Estimating IRI based on pavement distress type, density, and severity: Insights from machine learning techniques
This work addresses the costly measurement of pavement roughness for road management, offering a predictive method that could reduce expenses, though it appears incremental as it applies existing machine learning techniques to a specific domain problem.
This paper tackles the problem of estimating the International Roughness Index (IRI) for pavements by using machine learning to predict IRI based on distress types, densities, and severities, finding that this approach can reliably estimate IRI and that the estimates depend on pavement type and functional class.
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.