Hybrid ML/FWI method using GPR data to evaluate the tack coat characteristics in pavements: Experimental validation
Time resolution is one of the limiting factors when using Ground Penetrating Radar (GPR) techniques to characterize thin layers in the subsurface, such as the tack coat in pavements. To evaluate this residual bituminous emulsion at the interface between the wearing course and the binder course, we h...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Transportation Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666691X25000399 |
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| author | Grégory Andreoli Amine Ihamouten Franziska Schmidt Mai Lan Nguyen David Souriou Xavier Dérobert |
| author_facet | Grégory Andreoli Amine Ihamouten Franziska Schmidt Mai Lan Nguyen David Souriou Xavier Dérobert |
| author_sort | Grégory Andreoli |
| collection | DOAJ |
| description | Time resolution is one of the limiting factors when using Ground Penetrating Radar (GPR) techniques to characterize thin layers in the subsurface, such as the tack coat in pavements. To evaluate this residual bituminous emulsion at the interface between the wearing course and the binder course, we have developed an inverse method based on a hybrid data processing approach that combines machine learning (ML) algorithms with Full-Waveform Inversion (FWI). Adding the dielectric permittivity of the wearing course (extracted via FWI) as a structural a priori input into the SVM/SVR models has demonstrated the strong potential of this methodology on synthetic time domain signals. This research, proposes extending such a methodology through experimental campaigns. To carry out this study, three distinct campaigns have been planned, namely on: Hot-Mix Asphalt (HMA)-controlled slabs manufactured in the laboratory; a controlled full-scale structure using the Gustave Eiffel University fatigue carousel (Nantes, France); and a new, yet-to-be-used, road in France. These experiments serve to validate the performance improvements of various classification and regression SVM/SVR algorithms when adding the dielectric permittivity of the wearing course. Herein will be compared the results of the global approach, without preprocessing raw time domain signals, with the developed hybrid model. |
| format | Article |
| id | doaj-art-ed123c90944f41f78f2a71f9c0ce3490 |
| institution | Kabale University |
| issn | 2666-691X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Transportation Engineering |
| spelling | doaj-art-ed123c90944f41f78f2a71f9c0ce34902025-08-20T03:49:41ZengElsevierTransportation Engineering2666-691X2025-06-012010033910.1016/j.treng.2025.100339Hybrid ML/FWI method using GPR data to evaluate the tack coat characteristics in pavements: Experimental validationGrégory Andreoli0Amine Ihamouten1Franziska Schmidt2Mai Lan Nguyen3David Souriou4Xavier Dérobert5MAST/MIT Research Group, Gustave Eiffel University, Nantes, France; Corresponding author.MAST/LAMES Research Group, Gustave Eiffel University, Nantes, FranceMAST/EMGCU, Research Group, Gustave Eiffel University, Marne-la-Vallée, FranceMAST/LAMES Research Group, Gustave Eiffel University, Nantes, FranceFI-NDT, Nantes, FranceGERS/GeoEND Research Group, Gustave Eiffel University - FI-NDT, Nantes, FranceTime resolution is one of the limiting factors when using Ground Penetrating Radar (GPR) techniques to characterize thin layers in the subsurface, such as the tack coat in pavements. To evaluate this residual bituminous emulsion at the interface between the wearing course and the binder course, we have developed an inverse method based on a hybrid data processing approach that combines machine learning (ML) algorithms with Full-Waveform Inversion (FWI). Adding the dielectric permittivity of the wearing course (extracted via FWI) as a structural a priori input into the SVM/SVR models has demonstrated the strong potential of this methodology on synthetic time domain signals. This research, proposes extending such a methodology through experimental campaigns. To carry out this study, three distinct campaigns have been planned, namely on: Hot-Mix Asphalt (HMA)-controlled slabs manufactured in the laboratory; a controlled full-scale structure using the Gustave Eiffel University fatigue carousel (Nantes, France); and a new, yet-to-be-used, road in France. These experiments serve to validate the performance improvements of various classification and regression SVM/SVR algorithms when adding the dielectric permittivity of the wearing course. Herein will be compared the results of the global approach, without preprocessing raw time domain signals, with the developed hybrid model.http://www.sciencedirect.com/science/article/pii/S2666691X25000399Tack coatGPRMachine learningFWIHybridization |
| spellingShingle | Grégory Andreoli Amine Ihamouten Franziska Schmidt Mai Lan Nguyen David Souriou Xavier Dérobert Hybrid ML/FWI method using GPR data to evaluate the tack coat characteristics in pavements: Experimental validation Transportation Engineering Tack coat GPR Machine learning FWI Hybridization |
| title | Hybrid ML/FWI method using GPR data to evaluate the tack coat characteristics in pavements: Experimental validation |
| title_full | Hybrid ML/FWI method using GPR data to evaluate the tack coat characteristics in pavements: Experimental validation |
| title_fullStr | Hybrid ML/FWI method using GPR data to evaluate the tack coat characteristics in pavements: Experimental validation |
| title_full_unstemmed | Hybrid ML/FWI method using GPR data to evaluate the tack coat characteristics in pavements: Experimental validation |
| title_short | Hybrid ML/FWI method using GPR data to evaluate the tack coat characteristics in pavements: Experimental validation |
| title_sort | hybrid ml fwi method using gpr data to evaluate the tack coat characteristics in pavements experimental validation |
| topic | Tack coat GPR Machine learning FWI Hybridization |
| url | http://www.sciencedirect.com/science/article/pii/S2666691X25000399 |
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