Author(s)

L.K. STEWART & K.B. MORRILL

Abstract

The implementation of models for the prediction of structural behavior due to blast loads has become vital in the evaluation of threats. Most often, these models require complex finite element analysis and a background in structural and blast engineering to use effectively. Although the finite element models (FEMs) are robust and accurate, it is often necessary to evaluate structures and their potential risks in a relatively short time frame, much quicker than the time a complex, non-linear analysis would necessitate. Typically, simplified engineering tools, such as single degree of freedom models, are utilized in these scenarios but, while effective for some blast applications, often lack the fidelity necessary to sufficiently represent the variable (spatial and temporal) loading and non-linear response of the structure from a range of explosive events. This paper presents a methodology for a blast analysis tool whereby a FEM is used in conjunction with an artificial neural network to develop a physics-based fast running model. The methodology, which can be applied to a variety of engineering problems, is applied to residual capacity predictions for steel columns subjected to a range of vehicle-borne explosive threat scenarios. Through a set of validation scenarios, the model is shown to be an effective blast analysis tool capable of predictions of structural response within seconds and accessible for security and engineering professionals with varying technical backgrounds

Keywords

artificial neural network, blast, steel columns, structural models