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Integrating seismic and deflection methods to estimate pavement moduli Abdallah, Imad ; Yuan, Deren ; Nazarian, Soheil

By: Contributor(s): Publication details: Transportation Research Record, 2001Description: nr 1755, s. 43-50Subject(s): Bibl.nr: VTI P8167:1755Location: Abstract: Nondestructive testing (NDT) of pavements has made substantial progress during the past two decades. Most algorithms currently used to determine the remaining life of pavements rely on stiffness parameters determined from NDT devices. One major area of continual improvement is the reliable and rapid extraction of stiffness parameters from nondestructive field data. Two of the most common NDT methods used are the deflection and seismic-based methods. The spectral analysis of surface waves tests are the most common seismic method. In this method, time records obtained with vibration sensors are used to obtain an experimental dispersion curve, which, through an inversion procedure, provides an estimate of the elastic modulus profile of the pavement. Deflection bowls are also used to backcalculate modulus profiles of pavements through the inverse theory. The inversion processes used in both methods are complex and require either a significant computational effort or frequent operator intervention. Each method, of course, has its own limitations and strengths. To improve the accuracy of the predicted moduli, an algorithm for joint reduction of the deflection and seismic-based data has been developed. Thickness and modulus of each pavement layer are estimated in real time using artificial neural network models. An overview of a proposed joint inversion and its practical use in pavement analysis and design is presented. The joint reduction algorithm shows promise since it appears to be more robust and to yield more consistent results when compared with the process and results from each method independently.
Item type: Reports, conferences, monographs
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Nondestructive testing (NDT) of pavements has made substantial progress during the past two decades. Most algorithms currently used to determine the remaining life of pavements rely on stiffness parameters determined from NDT devices. One major area of continual improvement is the reliable and rapid extraction of stiffness parameters from nondestructive field data. Two of the most common NDT methods used are the deflection and seismic-based methods. The spectral analysis of surface waves tests are the most common seismic method. In this method, time records obtained with vibration sensors are used to obtain an experimental dispersion curve, which, through an inversion procedure, provides an estimate of the elastic modulus profile of the pavement. Deflection bowls are also used to backcalculate modulus profiles of pavements through the inverse theory. The inversion processes used in both methods are complex and require either a significant computational effort or frequent operator intervention. Each method, of course, has its own limitations and strengths. To improve the accuracy of the predicted moduli, an algorithm for joint reduction of the deflection and seismic-based data has been developed. Thickness and modulus of each pavement layer are estimated in real time using artificial neural network models. An overview of a proposed joint inversion and its practical use in pavement analysis and design is presented. The joint reduction algorithm shows promise since it appears to be more robust and to yield more consistent results when compared with the process and results from each method independently.

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