Machine learning-aided peak and residual displacement-based design method for enhancing seismic performance of steel moment-resisting frames by installing self-centering braces

•Prediction models of Cμ and Cr were developed for the RSMRF.•A peak and residual displacement-based design method was developed for retrofitting SMRFs.•The designed RSMRFs can achieve the desired peak and residual inter-story drift responses.•The designed RSMRFs have no repair requirements for afte...

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Veröffentlicht in:	Engineering structures Jg. 271; S. 114935
Hauptverfasser:	Hu, Shuling, Zhu, Songye, Shahria Alam, M., Wang, Wei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 15.11.2022 Elsevier BV
Schlagworte:	Buildings Control methods Degrees of freedom Design Displacement Drift Earthquakes Economic impact Learning algorithms Machine learning Moment-resisting frames Peak and residual displacement Post-earthquake repairability Prediction models Residual displacement-based design method Retrofitting Seismic activity Seismic energy Seismic engineering Seismic response Self-centering brace Steel Steel frames Stiffness Structural safety Peak and residual displacement Moment-resisting frames Self-centering brace Residual displacement-based design method Post-earthquake repairability Machine learning
ISSN:	0141-0296, 1873-7323
Online-Zugang:	Volltext
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