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 extraction of stiffness parameters from nondestructive field data. The spectral analysis of surface waves (SASW) method is one of the more frequently used NDT methods because of its capabilities in characterizing the near-surface layers effectively. 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. The inversion process requires a significant computational effort or frequent operator intervention. To improve the user-friendliness of the inversion process, an algorithm for the rapid reduction of the SASW data was developed. Thickness and modulus of each pavement layer are estimated in real time using artificial neural network models. These models serve a dual purpose:(a) the results from the neural network models can be used to approximate the layer properties of a given pavement section and (b) it can be used as a first approximation to the traditional inversion process. The reduction algorithm appears to be robust and to yield consistent results in almost real time.

Transportation agencies around the nation expend significant effort maintaining and rehabilitating existing roads. This endeavor requires continual monitoring and assessment of the pavements’ structural conditions. To evaluate the performance of the pavement, several techniques and methods have been developed and used. The most popular of these techniques are based on the use of nondestructive testing (NDT) procedures. An NDT method that has been increasingly gaining in popularity is the seismic method. Several techniques have been developed using seismic technology to evaluate the structural capacity and to detect defects and distress in pavements. The benefit of using seismic technology comes from its ability to evaluate and monitor changes in elastic pavement properties (moduli), the core parameters in the mechanistic pavement design. The seismic technique discussed in this paper is the spectral analysis of surface waves (SASW) method. It is a popular method used for elastic moduli profiling (1, 2). The SASW method involves two critical tasks in its overall algorithm:(a) construction of a dispersion curve and (b) inversion (backcalculation) of the dispersion curve for