Data exploration on standard asphalt mix analyses

• Published in: Journal of chemometrics 2009-06, Vol.23 (6), p.283-293
• Main Authors: TUSAR, Marjan, NOVIC, Marjana
• Format: Article
• Language: English
• Subjects: Asphalt; asphalt mix design; bitumen; Bitumens; Chemistry; counterpropagation; Exact sciences and technology; Exploration; General and physical chemistry; General. Nomenclature, chemical documentation, computer chemistry; Least squares method; Mathematical models; Mechanical properties; Neural networks; PLS; Regression; Regression analysis; Studies; Theory of reactions, general kinetics. Catalysis. Nomenclature, chemical documentation, computer chemistry
• ISSN: 0886-9383; 1099-128X; 1099-128X
• DOI: 10.1002/cem.1229

The purpose of our study was the evaluation of the most important factors that affect the volumetric and conventional mechanical properties of produced asphalt mix and the volumetric properties of built‐in asphalt layer. Asphalt mix design follows the standard procedure (Marshall procedure). We were interested not only in the quantity of bitumen specified by the Marshall procedure, but also in the quantity of stone aggregate fractions, temperatures of production and properties of bitumen that is used. The influence of these factors was investigated with several models. For the building of models we used 444 asphalt samples, analysed by one laboratory. To select the most important factors, several multiple linear regression (MLR) models, partial least squares (PLS) regression models and counterpropagation neural network models were made. Obtained models were tested with leave‐one‐out (LOO) and leave‐10%‐out cross‐validation procedures. The results of MLR and PLS models show that the independent variables are closely related. Among 21 variables there is only one found as less important. MLR and PLS models show better predictive ability than counterpropagation neural network models. The best MLR models will be employed for the preparation of the asphalt mix design (recipe) with some unknown material. Copyright © 2009 John Wiley & Sons, Ltd. The purpose of our study was the evaluation of the most important factors that affect the volumetric and conventional mechanical properties of asphalt. To select the most important factors, several multiple linear regression (MLR) models, partial least squares (PLS) regression models and counterpropagation neural network models were made. Among 21 variables there is only one found as less important. The best models will be employed for the preparation of the asphalt mix design with some unknown material.