Physics-based Demand Model and Fragility Functions of Industrial Tanks under Blast Loading

Blast hazards represent a serious threat to industrial facilities. Past explosion incidents highlight the severe consequences of such events. A probabilistic approach can help industries and designers mitigate the consequences of blast loading by better organizing industrial plants. In this paper, we propose a physics-based probabilistic demand model and formulate the reliability problem for industrial steel tanks under blast loading. Starting from a deterministic Single-Degree-of-Freedom (SDOF) model based on Donnell shallow-shell theory, we develop a correction term that improves the model accuracy due to the simplified representation of the SDOF model. We use Bayesian inference to estimate the unknown model parameters in the correction term and model error, combining predictions from the SDOF model with experimental data and any prior information. To illustrate, we estimate the reliability of an example cylindrical steel tanks subject to blast loading considering three damage levels. The reliability analysis yields a set of fragility curves that represent the conditional probability of the bending failure of the tank given a scaled distance, as the load intensity measure. Then, as an example, we use the developed fragility functions to estimate the reliability of a chemical industrial facility considering different explosion scenarios.