A minimum percent difference of 3.5 between the measured and predicted stiffness were reported for points loaded with lowest falling weight of 3 kN and tested at lowest water content of 3.
Copyright © National Academy of Sciences. by LWD and the stiffnesses predicted by backcalculation process increased with increasing the moisture contents and the applied loads.
Unless otherwise indicated, all materials in this PDF are copyrighted by the National Academy of Sciences. Distribution, posting, or copying of this PDF is strictly prohibited without written permission of the Transportation Research Board of the National Academy of Sciences. 1448, Strength and Deformation Characteristics of Pavement Sections.
The single most important advantage of using neural networks for backcalculation is speed.
In the context of backcalculation, a neural network can be trained to approximate the inverse function by repeatedly showing it forward problem solutions. An artificial neural network is a highly interconnected collection of simple processing elements that can be trained to approximate a complex, nonlinear function through repeated exposure to examples of the function. BACKCALCULATION OF FLEXIBLE PAVEMENT MODULI USING ARTIFICIAL NEURAL NETWORKSĪrtificial neural networks provide a fundamentally new approach to backcalculation of pavement layer moduli from falling-weight deflectometer deflection basins.