Performance-based probabilistic deflection capacity models and fragility estimation for reinforced concrete column and beam subjected to blast loading

Katchalla Bala Kishore, Jaswanth Gangolu, Mukesh K. Ramancha, Kasturi Bhuyan and Hrishikesh Sharma

Reliability Engineering and System Safety, 2022, vol. 227, issue C

Abstract: Due to the mounting of ongoing anti-social activities of explosions and inaccuracy of codes (UFC 3-340-02, Eurocode 1, IS 4991) current study has developed performance-based capacity models for Reinforced Concrete (RC) column/beam subjected to near field blast. The chosen three performance levels are deflection based and allied with four damage states of target (elastic limit with minimal damage, elasto-plastic with moderate damage, ultimate limit with total collapse). To estimate deflection levels, a numerical method LS-DYNA is used to acquire data using Arbitrary Lagrangian-Eulerian (ALE) method. Four models are accurately validated with experimental results and chosen parameters are deflection, damage pattern and pressure achieved. The deflection of the RC beam/column subjected to blast is reproduced with combination of mechanical model (UFC 3-340-02 code) and dimensionless explanatory functions. These functions are used to develop six probabilistic models for RC beam/column subjected to blast loading grounded on Bayesian inference and posterior statistics. These models account for all aleatoric and epistemic uncertainties in dynamic blast events. The estimated capacity models are compared with experimentation, and acquired results show the reliability of the probabilistic equations. This study also assessed fragility for three levels of normalized deflection demand and obtained plots are well authenticated.

Keywords: Blast loading; Performance levels; Finite element simulations; Probabilistic capacity models; Dynamic deflection capacity; Fragility estimation (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:227:y:2022:i:c:s0951832022003532

DOI: 10.1016/j.ress.2022.108729

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