Interpretation of Road Roughness Profile Data

High-speed road profiling is a technology that began in the 1960s when Spangler and Kelly developed the inertial profiler at the General Motors Research (GMR) Laboratory.(1) In the past decade, profiling instruments based on the GMR design have become everyday tools for measuring road roughness. The majority of States now own road profilers. As profiling capability has become the rule, rather than the exception, the most widely used summary index is the International Roughness Index (IRI). IRI has considerable merit as a summary profile-based roughness index. However, it was tailored to be measurable by a wide range of equipment, including response-type systems, rather than to be the best profile-based measure of any specific pavement quality.(2) State agencies with profiling instruments are building a data base of measurements that contain potential riches of information about the pavement surface condition. However, existing technology for extracting practical information from profiles often goes unused. With the vast amount of data being acquired, users are now asking (other than IRI) what can be learned from the profile data? An even more basic issue for many users involves the quality of the IRI data now being collected. Roughness measures that are supposedly on the standard IRI scale are submitted yearly to FHWA for the national Highway Performance Monitoring System (HPMS) data base, and discrepancies in profile-based data are being identified. This leads to questions of how to control the quality of IRI and other profile-based measures. For example, many State agencies use algorithms developed in-house for estimating serviceability, and the relationship of these indices to each other and to IRI is not well known.