International Roughness Index (IRI) is the primary index of Maryland pavement management system (PMS) and constitutes the data source for performance trend analysis, budget allocation and project selection. Maryland StateHighway Administration (MDSHA) makes a significant investment to collect,process, analyze and store IRI data every year over the entire network ofthe roads. Such reliance on IRI data and investment instigated the investigation of the confidence level embedded in the data and devising possible sampling scenarios. To evaluate the confidence level, repeatability error of the measurements were assessed. IRI data was repeatedly collected over a designated test loop under normal operating conditions to mimic network level data collection. Sampling scenarios were devised using Monte Carlo method based on network IRI data collected in 2008. The network was stratified based on functional classifications. Sampling errors in estimating the percentage of the roads in each of five IRI categories established by MDSHA were evaluated. The results indicated that the repeatability error of the measurements was less than 7%. The sampling results indicated that the State may survey a third of the network each year and expect estimation errors of less than 0.5% for all IRI categories. As such, MDSHA would not sacrifice considerably on the confidence of network condition evaluation.
Abstract