Print Email Facebook Twitter Crack monitoring method for an FRP-strengthened steel structure based on an antenna sensor Title Crack monitoring method for an FRP-strengthened steel structure based on an antenna sensor Author Liu, Z. (Wuhan University of Technology) Chen, Kai (Wuhan University of Technology) Li, Z. (TU Delft Transport Engineering and Logistics) Jiang, X. (TU Delft Transport Engineering and Logistics) Date 2017 Abstract Fiber-reinforced polymer (FRP) has been increasingly applied to steel structures for structural strengthening or crack repair, given its high strength-to-weight ratio and high stiffness-to-weight ratio. Cracks in steel structures are the dominant hidden threats to structural safety. However, it is difficult to monitor structural cracks under FRP coverage and there is little related research. In this paper, a crack monitoring method for an FRP-strengthened steel structure deploying a microstrip antenna sensor is presented. A theoretical model of the dual-substrate antenna sensor with FRP is established and the sensitivity of crack monitoring is studied. The effects of the weak conductivity of carbon fiber reinforced polymers (CFRPs) on the performance of crack monitoring are analyzed via contrast experiments. The effects of FRP thickness on the performance of the antenna sensor are studied. The influence of structural strain on crack detection coupling is studied through strain–crack coupling experiments. The results indicate that the antenna sensor can detect cracks in steel structures covered by FRP (including CFRP). FRP thickness affects the antenna sensor’s performance significantly, while the effects of strain can be ignored. The results provide a new approach for crack monitoring of FRP-strengthened steel structures with extensive application prospects. Subject Antenna sensorCrackingFRP thicknessFRP-strengthened steel structureResonant frequencySensitivity To reference this document use: http://resolver.tudelft.nl/uuid:3006c9cd-1ddf-4dbc-9dcc-7c30c3c5494b DOI https://doi.org/10.3390/s17102394 ISSN 1424-8220 Source Sensors, 17 (10) Part of collection Institutional Repository Document type journal article Rights © 2017 Z. Liu, Kai Chen, Z. Li, X. Jiang Files PDF sensors_17_02394.pdf 3.13 MB Close viewer /islandora/object/uuid:3006c9cd-1ddf-4dbc-9dcc-7c30c3c5494b/datastream/OBJ/view