Volume 12 , Issue 2 , PP: 25–31, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Ashraf Elhendawi 1 * , Moustafa Metwally 2
Doi: https://doi.org/10.54216/IJBES.120204
Building information modelling is increasingly applied to structure engineering information across the life cycle of built assets, but existing buildings are often underconnected to operational data for retrofit prioritisation. This research proposes a BIM-connected retrofit prioritisation model that converts building-performance information into an engineering information layer for initial screening. The method integrates BIM-aligned feature organisation, transparent machine learning, diagnostic validation, and scenario-driven screening to flag buildings for further assessment by engineers. The paper proposes a workflow for institutions and cities seeking to transition from disparate disclosure records to evidence-based retrofit prioritisation without relying on the immediate availability of digital twins. The results suggest that operational, geometric, and typological features can be used to generate interpretable screening markers that help guide engineering judgement, benchmarking, and incremental retrofit strategies. This research offers a replicable model that supplements, rather than substitutes for, in-depth audit and modelling.
Building information modelling , Engineering science , Retrofit prioritisation , Building energy performance , Interpretable machine learning , Portfolio decision support
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