Practical Models for Interpreting Traffic Speed Deflectometer Data
Abstract
For more than 30 years, FWDs have been the most viable device for structural pavement evaluation. During the last decade, several moving deflectometers have been introduced, among those, traffic speed deflectometer has been proven as the most utilized device. Since TSD can measure the deflection parameters of pavement at traffic speeds up to 50-60 mph (80-95 kph), it overcomes FWD shortcomings. However, the analysis of the data collected with TSD is in its infancy. Since TSD measures the vertical component of pavement velocity (a.k.a. deflection velocity), relationships to convert deflection velocities to deflections for further analyses are needed. Computer software 3D-Move, developed at the University of Nevada, Reno, was employed to simulate the responses of a large number of pavement sections subjected to TSD loading in terms of both deflections and deflection velocities. Symbolic regression approach was used to develop the best relationships with the least error between the deflections and deflection velocities. To take advantage of the wealth of pavement structural evaluation algorithms developed specifically for FWD, the same approach was used to develop relationships between the deflection bowls under TSD and FWD. 3D-Move was also employed to simulate the deflection bowls under FWD loading for the same pavement sections where the TSD responses were simulated. Symbolic regression approach was again used for developing those relationships. For almost real-time application of the TSD data to TSD deflection parameters for network level structural analyses, the critical responses (i.e., tensile strain at the bottom of the hot mix asphalt layer and compressive strain on top of subgrade among others) of the pavement sections used in the TSD simulations where also subjected to standard truck loading. A combination of well-known deflection indices extracted from TSD measurements were used to estimate those critical responses with reasonable accuracy. In all cases, utmost care was taken to only limit the parameters necessary for the models to those that can be readily measured with a TSD equipped with a ground penetrating radar for thickness assessment.
Subject Area
Transportation
Recommended Citation
Saremi, Setare Ghahri, "Practical Models for Interpreting Traffic Speed Deflectometer Data" (2018). ETD Collection for University of Texas, El Paso. AAI10931280.
https://scholarworks.utep.edu/dissertations/AAI10931280