Summary of Physics-inspired Deep Learning and Transferable Models For Bridge Scour Prediction, by Negin Yousefpour et al.
Physics-Inspired Deep Learning and Transferable Models for Bridge Scour Prediction
by Negin Yousefpour, Bo Wang
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents Scour Physics-Inspired Neural Networks (SPINNs), a hybrid framework combining deep learning and physics-based equations to predict bridge scours. SPINNs integrate empirical equations into neural networks, using site-specific historical data for training. The study explores base models like LSTM and CNN, as well as transferable models trained on aggregated datasets from multiple bridges versus site-specific models. Results show that SPINNs outperformed pure data-driven models in most cases, with some models reducing forecasting errors by up to 50%. Transferable models proved effective for bridges with limited data. The study also compares calibrated empirical equations derived from SPINNs with the commonly used HEC-18 model, showing improved accuracy. Overall, this work demonstrates the potential of physics-inspired machine learning methods for scour prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence to predict when a bridge might be damaged by water erosion. They created a new way to do this by combining computer models with real-world data and equations that describe how water behaves around bridges. The new system, called SPINNs, works better than previous systems in many cases, especially when there isn’t much data available about the specific bridge. This could be very helpful for making sure our bridges are safe. |
Keywords
» Artificial intelligence » Cnn » Deep learning » Lstm » Machine learning
