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 limits the ability to survey RAAC within buildings and monitor performance. Therefore, an accurate, complete and valid digital data capture process is needed to facilitate better RAAC performance and defect monitoring. This paper presents the development of an artificial intelligence (AI)-driven RAAC crack defect capture tool for improving the quality of RAAC survey data. Design/methodology/approach RAAC crack defect image data were collected, curated and trained. A deep learning approach was employed to train RAAC surveyed defects (cracks) images from two hospitals. This approach mitigated unavoidable occlusions/obstructions and unintended “foreign” objects and textures. Findings An automatic RAAC crack identification tool has been developed to be integrated into RAAC survey processes via an executable code. The executable code categorises RAAC survey images into “crack” or “non-crack” and can provide longitudinal graphical evidence of changes in the RAAC over time. Originality/value This paper identifies the role of AI in addressing the intrinsic defects data capture issues for RAAC and extends current debates on data-driven solutions for defect capture and monitoring.