In recent years, applying deep learning (DL) to assess structural damages has gained growing popularity in vision-based structural health monitoring (SHM). However, both data deficiency and class imbalance hinder the wide adoption of DL in practical applications of SHM. Common mitigation strategies include transfer learning, oversampling, and undersampling, yet these ad hoc methods only provide limited performance boost that varies from one case to another. In this work, we introduce one variant of the generative adversarial network (GAN), named the balanced semisupervised GAN (BSS-GAN). It adopts the semisupervised learning concept and applies balanced-batch sampling in training to resolve low-data and imbalanced-class problems. A series of computer experiments on concrete cracking and spalling classification were conducted under the low-data imbalanced-class regime with limited computing power. The results show that the BSS-GAN is able to achieve better damage detection in terms of recall and score than other conventional methods, indicating its state-of-the-art performance.

中文翻译：

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平衡半监督生成对抗网络，用于低数据不平衡类机制的损害评估

近年来，应用深度学习 (DL) 来评估结构损坏在基于视觉的结构健康监测 (SHM) 中越来越受欢迎。然而，数据不足和类别不平衡都阻碍了深度学习在 SHM 的实际应用中的广泛采用。常见的缓解策略包括迁移学习、过采样和欠采样，但这些临时方法仅提供有限的性能提升，具体情况因情况而异。在这项工作中，我们介绍了生成对抗网络 (GAN) 的一种变体，称为平衡半监督 GAN (BSS-GAN)。它采用半监督学习概念，在训练中应用平衡批量采样来解决低数据和不平衡类问题。在计算能力有限的低数据不平衡类制度下，进行了一系列混凝土开裂和剥落分类的计算机实验。结果表明，BSS-GAN 能够在召回率和 得分高于其他传统方法，表明其最先进的性能。