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New relationships between falling weight deflectometer deflections and asphalt pavement layer condition indicators Xu, Bing ; Ranjithan, S Ranji ; Kim, Y Richard

By: Contributor(s): Publication details: Transportation Research Record, 2002Description: nr 1806, s. 48-56Subject(s): Bibl.nr: VTI P8167:1806Location: Abstract: New relationships have been identified between the layer condition indicators of flexible pavements and falling weight deflectometer (FWD) deflections. Synthetic databases were generated using dynamic finite element analysis with nonlinear material models. The sensitivity of various deflection basin parameters (DBPs) to layer conditions was comprehensively examined on the basis of the developed databases. Three types of layer condition indicators were identified in the study, including DBPs, effective layer moduli, and stresses and strains. The DBPs identified from the sensitivity study were used in developing new relationships between the selected condition indicators and FWD deflections by applying regression and artificial neural network techniques. Even though these relationships include the complicated dynamic effect of FWD loading and nonlinear behavior of unbound materials, the time to obtain results from these procedures is insignificant, thus making the procedures suitable for field implementation.
Item type: Reports, conferences, monographs
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New relationships have been identified between the layer condition indicators of flexible pavements and falling weight deflectometer (FWD) deflections. Synthetic databases were generated using dynamic finite element analysis with nonlinear material models. The sensitivity of various deflection basin parameters (DBPs) to layer conditions was comprehensively examined on the basis of the developed databases. Three types of layer condition indicators were identified in the study, including DBPs, effective layer moduli, and stresses and strains. The DBPs identified from the sensitivity study were used in developing new relationships between the selected condition indicators and FWD deflections by applying regression and artificial neural network techniques. Even though these relationships include the complicated dynamic effect of FWD loading and nonlinear behavior of unbound materials, the time to obtain results from these procedures is insignificant, thus making the procedures suitable for field implementation.

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