发表机构
University of Florida（佛罗里达大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出波物理信息回归方法，通过局部色散曲线实现异构板空间厚度映射，在铝板数据上达到0.94相关系数。
AI 中文摘要
传统的导波结构健康监测方法通常假设材料属性均匀，这限制了其对具有空间变化厚度、损伤或材料属性的异构结构进行表征的能力。这些挑战常见于腐蚀评估、复合材料分层检测和结构退化监测中。本文提出了一种基于波物理信息回归的方法，通过提取结构上的局部色散曲线，实现对材料属性的空间分辨表征。我们的方法聚焦于一个高度可解释且灵活的物理信息框架，该框架可使用快速算法求解并获得稳健的数值解。本文讨论了该框架的数学设计、算法及其解释。该框架被应用于一个薄铝板（孔周围厚度不均匀）的导波波场成像数据集，以验证其实用性。该框架生成了准确的厚度图（与X射线CT验证的相关系数为0.94），并提取了这些区域内波的频率相关速度。
英文摘要
Traditional guided wave methods for structural health monitoring typically assume uniform material properties, which limit their ability to characterize heterogeneous structures with spatially varying thickness, damage, or material properties. These are challenges commonly encountered in corrosion assessment, composite delamination detection, and structural degradation monitoring. This paper presents a wave physics-informed regression approach that enables spatially resolved characterization of material properties by extracting local dispersion curves across a structure. Our approach focuses on a highly interpretable but flexible physics-informed framework that can be solved using fast algorithms and achieve robust numerical solutions. This paper discusses the mathematical design of the framework, the algorithm, and its interpretation. The framework was applied to a guided wave wavefield imaging dataset from a thin aluminum plate with non-uniform thickness around a hole to validate its practicality. The framework creates an accurate thickness map (correlation coefficient 0.94 with x-ray CT validation) as well as extracts the frequency-dependent velocities of waves within those regions.
