Squat reinforced concrete shear walls are stiff structural elements incorporated in buildings and other structures and are capable of resisting large seismic demands. However, when not properly designed, they are prone to shear-related brittle failure. To improve the seismic behaviour of these structural elements, a retrofitting bracing system incorporating superelastic Shape Memory Alloys (SMAs) was developed. Superelastic Shape Memory Alloys (SMAs) are smart materials with the ability to sustain and recover large pseudo-plastic deformations while dissipating energy. The SMA bracing system consists of tension-only SMA links coupled with rigid steel elements. The SMA links were designed to sustain and recover the elongation experienced by the bracing system, while the steel elements were designed to sustain negligible elastic elongations.
The SMA bracing system was installed on third-scale, 2000 mm × 2000 mm, shear walls, which were tested to failure under incremental reverse cyclic loading. The experimental results demonstrated that the tension-only SMA braces improve the seismic response of squat reinforced concrete walls. The retrofitted walls experienced higher strength, greater energy dissipation, and less permanent deformation. The re-centering properties of the SMA contributed to the reduction of pinching in the hysteretic response due mainly to the clamping action of the SMA bracings while recovering their original length. The walls were numerically simulated with the nonlinear finite element program VecTor2. The numerical simulations accurately captured the hysteretic response of both the original and the retrofitted walls. A parametric study was conducted to assess the effect of axial loading and size of the SMA braces.
Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/36167 |
Date | January 2017 |
Creators | Cortés Puentes, Wilmar Leonardo |
Contributors | Palermo, Dan |
Publisher | Université d'Ottawa / University of Ottawa |
Source Sets | Université d’Ottawa |
Language | English |
Detected Language | English |
Type | Thesis |
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