Performance of porous asphalt pavement based on seermingly unrelated equations approach

Road pavement deterioration models, which are currently used in the process of assessment and management of transportation infrastructures, are often simple models that capture only one aspect of the pavement performance over time. Very few models consider that different distress indicators might be correlated to each other or that there is a connection between the error terms. To address this last issue, this paper presents a system of seemingly unrelated regression equations (SURE) which allows to handle correlated error terms. In particular, three major indicators such as the side friction coefficient (SFC20°C), the mean-profile depth (MPD), and the international roughness index (IRI), were surveyed in continuous for a case study of a porous asphalt pavement and subsequently used in statistical analysis. The results show that estimation points have the signs expected and they are consistent with the literature. The likelihood ratio test proves that the model developed, which assumes a correlation among error terms, is statically more appropriate than uncorrelated models. The result found, with three degrees of freedom, corresponds to a p-value 0.150 and the null hypothesis cannot be rejected at any significance level less than this value.