Predicting the effectiveness of blast wall barriers using neural networks

Blast damage assessment of buildings and structural elements requires an accurate prediction of the blast loads in terms of the peak pressures and impulses. Blast loadings on structures have typically been evaluated using empirical relationships. These relationships assume that there are no obstacles between the explosive device and the target. If a blast barrier is used to protect personnel or a structure behind it, the actual blast loading environment will be significantly reduced for some distance behind the barrier. This paper is concerned with an accurate prediction of the area of effectiveness behind the barrier using experimental data and a neural network-based model. To train and validate the neural network, a database is developed through a series of measurements of the blast environment behind the barrier. The principal parameters controlling the blast environment, such as wall height, distance behind the wall, height above ground, and standoff distance are used as the training input data. Peak overpressure and peak scaled impulse are used as the outputs in the neural network configuration. The trained and validated neural network is used to develop contour plots of overpressure and impulse adjustment factors to simplify the process of predicting the effectiveness of blast barriers. The developed model is also deployed in a stand-alone application that is used as a fast-running predictive tool for the blast overpressures and impulses behind the wall.