期刊
SMART MATERIALS AND STRUCTURES
卷 23, 期 7, 页码 -出版社
IOP PUBLISHING LTD
DOI: 10.1088/0964-1726/23/7/075004
关键词
long-gauge fiber optic sensors; prestressing evaluation; curved continuous girder; deck-stiffened arch; structural health monitoring; early age cracking
Structural health monitoring (SHM) consists of the continuous or periodic measurement of structural parameters and their analysis with the aim of deducing information about the performance and health condition of a structure. The significant increase in the construction of prestressed concrete bridges motivated this research on an SHM method for the on-site determination of the distribution of prestressing forces along prestressed concrete beam structures. The estimation of the distribution of forces is important as it can give information regarding the overall performance and structural integrity of the bridge. An inadequate transfer of the designed prestressing forces to the concrete cross-section can lead to a reduced capacity of the bridge and consequently malfunction or failure at lower loads than predicted by design. This paper researches a universal method for the determination of the distribution of prestressing forces along concrete beam structures at the time of transfer of the prestressing force (e. g., at the time of prestressing or post-tensioning). The method is based on the use of long-gauge fiber optic sensors, and the sensor network is similar (practically identical) to the one used for damage identification. The method encompasses the determination of prestressing forces at both healthy and cracked cross-sections, and for the latter it can yield information about the condition of the cracks. The method is validated on-site by comparison to design forces through the application to two structures: (1) a deck-stiffened arch and (2) a curved continuous girder. The uncertainty in the determination of prestressing forces was calculated and the comparison with the design forces has shown very good agreement in most of the structures' cross-sections, but also helped identify some unusual behaviors. The method and its validation are presented in this paper.
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