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Using multiple sensors to approach pavement deterioration through multiple regression analysis and distribution fitting

Zhang, Dada (2021) Using multiple sensors to approach pavement deterioration through multiple regression analysis and distribution fitting. Masters thesis, Northern Arizona University.

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The measurement of road roughness is critical to road maintenance. How to adequately determine a threshold value to be used in computing processes in an attempt to determine the levels of pavement conditions has been a challenge. The project presents an approach using regression analysis and probability distribution fitting to analyze patterns and signals collected from a year-long pavement sensing activities on the I-10 corridors in Phoenix, Arizona. A vehicle is equipped with four sensors placed on the top of the control arms of the vehicle and another one is inside of the vehicle to gather the data for analysis. The result of the multiple regression analyses shows that the vibration data from the sensor located inside the vehicle should be analyzed individually and, to avoid statistical invalidity, it should not be included with the other four sensor groups in the analysis for condition assessments. Based on distribution models to fit all vibration data at a specified 99.99th percentile, the threshold values of 1.79 g and 1.28g were determined for use in identifying poor pavement conditions known as significant points in two road sections. Using a single sensor that was placed inside of the vehicle for pavement condition assessment, the threshold of 1.96 was determined through the upper bound of 95% confidence interval in standardization of log magnitudes. All selected significant points visually match with the IRI segments well in GIS software. The ANCOVA results also indicate that the interaction effects of sensor and pavement temperature exist at the significance level of 0.10 in both road sections which describes that the pavement temperature does play an important role in controlling pavement conditions. Based on the Time-Series analysis from the vibrationdata, the pavements are predicted to be deteriorated in two years if the maintenance and rehabilitation will not be scheduled in a timely manner. The paper concludes that using multiple regression analysis and distribution fitting method provides a promoting approach that can be used to help determine the level of different pavement conditions as well as facilitate decision making process for future maintenance.

Item Type: Thesis (Masters)
Publisher’s Statement: © Copyright is held by the author. Digital access to this material is made possible by the Cline Library, Northern Arizona University. Further transmission, reproduction or presentation of protected items is prohibited except with permission of the author.
Keywords: ANOVA; distribution fitting; multiple regression; pavement condition assessments; sensing patterns. Vibration data; Pavement deterioration
Subjects: T Technology > TE Highway engineering. Roads and pavements
NAU Depositing Author Academic Status: Student
Department/Unit: Graduate College > Theses and Dissertations
College of Engineering, Informatics, and Applied Sciences > Civil Engineering, Construction Management and Environmental Engineering
Date Deposited: 15 Feb 2022 18:03
Last Modified: 15 Feb 2022 18:03
URI: https://openknowledge.nau.edu/id/eprint/5707

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