Probabilistic damage quantification of an RC column subjected to blast loading

Safeguarding critical infrastructure from both intentional and accidental explosions is crucial to maintaining its operational functionality and ensuring personnel safety. In the context of protective design, probabilistic blast-damage evaluations of reinforced concrete (RC) structural components, such as columns, are required when dealing with uncertain threat conditions. While maximum dynamic deflection and residual axial capacity are standard damage criteria, their integration into a predictive model remains complex. This study develops a probabilistic framework for estimating blast damage based on these two key metrics. Utilizing numerical models validated against experimental data, the study evaluates the responses of RC columns across a diverse range of geometries, material properties, and blast-loading parameters. A Bayesian inference framework is adopted to quantify uncertainty in the probabilistic damage models using data generated from validated numerical simulations. Consequently, probabilistic formulations are proposed to estimate the maximum dynamic deflection and residual axial capacity. Furthermore, the framework is validated against the experimental results, demonstrating its reliability and precision. The developed framework provides a practical tool for risk-informed blast-resistant design and post-blast damage assessment for RC columns under free-air blast loading within the parameter ranges examined.