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A practical means of reducing the impact of blast loads on buildings is to introduce a barrier wall between the explosive device and the building. The height and location of the barrier wall are key design variables in terms of effectively reducing the peak positive and negative overpressure and impulse on the building. Until recently, set-ups that included a barrier between the explosive device and the building could only be modeled with consistent accuracy by using numeric simulation techniques. Unfortunately, these models require many hours of processing time to complete a simulation run, even for the fastest of today's computers. This has led several researchers to consider the use of advanced empirical modeling methods, specifically artificial neural networks, to overcome problems of computationally expensive simulations. Neural networks have the potential to make predictions of the influence of a barrier on blast propagation in a matter of seconds using a desktop computer, thus making it easier for designers to home-in on an optimal solution. Artificial neural networks appear to be well suited to this application, performing well for problems that are strongly non-linear and comprise many independent variables. This paper reports on past and on-going research in this field at AFRL Tyndall, using both scaled-live experimental data and simulated data to develop the neural models. The design and validation of these models are presented, and their successes and deficiencies are discussed. The paper concludes with an overview of current and future research plans to take this work to a state suitable for use in the field, and to extend it to problems comprising significantly more complicated configurations of structures than a barrier positioned between the explosive device and a building.