The objectives of this study were to develop neural networks capable of predicting pavement layer moduli rapidly and reliably and to determine correction factors for the high frequency/high speed FWD pavement responses to typical operating speed responses. Hence, significant error could result between calculated from FWD and measured strain responses from traffic loads at operational speeds. ![]() In addition, the loading frequency of the FWD impact loading can be considered similar to that of vehicle loading at a high speed. The use of artificial neural networks (ANN) is currently being studied as a more reliable methodology and an advanced alternative. These properties are currently obtained through an iterative process called backcalculation which has several limitations with one of the most notorious: the non-uniqueness of the results. ![]() ![]() The falling weight deflectometer (FWD) is commonly used to obtain material properties that can be used in mechanistic-empirical pavement design. The use of nondestructive deflection testing has become an integral part of the structural evaluation and rehabilitation process of pavements in recent years.
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