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Highway authorities make efforts to relate visual ratings with directly measured pavement condition data to reduce or remove the necessity of manual pavement condition surveys that involve subjectivity and potential safety risk of assessors. This research develops a set of relationships between pavement surface condition rating and objective pavement condition parameters to assist road asset managers in triggering periodic bituminous resurfacing programs at the network level. These condition parameters include cracking (% area affected), rutting (mm), texture loss (% of left wheel path texture), and roughness (m/km). In the literature, deterministic and probabilistic modelling approaches are used to predict visual surface inspection rating (SIR) from directly measured pavement distresses. The Factorial ANOVA results that are typically used have inferred that cracking and rutting interact with each other significantly for the asphalt surfaced road network. However, the percentages of variation explained by the linear regression models that predict SIR from cracking/rutting are low (24–31%). Alternatively, developed ordinal logistic models for predicting the probability of a road section being in any particular surface condition, with any quantified cracking/rutting data, prove to be statistically better with overall success rates of 46% and 51% for sprayed sealed and asphalt surfaced network, respectively.

Development and evaluation of relationships between surface condition rating and objective pavement condition parameters