Modeling blast wave propagation using artificial neural network methods

The paper reports on work concerned with the development of artificial neural network approaches to modeling the propagation of bomb blast waves in a built-up environment. A review of current methods of modeling blast wave propagation identifies a need for a modeling system that is both fast and versatile in its scope of application. This is followed by a description of a preliminary study that used artificial neural networks to estimate peak pressures on buildings protected by simple blast barriers, using data generated from, first, an existing empirical model and, second, miniature bomb-barrier-building experiments. The first of these studies demonstrates the viability of the approach in terms of producing accurate results very rapidly. However, the study using data from live miniature bomb-barrier-building experiments was inconclusive due to a poor distribution of the sample data. The paper then describes on-going research refining this artificial neural network approach to allow the modeling of the time-wise progress of the blast wave over the surfaces of critical structures, facilitating a three-dimensional visualization of the problem. Finally, the paper outlines a proposed novel method of modeling blast wave propagation that uses a coarse-grain simulation approach combined with artificial neural networks, which has the goal of extending modeling to complicated geometries while maintaining rapid processing.