  Surface roughness is primary measure of pavement performance that has been
associated with ride quality and vehicle operating costs. Of all the surface
roughness indicators, the International Roughness Index (IRI) is the most
widely used. However, it is costly to measure IRI, and for this reason, certain
road classes are excluded from IRI measurements at a network level. Higher
levels of distresses are generally associated with higher roughness. However,
for a given roughness level, pavement data typically exhibits a great deal of
variability in the distress types, density, and severity. It is hypothesized
that it is feasible to estimate the IRI of a pavement section given its
distress types and their respective densities and severities. To investigate
this hypothesis, this paper uses data from in-service pavements and machine
learning methods to ascertain the extent to which IRI can be predicted given a
set of pavement attributes. The results suggest that machine learning can be
used reliably to estimate IRI based on the measured distress types and their
respective densities and severities. The analysis also showed that IRI
estimated this way depends on the pavement type and functional class. The paper
also includes an exploratory section that addresses the reverse situation, that
is, estimating the probability of pavement distress type distribution and
occurrence severity/extent based on a given roughness level.
