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Hierarchical development of training database for artificial neural network-based damage identification

机译：基于人工神经网络的损伤识别训练数据库的分层开发

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Though serving as an effective means for damage identification, the capability of an artificial neural network (ANN) for quantitative prediction is substantially dependent on the amount of training data. In virtue of a concept of "Digital Damage Fingerprints" (DDF), a hierarchical approach for the development of training databases was proposed for ANN-based damage identification. With the object of exploiting the capability of ANN to address the key questions: "Is there damage?" and "Where is the damage?", the amount of training data (damage cases) was increased progressively. Mutuality was established between the quantity of training data and the accuracy of answers to the two questions of interest, and was experimentally validated by identifying the position of actual damage in carbon fibre-reinforced composite laminates. The results demonstrate that such a hierarchical approach is capable of offering prediction as to the presence and location of damage individually, with substantially reduced computational cost and effort in the development of the ANN training database.
机译：虽然是一种有效的损伤识别方法，但人工神经网络（ANN）进行定量预测的能力基本上取决于训练数据的数量。根据“数字损伤指纹”（DDF）的概念，提出了一种用于开发训练数据库的分层方法，用于基于ANN的损伤识别。目的是利用ANN的能力来解决关键问题：“是否存在损害？”和“损坏在哪里？”，训练数据（损坏案例）的数量逐渐增加。在训练数据的数量和对两个感兴趣的问题的答案的准确性之间建立了相互关系，并通过确定碳纤维增强复合材料层压板中实际损坏的位置进行了实验验证。结果表明，这种分层方法能够单独提供关于损坏的存在和位置的预测，同时大大减少了ANN训练数据库的开发所需的计算成本和工作量。

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