Author: Waleed Aleadelate

Publisher:

Published: 2016

Total Pages: 122

ISBN-13: 9781369182064

In Wyoming, most county paved roads were built decades ago without following minimum design standards. However, the recent increase in industrial/mineral activities in the state requires developing a Pavement Management System (PMS) for local paved roads. Thus, The Wyoming Technology Transfer Center/Local Technical Assistant Program (WYT2/LTAP) is currently in the process of developing a PMS for county roads. The PMS which is being developed uses the present serviceability index (PSI) as a pavement performance parameter. The primary steps in the development process show that there are two major issues related to the development of county roads PMS; the none availability of suitable PSI prediction models for county roads and the high costs related to the pavement condition data collection process. This study comprises of two parts. The first part of this study deals with the development of exclusive county roads PSI models. The developed PSI models for county roads are based on: International Roughness Index (IRI), Pavement Condition Index (PCI), and rut depth for flexible pavements only. Ten panelists from Wyoming rated 30 pavement sections that were randomly selected at different distresses’ levels using two vehicles (SUV and Sedan). Regarding the rating process, the F-test results for equal variances indicated that the seating position, age, and gender were not significant to the rating process. However, the vehicle’s type was significant. One model (Sedan model) was proposed to be used in the county roads PMS. The newly proposed model explains 80 percent of the variations in the PSI values of county roads (Adjusted R2 = 0.80). In addition, the new model seems to provide more realistic representation of the county roads conditions. In the second part of this study, modern smartphones are proposed as a cost effective solution to minimize the costs of collecting pavement condition data. Modern smartphones are equipped with many useful sensors such as gyroscopes, magnetometers, GPS receivers and 3D accelerometers. Smartphones’ 3D accelerometer was used for collecting a vehicle’s vertical acceleration data. Through the use of various signal processing and pattern recognition techniques such as cross correlations, Welch periodograms, and variance analyses, the measured signals (time series acceleration data) were identified and correlated with the actual IRI values. It was found that the variance among the vertical acceleration measurements was the key feature for classifying the measured signals. A validation analysis was also conducted to measure the reliability of this methodology. The initial validation results suggested that, using the aforementioned methodology, the smartphones used could predict with high certainty the actual IRI values. In addition, the difference between the predicted and the actual IRI values was not statistically significant. The smartphones data were collected over 20 roadway segments. The selected segments have various lengths and geometric features reflecting the actual roadway segments under any PMS.