Estimating asphalt mixture volumetric properties using seemingly unrelated regression equations approaches

Abstract As the asphalt pavement industry seeks to manufacture products with low variability, advances in modeling approaches that help optimize asphalt materials are lagging behind. Typically, well-controlled paving projects provide asphalt mixture volumetric properties with low variability, while poorly controlled projects result in higher variability in asphalt mixture volumetric properties. Additionally, on many occasions, the management of all the factors influencing asphalt mixture production and construction is inadequate. The work described in this paper demonstrates that seemingly unrelated regression equations (SURE) approaches can be used to estimate asphalt mixture volumetric properties using mixture design, material properties, and testing inputs. SURE approaches can help evaluate asphalt mixture production and placement by accounting for the deficiencies and limitations in quality assurance data. Moreover, SURE approaches analyze asphalt mixtures in a concise, yet robust, manner. The findings of this study contribute to a better understanding of variability in asphalt material production and placement.