用实验频率响应函数对杆束进行材料降解反向识别
Qi Chen1, Carol Featherston1, David Kennedy1
1School of Engineering, Cardiff University, Cardiff CF24 3AA, UK.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
本研究引入了一种新的随机框架,用于识别使用卡鲁宁-洛耶夫扩展和贝叶斯推理的梁中的结构材料降解 (SMD). 该方法准确地定位和量化材料衰变,确保物理可信性和算法稳定性.
科学领域:
- 结构健康监测 结构健康监测
- 计算力学 计算力学 计算力学
- 材料科学 材料科学 材料科学
背景情况:
- 结构材料退化 (SMD) 对基础设施完整性构成风险.
- 准确识别SMD对于安全和维护至关重要.
- 现有的方法往往在高维度和物理可信性方面扎.
研究的目的:
- 开发一个随机框架,用于反向识别SMD在杆梁.
- 将卡鲁宁-洛埃夫扩展与贝叶斯推理结合起来,用于概率 SMD 描述.
- 实施两阶段的约束策略,以提高稳定性和物理现实性.
主要方法:
- 利用卡鲁宁-洛耶夫 (KL) 扩展用于材料衰变的低维光谱参数化.
- 在贝叶斯推理方案中,集成KL扩展与实验频率响应函数 (FRF) 数据.
- 采用了两阶段的约束策略:识别期间的物理规范化和选择性的融合后规范化.
主要成果:
- 成功地将SMD定位和量化在一个带有切割的钢吊杆梁中.
- 证明了框架能够提供SMD的全场概率描述的能力.
- 验证了自适应约束策略在平衡稳定性和物理可信性的有效性.
结论:
- 拟议的随机框架准确地识别了结构材料的退化.
- 两个阶段的约束策略有效地解决了反向识别方面的挑战.
- 该方法提供了一种可靠的方法,用于对结构中的材料衰变进行概率评估.
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