Porosity Prediction And Virtual Gradation Design Of Asphalt Mixture

Asphalt pavement is becoming the most popular used pavement type in past decades.The performance of asphalt mixture determines the overall road service capability,while the mixture gradation directly affects the asphalt mixture’s compaction,porosity,structure type,segregation,modulus and strength.Therefore,gradation design is one of the key step of road construction.However,the current gradation design methods are still mainly based on “trial and error” method and it highly relies on the designers’ experience.Meanwhile,“trial and error” method is becoming increasingly expensive since the lack of high quality aggregate sources.In addition,existing gradation design theories are relatively ideal without considering aggregates features,while aggregates are irregular particles that will impact the mixture performance.With the rapid development of testing and analysis method such as image processing,numerical simulation,machine learning algorithm,intellectual aggregate,etc.,it is possible for researchers to study the gradation influence from different perspectives.The using of above methods and technologies will improve the gradation design method by transferring it from “trial and error method” to “performance-based computing method”.In this paper,a functional classification method of mineral aggregate was proposed based on the Beilay method.The aggregates were divided into four categories according to the different roles of aggregate playing in the mixture: large,medium,small and mineral powder.On the basis of this classification,the large particles that forming the skeleton,the medium particles that interfering skeleton and the small particles and mineral powder that filling the skeleton were studied respectively.The quantitative relationship and prediction method in various aggregate gradation and mixture porosity were established by applying discrete element method(DEM),BP neural network algorithm,EXCEL-VBA and digital image technology.Meanwhile,the results were verified and corrected by combining conventional lab tests with 3D printing aggregate compaction tests.At the same time,the shape of large particle aggregate was also studied.the 3D image files of aggregate particles were obtained by 3D scanner,and the MATLAB minimum box algorithm was selected to obtain the 3D information of aggregate.In that way,the quantitative relationship between various aggregate shape parameters and the volume characteristics of mixture was established,and the classification method of aggregate shape was determined.On the basis of this,the compaction porosity characteristics of the mixture in a single shape and multiple shapes were analyzed respectively.Correspondingly,the parameters such as aggregate shape correction coefficient and aggregate shape filling reduction coefficient were proposed to calculate the porosity of large particles with real aggregate shape characteristics and volume ratio.Combined with the research results of gradation and aggregate shape,the prediction of asphalt mixture porosity was finally realized,and a complete virtual gradation design method of asphalt mixture was formed.Through building the virtual gradation reference manual and the related program software,the practical application of virtual gradation method was realized.