Defect identification is an important issue in structural health monitoring. Herein, originated from inverse techniques, a merging approach is established by the numerical manifold method (NMM) and whale optimization algorithm-back propagation (WOA-BP) cooperative neural network to identify hole defects in heat conduction problems. On the one hand, the NMM can simulate varying hole configurations on a fixed mathematical cover, which eases the generation of “big data” for the training of neural network to a large extent. On the other hand, the WOA, a global optimization algorithm, is adopted to optimize the initial weights and thresholds of the BP neural network to alleviate its frequently encountered local optimum phenomenon. The boundary temperatures of sampling points by the NMM and the associated hole geometries are used for the learning of WOA-BP neural network, which is then applied to predict the hole defects. Numerical examples concerning the detection of circular/ elliptical holes demonstrate that the proposed method possesses higher accuracy and satisfying robustness in holes prediction compared with standard BP network under the same condition. The present work provides a convenient pathway and great potential in application of structural health monitoring.

中文翻译：

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热传导问题中 NMM 和 WOA-BP 协作神经网络的空穴识别合并方法


缺陷识别是结构健康监测中的一个重要问题。本文源自逆向技术，通过数值流形法 （NMM） 和鲸鱼优化算法-反向传播 （WOA-BP） 合作神经网络建立了一种合并方法，以识别热传导问题中的空穴缺陷。一方面，NMM 可以在固定的数学覆盖上模拟不同的孔配置，这在很大程度上简化了神经网络训练的“大数据”的生成。另一方面，采用全局优化算法 WOA 来优化 BP 神经网络的初始权重和阈值，以缓解其经常遇到的局部最优现象。利用NMM采样点的边界温度和相关的空穴几何形状来学习WOA-BP神经网络，然后将其用于预测空穴缺陷。关于圆形/椭圆形孔检测的数值实例表明，在相同条件下，与标准 BP 网络相比，所提方法在孔洞预测中具有更高的精度和令人满意的鲁棒性。目前的工作为结构健康监测的应用提供了便捷的途径和巨大的潜力。