通过将GRU和注意力机制结合起来,开发出高性的新构成模型
Feng Li1, Tianbo Peng1,2
1College of Civil Engineering, Tongji University, Shanghai 200092, China.
Polymers
|March 13, 2024
概括
高阻尼 (HDR) 轴承对于抗震桥梁设计至关重要. 使用GRU和注意力的新深度学习模型准确预测HDR行为,减少复杂性和计算时间,以便更好地分析地震响应.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 计算力学 计算力学 计算力学
背景情况:
- 高阻尼 (HDR) 轴承对于桥梁的地震能量消耗至关重要.
- 现有的HDR构成模型缺乏对温度,延展率和穆林斯效应等因素的全面分析.
- 准确的建模对于预测地震事件期间桥梁性能至关重要.
研究的目的:
- 开发一种新的深度学习方法来建模HDR轴承的机械行为.
- 研究关键因素对HDR构成模型的影响.
- 为了提高HDR行为分析的预测准确性和效率.
主要方法:
- 使用了深度学习方法,结合了Gate Recurrent Unit (GRU) 和注意力机制.
- 分析了来自HDR标本的压缩剪切测试数据,以确定时间序列特征.
- 开发了一个基于机制的构成模型,结合了深度学习预测.
主要成果:
- GRU注意力模型准确地预测了HDR标本的机械行为.
- 与仅使用GRU相比,提出的方法证明了模型复杂性和计算时间的减少.
- 构成模型成功地分析了最大幅度和周期对HDR压力-应变关系的影响.
结论:
- 集成的GRU-attention深度学习模型为构建HDR构成模型提供了一个有希望的新方法.
- 这种方法为评估在地震负载条件下的HDR轴承响应提供了坚实的基础.
- 了解最大幅度的影响对于使用HDR轴承的桥梁的地震设计至关重要.
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