Publication | Closed Access
Computation of Pavement Vertical Surface Deflections from Traffic Speed Deflectometer Data: Evaluation of Current Methods
24
Citations
11
References
2018
Year
Highway PavementPavement EngineeringEngineeringPavement DesignStructural PerformanceStructural EngineeringGeotechnical EngineeringPavementsCalibrationPavement-layer ModuliSystems EngineeringTraffic SimulationVertical Surface DeflectionsTransportation EngineeringStructural CapacityFull-scale MeasurementStructural Health MonitoringTraffic EngineeringCurrent MethodsCivil EngineeringConstruction Engineering
Vertical surface deflections and deflection indices are common parameters used by pavement engineers to the evaluate structural condition of a pavement. At the network level, traffic speed deflectometers (TSDs) have been evaluated and found to meet a minimum set of specifications for structural evaluation of pavements, including accuracy and precision requirements. In order to realize the full benefits of TSD data in traditional pavement structural evaluation applications, TSD-measured deflection slopes are converted to surface deflections using an algorithm, referred hereafter as a deflection algorithm. The objective of this study is to evaluate commonly used deflection algorithms with implications regarding estimation of remaining structural capacity and back-calculation of pavement-layer moduli. This study evaluates the efficacy of several deflection algorithms proposed in the literature by comparing their results with those of a dynamic analytical model of measured TSD slope data. The study showed all deflection algorithms produced comparable deflection indices and are therefore appropriate for estimation of remaining structural capacity in network-level application. One algorithm was found to produce more reasonable back-calculated pavement-layer moduli from deflections than those produced by the other two deflection algorithms. When the measured TSD data contain anomalous data, it is recommended to use a filtering method to screen and exclude or correct anomalous data before applying deflection algorithms.
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