Two backpropagation neural networks were trained to backcalculate pavement moduli for three-layer flexible pavement profiles. Such speed makes real-time backcalculation of moduli possible. Neural networks trained in this study are more than three orders of magnitude faster than conventional gradient search algorithms. 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.
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