Summary of Deep Learning in Earthquake Engineering: a Comprehensive Review, by Yazhou Xie
Deep Learning in Earthquake Engineering: A Comprehensive Review
by Yazhou Xie
First submitted to arxiv on: 15 May 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 A machine learning educator writing for a technical audience can generate the following medium-difficulty summary: This paper surveys the growing interest in using Deep Learning (DL) to address challenging problems in earthquake engineering. The authors highlight the limitations of domain-specific methods in addressing issues like uncertainty in earthquake occurrence, unpredictable seismic loads, nonlinear structural responses, and community engagement. DL offers promising solutions by leveraging its data-driven capacity for nonlinear mapping, sequential data modeling, automatic feature extraction, dimensionality reduction, optimal decision-making, etc. The paper discusses methodological advances in various applicable DL techniques, such as multi-layer perceptron (MLP), convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), autoencoder (AE), transfer learning (TL), reinforcement learning (RL), and graph neural network (GNN). The authors explore different research topics, including vision-based seismic damage assessment and structural characterization, seismic demand and damage state prediction, seismic response history prediction, regional seismic risk assessment and community resilience, ground motion (GM) for engineering use, seismic response control, and the inverse problem of system/damage identification. Suitable DL techniques are identified for each research topic, emphasizing the preeminence of CNN for vision-based tasks, RNN for sequential data, RL for community resilience, and unsupervised learning for GM analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how Deep Learning (DL) can be used to solve big problems in earthquake engineering. Earthquake engineers have been working on this problem for decades, but they still struggle with things like predicting when earthquakes will happen, figuring out what kind of damage an earthquake will cause, and keeping people safe during an earthquake. DL is a powerful tool that can help with these problems by looking at lots of data and finding patterns. The paper looks at different types of DL, like convolutional neural networks (CNN) and recurrent neural networks (RNN), and how they can be used to solve different problems in earthquake engineering. |
Keywords
» Artificial intelligence » Autoencoder » Cnn » Deep learning » Dimensionality reduction » Feature extraction » Gan » Generative adversarial network » Gnn » Graph neural network » Machine learning » Neural network » Reinforcement learning » Rnn » Transfer learning » Unsupervised
