Artificial Intelligence (AI) for Reinforced Autoclaved Aerated Concrete (RAAC) crack defect identification

Purpose: Reinforced Autoclaved Aerated Concrete (RAAC) panels have been extensively used in the UK since the 1960s as structural roofs, floors and walls. The lack of a longitudinal, objective, consistent defect data capture process has led to inaccurate, invalid and incomplete RAAC data, which limit...

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Bibliographic Details
Main Authors: Karen Blay, Chris Gorse, Chris Goodier, Jack Starkey, Seongha Hwang, Sergio Pialarissi-Cavalaro
Format: Default Article
Published: 2024
Subjects:
Building
Reinforced Autoclaved Aerated Concrete (RAAC)
Deep Learning
Cracks
Data quality
Defects
Online Access:https://hdl.handle.net/2134/27376719.v1
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https://hdl.handle.net/2134/27376719.v1

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