Abstract
The United States is facing grave infrastructure challenges related to repairing aging roads with limited resource allocation. Every year, large sums of money are invested into repairs, but these repairs are still inadequate. In order to best prioritize road maintenance investment, city officials should know the grade of all roads prior to any action of repair. Two major parameters are widely used to assess road surface conditions: International Roughness Index (IRI) and Pavement Condition Index (PCI). IRI is a standardized and widely used parameter to quantify road roughness and riders' comfort level for major highways. A low IRI value indicates a smooth road and a high value indicates that the road has distresses, such as potholes or deep depressions. One limitation of the current IRI measurement is that the laser profilometers used with accelerometers are incapable of operating on a wet road surface. Another is IRI's inaccuracy due to the speed effect and complicated road conditions (potholes, manholes, etc.). PCI has been used widely on urban roads. A high PCI value indicates a good road and a low value indicates a poor road. The limitations of current PCI measurement include the high cost, difficulty of manually gathering measurements without traffic interruptions , and low efficiency on data processing due to large amounts of pictures. To overcome these limitations, this dissertation develops two new sensor systems for IRI measurement: a directional microphone and dynamic tire pressure sensor (DTPS) with an axle accelerometer. These sensors are all mounted on a moving vehicle. This research develops that IRI measurement use a directional microphone and a probabilistic method to analyze the probability density function (PDF) of acoustic data collected while driving. Acoustic response of tire/road interactions was measured. Weibull distribution of the acoustic data was applied to the IRI estimation on Superpave, Stone Matrix Asphalt (SMA), and Open Grade Friction Coarse (OGFC) roads with IRI values less than 2 m/km. IRI measurement using DTPS with an axle accelerometer analyzes the tire pressure change inside the tire with an axle accelerometer. Speed effect has been minimized in the derivation. Therefore, it is suitable for both urban roads and state/interstate highways. Field road tests have been conducted to validate the accuracy for the cities of Brockton and Boston, MA, and for part of interstate highway I-95 MA, state highway US-1 and US-128. A certification test was also completed at New Bedford Regional Airport administered by the Massachusetts Department of Transportation. Results showed that the higher the IRI values, the lower the PCI values. It is possible to use IRI to assess road conditions for both urban roads and highways and indirectly infer PCI values of urban roads. The advantages of this method are that it works under all weather conditions since the sensor is inside the tire, and it does not interrupt the traffic. The speed effect, which was encountered in the method which used laser profilometers with accelerometers, is considerably minimized in the DTPS approach since this approach does not require the systematic integration of acceleration data. Therefore, it works for both highways and urban roads. Meanwhile, a miniature fixture was designed to further the simplification of the mounting process for easy installation. An energy harvesting system was also designed and tested at a speed of up to 52 km/h in the lab in order to power the DTPS sensing system. Not only can this energy harvester be used to power the DTPS system, but it can also potentially be used as an independent energy harvester to recharge car batteries and to power vehicle based sensors, including their wireless transmitters and vehicle computer chips. This new development will enable continuous, network-wide assessments of roadway conditions to effectively and efficiently make the right repair, at the right time, in the right place.
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