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.

翻译：在常用的无损检测技术中，探地雷达（GPR）目前是法国评估路面状况最广泛采用的方法之一。然而，传统雷达系统及其正向处理方法在物理和几何表征极薄层（如粘层）方面存在局限性。但采用基于逆方法的机器学习技术对GPR数据进行分析表明，尽管原始B扫描数据因低时频分辨率存在掩盖效应，仍可通过数值方法识别粘层特性。因此，本文提出将已在前序数值研究中验证的逆方法机器学习技术应用于两种不同路面结构的实验案例。第一个案例是在法国南特古斯塔夫·埃菲尔大学的路面疲劳试验转台上对已知路面结构进行验证，第二个案例则聚焦于法国旺代省一条新建真实道路。两项案例中，SVM/SVR方法的性能证明了监督学习方法在分类和估算粘层乳化沥青配比方面的有效性。