Dennis, A. orcid.org/0000-0002-3347-2747 and Rigby, S. orcid.org/0000-0001-6844-3797 (2023) Prediction of blast loads using machine learning approaches. In: Earthquake Engineering and Dynamics for a Sustainable Future. 2023 Society for Earthquake and Civil Engineering Dynamics (SECED) Conference, 14-15 Sep 2023, Cambridge, UK. Society for Earthquake and Civil Engineering Dynamics (SECED)
Abstract
The assessment of human injuries and structural damage following the detonation of a high explosive requires an understanding of blast load parameters. Use of physical experiments or physics-based numerical tools require large amounts of time and expertise, often restricting their use to deterministic analyses. Since explosive events are inherently unpredictable and key variables (e.g. charge size, mass, composition, location) may not be known a priori, there is a clear need for rapid analysis tools that can embrace this uncertainty in a probabilistic framework. Machine learning tools have been developed for this purpose, however, the features of the problem that are selected as model inputs can result in predictions being fixed to a single domain, thus requiring the tool to be retained for every new scenario. This paper details how the Directionencoded Neural Network (DeNN), a novel Machine Learning method, takes inspiration from the operation of robot vacuum cleaners to prevent this issue by considering the surroundings of each prediction point. Through comparisons to a traditional Artificial Neural Network (ANN), provided with global domain inputs, it is shown that the DeNN’s unique feature selection process allows for predictions in domains of variable sizes with movable obstacles, ultimately producing a tool that can be used in a range of studies without requiring additional task-specific training.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 Society for Earthquake and Civil Engineering Dynamics (SECED). This is an author-produced version of a paper subsequently published in Earthquake Engineering and Dynamics for a Sustainable Future: Proceedings of the Society for Earthquake and Civil Engineering Dynamics (SECED) Conference. This version is distributed under the terms of the Creative Commons Attribution-NonCommercial Licence (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. You may not use the material for commercial purposes. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Civil and Structural Engineering (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council 2574387 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 22 Sep 2023 10:47 |
Last Modified: | 03 Jan 2024 15:03 |
Published Version: | https://www.seced.org.uk/index.php/seced-2023-proc... |
Status: | Published |
Publisher: | Society for Earthquake and Civil Engineering Dynamics (SECED) |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:203470 |