About the research
Pavement roughness serves as a critical indicator of the overall ride quality of road surfaces. Elevated levels of pavement roughness can give rise to concerns regarding driving safety, fuel consumption, and exhaust gas emissions. The International Roughness Index (IRI) stands as the globally accepted standard for quantifying road surface roughness. Although one of the primary responsibilities of local public agencies (LPAs) involves monitoring and maintaining appropriate IRI levels for the local road system, existing techniques used to collect IRI data, such as using high-speed and walking profilometers, typically entail high annual costs for network-level inspection. Consequently, LPAs are in need of a cost-effective IRI data collection system that enables them to gather pavement performance data annually. Smartphones come equipped with an array of sensors, including multi-axis accelerometers and global positioning systems (GPS), that present an efficient and economical approach for collecting vehicle suspension data, specifically vertical acceleration. These data can then be harnessed to estimate pavement profiles and roughness. This study endeavored to develop a low-cost, smartphone-based, nonproprietary data collection system designed for use by LPAs to gather pavement roughness data on an annual basis. This adaptable system can be implemented on Android phones, iPhones, or custom-developed smart boxes that incorporate accelerometers and GPS units. All data gathered in this way can be seamlessly transferred to a cloud-based server and subsequently processed using a Python-based algorithm to compute the IRI. The accuracy of such a system was assessed using four distinct smartphones, one custom-developed smart box, and a Class 1 high-speed profilometer that was used to establish reference IRI values. The field data collection program encompassed 24 Story County sites selected to validate the accuracy of IRI measurements. The results demonstrate that the smartphone-based and smart box systems developed offer a dependable, low-cost, and user-friendly solution for LPAs to use in assessing local road roughness. A prototype smartphone application for detecting road surface distress through captured videos and images was also developed and evaluated.