Optimization of data collection needs for manual and automated network-level pavement condition ratings based on transverse variability and neural networks.

The paper deals with two approaches to optimizing pavement condition surveys for the urban pavement network of the City of Winnipeg, Manitoba. First, a nonparametric statistical test was applied to assess the transverse variability of the data. The test compared the ratings for one lane with those of all lanes of each segment. The test concluded that the medians of the two groups are equal at a 92% confidence interval and that there are observed biases in the data. The bias can be eliminated if the surveyed lane is selected randomly. The second approach was to predict visual survey scores from automated (laser-based) measurement of rut depth and international roughness index (IRI). A resilient back-propagation algorithm was selected, and the Kappa coefficient was used to examine the strength of the agreement. The results showed that only moderate agreement was achieved and that additional data elements are required to improve the predictive ability of the model. [ABSTRACT FROM AUTHOR]