基于贝叶斯图神经网络的应变裂纹定位
Bayesian graph neural networks for strain-based crack localization
AI总结：
本研究提出一种结合图神经网络和贝叶斯神经网络的数据驱动方法，通过结构离散位置的动态应变测量近似裂纹位置的后验分布，解决小缺陷应变衰减导致的裂纹定位难题。
摘要：
基于振动的损伤定位技术的一个常见缺陷是，局部损伤（即微小裂纹）对结构的频谱特性影响有限。相反，即使是最小的缺陷，在特定载荷条件下，也会引起具有可预测空间构型的局部应变集中。然而，小缺陷对应变的影响随着距缺陷距离的增加而迅速衰减，使得基于应变的定位相当具有挑战性。在本研究中，我们尝试以完全数据驱动的方式，近似在结构上任意离散位置给定任意动态应变测量值时的裂纹位置后验分布。所提出的技术利用图神经网络（GNNs）和贝叶斯神经网络可扩展学习的最新进展。该技术在通过离散位置的动态应变场测量模式推断未知裂纹位置的问题上进行了演示。数据集由在随机时变激励下具有随机采样裂纹几何形状和方向的空心管模拟组成。
英文摘要：
A common shortcoming of vibration-based damage localization techniques is that localized damages, i.e. small cracks, have a limited influence on the spectral characteristics of a structure. In contrast, even the smallest of defects, under particular loading conditions, cause localized strain concentrations with predictable spatial configuration. However, the effect of a small defect on strain decays quickly with distance from the defect, making strain-based localization rather challenging. In this work, an attempt is made to approximate, in a fully data-driven manner, the posterior distribution of a crack location, given arbitrary dynamic strain measurements at arbitrary discrete locations on a structure. The proposed technique leverages Graph Neural Networks (GNNs) and recent developments in scalable learning for Bayesian neural networks. The technique is demonstrated on the problem of inferring the position of an unknown crack via patterns of dynamic strain field measurements at discrete locations. The dataset consists of simulations of a hollow tube under random time-dependent excitations with randomly sampled crack geometry and orientation.
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