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Runway Stiffness Evaluation Using an Artificial Neural Systems Approach

A critical issue concerning the deterioration of ageing road infrastructure all around the world is the need to rapidly and cost-effectively evaluate the present condition of pavement infrastructure. Non-Destructive Test (NDT) and evaluation methods are well-suited for characterizing materials and determining structural integrity of pavement systems. The Falling Weight Deflectometer (FWD) is a Nondestructive Test (NDT) equipment used to assess the structural condition of airfield pavement systems and to determine the moduli of pavement layers which are not only good condition indicators, but are also necessary inputs for conducting mechanistic based pavement structural analysis. In this study, Artificial Neural Systems (ANSs) based models were used to predict flexible airport pavement layer moduli from realistic FWD deflection basins acquired at the U.S. Federal Aviation Administration's (FAA's) National Airport Pavement Test Facility (NAPTF). A finite-element pavement structural model, which can account for nonlinear, stress-dependent behavior of pavement geomaterials, was used to generate the ANS training and testing database. The pavement stiffness uniformity characteristics of a high- strength flexible test section at the NAPTF were successfully mapped from FWD deflection data using the ANS based backcalculation models.