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- W4221024257 abstract "Most of the buildings in Saudi Arabia were constructed using reinforced concrete (RC) ordinary moment frames (OMFs), which, in most cases, had discontinuous bottom beam rebars across the beam-column joints. Consequently, they may be susceptible to the potential of progressive collapse owing to accidental loss of one (or more) exterior column(s) in an extreme event. Therefore, it is imperative to examine the progressive collapse robustness of RC OMF buildings. In this paper, 16 half-scale RC OMF assemblies (skeletal frames comprising of multi-span beams and two-story columns) were investigated using validated FE modeling for progressive collapse potential in the event of middle column removal. The analysis was carried out with the help of LS-DYNA software. It incorporated nonlinear constitutive modeling of concrete and steel rebars considering the effect of strain rate, and modeling of bond-slip behavior at the interaction of steel rebars with concrete. Out of the 16 assemblies, 12 specimens represented the actual detailing of an existing 8-story building. The studied variables included axial load on columns, percentage of continuous bottom beam rebars across the beam-column joint (with respect to the total number of bottom rebars), and number of beam spans. The performance of the 12 assemblies was evaluated in accordance with mode of failure and characteristics of load–displacement envelope. A simple approach was suggested to assess the progressive collapse risk level of RC OMFs. Five risk levels were proposed, ranging from very low to very high. Very high to high risk was predicted for the 12 studied assemblies. The last four assemblies of the analysis matrix were newly designed to reduce the progressive collapse risk to a moderate (or low) level." @default.
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- W4221024257 date "2022-06-01" @default.
- W4221024257 modified "2023-10-02" @default.
- W4221024257 title "High-fidelity FE models for assessing progressive collapse robustness of RC ordinary moment frame (OMF) buildings" @default.
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- W4221024257 doi "https://doi.org/10.1016/j.engfailanal.2022.106228" @default.
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