Prediction Of Airblast Loads In Complex
Environments Using Artificial Neural Networks
Author(s)
A. M. Remennikov & P. A. Mendis
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 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 used as the training input data.
The peak pressures and impulses were used as the outputs in the neural network
configuration.
Keywords: neural networks, blast loads, urban environment, explosion,
numerical simulation.
1 Introduction
Protecting civilian buildings from the threat of terrorist activities is one of the
most critical challenges for structural engineers today. Events of the past few
years have greatly heightened the awareness of structural designers of the threat
Keywords
neural networks, blast loads, urban environment, explosion,
numerical simulation.
Related Book
Other papers in this volume
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