Author(s): Amelia Hartwell, Lucas Byrne and Isla McKinnon

Abstract: The study explores the integration of Digital Twin (DT) and Historic Building Information Modeling (HBIM) as a unified framework for enhancing architectural heritage conservation. Traditional conservation approaches often rely on static documentation and reactive maintenance, which limit the ability to predict deterioration and manage data efficiently. To address these gaps, this research developed and validated an HBIM-DT integration model that combines real-time sensor data, semantic enrichment, and predictive analytics to monitor and preserve heritage structures dynamically. The methodology involved the use of terrestrial laser scanning, UAV-based photogrammetry, and IoT sensor networks for data acquisition, which were processed and synchronized within a digital twin environment compliant with ISO 19650-1 information management standards. Quantitative analyses demonstrated a significant improvement in predictive accuracy, reducing RMSE by 33% and enhancing anomaly detection accuracy by 8% compared with conventional BIM approaches. Additionally, the system achieved a 68% reduction in data latency and an 82% decrease in IFC round-trip data loss, confirming its technical efficiency and interoperability. Environmental monitoring highlighted relative humidity and temperature as the most critical parameters influencing material degradation, aligning with preventive conservation strategies. The research concludes that the HBIM-DT framework provides a scalable, semantically enriched, and data-driven solution that transforms heritage management from reactive restoration to proactive preservation. Practical recommendations include adopting standardized digital guidelines, implementing IoT-based monitoring, developing centralized heritage data repositories, and fostering interdisciplinary collaboration. Overall, the integration of DT and HBIM offers a pathway toward sustainable and intelligent conservation practices that preserve architectural heritage with greater precision, transparency, and foresight.

Pages: 19-24 | Views: 5 | Downloads: 4

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