Prediction of airblast loads in complex environments using artificial neural networks

Abstract

Predicting non-ideal airblast loads is presently a complex computational art requiring many hours of high-performance computing to evaluate a single blast scenario. The goal of this research is to develop a method for predicting blast loads in a non-ideal environment in real time. The proposed method is incorporated in a fast-running model for rapid assessment of blast loads in complex configurations such as a dense urban environment or a blast environment behind a blast barrier. This paper is concerned with an accurate prediction of the blast loads from a bomb detonation using a neural network-based model. The approach is demonstrated in application to the problem of predicting the blast lo..