Correlations and Analyses of Longitudinal Roughness Indices

Abstract

ABSTRACT This paper analyses some of the most widely used indices for the characterization of longitudinal roughness. Measured longitudinal profiles available in the Long-Term Pavement Performance (LTPP) program database were used. A total of 207 pavement sections, in 42 States of the USA, were used in these analyses. Using a suitable software, the International Roughness Index (IRI), the Standard Deviation of longitudinal roughness (σ) and the Root Mean Square of Vertical Acceleration (RMSVA) with different base lengths were computed for every measured longitudinal pavement profile. Linear regression analyses between the IRI and σ statistics produced a very high correlation (R2=0.93). Multivariate regression between IRI and the RMSVA also showed a high correlation (R2=0.96) for base lengths of 1.0 m and 3.5 m. On the other hand, when the RMSVA and σ statistics were analyzed, it was observed that all regressions including a RMSVA with the base length of 1.5m presented a nearly perfect correlation (R2≅1.00). Probabilistic analyses were also carried out using the First Order Second Moment (FOSM) method applied to the regression models obtained between the IRI × RMSVA and σ × RMSVA. Thus, it was possible to determine the relative contribution of different base lengths used in the RMSVA statistic on each roughness index. It was concluded that the base length of 1.5 m contributed in average with 96% of roughness values computed for σ statistic, whereas the base length of 1.0 m was responsible for 74.5% of computed IRI values.