A Neural-Network Model-Based Engineering Tool for Blast Wall Protection of Structures

Abstract

Blast barrier walls have been shown to reduce blast loads on structures, especially in urban environments. Analysis of existing test and simulation data for blast barrier response has revealed that a need still exists to determine the bounds of the problem and produce a fast-running accurate model for the effects of barrier walls on blast wave propagation. Since blast experiments are very time intensive and extremely cost prohibitive, it is vital that computational capabilities be developed to generate the required data set that can be utilized to produce simplified design tools. The combination of high fidelity model-based simulation with artificial neural network techniques is proposed in this paper to manage the challenging problem. The proposed approach is demonstrated to estimate the peak pressure, impulse, time of arrival, and time of duration of blast loads on buildings protected by simple barriers, using data generated from validated hydrocode simulations. Once verified and validated, the proposed neural-network model-based simulation procedure would provide a very efficient solution to predicting blast loads on the structures that are protected by blast barrier walls.