Visual building condition assessment is resource-intensive, particularly when inspections are repeated across large areas. This study seeks to reduce this burden by predicting the visual condition of surface-level architectural and structural elements and the latent risk in underlying elements using condition graphs. The proposed Scan-to-Condition prediction workflow builds on Scan-to-BIM and extends it to support ongoing inspection and prediction. This is demonstrated in a case study of an 818 m2 mixed-use building in Melbourne that lacked prior BIM documentation. The workflow consisted of 4 components: (1) an optimised terrestrial laser scanning protocol for repeatable documentation and manageable data volumes, (2) a Scan-to-BIM modelling schema enriched with element-level confidence metrics and hidden-profile inference (3) a unified, multimodal condition documenting platform, and (4) an automated BIM-to-Graph Markup Language (GraphML) conversion method that generated graph representations for condition prediction using a custom Graph Attention Network (GAT) model. The workflow was designed as an iterative cycle in which baseline and follow-up point clouds, a semantically enriched BIM, and inspector reports were progressively integrated to form a longitudinal dataset. Future predictions are proposed to drive inspections and maintenance, progressively reducing manual human inspections over time.
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