The evolution of spectral analysis of surface wave method – a review
Spectral analysis of surface wave (SASW) method for in-situ non-destructive testing of stiffness profiles of soil and pavement sites have undergone various improvements since its inception during the 1980s. Improvements have been in both data-acquisition (sampling and sensors) as well as in dat...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en en |
| Published: |
Books & Journals Private Ltd.
2021
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/93653/7/93653_The%20evolution%20of%20spectral%20analysis%20of%20surface%20wave%20method%20%E2%80%93%20a%20review_SCOPUS.pdf http://irep.iium.edu.my/93653/8/93653_The%20evolution%20of%20spectral%20analysis%20of%20surface%20wave%20method%20%E2%80%93%20a%20review.pdf http://irep.iium.edu.my/93653/ http://www.jmmf.info/2021/?post_type=current-issues |
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| Summary: | Spectral analysis of surface wave (SASW) method for in-situ
non-destructive testing of stiffness profiles of soil and
pavement sites have undergone various improvements since
its inception during the 1980s. Improvements have been in
both data-acquisition (sampling and sensors) as well as in
data-processing (forward calculation and inversion
algorithm) aspects. The present study explores the relative
effectiveness of using SASW and its automation. Precise
recording of amplitude value has the potential to further
improve the effectiveness and develop the surface wave
testing methods. Different approaches for interpreting the
dispersion curve and their potential regarding sensitivity to
noise, reliability, and capability to extract significant
information were investigated. Finally, the suitable
algorithms, finite element modelling, data acquisition and
processing and the inversion procedure to provide the
reliable and robust stiffness profile were illustrated in this
study. After reviewing a few inversion analysis techniques, a
non-linear minimization technique could perform
reasonably well, which is fully automated despite some
limitations. The artificial neural network could be
implemented to generate the shear wave velocity profile
from the dispersion curve and perform well for the upper
layer’s parameters. |
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