Effect of mechanical training on the properties of superelastic shape memory alloys for seismic applications

Jason McCormick; Laura Barbero; Reginald DesRoches

doi:10.1117/12.599863

17 May 2005 Effect of mechanical training on the properties of superelastic shape memory alloys for seismic applications

Jason McCormick, Laura Barbero, Reginald DesRoches

Author Affiliations +

Proceedings Volume 5764, Smart Structures and Materials 2005: Smart Structures and Integrated Systems; (2005) https://doi.org/10.1117/12.599863
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2005, San Diego, California, United States

Abstract

The objective of this study is to evaluate the effect that mechanical training has on the properties of NiTi based shape memory alloys. The unique mechanical behavior of shape memory alloys, which allows the material to undergo large deformations while returning to their original undeformed shape through either the shape memory effect or superelastic effect, has shown potential for use in seismic design and retrofit applications for civil engineering structures. However, cyclic loading has been shown to degrade the energy dissipation capacity and decrease the recentering capability of the material due to fatigue effects. It has been recommended that mechanical training of superelastic shape memory alloys prior to use in applications can limit these fatigue effects. A factorial experimental design is employed to explore the optimal number of mechanical training cycles, strain level of training, and the effect of the loading rate after training in order to minimize the degradation in the loading plateau stress, residual strain, and equivalent viscous damping properties. The results presented can serve as a guide to optimizing the properties of NiTi shape memory alloys for seismic applications. The ability to obtain stable properties of shape memory alloys under a specified training schedule further supports the eventual implementation of the material into actual building and bridge systems as seismic design and retrofit devices.

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Jason McCormick, Laura Barbero, and Reginald DesRoches "Effect of mechanical training on the properties of superelastic shape memory alloys for seismic applications", Proc. SPIE 5764, Smart Structures and Materials 2005: Smart Structures and Integrated Systems, (17 May 2005); https://doi.org/10.1117/12.599863

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