Pannell, J., Rigby, S. orcid.org/0000-0001-6844-3797 and Panoutsos, G. (2023) Application of transfer learning for the prediction of blast impulse. International Journal of Protective Structures, 14 (2). pp. 242-262. ISSN 2041-4196
Abstract
Transfer learning offers the potential to increase the utility of obtained data and improve predictive model performance in a new domain, particularly useful in an environment where data is expensive to obtain such as in a blast engineering context. A successful application in this respect will improve existing surrogate modelling approaches to allow for holistic and efficient strategies to protect people and structures subjected to the effects of an explosion. This paper presents a novel application of transfer learning for the prediction of peak specific impulse where we demonstrate that previous knowledge learned when modelling spherical charges can be transferred to provide a performance benefit when modelling cylindrical charges. To evaluate the influence of transfer learning, two artificial neural network architectures were stress tested for three levels of random data removal: the first model (NN) did not implement transfer learning whilst the second model (TNN) did by including a bolt-on network to a previously published NN model trained on the spherical dataset. It is shown the TNN consistently outperforms the NN, with this out-performance increasing as the proportion of data removed increases and showing statistically significant results for the low and high threshold with less variability in all cases. This paper indicates transfer learning applications can be used successfully with considerable benefit with respect to surrogate modelling in a blast engineering context.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2022. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
Keywords: | machine learning; transfer learning; blast; computational fluid dynamics; data-driven modelling |
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 2132491 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 25 May 2022 12:54 |
Last Modified: | 10 Jul 2024 12:00 |
Status: | Published |
Publisher: | Multi-Science Publishing |
Refereed: | Yes |
Identification Number: | 10.1177/20414196221096699 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:187344 |