The Use of Wavelet Analysis to Improve the Accuracy of Pavement Layer Thickness Estimation Based on Amplitudes of Electromagnetic Waves

M. Wutke , Anna Lejzerowicz , Andrzej Garbacz

Abstract

The article discusses one of the methods of dielectric constant determination in a continuous way, which is the determination of its value based on the amplitude of the wave reflected from the surface. Based on tests performed on model asphalt slabs, it was presented how the value of the dielectric constant changes depending on the atmospheric conditions of the measured surface (dry, covered with water film, covered with ice, covered with snow, covered with de-icing salt). Coefficients correcting dielectric constants of hot mix asphalt (HMA) determined in various surface atmospheric conditions were introduced. It was proposed to determine the atmospheric conditions of the pavement with the use of wavelet analysis in order to choose the proper dielectric constant correction coefficient and therefore improve the accuracy of the pavement layer thickness estimation based on the ground penetrating radar (GPR) method.
Author M. Wutke
M. Wutke,,
-
, Anna Lejzerowicz (FCE / IRB)
Anna Lejzerowicz,,
- The Institute of Roads and Bridges
, Andrzej Garbacz (FCE / ICE)
Andrzej Garbacz,,
- The Institute of Civil Engineering
Journal seriesMaterials, ISSN 1996-1944
Issue year2020
Vol13
No14
Pages1-18
Publication size in sheets0.85
Article number3214
Keywords in Englishground penetrating radar (GPR); HMA dielectric constant; wavelet analysis; road pavement thickness estimation
ASJC Classification2500 General Materials Science
DOIDOI:10.3390/ma13143214
Languageen angielski
LicenseJournal (articles only); author's original; Uznanie Autorstwa (CC-BY); after publication
File
WUT3b1b6c20b6084515851571af1f6d5312.pdf 4.13 MB
Score (nominal)140
Score sourcejournalList
ScoreMinisterial score = 140.0, 15-09-2020, ArticleFromJournal
Publication indicators WoS Citations = 0; Scopus SNIP (Source Normalised Impact per Paper): 2017 = 1.285; WoS Impact Factor: 2018 = 2.972 (2) - 2018=3.532 (5)
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