Spectral Analysis for Surface Wave (SASW) is a widely practiced NDT method due to its ability to identify the
shear velocity profile of subsurface layers. However, the SASW method is limited to point-to-point inspection
because all data has to go through an inversion process, which is iterative and manual. Some automated iteration
techniques were developed to improve the efficiency of inversion analysis. These attempts did not change the
situation much because they were still based on the guess-and-check procedure incorporated with a forward analysis.
In this paper, a new inversion analysis algorithm is proposed to estimate the shear velocity profile rapidly without
performing conventional forward analysis. Unlike conventionally determining the dispersion curve with a stiffness
matrix or something similar, the dispersion curve of a layered structure is assumed to be a weighted combination of
the shear velocity profile. The weighting factors are determined according to the variation of particle displacement
with depth for a specified wavelength of surface wave. Based on this assumption, a fast inversion algorithm is
established to estimate the shear velocity profile from a given dispersion curve. No prior knowledge of the test site
or personal expertise is needed because this method does not require the initial values of the layer depths and shear
velocities. This new method allows the SASW method to be a fully automatic or even real-time reporting method for
highway pavement detection. The accuracy of this fast inversion algorithm is verified by comparing the results to
those of the conventional algorithm.
SASW (Spectral Analysis of Surface Waves) is practical and relatively effective in characterizing subsurface ground
truth. According to the surface wave in the interesting range of frequency, some criteria for source-receiver configuration
are employed and limit the applications. Challenges emerge when SASW is applied to study the surface wave involving
multiple modes effect and when the source is near the receiver. In such cases, multiple modes effects and evanescent
wave fields are present in array sensing and might weaken the inversion accuracy of pavement subsurface profile. In this
work, these issues were investigated and a complex wave number estimation based method was proposed. The complex
wave number was estimated by iterative linear exponential fitting from wave field model to response measurements.
Evanescent wave for near field and multiple modes effects were focused in the proposed method. Finally, simulated
signals from FEA model were processed to demonstrate the algorithm and the results were discussed.
A large scale finite element model with high mesh resolution is established to simulate the ground truth of regular
highway pavement structure with subsurface debonding defects. The simulation is motivated by non-destructive testing
methods that derive information from the acoustic radiation of the surface wave. These NDT (Non-Destructive Testing) signals come from solid elastic wave propagation beneath pavement surface, which then couple with acoustic wave in air above the pavement surface. In this article, 2 main debonding phenomena, which are conventionally hidden below the pavement surface, are modeled and also compared with a healthy (perfectly intact) pavement structure model. Both the impact-response transient analysis and frequency spectrum analysis have been given to show a new opportunity to detect the subsurface debonding in pavement non-destructively through acoustic signals from heights above the pavement surface which are incorporated with ground truth information.
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