A Digital Twin-Assisted Multi-Module Revit/HBIM Information-Integration Framework for Preventive Conservation of Chaoshan Overseas-Chinese Residences

Historic overseas-Chinese residences in hot–humid coastal regions face interconnected challenges involving surface deterioration, environmental performance, continued use, and long-term sustainability. Existing digital conservation studies often address these issues separately, resulting in fragmented information, weak spatial associations, and discontinuities across survey, diagnosis, intervention, and reinspection. This study develops a Revit/HBIM-based, Digital Twin-assisted multi-module information-integration and updating framework using Yingchuan Shijia, Jingzu Jiashu, Mingde Jiashu, and Chaoyang Xiyuan as complementary cases. Multi-source digital documentation, AI-assisted deterioration screening, building-performance simulation, adaptive-use assessment, and operational-carbon evaluation are integrated within a unified Revit/HBIM environment. The principal innovation lies in a unified parametric information architecture that organizes heterogeneous conservation evidence through stable Building–Space–Component–Surface–System identities, M0–M3 condition-localization logic, and persistent Scenario, Verification, Revision, and Predecessor relationships, enabling cross-module retrieval, evidence-to-decision linkage, and traceable updating. Previously established results are incorporated as module-level evidence rather than presented as new findings. The adopted YOLOv8 deterioration-screening study reported a Precision of 0.85, Recall of 0.72, mAP50 of 0.83, and mAP50–95 of 0.58, while previous Jingzu Jiashu simulations showed summer comfortable-hour ratios increasing from 7.29–7.78% to 32.00–42.45%. Chaoyang Xiyuan is introduced as a focal additional case extending the framework to adaptive reuse and sustainability assessment. Its earlier residential-use assessment reported 60.95 kgCO2e/(m2·a), compared with 56.14 kgCO2e/(m2·a) for the reference building; the later restaurant-inclusive scenario reported 295.81 versus 404.45 kgCO2e/(m2·a), with cooking-related emissions accounting for 78.9% of the design total. The proposed Digital Twin operates as an asynchronous, human-reviewed information-updating mechanism connecting physical assets, HBIM representations, professional evidence, conservation decisions, intervention, and reinspection. The framework provides a structured and traceable decision-support basis for long-term preventive conservation and sustainable activation.