An advanced intensity measure for aftershock collapse fragility assessment of structures. (October 2022)

• Record Type: Journal Article
• Authors: Yakhchalian, Mansoor; Yakhchalian, Masood
• Is Part Of: Structures. Volume 44(2022)
• Page Start: 933
• Page End: 946
• Publication Date: 2022-10
• Subjects: Intensity measure -- Mainshock-aftershock sequences -- Collapse capacity -- Efficiency -- Sufficiency; Structural engineering -- Periodicals; 624.1
• DOI: 10.1016/j.istruc.2022.07.076
• Languages: English
• ISSNs: 2352-0124

Abstract

Abstract: A ground motion intensity measure (IM) characterizes the damage potential of seismic ground motion on a structure and makes a connection between the structural response analysis and seismic hazard analysis. During earthquake events, aftershocks (ASs) are usually observed following a mainshock (MS), having no time for repairing between the events. For developing a framework to integrate aftershock seismic hazard into performance-based earthquake engineering, it is essential to apply an IM that accounts for the damage state experienced by the structure under the MS event. In the present study, the ability of some well-known and advanced IMs to predict the collapse capacity of reinforced concrete moment resisting frames (RC MRFs) under mainshock-aftershock (MS-AS) sequences with low dispersion is investigated. This ability for an IM is denoted as its efficiency. Furthermore, the potential bias in the collapse capacity values predicted using the IMs, with regard to ground motion characteristics is tested. The ability of an IM for reducing the bias to predict the seismic response or damage state capacity is denoted as sufficiency. The results show that the efficiency of the IMs for collapse capacity prediction of the structures under MS-AS sequences decreases when compared with the results obtained from only MS records as a prevalent method. In addition, most of the IMs that have high efficiency, do not have high sufficiency with regard to scale factor or magnitude (or both). The multi-objective particle swarm optimization algorithm is applied to develop an optimal IM with high efficiency and sufficiency for collapse capacity prediction of RC MRFs.