Digital twin-based simulation for old city renovation and cultural heritage conservation

Abstract Preserving old city environments and culturally significant heritage structures presents increasing challenges due to aging materials, environmental stresses, and the limitations of traditional conservation methods. This study proposes a comprehensive digital twin (DT)-based simulation framework designed to enhance renovation planning, structural monitoring, and long-term preservation of historic urban areas. Multi-source data—including LiDAR point clouds, photogrammetry imagery, geographic information systems layers, environmental sensor streams, and historical architectural records—were integrated to generate a high-fidelity 3D representation of a selected heritage zone. The accuracy of the reconstructed DT was validated through ground-truth surveys, demonstrating strong geometric consistency and multi-source data alignment. Predictive simulation models combining machine learning algorithms and physics-based analyses were employed to assess structural behavior, environmental stress factors, and deterioration dynamics. Results revealed clear identification of moisture-sensitive façades, vibration-prone structural components, and temperature-induced risk zones. Scenario-based conservation evaluations showed that targeted interventions, particularly when combined, substantially reduced deterioration risks and improved structural resilience. Expert assessments further confirmed the interpretability, reliability, and practical utility of the DT framework. Overall, the proposed system enables proactive, data-driven conservation decisions by providing real-time monitoring, predictive analytics, and visualized scenario comparisons. This research demonstrates the potential of DT technology to support sustainable cultural heritage preservation and informed urban renewal strategies.