Visual deep learning with physics constraints for local scour evolution prediction at monopiles Article Swipe
Bingjing Lu
,
Jingjing Zuo
,
Mohammad Shahhosseini
,
Hui Wang
,
Haichao Liu
,
Minxi Zhang
,
Guoliang Yu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.joes.2024.04.001
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.joes.2024.04.001
Local scour threatens the safety of marine structures, necessitating precise prediction of scour evolution around these structures. A visually oriented deep learning model, called Disentangled Physics-constrained Prediction (DPP), was proposed in this study to predict scour evolution at monopiles reliably. It integrates scouring physics with advanced video prediction through a two-branch architecture. The Physics-constrained Recurrent Module (PRModule) branch leverages Recurrent Neural Networks (RNNs) for temporal differentiation, ensuring accurate prediction of scouring-related physical information. Meanwhile, the Convolutional Long-Short-Term Memory (ConvLSTM) branch captures spatial and temporal dynamics in scouring videos, focusing on the prediction of residual features. DPP outperformed three baseline models in predicting the scour evolution at monopiles. Across three scouring scenarios, DPP achieved a 14.2% decrease in Root Mean Squared Error, a 14.7% reduction in Mean Absolute Error, and an 8.1% increase in Structural Similarity on average, compared to the most advanced baseline model. The predicted scouring frames are found to agree well with the true frames, demonstrating DPPs potential as a valuable tool to protect marine infrastructures.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.joes.2024.04.001
- OA Status
- diamond
- Cited By
- 4
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394985057
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4394985057Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.joes.2024.04.001Digital Object Identifier
- Title
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Visual deep learning with physics constraints for local scour evolution prediction at monopilesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-21Full publication date if available
- Authors
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Bingjing Lu, Jingjing Zuo, Mohammad Shahhosseini, Hui Wang, Haichao Liu, Minxi Zhang, Guoliang YuList of authors in order
- Landing page
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https://doi.org/10.1016/j.joes.2024.04.001Publisher landing page
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.joes.2024.04.001Direct OA link when available
- Concepts
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Convolutional neural network, Residual, Baseline (sea), Recurrent neural network, Deep learning, Artificial intelligence, Mean squared error, Artificial neural network, Computer science, Mathematics, Geology, Algorithm, Statistics, OceanographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4Per-year citation counts (last 5 years)
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46Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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