In order to best prioritize road maintenance, the level of deterioration must be known for all roads in a city’s network. Pavement Condition Index (PCI) and International Roughness Index (IRI) are two standard methods for obtaining this information. However, IRI is substantially easier to measure. Significant time and money could be saved if a method were developed to estimate PCI from IRI. This research introduces a new method to estimate IRI and correlate IRI with PCI. A vehicle-mounted dynamic tire pressure sensor (DTPS) system is used. The DTPS measures the signals generated from the tire/road interaction while driving. The tire/road interaction excites surface waves that travel through the road. DTPS, which is mounted on the tire’s valve stem, measures tire/road interaction by analyzing the pressure change inside the tire due to the road vibration, road geometry and tire wall vibration. The road conditions are sensible to sensors in a similar way to human beings in a car. When driving on a smooth road, tire pressure stays almost constant and there are minimal changes in the DTPS data. When driving on a rough road, DTPS data changes drastically. IRI is estimated from the reconstructed road profile using DTPS data. In order to correlate IRI with PCI, field tests were conducted on roads with known PCI values in the city of Brockton, MA. Results show a high correlation between the estimated IRI values and the known PCI values, which suggests that DTPS-based IRI can provide accurate predictions of PCI.
Classifications of road conditions are crucial because officials prioritize road maintenance decisions based on them.
Pavement condition index (PCI) surveys are performed manually and used by many cities in the U.S. to evaluate road
surface conditions. In this research, a more efficient method is used to detect road surface conditions. This method applies a probabilistic analysis to acoustic pressure data collected from a vehicle-mounted microphone. The data is collected while the driving and processed in real time. Acoustic pressure data contains information on road surface conditions because acoustic pressures change when the tire impacts different road surfaces. This change is audible to human ears, for example, a driver transitions from a normal road to a bridge. The acoustic pressure data used in this research was collected from roads with known PCI values that are used as a reference. To reveal the dominant common features and neglect trivial differences within a certain length of road, a probabilistic method is used to evaluate road surface conditions. This approach uses the Weibull probability density function (PDF) to evaluate road surface conditions. This distribution was chosen because it is closest to the actual PDF among other distributions such as the normal distribution and lognormal distribution. A key finding of this paper is that the Weibull PDF shows the largest change between roads with different PCI values. Another finding is that the Weibull pdf changes when the van hits road defects such as cracks and patches.