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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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School of Civil and Environmental Engineering
Ph.D. Thesis Defense Announcement
A Methodology for Quantifying and Improving Pavement Condition Estimation and Forecasting By Integrating Smartphone and 3D Laser Data
By
Anirban Chatterjee
Advisor:
Dr. Yi-Chang (James) Tsai (CEE)
Committee Members:
Dr. Adjo Amekudzi-Kennedy (CEE), Dr. Zhaohua Wang (CRP), Dr. Umit V. Catalyurek (CSE), Dr. Richard Vuduc (CSE)
Date & Time: Friday, July 12, 1:30pm
Location: Mason 2119
This thesis aims to combine data from accurate but expensive 3D laser scanners and inexpensive and frequently used smartphones for improving pavement condition estimation and forecasting using both technologies. This thesis presents 1) A methodology for registering both 3D laser data and smartphone sensor data onto a common GIS model of the road network; 2) A methodology for single-run pavement condition estimation using smartphone data trained using labeled data from a 3D laser scanner; 3) A methodology for combining multiple-run pavement condition estimates from both smartphones and 3D laser scanners with an associated confidence level; and 4) A methodology to improve pavement condition forecasting using 3D laser data by combining updated evidence obtained from crowdsourced smartphone data. Data was collected on a test route consisting of diverse pavement conditions and road classifications to test and validate the proposed methodologies. The data registration methodology was validated by observing a large peak in cross-correlation between pavement distress metrics calculated from multiple runs registered using the proposed methodology. The methodologies for pavement condition estimation and forecasting were validated by observing low median error for International Roughness Index (IRI) estimation and forecasting. The proposed method for associating a confidence level with each estimate was able to successfully separate low and high error estimates derived from smartphone data, allowing transportation agencies to use smartphone derived pavement condition estimates with low error. This research will strongly improve the utility of both 3D laser technologies and smartphones for pavement condition assessment and forecasting.
