Advanced crack detection in reinforced autoclaved aerated concrete using generative data augmentation and enhanced segmentation

Cracks in reinforced autoclaved aerated concrete (RAAC) pose significant structural risks, including water ingress, corrosion of reinforcement, and, in extreme cases, potential collapse. The challenge is worsened by the lack of accessible RAAC crack data, making it difficult to develop accurate and...

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Bibliographic Details

Main Authors:	Seongha Hwang, Karen Blay, Chris Goodier, Chris Gorse, Sergio Cavalaro
Format:	Default Article
Published:	2025
Subjects:	Civil engineering Reinforced autoclaved aerated concrete (RAAC) Crack detection Generative adversarial networks (GANs) Autoclaved aerated concrete (AAC)
Online Access:	https://hdl.handle.net/2134/28533482.v1
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