Topology optimization and seismic collapse assessment of shape memory alloy (SMA)-braced frames: Effectiveness of Fe-based SMAs

Aydin HASSANZADEH, Saber MORADI

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Front. Struct. Civ. Eng. ›› 2022, Vol. 16 ›› Issue (3) : 281-301. DOI: 10.1007/s11709-022-0807-3
RESEARCH ARTICLE

Topology optimization and seismic collapse assessment of shape memory alloy (SMA)-braced frames: Effectiveness of Fe-based SMAs

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Abstract

This paper presents a seismic topology optimization study of steel braced frames with shape memory alloy (SMA) braces. Optimal SMA-braced frames (SMA-BFs) with either Fe-based SMA or NiTi braces are determined in a performance-based seismic design context. The topology optimization is performed on 5- and 10-story SMA-BFs considering the placement, length, and cross-sectional area of SMA bracing members. Geometric, strength, and performance-based design constraints are considered in the optimization. The seismic response and collapse safety of topologically optimal SMA-BFs are assessed according to the FEMA P695 methodology. A comparative study on the optimal SMA-BFs is also presented in terms of total relative cost, collapse capacity, and peak and residual story drift. The results demonstrate that Fe-based SMA-BFs exhibit higher collapse capacity and more uniform distribution of lateral displacement over the frame height while being more cost-effective than NiTi braced frames. In addition to a lower unit price compared to NiTi, Fe-based SMAs reduce SMA material usage. In frames with Fe-based SMA braces, the SMA usage is reduced by up to 80%. The results highlight the need for using SMAs with larger recoverable strains.

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topology optimization / shape memory alloy / Fe-based SMA / steel braced frames / performance-based seismic design / collapse assessment

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Aydin HASSANZADEH, Saber MORADI. Topology optimization and seismic collapse assessment of shape memory alloy (SMA)-braced frames: Effectiveness of Fe-based SMAs. Front. Struct. Civ. Eng., 2022, 16(3): 281‒301 https://doi.org/10.1007/s11709-022-0807-3

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Acknowledgements

The research presented in this paper was financially supported by the Ryerson University Faculty of Engineering and Architectural Science and the Natural Sciences and Engineering Research Council of Canada (NSERC) through Discovery Grant. The authors gratefully acknowledge these financial supports.

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2022 Higher Education Press
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