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https://hdl.handle.net/1822/88961
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Campo DC | Valor | Idioma |
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dc.contributor.author | Rasol, Mezgeen | por |
dc.contributor.author | Pais, Jorge C. | por |
dc.contributor.author | Perez-Gracia, Vega | por |
dc.contributor.author | Solla, Mercedes | por |
dc.contributor.author | Fernandes, Francisco M. | por |
dc.contributor.author | Fontul, Simona | por |
dc.contributor.author | Ayala-Cabrera, David | por |
dc.contributor.author | Schmidt, Franziska | por |
dc.contributor.author | Assadollahi, Hossein | por |
dc.date.accessioned | 2024-02-22T11:39:25Z | - |
dc.date.issued | 2022-02-08 | - |
dc.identifier.issn | 0950-0618 | por |
dc.identifier.uri | https://hdl.handle.net/1822/88961 | - |
dc.description.abstract | Suitable road pavements assessment becomes essential to provide safe traffic movements of people and goods. Moreover, a reliable transportation network is a crucial aspect of economic growth. Road pavements are subjected to various factors that influence overall performance (e.g., traffic load, temperature, moisture, delamination of the pavement layers, subsurface condition, etc.). These factors can reduce the infrastructure's life and decrease the circulation comfort of the vehicles in the transportation network. Early inspection of pavements optimizes maintenance and repairing methodologies, decreasing the maintenance cost and increasing the lifespan of the road pavements. Non-destructive techniques are strongly recommended to achieve accurate and valuable information from the subsurface condition. Ground Penetrating Radar (GPR) is a non-destructive geophysical method widely used on infrastructure assessment, particularly in road pavements, due to its low operation cost, time-saving, non-invasive, and less workforce. This paper presents a critical state of the art of applying GPR to diagnose road pavement and detect inner damages such as debonding, sinkholes, moisture, etc. The incorporation of the GPR with other complementary techniques in pavement inspection is also discussed. Through the review, the GPR capabilities for road inspection and evaluation of subsurface identification have been successfully demonstrated and validated in numerous studies and case studies. Finally, the application of more recent processing techniques to support decision-making owners/operators, such as machine learning and intelligent data analysis methods, and the future challenges on the GPR application in road pavements are introduced. | por |
dc.description.sponsorship | - This project has received funding from the European Union's Hori-zon 2020 research and innovation program under grant agreements No. 769129 (PANOPTIS) and No. 955356 (HERON) . The project has also partially supported by the GAIN, Xunta de Galicia, through the project ENDITi (Ref. ED431F 2021/08) . Rasol M. acknowledges the financial support from Gustave Eiffel University, and Solla M. acknowledges the grant RYC2019-026604-I funded by MCIN/AEI/10.13039/501100011033 and by "ESF Investing in your future". | por |
dc.language.iso | eng | por |
dc.publisher | Elsevier 1 | por |
dc.relation | MCIN/AEI/10.13039/501100011033 | por |
dc.relation | RYC2019-026604-I | por |
dc.rights | closedAccess | por |
dc.subject | GPR | por |
dc.subject | Road transport pavement | por |
dc.subject | NDT | por |
dc.subject | Damages | por |
dc.subject | Intelligent data analysis | por |
dc.subject | Machine learning | por |
dc.subject | Inspection and monitoring | por |
dc.subject | Deep learning | por |
dc.subject | Decision-making | por |
dc.title | GPR monitoring for road transport infrastructure: a systematic review and machine learning insights | por |
dc.type | article | - |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0950061822003774?via%3Dihub | por |
oaire.citationVolume | 324 | por |
dc.date.updated | 2024-02-08T23:47:53Z | - |
dc.identifier.eissn | 1879-0526 | por |
dc.identifier.doi | 10.1016/j.conbuildmat.2022.126686 | por |
dc.date.embargo | 10000-01-01 | - |
dc.subject.wos | Science & Technology | - |
sdum.export.identifier | 13200 | - |
sdum.journal | Construction and Building Materials | por |
Aparece nas coleções: | ISISE - Artigos em Revistas Internacionais |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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SCIE-32_GPR monitoring for road transport infrastructure A systematic review and machine learning insights.pdf Acesso restrito! | 16,49 MB | Adobe PDF | Ver/Abrir |