论文标题
通过神经网络辅助电阻纸在自感应材料中的空间损伤表征：一项计算研究
Spatial Damage Characterization in Self-Sensing Materials via Neural Network-Aided Electrical Impedance Tomography: A Computational Study
论文作者
论文摘要
连续的结构健康监测（SHM）和综合的非破坏性评估（NDE）对于确保高风险工程结构的安全运行很重要。最近，对SHM和NDE的压电纳米复合材料受到了很多关注。这些材料是自感应的，因为它们的电导率会因变形和损坏而发生变化。结合电阻抗断层扫描（EIT），可以绘制有害效果。但是，EIT遭受了重要的局限性 - 计算在计算上昂贵，提供了关于损害形状的模糊信息，并且如果它们靠近，可能会错过多个损害。在本文中，我们采用一种新型的神经网络方法来量化损害指标，例如大小，数字和EIT数据的位置。该网络是使用校准的模拟常规训练的，该常规座校准了对压电性碳纳米纤维改造的环氧树脂的实验数据。我们的结果表明，网络可以预测99.2％精度的损害数量，相对于平均误差为2.46％的平均半径，量化损伤大小，并在平均0.89％的误差下对域长度量化损坏位置。这些结果是将自感应材料和EIT的组合转化为现实世界SHM和NDE的重要第一步。
Continuous structural health monitoring (SHM) and integrated nondestructive evaluation (NDE) are important for ensuring the safe operation of high-risk engineering structures. Recently, piezoresistive nanocomposite materials have received much attention for SHM and NDE. These materials are self-sensing because their electrical conductivity changes in response to deformation and damage. Combined with electrical impedance tomography (EIT), it is possible to map deleterious effects. However, EIT suffers from important limitations -- it is computationally expensive, provides indistinct information on damage shape, and can miss multiple damages if they are close together. In this article we apply a novel neural network approach to quantify damage metrics such as size, number, and location from EIT data. This network is trained using a simulation routine calibrated to experimental data for a piezoresistive carbon nanofiber-modified epoxy. Our results show that the network can predict the number of damages with 99.2% accuracy, quantify damage size with respect to the averaged radius at an average of 2.46% error, and quantify damage position with respect to the domain length at an average of 0.89% error. These results are an important first step in translating the combination of self-sensing materials and EIT to real-world SHM and NDE.
