In this regard, theįourier-based extraction of static deflections faces two key challengesĬaused by measurement errors, namely, the problem of the so-calledīaseline offset and a poor signal-to-noise ratio characterizing the Time histories for the preprocessing procedure. Stemming from the Fourier analysis of transient deflection records.ĭespite its potential for improving the pavement diagnosis fromįWD records, the approach necessitates the use of entire deflection Peak values of the pavement response are replaced, as an input toīackcalculation, by their zero-frequency (i.e., static) counterparts In the presence of a shallow stiff layer. Within the framework of conventional elastostatic analyses, especially Recently, a simple and efficient preprocessing procedure invoking the concepts of the Fourier transformĪnd frequency response functions was proposed (6) that potentiallyĬould elevate the FWD backcalculation of pavement elastic layers Nature, phenomena such as wave reflection and refraction as wellĪs the viscoelastic response of asphalt concrete are unaccounted for In particular, because the FWD test is decidedly dynamic in While interpreting the dynamic (i.e., peak) deflections of the pavement The prevailing methods of backcalculation assume static deformation Dynamic models (1, 2) and artificial neural networks (3–5) haveīeen employed to circumvent these difficulties, elastostatic backcalculation remains the dominant method of analysis.
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