经常性神经网络的趋同和稳定性分析,用于在地震负荷下快速评估结构损坏
Feng Zeng1,2, Fujiang Chen1,2, Yongyi Yang1
1School of Emergency Management, Xihua University, Chengdu, Sichuan, China.
PloS one
|November 7, 2025
概括
我们创建了一个稳定,轻量级的LSTM模型用于地震损害评估,提高准确性和实时性能,即使有传感器噪声. 这种方法增强了结构性健康监测系统.
科学领域:
- 结构健康监测 结构健康监测
- 机器学习用于工程.
- 地震数据分析的分析.
背景情况:
- 经常性神经网络 (RNN) 模型与非静止的地震数据和传感器噪声作斗争,导致不稳定的损害评估.
- 优化RNN用于地震响应分析是具有挑战性的,因为梯度问题 (爆炸/消失).
研究的目的:
- 开发一个稳定性控制的,轻量级的长短期记忆 (LSTM) 网络,以进行强大的地震损害评估.
- 提高地震损害检测系统的准确性,稳定性和实时性能.
主要方法:
- 实施了稳定性控制的LSTM,具有梯度超标处罚和时间注意力门.
- 利用多尺度的滑动窗口推断来实现稳定的长期预测.
- 使用离散时间状态空间视图分析稳定性,将雅科比的光谱规范和注意力收益限制在内.
主要成果:
- 在10dB的噪音下实现了低损耗 (<0.01) 和最小的梯度爆炸.
- 与标准RNN基线相比,证明了卓越的准确性,稳定性 (σ(out) =0.032) 和降低的延迟.
- 确认了实时部署可行性,在100 Hz的Jetson Nano上<5ms的端到端延迟.
结论:
- 拟议的稳定性控制的LSTM为地震损害评估提供了强大而高效的解决方案.
- 该方法显著改进了现有的基于RNN的方法,使可靠的实时结构健康监测成为可能.
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