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：以振动为基础的损害定位技术的一个常见缺点是局部损害,即小裂缝,对结构的光谱特性影响有限,相比之下,即使是最小的缺陷,在特定装载条件下,也会导致可预见空间配置的局部菌株浓度;然而,小缺陷对菌株衰减的影响,与缺陷相距很远,使基于菌株的局部化变得相当具有挑战性。在这项工作中,试图以完全以数据驱动的方式对裂缝位置的后方分布进行近似,因为对结构的任意离散地点进行了任意的动态强度测量。拟议的技术杠杆图神经网络(GNN)和Bayesian神经网络可扩展学习的最新发展。该技术的证明是通过离散地点的动态压力场测量模式推断出未知裂痕位置的问题。数据集包括随机根据随机根据时间进行的抽查,对空管进行模拟,并有随机抽样的裂缝测量和定向。