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Prediction of airblast loads in complex environments using artificial neural networks

Chapter


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 networkbased

    model. The approach is demonstrated in application to the problem of

    predicting the blast loads in city streets. To train and validate the neural

    networks, a database of blast effects was developed using the Computational

    Fluid Dynamics (CFD) blast simulations. The blast threat scenarios and the

    principal parameters describing the street configurations and the blast wall

    geometry were lIsed as the train ing input data. The peak pressures and impulses

    were lIsed as the outputs in the neural network configuration.

Publication Date


  • 2013

Citation


  • Remennikov, A. M. & Mendis, P. A. (2013). Prediction of airblast loads in complex environments using artificial neural networks. In S. Syngellakis (Eds.), Design against blast: Load definition & structural response (pp. 53-62). Southampton: WIT.

Scopus Eid


  • 2-s2.0-36148956189

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/582

Book Title


  • Design against blast: Load definition & structural response

Start Page


  • 53

End Page


  • 62

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 networkbased

    model. The approach is demonstrated in application to the problem of

    predicting the blast loads in city streets. To train and validate the neural

    networks, a database of blast effects was developed using the Computational

    Fluid Dynamics (CFD) blast simulations. The blast threat scenarios and the

    principal parameters describing the street configurations and the blast wall

    geometry were lIsed as the train ing input data. The peak pressures and impulses

    were lIsed as the outputs in the neural network configuration.

Publication Date


  • 2013

Citation


  • Remennikov, A. M. & Mendis, P. A. (2013). Prediction of airblast loads in complex environments using artificial neural networks. In S. Syngellakis (Eds.), Design against blast: Load definition & structural response (pp. 53-62). Southampton: WIT.

Scopus Eid


  • 2-s2.0-36148956189

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/582

Book Title


  • Design against blast: Load definition & structural response

Start Page


  • 53

End Page


  • 62