GPR analysis to detect subsidence: a case study on a loaded reinforced concrete pavement

ABSTRACT

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.

KEYWORDS:

• Ground penetrating radar
• amplitude features
• textural features
• interpretational strategies
• penetrometer
• subsidence detection
• construction

Acknowledgements

Authors acknowledge the support given by the GEONOX S.L. company for providing the geotechnical study. This work has received funding from the Xunta de Galicia – GAIN – through the project ENDITí (Ref. ED431F 2021/08) and from the Agencia Estatal de Investigación (AEI) of Spain under the project PID2019-105221RB-C44. M. Solla acknowledges the grant RYC2019–026604–I funded by the MCIN/AEI/10.13039/501100011033 and by the ‘ESF Investing in your future’.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Agencia Estatal de Investigación [grant number PID2019-105221RB-C44/AEI/10.13039/501100011033] and Xunta de Galicia - GAIN [grant number ED431F 2021/08]. M. Solla acknowledges the grant RYC2019–026604–I funded by MCIN/AEI/10.13039/501100011033 and by “ESF Investing in your future”.