Probabilistic demand models and reliability based code calibration for reinforced concrete column and beam subjected to blast loading

Jaswanth Gangolu, Katchalla Bala Kishore and Hrishikesh Sharma

Reliability Engineering and System Safety, 2023, vol. 240, issue C

Abstract: Performance-based probabilistic demand models for reinforced concrete (RC) columns and beams have been estimated due to ongoing blast loading attacks on structures and inadequate design codes. The unknown model parameters are evaluated using the Bayesian approach. The required reliable data is generated using LS-DYNA numerical experimental design with the Arbitary Lagrangian-Eulerian (ALE) method. This numerical method is based on finite element (FE) analysis, and all realistic material and geometrical variables are exposed to different combinations of detonation mass and stand-off distance. These developed probabilistic models are based on the deflection-based mechanical equation from the UFC 3–340–02 code and dimensionless correction terms. A comparison study with experimentation and fragility analysis demonstrates the efficiency of the models. Due to the inadequacy of existing Load and Resistance Factors for Design (LRFD) which are usually ‘1.0′ in available codes, the current study developed new LRFD factors for chosen performance levels based on probabilistic capacity and demand models. Furthermore, hazard curves for mass and standoff distance are evaluated to determine the dataset's distribution. Grounded on these curves the total probability of failure for an RC column constructed at the Supreme Court of India (an important building) has analysed and obtained results are promising.

Keywords: Probabilistic demand models; Blast loading; Hazard curves; Reliability code calibration; Load and resistance factors for design; Fragility estimation; Total probability of failure (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:240:y:2023:i:c:s095183202300491x

DOI: 10.1016/j.ress.2023.109577

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