Optimization-based minimum-cost seismic retrofitting of hysteretic frames with nonlinear fluid viscous dampers

Nicolo Pollini*, Oren Lavan, Oded Amir

*Corresponding author for this work

    Research output: Contribution to journalJournal articleResearchpeer-review

    889 Downloads (Pure)

    Abstract

    In this paper, we discuss an optimization-based approach for minimum-costseismic retrofitting of hysteretic frames with nonlinear fluid viscous dampers.The proposed approach accounts also for moment-axial interaction in the struc-tural elements, to consider a more realistic coupling between added dampersand retrofitted structure. The design variables of the problem are the dampingcoefficients of the dampers. Indirectly, the design involves also the stiffness coef-ficients of the supporting braces. In the optimization analysis, we minimize arealistic retrofitting cost function with constraints on inter-story drifts undera suite of ground motion records. The cost function includes costs related tothe topological and mechanical properties of the dampers' designs. The struc-ture is modeled with a mixed finite element approach, where the hystereticbehavior is defined at the beams' and columns' cross sections level. We con-sider damper-brace elements with a visco-elastic behavior characterized by theMaxwell model. The dampers' viscous behavior is defined by a fractional powerlaw. Promising results obtained for a two-story, a nine-story, and a 20-story 2-Dframes are presented and discussed.
    Original languageEnglish
    JournalEarthquake Engineering and Structural Dynamics
    Volume47
    Issue number15
    Pages (from-to)2985-3005
    Number of pages21
    ISSN0098-8847
    DOIs
    Publication statusPublished - 2018

    Keywords

    • Hysteretic structures
    • Maxwell model
    • Optimization
    • Seismic retrofitting
    • Viscous dampers

    Fingerprint

    Dive into the research topics of 'Optimization-based minimum-cost seismic retrofitting of hysteretic frames with nonlinear fluid viscous dampers'. Together they form a unique fingerprint.

    Cite this