Seismic Performance of CFRP-Retrofitted Large-Scale Square RC Columns with High Axial Compression Ratios

RIS ID

128048

Publication Details

Wang, D., Huang, L., Yu, T. & Wang, Z. (2017). Seismic Performance of CFRP-Retrofitted Large-Scale Square RC Columns with High Axial Compression Ratios. Journal of Composites for Construction, 21 (5), 1-12.

Abstract

Numerous reinforced concrete frames have collapsed in major earthquakes because of severe local damage at the ends of columns. Many of these frames were designed and built according to out-of-date codes in which earthquake loads were either not taken into account or not sufficiently considered. To date, there are still a large number of such nonductile RC frames serving in the potential earthquake regions across the world. Thus, there is an urgent need for preearthquake retrofitting and strengthening of these RC frames to achieve desirable seismic performance. In this paper, a total of 11 large-scale cantilever square RC columns were constructed and nine of them were retrofitted with carbon fiber-reinforced polymers (CFRP) wraps at the column ends. These columns were then tested under combined constant axial compression and cyclic lateral displacement excursions. The variables in the tests included the axial compression ratio, the thickness of CFRP wraps, and the column size. The failure modes, hysteretic responses, energy dissipation, stiffness degradation, and equivalent viscous damping ratio of the tested columns were presented and interpreted. The test results showed that the ductility and energy dissipation capacities of nonductile RC columns can be significantly improved with the potential plastic hinge regions of the columns being retrofitted. The retrofitted columns were found to exhibit satisfactory seismic performance even when tested under an ultrahigh axial compression level (i.e., 0.75). Based on the test results, the degradation of the effective stiffness and the unloading stiffness of the columns was discussed and empirical equations were proposed through nonlinear regression analysis.

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