Volume 12 , Issue 2 , PP: 47–59, 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
Aman, J., Kim, J. B., & Verniz, D. (2023). AI-integrated urban building energy simulation: A framework to forecast the morphological impact on daylight availability. In Proceedings of eCAADe 2023.
Doukari, O., Scoditti, E., Kassem, M., & Greenwood, D. (2023). A BIM-based techno-economic frame- work and tool for evaluating and comparing building renovation strategies. Journal of Information Technology in Construction, 28, 246–265. https://doi.org/10.36680/j.itcon.2023.012
Hu, Z., Borsato, M., Geyer, P., & others. (2024). Explainable AI for engineering design: A unified approach of systems engineering and component-based deep learning demonstrated by energy-efficient building design. Advanced Engineering Informatics, 62, Article 102843. https://doi.org/10.1016/j.aei.2024.102843
Itanola, M., Tokede, O., Rotimi, J. O. B., & others. (2024). The impact of digital technologies on energy-efficient buildings: A scientometric and systematic review. Sustainable Buildings, 9, Article 7. https://doi.org/10.1051/sbuild/2024007
Jiang, Y., Li, M., Guo, D., Wu, W., & Zhong, R. Y. (2023). Digital twin-enabled smart modular integrated construction system. Automation in Construction, 152, Article 104911. https://doi.org/10.1016/j.autcon.2023.104911
Jin, N., Yang, F., Mo, Y., Zeng, M., & Yang, X. (2022). Highly accurate energy consumption forecasting model based on parallel LSTM neural networks. Advanced Engineering Informatics, 51, Article 101442. https://doi.org/10.1016/j.aei.2021.101442
Mirarchi, C., Pavan, A., De Marco, F., & Wang, X. (2024). Semantic enrichment of BIM models for building asset information management. Buildings, 14(4), Article 1122. https://doi.org/10.3390/buildings14041122
Olu-Ajayi, R., Alaka, H., Sulaimon, I., Sunmola, F., & Ajayi, S. (2022). Machine learning for energy performance prediction at the design stage of buildings. Energy and Built Environment, 3(4), 507–521. https://doi.org/10.1016/j.enbenv.2021.05.002
Panchalingam, R., & Chan, K. C. (2021). A state-of-the-art review on artificial intelligence for smart buildings. Intelligent Buildings International, 13(4), 203–226. https://doi.org/10.1080/17508975.2019.1613219
Sari, M., Moghadam, M., & Al-Hussein, M. (2023). Machine learning-based energy use prediction for the smart building energy management system. Journal of Information Technology in Construction, 28, 645–671. https://doi.org/10.36680/j.itcon.2023.033
Shapi, M. K. M., Ramli, N. A., & Awalin, L. J. (2021). Energy consumption prediction by using machine learning for smart building: Case study in Malaysia. Developments in the Built Environment, 5, Article 100037. https://doi.org/10.1016/j.dibe.2020.100037
Shen, Y., & Pan, Y. (2023). BIM-supported automatic energy performance analysis for green building design using explainable machine learning and multi-objective optimization. Applied Energy, 333, Article 120575. https://doi.org/10.1016/j.apenergy.2022.120575
Shi, Q., Lai, X., Xie, X., & Zuo, J. (2022). Machine learning in building energy management: A critical review. Frontiers of Engineering Management, 9, 376–391. https://doi.org/10.1007/s42524-021-0181- 1
Singh, M. M., Singaravel, S., Klein, R., & Geyer, P. (2020). Quick energy prediction and comparison of options at the early design stage. Advanced Engineering Informatics, 46, Article 101185. https://doi.org/10.1016/j.aei.2020.101185
Tao, L. (2024). Application research of deep learning-based BIM technology in building energy consumption renovation. International Journal of Low-Carbon Technologies, 19, 2357–2368. https://doi.org/10.1093/ijlct/ctae182
