GPR analysis to detect subsidence: a case study on a loaded reinforced concrete pavement
Subsidence seriously affects the structural stability and safety of pavements and foundation soils. In heavy-loaded pavements, there is a risk of floor sinking and further construction collapse; hence, there is a need to develop efficient methodologies to detect subsidence earlier. This work proposes the use of ground penetrating radar (GPR) as a solution to non-invasively inspect the subsoil. Furthermore, as the interpretation of the GPR data is arguably subjective and highly dependent on who interprets it, different imaging techniques are herein exploited to improve the interpretability and detection of subsidence and settlement phenomena. The approach was applied to a heavily loaded reinforced concrete pavement servicing a manufacturing facility. Amplitude- and texture-based imaging methods were used to detect subsidence. The interpretation of such imaging was validated with additional geotechnical studies, which show that the proposed methods provide reliable results with good agreement between techniques.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/44544515
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Supplemental Notes:
- © 2022 Informa UK Limited, trading as Taylor & Francis Group 2022. Abstract reprinted with permission of Taylor & Francis.
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Authors:
- Solla, Mercedes
- Fernández, Norberto
- Publication Date: 2023
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: 2027420
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Serial:
- International Journal of Pavement Engineering
- Volume: 24
- Issue Number: 2
- Publisher: Taylor & Francis
- ISSN: 1029-8436
- Serial URL: http://www.tandf.co.uk/journals/titles/10298436.html
Subject/Index Terms
- TRT Terms: Case studies; Foundation soils; Geological subsidence; Ground penetrating radar; Ground settlement; Image analysis; Reinforced concrete pavements; Subsoil
- Subject Areas: Geotechnology; Highways; Pavements;
Filing Info
- Accession Number: 01917071
- Record Type: Publication
- Files: TRIS
- Created Date: Apr 29 2024 6:31PM