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Use of Distributed Fiber Optic Sensors to Detect Damage in a Pavement

Abstract : This paper presents the feasibility of damage detection in the asphalt pavements by embedded fiber optics as a new non-destructive inspection technique. The distributed fiber optic sensing technology called ÒRayleigh techniqueî was used in this study. The main advantage of this technique is that it allows to measure strains over long length of fiber optic with a high spatial resolution, less than 1 cm. By comparing strain profiles measured at different time, an attempt was made to link strain changes with the appearance of damage (cracking) in the pavement. This non-destructive method was evaluated on the IFSTTAR accelerated pavement testing facility, in a bituminous pavement. In our experimentation, the optical fibers were placed near the bottom of the asphalt layer. The application of 728 000 heavy vehicle loads (65 KN dual wheel loads) was simulated in the experiment. Optical fiber measurements were made at regular intervals and surface cracking of the pavement was surveyed. After some traffic, a significant increase of strains was detected by the optical fibers at different points in the pavement structure, before any damage was visible. Later, cracking developed in the zones where the strain profiles were modified, thus indicating a clear relationship between the increased strains and crack initiation. These first tests demonstrate that distributed fiber optic sensor based on Rayleigh sensing technique can be used to detect crack initiation and propagation in pavements, by monitoring strain profiles in the bituminous layers.
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Submitted on : Thursday, July 10, 2014 - 9:58:20 AM
Last modification on : Saturday, January 15, 2022 - 3:51:49 AM
Long-term archiving on: : Friday, October 10, 2014 - 10:56:49 AM


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  • HAL Id : hal-01022035, version 1



Xavier Chapeleau, Juliette Blanc, Pierre Hornych, Jean-Luc Gautier, Jean Carroget. Use of Distributed Fiber Optic Sensors to Detect Damage in a Pavement. EWSHM - 7th European Workshop on Structural Health Monitoring, IFFSTTAR, Inria, Université de Nantes, Jul 2014, Nantes, France. ⟨hal-01022035⟩



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