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Identifications of Structural State Parameters of Concrete Columns with Self-sensing Basalt-fiber-reinforced Polymer Bars



In general, for the implementation of structural health monitoring (SHM) based on strain monitoring, the directly measured strain is used as a structural parameter or index for damage location. However, often a critical range of strains of all the structural members is not enough to evaluate the service performance and safety of structures. Instead, critical ranges of global structural parameters, such as deformation, load and rotation, are provided in structural design codes and standards to define the required serviceability. Therefore, it is a need to derive structural parameters from measured strain distributions. A self-sensing basalt-fiber-reinforced polymer bar that uses internal embedded long-gage fiber optic sensors (FBGs) is designed to implement internal strain measurement of reinforced concrete (RC) columns. A fiber-modelbased algorithm is presented that uses the internal strain distribution to identify the structural state parameters, such as stress distribution, curvature distribution, displacement, and load. To determine the most effective implantation method, two schemes with different anchorage lengths and sensor locations are compared. Base on the identification of fiber model-based analysis, the effective sensing unit number is discussed to determine the structural state parameters of the tested RC columns. The computational accuracy of the fiber-model-based algorithm is discussed and it is demonstrated that the measured strain distribution can be used to calculate the structural state parameters before and after flexural yielding.

doi: 10.12783/SHM2015/49

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