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Reliable fragility functions for seismic collapse assessment of reinforced concrete special moment resisting frame structures under near‐fault ground motions
Author(s) -
Yakhchalian Masood,
Yakhchalian Mehrzad,
Yakhchalian Mansoor
Publication year - 2019
Publication title -
the structural design of tall and special buildings
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.895
H-Index - 43
eISSN - 1541-7808
pISSN - 1541-7794
DOI - 10.1002/tal.1608
Subject(s) - fragility , scalar (mathematics) , ground motion , structural engineering , moment (physics) , particle swarm optimization , incremental dynamic analysis , reinforced concrete , seismic moment , geology , mathematics , fault (geology) , computer science , seismology , engineering , mathematical optimization , physics , geometry , classical mechanics , thermodynamics
Summary A collapse fragility function shows how the probability of collapse of a structure increases with increasing ground motion intensity measure (IM). To have a more reliable fragility function, an IM should be applied that is efficient and sufficient with respect to ground motion parameters such as magnitude ( M ) and source‐to‐site distance ( R ). Typically, pulse‐like near‐fault ground motions are known by the presence of a velocity pulse, and the period of this pulse ( T p ) affects the structural response. The present study investigates the application of different scalar and vector‐valued IMs to obtain reliable seismic collapse fragility functions for reinforced concrete special moment resisting frames (RC SMRFs) under near‐fault ground motions. The efficiency and sufficiency of the IMs as the desirable features of an optimal IM are investigated, and it is shown that seismic collapse assessments by using most of the IMs are biased with respect to T p . The results show that ( Sa ( T 1 ), Sa ( T 1 )/DSI) has high efficiency and sufficiency with respect to M , R , T p , and scale factor for collapse capacity prediction of RC SMRFs. Moreover, the multiobjective particle swarm optimization algorithm is applied to improve the efficiency and sufficiency of some advanced scalar IMs, and an optimal scalar IM is proposed.