On the variants of SVM methods applied to GPR data to classify tack coat characteristics in French pavements: two experimental case studies - Université Gustave Eiffel Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

On the variants of SVM methods applied to GPR data to classify tack coat characteristics in French pavements: two experimental case studies

Résumé

Among the commonly used non-destructive techniques, the Ground Penetrating Radar (GPR) is one of the most widely adopted today for assessing pavement conditions in France. However, conventional radar systems and their forward processing methods have shown their limitations for the physical and geometrical characterization of very thin layers such as tack coats. However, the use of Machine Learning methods applied to GPR with an inverse approach showed that it was numerically possible to identify the tack coat characteristics despite masking effects due to low timefrequency resolution noted in the raw B-scans. Thus, we propose in this paper to apply the inverse approach based on Machine Learning, already validated in previous works on numerical data, on two experimental cases with different pavement structures. The first case corresponds to a validation on known pavement structures on the Gustave Eiffel University (Nantes, France) with its pavement fatigue carousel and the second case focuses on a new real road in Vendée department (France). In both case studies, the performances of SVM/SVR methods showed the efficiency of supervised learning methods to classify and estimate the emulsion proportioning in the tack coats.
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Dates et versions

hal-04323965 , version 1 (05-12-2023)

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Grégory Andreoli, Amine Ihamouten, Mai Lan Nguyen, Cyrille Fauchard, Yannick Fargier, et al.. On the variants of SVM methods applied to GPR data to classify tack coat characteristics in French pavements: two experimental case studies. 2023 12th International Workshop on Advanced Ground Penetrating Radar (IWAGPR), LNEC, Jul 2023, Lisbonne, Portugal. pp.1-5, ⟨10.1109/IWAGPR57138.2023.10329070⟩. ⟨hal-04323965⟩
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