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- Title
Balanced semisupervised generative adversarial network for damage assessment from low‐data imbalanced‐class regime.
- Authors
Gao, Yuqing; Zhai, Pengyuan; Mosalam, Khalid M.
- Abstract
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 Fβ score than other conventional methods, indicating its state‐of‐the‐art performance.
- Subjects
GENERATIVE adversarial networks; DEEP learning; STRUCTURAL health monitoring; SUPERVISED learning; CRACKING of concrete; CONCEPT learning
- Publication
Computer-Aided Civil & Infrastructure Engineering, 2021, Vol 36, Issue 9, p1094
- ISSN
1093-9687
- Publication type
Article
- DOI
10.1111/mice.12741