Title:
A spatiotemporal methodology for pavement rut characterization and deterioration analysis using long-term 3D pavement data
A spatiotemporal methodology for pavement rut characterization and deterioration analysis using long-term 3D pavement data
Author(s)
Wang, Chieh
Advisor(s)
Tsai, Yi-Chang James
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Abstract
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, a 1D indicator that is insufficient to characterize the 3D shape of rutting 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 characterizing 3D rut shape and its deterioration behaviors 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 multi-timestamp 3D data for characterizing and studying 3D rutting and its deterioration. Therefore, the objective of this dissertation is to develop a methodology to utilize 3D sensing technology for characterizing 3D rut shape and analyzing its deterioration behavior. 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 the spatial and temporal characteristics of rutting. 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 characterizing and studying deterioration behaviors of rutting under different roadway and traffic characteristics. Results of the case study show that the proposed boundary-based registration method can accurately register multi-timestamp 3D pavement data in the 3D space. Visualization of the registered data demonstrates that the proposed methodology is capable of reflecting the detailed 3D rut shape and quantifying its deterioration over time. The multi-scale deterioration analysis demonstrates that the proposed methodology not only can be utilized to support state DOTs' routine pavement performance evaluation and monitoring practices, but also enables the analysis of detailed rut shape and its deterioration down to the individual rut level. The sensitivity analysis on sampling intervals can significantly reduce data storage and processing needs and help state DOTs utilize 3D sensing technology effectively. The rut classification study shows promising results that permit the application of the proposed methodology to identify causes of rutting. The proposed methodology is one of the first efforts that utilize long-term 3D pavement data to characterize 3D rutting and quantify its deterioration. Results of this dissertation will play a key role in advancing state DOTs' utilization of sensing technology for enhanced pavement evaluation and monitoring practices. Methods of this dissertation will serve as a cornerstone for future 3D pavement deterioration and classification research that can lead to improved pavement design, performance modeling, and maintenance decisions.
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Date Issued
2017-01-13
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Text
Resource Subtype
Dissertation