The 50th anniversary of the Convention Concerning the Protection of World Cultural and Natural Heritage in 2022 prompted a global reflection on digital transformation in heritage conservation. Heritage Building Information Modeling (HBIM), first introduced in 2009, has emerged as a key paradigm for digital design and management of heritage assets. Initially focused on 3D modeling, HBIM's scope has broadened to encompass the entire lifecycle of sustainable preservation. This paper redefines HBIM as "a collaborative approach based on digital technology that embeds heritage asset data in 3D computer models throughout the lifecycle of its conservation." The successful application of HBIM in various projects, including facilities management of the Milan Cathedral and fire safety analysis of the School of Air Warfare in Florence, highlights its potential. The integration of emerging technologies like Microsoft SQL Server, artificial intelligence, the Internet of Things, Web-GIS, and virtual/augmented reality further enhances HBIM's capabilities, enabling automated diagnosis, real-time monitoring, and immersive visualizations. However, a comprehensive analysis of research hotspots and trends in HBIM, along with a complete, practical workflow, was lacking. This study aims to address this gap.

Several recent systematic reviews have touched upon aspects of HBIM. Pocobelli et al. (2018) focused on HBIM's application in the initial stages of conservation, emphasizing digitization and automation. López et al. (2018) reviewed HBIM's implementation in cultural heritage, exploring different modeling approaches. Yang et al. (2020) discussed the integration of HBIM with other information technologies. However, these reviews lacked a holistic analysis of research status, co-citation networks, and detailed trend analysis using keyword co-word and cluster analysis. This current study addresses these gaps by providing a systematic analysis of HBIM research from 2010 to 2022, covering publication volume, funding agencies, countries/regions, publication sources, and institutions. It also performs co-citation network analysis of journals and literature to reveal correlations and collective knowledge in specific research areas, utilizing keyword co-word analysis and cluster analysis to uncover research hotspots and evolutionary trends.

This study employs scientometrics, a quantitative approach to study science itself, using the CiteSpace knowledge graph analysis tool. 372 highly relevant documents from the Web of Science core collection (2010-March 2022) were analyzed using keywords like "Heritage Building Information Modeling," "Heritage conservation," and related terms. Data filtering narrowed the results to relevant research areas (engineering, construction, computer science). CiteSpace's functionalities—literature co-citation analysis, keyword co-occurrence analysis, cluster analysis, and keyword emergence detection—were employed. The analysis incorporates Ronald S. Burt's structural hole idea, Kuhn's paradigm shift theory, and Kleinberg's algorithm for detecting frequency bursts to provide a comprehensive understanding of HBIM's development. The study analyzed annual publication volume, geographical distribution, publication sources, institutions involved, funding agencies, journal co-citation networks, literature co-citation networks, keyword co-occurrence networks, keyword clustering, and citation surge analysis to understand the evolution and current state of HBIM research.

The annual publication volume analysis reveals that HBIM research was primarily in the conceptualization phase from 2010 to 2016. However, since 2017, research has accelerated, indicating the field's maturation. Italy leads in HBIM publications (23.4%), followed by Spain (12.6%), Portugal, and China. The *Journal of Cultural Heritage* published the most HBIM-related articles. The Polytechnic University of Milan stands out among institutions in terms of publication volume and annual publication rate. The European Commission is the largest funding agency for HBIM research. Journal co-citation analysis identified *Journal of Cultural Heritage* as the most influential journal in the field. Literature co-citation analysis highlighted practical case studies as research hotspots, focusing on high-quality HBIM model creation and assisted management. Cluster analysis revealed 10 clusters (semiautomatic 3D modeling, heritage complex, Magoksa temple stone pagoda, Italian medieval castle, laser scanner, heritage information system, pilot study, virtual reality, National Palace, and fine surveying) representing research frontiers. Keyword co-occurrence analysis identified "cultural heritage," "building," "model," "documentation," "point cloud," and "BIM" as core research keywords. Keyword surge analysis showed a shift in research focus from point cloud measurement and algorithms (2010–2014) to practical applications (2014–2018), and finally to preventive conservation and VR (2018–present). Citation surge analysis identified key milestone studies.

Based on the keyword cluster analysis, a three-level HBIM application workflow is proposed: modeling, data exchange and database creation, and auxiliary management. The modeling phase uses terrestrial laser scanners (TLS) and photogrammetry, often combined, to create high-quality 3D models. Data exchange and database creation are crucial for interoperability and knowledge management; the use of IFC and ontologies is essential. Auxiliary management integrates structural analysis, maintenance management, energy performance assessment, and other data for comprehensive heritage asset management. The integration of artificial intelligence, wireless sensor networks, and mobile applications are discussed as important areas for future development, enabling automated damage detection, real-time monitoring, and efficient data collection through crowd-sourced initiatives. This workflow provides a more complete and practical guide for HBIM applications.

This study provides a comprehensive scientometric analysis of HBIM research, identifying key research clusters, trends, and future directions. The proposed three-level HBIM workflow offers a practical framework for heritage asset management. Future research should focus on integrating AI for automated diagnosis and monitoring, utilizing wireless sensor networks for real-time data acquisition, and developing mobile applications for crowd-sourced data collection and analysis. These advancements will further enhance the efficiency and effectiveness of HBIM in preserving cultural heritage.

This study has some limitations. The reliance on the Web of Science database might exclude relevant studies from other databases. The manual screening of irrelevant literature introduces potential bias. The evolving terminology in the HBIM field also affects the precision of sample selection. Future research could address these limitations by incorporating data from multiple databases and developing more refined keyword search strategies as the field's terminology matures.