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Edition:Final Report (September 1, 2019-May 1, 2021)
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Abstract:The main objective of this research was to develop and deploy a wireless crack sensing system that can measure and monitor cracks for both concrete and steel bridge structures. This system contained the sensing unit, wireless data transmitting system, as well as a data processing unit. The sensing unit consisted of single or arrays of advanced thin film-based sensing nodes that are capable to measuring crack induced strains in bridge structures. This thin film sensing node contained soft polymer film (polyvinylidene difluoride, PVDF) embedded with conductive nanoparticles (graphene) or atomically thin films. An additional fiber-coated sensor was explored as a sensor to detect large crack growth. The piezoresistive mechanism was considered to convert the strain to electrical resistance of the sensing unit. The sensing unit was also connected to a wireless transmitting system to broadcast the signal wirelessly. The wireless transmitting system utilized a blue-tooth technology that can enable the sensing unit to broadcast electronic signals in terms of electrical resistance. These signals were received by a mobile device (laptop or cellphone) that can convert the electrical resistance information into the measured stains. These measurements were processed using the data processing unit. The data processing unit received the electrical resistance data and converted it into measured strains. The machine learning approach was also taken to train the software to be able to automatically detect the abnormalities in the measured strain for critical crack growth detection.
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