In this paper, a passive structural health monitoring (SHM) method capable of detecting the presence of damage in carbon fibre/epoxy composite plates is developed. The method requires the measurement of strains from the considered structure, which are used to set up, train, and test artificial neural networks (ANNs). At the end of the training phase, the networks find correlations between the given strains, which represent the ‘fingerprint’ of the structure under investigation. Changes in the distribution of these strains is captured by assessing differences in the previously identified strain correlations. If any cause generates damage that alters the strain distribution, this is considered as a reason for further detailed structural inspection. The novelty of the strain algorithm comes from its independence from both the choice of material and the loading condition. It does not require the prior knowledge of material properties based on stress-strain relationships and, as the strain correlations represent the structure and its mechanical behaviour, they are valid for the full range of operating loads. An implementation of such approach is herein presented based on the usage of a distributed optical fibre sensor that allows to obtain strain measurement with an incredibly high resolution.

在本文中，开发了一种能够检测碳纤维/环氧树脂复合板中是否存在损伤的被动结构健康监测（SHM）方法。该方法需要从所考虑的结构中测量应变，然后将其用于建立，训练和测试人工神经网络（ANN）。在训练阶段的最后，网络发现给定应变之间的相关性，这些相关性代表了所研究结构的“指纹”。通过评估先前确定的应变相关性的差异，可以捕获这些菌株分布的变化。如果有任何原因产生会改变应变分布的损坏，则认为这是进行进一步详细结构检查的原因。应变算法的新颖性在于它与材料选择和加载条件无关。它不需要基于应力-应变关系的材料特性的先验知识，并且由于应变相关表示结构及其机械性能，因此它们在整个工作负载范围内都是有效的。本文基于分布式光纤传感器的使用提出了这种方法的实现，该分布式光纤传感器允许以难以置信的高分辨率获得应变测量。