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School of Civil and Environmental Engineering
Ph.D. Thesis Defense Announcement
A Spatiotemporal Methodology for Pavement Rut Characterization and Deterioration Analysis Using Long-Term 3D Pavement Data
By
Chieh Wang
Advisor:
Dr. Yi-Chang James Tsai (CEE)
Committee Members:
Dr. James Lai (CEE), Dr. Adjo Amekudzi-Kennedy (CEE), Dr. Zhaohua Wang (CGIS),
Dr. Elliot Moore II (ECE), Dr. Michael D. Meyer (WSP|Parsons Brinkerhoff)
Date & Time: Monday, November 21, 2016, 1:30 PM
Location: Sustainable Education Building, 122
Pavement rutting, defined as the permanent longitudinal deformation in the wheelpaths of the road, is an important type of pavement distress that is required to be monitored by the Highway Performance Monitoring System (HPMS) and the Moving Ahead for Progress in the 21st Century Act (MAP-21). Traditionally, performance of ruts has been measured by rut depth. However, rut depth is insufficient to characterize 3D rut shape and its deterioration, which are essential for identifying causes and determine adequate and timely treatment methods.
With the advancement in sensing technology, continuous 3D pavement surface can now be accurately measured at 1 mm intervals, which are equivalent to more than 4,000 points instead of the traditional 3 or 5 points. This technology provides a great opportunity for capturing detailed 3D rut shapes in the real-world environment. Although preliminary calibration and validation of 3D sensing technology have been undertaken, there is a lack of methods to utilize this technology for reliable comparison of 3D rut shapes over time. Therefore, the objective of this dissertation is to develop a methodology to register long-term 3D pavement data spatially and temporally, and utilize the registered data for characterizing 3D rut shape and its deterioration.
The proposed methodology includes (1) a boundary-based 3D data registration method for matching multi-timestamp 3D transverse profiles in 3D space; (2) visualization of 3D rut shape and its deterioration over time; and (3) characterization of 3D rut shape and quantification of rut deterioration behavior. A sensitivity analysis is performed through an iterative static sampling simulation to assess the effect of different data sampling intervals on 3D rut characteristics. The proposed methodology is further applied to develop a rut classification field study that utilizes the characteristics of 3D rut shape and its deterioration behavior to classify the causes of rutting. Case studies, using 3D pavement data collected between 2012 and 2016 on State Route 26, State Route 275, and Interstate Highway 95, are conducted to demonstrate the capability of the proposed methodology in quantifying deterioration behaviors of rutting under different roadway and traffic characteristics.
The proposed methodology is one of the first efforts that utilize long-term 3D pavement data to characterize 3D rutting and its deterioration. Results of this dissertation will play a key role in advancing the utilization of sensing technology for enhanced pavement evaluation and monitoring practices. Methods of this dissertation will serve as the cornerstone for future 3D pavement deterioration and classification research that can lead to improved pavement design, performance modeling, and maintenance decisions.
