Summary of Deep Learning-based Modularized Loading Protocol For Parameter Estimation Of Bouc-wen Class Models, by Sebin Oh et al.
Deep learning-based modularized loading protocol for parameter estimation of Bouc-Wen class models
by Sebin Oh, Junho Song, Taeyong Kim
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP)
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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 research proposes a novel deep learning-based protocol for optimizing parameter estimation in Bouc-Wen (BW) class models. The protocol consists of two key components: constructing optimal loading histories and using convolutional neural networks (CNNs) to rapidly estimate parameters. Each component is modularized into independent sub-modules, tailored to distinct hysteretic behaviors such as basic hysteresis, structural degradation, and pinching effect. Three CNN architectures are developed to capture the path-dependent nature of these behaviors, which are then trained on diverse loading histories to identify minimal sequences (loading history modules). These modules are combined to construct an optimal loading history, while the three CNN models serve as rapid parameter estimators. Numerical evaluations demonstrate that the proposed protocol reduces total analysis time while maintaining or improving estimation accuracy for various hysteretic behaviors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study develops a new way to improve our understanding of how materials behave under different loads. It creates a system that uses deep learning and neural networks to quickly estimate key properties of materials, called Bouc-Wen class models. These models are important for designing buildings and structures that can withstand natural disasters like earthquakes. The system works by breaking down the process into smaller steps and using special types of artificial intelligence (AI) to learn from different patterns in how materials behave. This allows it to quickly and accurately estimate the properties of various materials, which is a big improvement over current methods. |
Keywords
» Artificial intelligence » Cnn » Deep learning
