Backcalculation of flexible pavement moduli from falling weight deflectometer data using artificial neural networks

Abstract

The falling weight deflectometer (FWD) test is a widely used nondestructive test for assessing the structural integrity of pavement systems. The FWD test entails dropping a weight onto the pavement surface to produce an impulse load and measures the resulting pavement deflections at numerous locations. Artificial neural network is used to approximate the backcalculation function of the pavement layer moduli from FWD in real time. By dissociating backcalculation speed from model complexity, the neural network approach opens up a host of possibilities for incorporating additional test data and more complex material behaviors into the pavement analysis.

Publication Title

Manuals and Reports on Engineering Practice, American Society of Civil Engineers

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