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

2023; Elsevier BV; Volume: 240; Linguagem: Inglês

10.1016/j.ress.2023.109577

1879-0836

Jaswanth Gangolu, Katchalla Bala Kishore, Hrishikesh Sharma,

Concrete Corrosion and Durability

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.