  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 Vend{\'e}e 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.
