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    Evaluating prestress losses in a prestressed concrete girder railway bridge using distributed and discrete fibre optic sensors

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    1-s2.0-S0950061820305237-main.pdf (3.977Mb)
    Date
    2020
    Author
    Ye, Cong
    Butler, Liam J.
    Elshafie, Mohammed Z.E.B.
    Middleton, Campbell R.
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    Abstract
    This paper evaluates the data collected during a comprehensive monitoring campaign aimed at capturing the prestress loss behaviour of four 11.9 m prestressed concrete beams. These beams formed part of the superstructure of a newly-constructed railway bridge in Staffordshire, U.K. Two types of prestressed concrete beams were monitored, two TY7 internal beams and two TYE7 edge beams. Both distributed and discrete fibre optic sensor (FOS) systems were used to measure strain and temperature for the first two and a half years since the beams were cast. Prestress loss mechanisms were investigated in detail including immediate prestress losses due to elastic shortening of concrete and time-dependent prestress losses due to steel relaxation, concrete shrinkage and creep. Prestress loss predictions were calculated using both European and American standards, which were then compared with measured prestress losses. Both simplified and advanced time-step methods were used to provide more refined loss predictions by taking into account the interrelationships between various prestress loss mechanisms and the total prestress force at the time of interest. To provide better interpretation of the monitoring measurements, a sensitivity analysis was performed to evaluate the effects of various input parameter uncertainties on prestress loss predictions. It was found that (i) time-step methods produced prestress loss estimates that were lower compared with the simplified method; and (ii) code estimates of prestress losses using measured material properties gave reasonable agreement with the field measurements. As structurally-integrated FOS systems are becoming more commonplace and hold great potential, it is envisaged that they enable better understanding of field performance and thus facilitate data-informed asset management.
    DOI/handle
    http://dx.doi.org/10.1016/j.conbuildmat.2020.118518
    http://hdl.handle.net/10576/57448
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    • Civil and Environmental Engineering [‎869‎ items ]

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