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This paper presents a procedure for simplified collapse probability prediction of RC frame buildings when subjected to ground motion intensities commonly used in designing buildings. For this purpose, seismic collapse probability of a range of RC moment resisting frame buildings are assessed at the design basis earthquake (DBE) and the maximum considered earthquake (MCE) levels of seismic intensity by conducting more than a thousand nonlinear response history analyses (NRHA) using a suite of 40 seismic ground motion records. The 13 buildings included in the investigation have different heights (5–15 storeys) and footprints (3–6 bays), are designed to different target maximum inter‐storey drifts (within the code specified limit), and have critical members detailed to sustain different levels of confinement and anti‐buckling demands. The NRHA results consistently demonstrate that the design inter‐storey drift and the anti‐buckling detailing of longitudinal rebars in the critical frame members strongly influence the collapse probability of RC frame buildings. The results also show that while RC frame buildings designed to reach 2.5% (or less) inter‐storey drifts and detailed for ductile seismic performance can successfully avoid collapse at DBE, avoidance of collapse at DBE cannot be guaranteed if RC frames are designed to reach a target drift of more than 2% and not detailed for full ductility. At MCE, collapse probability is found to consistently exceed 10% unless the building is designed to incur no more than 1% plastic drift and the frame members have ductile detailing. Based on the NRHA results of the 13 case study buildings, easy‐to‐use equations relating their seismic collapse probability at MCE with the design drift and a quantifiable measure of ductile detailing are generated and validated by comparing with the collapse probability obtained by NRHA of an independent RC frame building. The proposed equations provide a rapid, yet reasonably reliable tool for performance‐based seismic design of RC frame buildings, and enable designers to estimate seismic collapse probability of RC frame buildings without having to conduct cumbersome NRHA. [ABSTRACT FROM AUTHOR]

Variation in the seismic collapse fragility of reinforced concrete frame buildings predicted using different ground motion (GM) selection methods is investigated in this paper. To simulate the structural collapse, a fiber-element modelling approach with path-dependent cyclic nonlinear material models that account for concrete confinement and crushing, reinforcement buckling as well as low cycle fatigue is used. The adopted fiber analysis approach has been found to reliably predict the loss in vertical load carrying capacity of structural components in addition to the sidesway mode of collapse due to destabilizing P-Δ moments at large inelastic deflections. Multiple stripe analysis is performed by conducting response history analyses at various hazard levels to generate the collapse fragility curves. To select GMs at various hazard levels, two alternatives of uniform hazard spectrum (UHS), conditional mean spectrum (CMS) and generalized conditional intensity measure (GCIM) are used. Collapse analyses are repeated based on structural periods corresponding to initial un-cracked stiffness and cracked stiffness of the frame members. A return period-based intensity measure is then introduced and applied in estimating collapse fragility of frame buildings. In line with the results of previous research, it is shown that the choice of structural period significantly affects the collapse fragility predictions. Among the GM selection methods used in this study, GCIM and CMS methods predict similar collapse fragilities for the case study building investigated herein, and UHS provides the most conservative prediction of the collapse capacity, with approximately 40% smaller median collapse capacity compared to the CMS method. The results confirm that collapse probability prediction of buildings using UHS offers a higher level of conservatism in comparison to the other selection methods. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]