Integration of NDT data for monitoring road pavement distresses in hydrogeologically complex areas

crossref(2024)

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摘要
Achieving a comprehensive understanding of the health condition of the pavement asset is a crucial step for road network managers to establish an efficient maintenance and rehabilitation program. This is particularly true when the managed infrastructure traverses hydrogeologically complex areas that are reported to be highly vulnerable to both surface and deep hydraulic phenomena. Often, these areas are simultaneously affected by geotechnical events such as landslides and settlements. Monitoring the evolution of the effects related to these occurrences is indeed crucial to understand the actual risks to traffic, predict the remaining service life, and assess the overall resilience of the transport network to major natural events.The integration of non-destructive testing (NDT) data, typically collected through high-productivity surveys, is now widely recognized as a method for gaining a deep understanding of pavement decay phenomena, with significant implications for the reliability of their evolution predictions.This study presents the results of the integration of ground-penetrating radar (GPR) and mobile laser scanner (MLS) developed within the context of monitoring the A3 motorway, near the city of Salerno, Southern Italy. In particular, the focus is on a specific road stretch enclosed between two viaducts and affected by a remarkably complex hydrogeological scenario.The integrated analysis revealed the possibility of identifying severe distress occurring at the subgrade level, successfully linked to underground water movements induced by the relationship between slope morphography and road embankment.AcknowledgementsThis research is supported by the Italian Ministry of Education, University and Research under the National Project “EXTRA TN”, PRIN2017, Prot. 20179BP4SM. In addition, this study was funded by Regione Lazio through the Project “PIASTRE” (PR FESR Lazio 2021-2027). We would like to thank C.U.G.RI. (Inter-University Research Center for the Prediction and Prevention of Major Hazards), Leica Geosystems for the collaboration in field survey operations and the Consorzio Stabile SIS S.c.p.a. for the logistical support and assistance.
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