Data-driven modelling of blast waves for safer cities

Currently, blast protection engineers are not equipped with adequate or appropriate tools to study holistic blast behaviour in complex urban environments. As such, blast protection solutions are implemented at a local level only, i.e. "what happens to this particular structural element if a bomb of this size is placed in this exact location". This project aims to develop a new analysis method that can rapidly model the propagation of a blast wave in a cityscape or urban environment, in order for blast protection engineers and city planners to build blast protection solutions into the fabric of cities and public spaces, as opposed to being optional 'bolt-ons'. To do this, we will take advantage of state-of-the-art Machine Learning methodologies and develop a data-driven blast propagation emulator, based on approximations to physical behaviour learnt through interrogation of both advanced numerical modelling and detailed experimental data gathered at the world-leading University of Sheffield Blast and Impact Laboratory.

High-fidelity physics-based solvers can provide accurate answers to very specific situations. These approaches, however, become unwieldy and unsuitable even when considering only a small number of potential situations. On the other hand, approximate design tools can evaluate hundreds of thousands of permutations within seconds, but currently the available approaches are lacking in sophistication and cannot accurately model blast behaviour in complex environments, when a blast wave may coalesce, diffract, and reflect. Data-driven modelling based on Machine Learning (ML) offers the potential to combine the best of both worlds with respect to the state-of-the art in blast load quantification: quick-running solvers that can simulate events with many, many potential permutations, but with physically valid and verified assumptions underpinning the analyses. Research in data-driven modelling under high uncertainty will initially be investigated, followed by physics-constraint supervised learning in ML modelling structures.Machine learning has shown enormous potential for pattern recognition in large data sets, and has recently been used to forecast ocean wave conditions [James et al. (2017) A Machine Learning Framework to Forecast Wave Conditions. Journal of Coastal Engineering]. Here, the developed ML model was able to predict ocean waves in 1/1000th the computation time of a physics-based model. Such an approach extended to the prediction of blast wave propagation will provide a more robust and accurate blast load predictor with accuracy commensurate with FEA.ML requires large data input. Initially, the modelling structure will be trained using geometric 3D point clouds measured from the Civil and Structural Engineering Urban Flows Laboratory, with the intention of subsequently being able to rapidly recreate any cityscape and therefore blast effects within that cityscape. Select numerical analyses will provide training data (a grid of points in 3D space provides 4 dimensions of training data for each run) based on the imported geometries, with the intention of developing a more generalised predictive approach.
Finally, the developed model will be validated against a series of well-controlled experimental tests conducted at the world-leading University of Sheffield Blast and Impact Laboratory. Here, blast pressures in complex geometries will be measured using state-of-the-art high magnitude pressure gauges sampled at ~5 MHz to ensure a rich suite of data is gathered.