Speaker
Description
Abstract. The surface roughness in terms of the International Roughness Index (IRI) is critical in assessing pavement quality, affecting vehicle operation costs, driver safety, and comfort. The IRI model in PMED is linear between the initial IRI and other distress. The initial IRI is crucial for IRI model calibration and pavement design. This paper addresses the challenges and data limitations in estimating the initial IRI. It outlines a systematic approach to back-cast the initial IRI, using data from the MDOT sensor database for flexible and rigid pavements. The MDOT sensor database records IRI measurements from 1998 to 2019 for every 0.1-mile (0.161 km) road segment, but the initial IRI may not be available for all sections, especially the older ones. Further, the IRI time series plots show different trends, making having a single back-casting method for all pavement sections impractical. The study employs five distinct methods for estimating the initial IRI, considering variations in measured IRI trends across different pavement sections. A total of 424 flexible and 113 rigid sections were used in this study. A flowchart is provided to help the selection of the most suitable method based on specific criteria. Threshold values are established to ensure reasonable initial IRI estimates for different pavement types and fix types, enhancing the accuracy of IRI models for calibration. The study's findings reveal acceptable initial IRI distributions and time series trends.
