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.

翻译：基于振动的损伤定位技术普遍存在缺点：局部损伤（如微小裂纹）对结构频谱特性的影响有限。相反，在最微小的缺陷在特定载荷条件下，也会在可预测的空间构型中产生局部应变集中。然而，小缺陷对应变的影响会随距缺陷距离增大而快速衰减，这使得基于应变的定位极具挑战性。本研究尝试在全数据驱动框架下，基于结构上任意离散位置处的动态应变测量值，近似裂纹位置的后验分布。所提出的技术利用图神经网络（GNN）与贝叶斯神经网络可扩展学习的最新进展。该技术通过离散位置上动态应变场测量模式，验证了未知裂纹位置推断问题。数据集包含空心管在随机时变激励下的模拟结果，其中裂纹几何形状和方向均为随机采样。