United States Department of Transportation
i
Study of Temporal Pavement Cracking in 3D to Determine Optimal Time and Cost-Effective Treatment Methods
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2020-01-01
[PDF-2.62 MB]
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Edition:August 2016 – January 2020
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Abstract:The Georgia Department of Transportation (GDOT) and many of its counterparts now collect the full-extent, high-resolution 3D pavement data to provide a full coverage (or continuous) of pavement distress data on its roadways. This project focuses on exploring the potential for taking full advantage of 3D pavement data in support of using slab-level pavement distresses to forecast the performance and treatment (such as slab replacement) of jointed plain concrete pavement (JPCP). A methodology, consisting of joint/crack detection, joint/crack clustering method, crack classification, faulting computation, and slab condition criteria, was developed for effectively extracting the distresses (i.e., cracking and faulting) on each slab and classifying the slab condition based on GDOT’s Jointed Plain Concrete Pavement Condition Evaluation System (JPCPACES) to support various analyses at the slab level. Six years’ 3D pavement data on five test sections was processed to generate the time-series slab condition data to explore the deterioration behavior. A slab condition forecasting model was developed based on a multi-stage Markov chain modeling, which use different transition probability matrixes (TPMs) to represent the slab deterioration behavior (change in condition) in three stages defined by the slab condition. A dynamic linear regression model, coupled with the default faulting growth rate for each design category derived from CPACES, was developed for predicting faulting on Georgia’s JPCP. A case study was conducted on 160 miles of I-16 (eastbound) to demonstrate the feasibility and the use of the developed models (methodology) for predicting future JPCP condition and M&R needs.
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