A Machine Learning Scheme For Estimating The Diameter of Reinforcing Bars Using Ground Penetrating Radar

Iraklis Giannakis, Antonios Giannopoulos, Craig Warren

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
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Abstract

Ground Penetrating Radar (GPR) is a well- established tool for detecting and locating reinforcing bars (rebars) in concrete structures. However, using GPR to quantify the diameter of rebars is a challenging problem that current processing approaches fail to tackle. To that extent, we have developed a novel machine learning framework that can estimate the diameter of the investigated rebar within the resolution range of the employed antenna. The suggested approach combines neural networks and a random forest regression, and has been trained entirely using synthetic data. Although the training process relied only on numerical training sets, nonetheless, the suggested scheme is successfully evaluated in real data indicating the generalization capabilities of the resulting regression. The only required input of the proposed technique is a single A-scan, avoiding laborious measurement configurations and multi-sensor approaches. Additionally, the results are provided in real-time making this method practical and commercially appealing.
Original languageEnglish
Pages (from-to)461-465
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume18
Issue number3
Early online date11 Mar 2020
DOIs
Publication statusPublished - Mar 2021

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