Correlation model between mesostructure and gradation of asphalt mixture based on statistical method

Asphalt mixture has complex gradation and mesostructure. Accurate prediction of the relationship between gradation and mesostructure is of great significance for the establishment of mesostructure numerical simulation model and image-based gradation detection. In this paper, featurization, stepwise regression, econometric hypothesis test are utilized for establishing the predicting models. Firstly, asphalt mixtures with 64 kinds of gradation are scanned by Computed Tomography (CT) to obtain the mesostructure images; Then a series of mesostructure parameters of voids and aggregates are put forward. On this basis, the relationship model between gradation and mesostructure is established and verified by featurization and statistical modeling method. The results show that for predicting the passing percentage of the 4.75 mm sieve and the mean value of average distance between aggregate centroids for 9.5–4.75 mm aggregates, the prediction error of passing percentage is acceptable. It illustrates that the relationship model between gradation and mesostructure established by statistical method is effective, and it is significance for material design and testing under the condition of big data in the future.