IRI Prediction Model for Use in Network-Level Pavement Management Systems

Abstract

This paper describes the development and validation of an empirical model for predicting the International Roughness Index (IRI) over time. The model is designed to balance mathematical complexity and ease of implementation in network-level pavement management systems. The predicted pavement roughness is modeled as a function of the initial IRI (post construction or treatment) and pavement age. The model accounts for the effects of climate, subgrade, treatment type, pavement type, traffic loading, and functional system (urban or rural) through the use of calibration coefficients. Representative roadway sections are selected from a 10-year (2005 to 2014) pavement management database provided by the Texas Department of Transportation (TxDOT). To validate the model, the IRI data observed in 2015 is compared with the 2015 predicted IRI. The reasonableness and sensitivity of the model are also evaluated. The results show that the proposed model can be a useful tool for predicting IRI in network-level pavement management systems.