通过神经网络改进EFDD,用于阻尼结构健康监测的阻尼识别
Yuanqi Zheng1, Chin-Long Lee1, Jia Guo2
1Department of Civil and Environmental Engineering, University of Canterbury, Christchurch 8041, New Zealand.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
神经网络增强了用于结构健康监测 (SHM) 的阻尼识别. 一种新的网格重量方法提高了早期结构损坏检测的准确性和稳定性.
科学领域:
- 结构工程 结构工程
- 机器学习 机器学习
- 结构健康监测 结构健康监测
背景情况:
- 阻尼是结构健康监测 (SHM) 的敏感指标,可以检测微妙的损伤.
- 目前的阻尼识别方法,包括增强频域分解 (EFDD),缺乏准确性和稳定性.
- 实际的SHM应用受到这些识别方法缺陷的限制.
研究的目的:
- 为了提高SHM中减压识别的准确性和稳定性.
- 用数据驱动的神经网络模块取代EFDD中的启发式参数选择.
- 开发一种更可靠的基于缓冲的损坏检测技术.
主要方法:
- 将神经网络集成到EFDD工作流程中.
- 开发一个神经模块的逐步嵌入策略.
- 使用多层感知器实现端到端的网格重量框架.
- 通过数值模拟和摇桌实验进行验证.
主要成果:
- 拟议的电网重量EFDD在识别阻尼变化方面表现出卓越的稳定性和灵敏性.
- 早期的结构损伤检测得到了显著改善.
- 发现阻尼指标的有效性受到结构材料系统的影响.
结论:
- 将神经网络集成到EFDD中可以提高SHM的阻尼识别.
- 电网重量EFDD方法提供了一种可靠和可解释的损害检测方法.
- 这种数据驱动的方法克服了阻尼分析中传统启发式方法的局限性.
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