On the variants of SVM methods applied to GPR data to classify tack coat characteristics in French pavements: two experimental case studies
This work addresses the challenge of non-destructive assessment of pavement conditions in France, specifically for thin layers, but is incremental as it applies an existing inverse ML method to new experimental data.
The study tackled the problem of characterizing thin tack coat layers in pavements using Ground Penetrating Radar (GPR) data by applying SVM/SVR methods with an inverse machine learning approach, achieving effective classification and estimation of emulsion proportioning in two experimental case studies.
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{é}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.