UTC 2022 Funding - Cycle 1 Research Projects

Project No.: CY1-OU-OSU-TTI-06
Title: Rapid Assessment of Network-Level Pavement Conditions Using Novel Tools
Performing Institution: University of Oklahoma, Oklahoma State University and Texas A&M Transportation Institute
Principal Investigator: Syed Ashik Ali, University of Oklahoma; Joshua Qiang Li, Oklahoma State University; Garrett Dorsett, Texas A&M Transportation Institute; Kenneth Hobson, University of Oklahoma; Musharraf Zaman, University of Oklahoma
Start and Anticipated Completion Dates: 10/1/2023-9/30/2024
Abstract: Roadway pavements constitute a critical element of surface transportation infrastructure. With a large portion of pavements in poor condition and reaching the end of their service lives, pavement maintenance and rehabilitation are becoming increasingly challenging tasks for many state DOTs, including DOTs in Region 6. Recent developments have spotlighted the Traffic Speed Deflection (TSD) Device as a valuable technology for measuring surface deflections at short intervals and capturing data on roughness, texture, and rutting at traffic speed. The evaluation of pavement conditions and their rating typically depend on such parameters as deflections, slope deflection indices, structural considerations, and remaining service life. In this context, the potential advantages of deriving network-level pavement condition ratings from the TSD data could be enhanced through the implementation of other novel technologies developed by the consortium members collaborating on this project. Lack of access to a TSD device and high costs associated with data collection necessitate the pursuit of innovative in-house technologies, which will not only increase efficiency but reduce costs significantly. As part of a pooled fund study (TPF-5 (385)) participated by the Oklahoma Department of Transportation, pavement conditions data from I-35 and I-40 in Oklahoma were collected recently using a TSD. The proposed study focuses on developing tools for analyzing these TSD data for network-level assessment or rating of the associated pavements. A complementary objective is to collect data from the same pavements using in-house technologies, namely Pave3D 8K available at Oklahoma State University (OSU), and an air-coupled Ground Penetrating Radar (GPR) and Fast Falling Weight Deflectometer (FFWD) available at Texas A&M Transportation Institute (TTI). For this purpose, with the assistance of the Strategic Asset and Performance Management (SAPM) personnel at ODOT, the research team seeks to gain access to the TSD data from I-35 and I-40 and review these data closely. Leveraging different pavement condition indicators, the University of Oklahoma (OU) team will divide the I-35 and I-40 pavement sections into five different categories, namely very poor, poor, fair, good, and excellent. This categorization will facilitate the subsequent selection of experimental sites for an in-depth evaluation, each spanning 3 to 5 miles. The OSU team will employ Pave3D 8K for the acquisition of 2D/3D surface images and detailed pavement roughness and texture data from the evaluation sites. FFWD and GPR surveys will be conducted by TTI at the selected I-35 and I-40 sections. Based on the pavement conditions, cores will be extracted selectively from both distressed and good locations. Visual observations of the extracted cores and limited laboratory test results will be used to validate the pavement rating from the TSD data and Pave3D 8K and FFWD data. The research teams from all three institutions will work together to establish pavement condition thresholds. These thresholds can be used readily by ODOT and other DOTs in Region 6. These thresholds can be adjusted in the future as more network-level data becomes available. This study will help develop a data-driven application process for network-level pavement evaluation. Through selecting pertinent parameters and establishing operational thresholds, this study will assist state DOTs in rapidly identifying and prioritizing pavements in need of maintenance, rehabilitation, and reconstruction. The need for such rapid identification and prioritization is important in addressing the impacts of climate change and severe weather.
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