This paper introduces a groundbreaking approach to survey design leveraging Building Information Modeling (BIM) and Artificial Intelligence (AI) for dynamic monitoring and semantic segmentation in the preservation and conservation of historic buildings. Traditional survey methods often struggle to capture the intricate details and evolving conditions of historical structures. In response, this research proposes a novel paradigm that integrates BIM technology with AI algorithms to enhance the accuracy, efficiency, and depth of building surveys. By employing BIM, a comprehensive digital representation of the building is created, facilitating real-time data integration and analysis. AI techniques, particularly semantic segmentation, are then applied to this BIM model to automatically identify and classify areas of degradation within historic buildings. This dynamic monitoring system enables preservationists and conservationists to detect structural vulnerabilities and deterioration promptly, allowing for timely intervention and maintenance. Through a case study analysis, the efficacy of the proposed approach is demonstrated in identifying and revealing areas of degradation in historic buildings. The integration of BIM and AI not only streamlines the survey process but also provides invaluable insights into the condition of heritage structures, guiding informed decision-making in their preservation and conservation efforts. This research represents a significant advancement in survey methodologies, offering a transformative framework for the sustainable management of cultural heritage assets.