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An overview of ground-penetrating radar signal processing techniques for road inspections

Open access
Benedetto, Andrea, Tosti, Fabio, Bianchini Ciampoli, Luca, D’Amico, Fabrizio·2017·Signal Processing

Abstract

Ground-penetrating radar (GPR) was firstly used in traffic infrastructure surveys during the first half of the Seventies for testing in tunnel applications. From that time onwards, such non-destructive testing (NDT) technique has found exactly in the field of road engineering one of the application areas of major interest for its capability in performing accurate continuous profiles of pavement layers and detecting major causes of structural failure at traffic speed. This work provides an overview on the main signal processing techniques employed in road engineering, and theoretical insights and instructions on the proper use of the processing in relation to the quality of the data acquired and the purposes of the surveys. GPR is an increasingly used NDT technique in pavement applicationsProper choice of the signal processing techniques according to the GPR data sourceOverview of signal processing techniques to use prior to any post-processing stepOverview of signal processing techniques to perform after the GPR data collection

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Authors
Benedetto, A., Tosti, F., Bianchini Ciampoli, L., & D’Amico, F.
Venue
Signal Processing
Publisher
Elsevier BV
Published
2017-03
Volume
132
Pages
201-209
ISSN
0165-1684 (ISSN-L)
OpenAlex
W2395626468
Cited by
362
References
0
FWCI
21.05
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: An overview of ground-penetrating radar signal processing techniques for road inspections. https://app.cochat.ai/papers/10.1016/j.sigpro.2016.05.016
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Benedetto, A., Tosti, F., Bianchini Ciampoli, L., & D’Amico, F. (2017). An overview of ground-penetrating radar signal processing techniques for road inspections. Signal Processing. https://doi.org/10.1016/j.sigpro.2016.05.016

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.