt mix design, refers to the percentage of void volume between the aggregate skeletons to the total mix volume, provides accommodation for asphalt and ensures adequate durability and stability of the mix. Too low VMA means that insufficient asphalt cement content is very likely to cause premature hardening and reduction of fatigue cracking resistance of the mixture, and too high VMA means that the aggregate framework structure is poor and the asphalt consumption is too high, so that rutting capability of the mixture is obviously weakened. Currently, the primary method of determining asphalt mix VMA is through laboratory measurements. This process typically involves weighing aggregate according to a target grading, heating, mixing with asphalt, shaping the test piece with a gyratory compactor (SGC) or Marshall compaction machine, and finally calculating the VMA by volumetric parameter measurements. Although direct, this method has significant limitations, such as long experimental time, low efficiency, high cost, and inability to reveal the mechanism of formation. Therefore, a scientific, accurate and effective method for predicting the mineral aggregate clearance rate of the asphalt mixture is needed in the art, so that the design efficiency of the asphalt mixture is fundamentally improved, and the research and development cost is reduced. Disclosure of Invention The invention aims to solve the problem of providing a method for predicting the mineral aggregate clearance rate of an asphalt mixture and computer equipment, which can realize accurate prediction of the mineral aggregate clearance rate of the asphalt mixture. In order to solve the technical problems, the invention provides a mineral aggregate clearance prediction method of asphalt mixtures and computer equipment, which comprise the steps of respectively obtaining particle size distributions of the asphalt mixtures with different proportions, respectively calculating basic total specific volume of the asphalt mixtures with different proportions according to the particle size distributions, respectively calculating comprehensive total specific volume of the asphalt mixtures according to the basic total specific volume of all the asphalt mixtures, respectively calculating the mineral aggregate clearance of the asphalt mixtures according to the comprehensive total specific volume, calculating main control particle size of the asphalt mixtures according to the particle size distributions of the asphalt mixtures, calculating a main particle size range according to the main control particle size, dividing the asphalt mixtures into skeleton particles, filling particles and suspension particles according to the main particle size range, calculating an optimal distribution function by utilizing an optimization algorithm, respectively calculating the basic specific volume of the asphalt mixtures according to the skeleton particles, the filling particles and the suspension particles, and calculating the basic specific volume of the asphalt mixtures according to the main particle size range and the optimal distribution function. As an improvement of the scheme, the method for calculating the main control particle size of the asphalt mixture according to the particle size distribution of the asphalt mixture comprises the steps of determining the nominal maximum particle size of the asphalt mixture according to the particle size distribution of the asphalt mixture, calculating the initial control particle size according to the nominal maximum particle size, verifying the initial control particle size by using the coarse aggregate proportion, and if the coarse aggregate proportion is in a preset range, determining the initial control particle size as the main control particle size. The method comprises the steps of constructing a quantization difference objective function according to particle size distribution and a distribution function, solving the minimum value of the quantization difference objective function according to the particle size distribution by using a numerical optimization algorithm, taking a parameter corresponding to the minimum value of the quantization difference objective function as an optimal parameter, and bringing the parameter into the distribution function to obtain the optimal distribution function. As an improvement of the scheme, the method for calculating the specific volumes of the framework particles, the filling particles and the suspended particles respectively by using the stacking model comprises the steps of calculating the total specific volume of the framework