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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Vydané v:Engineering structures Ročník 271; s. 114935
Hlavní autori: Hu, Shuling, Zhu, Songye, Shahria Alam, M., Wang, Wei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Kidlington Elsevier Ltd 15.11.2022
Elsevier BV
Predmet:
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
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Popis
Shrnutí:•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 after MCE excitations. Conventional steel moment-resisting frames (SMRFs) absorb seismic energy through steel yielding behavior, leading to significant residual displacement. Although steel yielding behavior can ensure the seismic safety of SMRFs under strong earthquakes, excessive residual displacement may lead to post-earthquake demolition decisions, causing a large amount of economic loss. This paper aims to develop a peak and residual displacement-based design (PRDBD) method for controlling the peak and residual inter-story drift responses of SMRFs by installing self-centering braces. The peak and residual displacements are both set as the design targets in the proposed PRDBD method. To this end, the machine learning prediction models of inelastic and residual displacement ratios were first developed based on the median responses of single-degree-of-freedom systems under earthquakes. The detailed design steps of the proposed PRDBD method were subsequently introduced. The three- and nine-story demonstration buildings were retrofitted by using the PRDBD method with two different design targets. Static and dynamic analyses were conducted to validate the effectiveness of the proposed PRDBD method. The static analysis results indicated that the self-centering braces could efficiently enhance the SMRF’s stiffness and strength. The retrofitted SMRFs showed no strength deterioration, whereas the original SMRFs showed obvious strength deterioration at the roof drifts of 3.2% and 2.5% in the three- and nine-story buildings, respectively. The dynamic analysis results confirm that the self-centering braces can efficiently reduce the peak and residual inter-story drift responses of the existing SMRFs and the retrofitted SMRFs can achieve the peak and residual inter-story performance objectives under the considered seismic intensity. Moreover, the retrofitted SMRFs can be fully recoverable after maximum considered earthquakes by controlling the maximum residual inter-story drift lower than 0.2%.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0141-0296
1873-7323
DOI:10.1016/j.engstruct.2022.114935