Research hotspots and trends in heritage building information modeling: A review based on CiteSpace analysis

The paper situates HBIM within the context of UNESCO’s 1972 World Heritage Convention and the 50th anniversary reflection emphasizing digital transformation of heritage conservation. HBIM, first introduced in 2009, is redefined here as a collaborative, digital, lifecycle approach embedding heritage asset data in 3D models to support sustainable preservation. The introduction reviews the significance of HBIM in organizing and archiving architectural information, supporting decision-making, and enabling practical applications such as facilities management, fire safety analysis, and VR-based inspection and education. It highlights the emergence of enabling technologies (SQL Server, AI, IoT, Web-GIS, VR/AR) and summarizes prior systematic reviews focused on digitization/automation, effective implementation, and integration with other IT domains. The study poses the need for: a systematic analysis of research status (outputs, funding, geography, sources, institutions); co-citation network analyses to reveal knowledge structures; and keyword/cluster-based trend analyses to identify hotspots and evolution.

The authors synthesize prior HBIM reviews: Pocobelli et al. (2018) emphasized early-stage conservation processes, digitization, and automation; López et al. (2018) reviewed HBIM literature and implementation effectiveness across building families; Yang et al. (2020) discussed HBIM’s integration with computer graphics, photogrammetry, GIS, and ontology. Despite these, gaps remain in comprehensive scientometric mapping of HBIM’s global research status, knowledge structures through co-citation analyses, and longitudinal hotspot/trend detection using keyword co-occurrence and clustering. The introduction also surveys successful HBIM applications (e.g., Milan Cathedral FM, fire safety analyses, VR use in heritage) and enabling technologies (databases, AI for pathology/crack detection, IoT/Web-GIS for real-time diagnostics, VR/AR for risk management).

The study employs scientometrics and scientific knowledge mapping using CiteSpace. Data source: Web of Science Core Collection. Search strategy combined subject terms such as “Heritage Building Information Modeling,” “Heritage conservation,” “Historic Building Information Modeling,” “point clouds,” “heritage building,” and “Digital Management.” Records were limited to 2010–March 2022. Initial retrieval yielded 577 articles. Non-relevant items (e.g., HBIM acronym in other domains) were filtered by research categories (engineering, construction building technology, architecture, computer science), followed by manual screening of titles, abstracts, and texts to retain 372 highly relevant documents. Data export: WoS plain text, deduplicated and formatted via R. CiteSpace settings: time slice 2010–2022, 1-year per slice, default parameters otherwise. Analyses included: publication trends; country/region, journal, institution, and funder distribution; journal and literature co-citation networks (with minimum spanning tree pruning), identification of key nodes via betweenness centrality; cluster analyses (default and automatic TF*IDF labeling); timeline views; keyword co-occurrence networks; keyword clustering and emergence (burst) detection; and citation burst analyses. The knowledge mapping interprets node size (frequency/citations), color (time), edge thickness (relationship intensity), and highlights high-centrality nodes.

- Publication trends: 2010–2016 showed low and fluctuating output across databases, indicating a conceptualization phase. From 2017, outputs increased, with WoS surpassing 60 in 2019 and peaking at 90 in 2020, signaling an accelerated accumulation phase; WoS decreased in 2021 while CNKI/Scopus continued rising. - Geography: Nearly 60 countries/regions contribute. Italy leads (115 publications, 23.4% of total; betweenness centrality 0.38), followed by Spain (62; 12.6%). Others include Portugal (34), China (31), USA (20; centrality 0.30), England (18; centrality 0.25), France (18). The UK, despite lower volume, has high centrality, indicating influence. - Journals: Journal of Cultural Heritage has the highest co-citation frequency (190; centrality 0.20), followed by ISPRS Archives, Automation in Construction, and International Journal of Architectural Heritage (all with frequency >100 and centrality >0.1). Publication sources with most HBIM articles include Journal of Cultural Heritage (41), Applied Sciences (Basel) (37), International Journal of Architectural Heritage (34), ISPRS Int. J. Geo-Information (31), Remote Sensing (21). - Institutions: Politecnico di Milano leads (21 publications; annual rate 3.0), followed by University of Minho (16), University of Seville (11; annual rate 2.75), Politecnico di Torino (11), University of Lisbon (9), etc. Institutional collaborations are relatively scattered, with Politecnico di Milano being a central connector. - Funders: The European Commission supported the most publications (44), followed by the Spanish Government (18), National Natural Science Foundation of China (16), Italy’s Ministry of Education Universities and Research (10), and Portuguese Foundation for Science and Technology (10), indicating strong EU investment in HBIM. - Co-citation of literature: The most cited article is López et al. (2018) H-BIM review. High-centrality works include Bruno & Roncella (2018) on a restoration-oriented HBIM system (highest betweenness). Hotspots in highly cited works focus on high-quality HBIM modeling, structural simulation/integration (Cloud-to-BIM-to-FEM), semantically aware modeling from TLS, and diagnosis-aided management. - Co-citation clusters (literature): Ten clusters identified, including semiautomatic 3D modeling (#0), heritage complex (#1), Magoksa temple stone pagoda (#2), Italian medieval castle (#3), laser scanner (#4), heritage information system (#5), pilot study (#6), virtual reality (#7), National Palace (#8), and fine surveying (#9). Four prominent clusters discussed in depth: semiautomatic 3D modeling, heritage information system, pilot study, and virtual reality. - Keyword co-occurrence: 344 keyword nodes after pruning. Top/high-centrality keywords: cultural heritage (freq 86), model (53), building (43), documentation (31), point cloud (29), BIM (24). Keywords show strong interconnections; “model” gains sustained attention since 2017. - Keyword clustering: 14 clusters reflecting research hotspots/frontiers: #0 case studies, #1 heritage complex, #2 architectural heritage, #3 detailed application, #4 structural analysis, #5 masonry structure, #6 3D modeling approach, #7 laser scanner, #8 heritage building maintenance management, #9 energy performance, #10 convective heat transfer, #11 digital archive, #12 wide-angle lenses, #13 partial matching. Case studies and detailed applications are persistently hot; wide-angle lenses and partial matching are emergent. - Keyword bursts (trends): 2010–2014 emphasized close-range photogrammetry, calibration, algorithms (measurement/recognition focus). 2014–2018 shifted to practical applications (seismic/damage assessment, terrestrial laser scanning). Since 2018, focus expands to laser, information, built heritage, HBIM, preventive conservation, virtual reality, and seismic vulnerability, indicating accelerating interest and a move toward preventive approaches. - Citation bursts: Post-2018 saw as many bursty citations as the prior eight years combined, underscoring increased attention. Milestone bursty works include Volk et al. (2014), Oreni et al. (2014), and Lee et al. (2019).

The scientometric mapping addresses the study’s aims by revealing HBIM’s maturation from conceptualization to accelerated growth, identifying influential countries, journals, institutions, and funders, and delineating knowledge bases and evolving hotspots through co-citation and keyword analyses. Building on these insights, the authors propose a systematic HBIM workflow for heritage applications across three levels: - Modeling: Integrates TLS, UAV photogrammetry/SfM, 3D Time-of-Flight, and point-cloud-to-BIM processes, with LOD strategies (e.g., CityGML LOD1–3; IFC LOD300). Methods for capturing and modeling complex microstructures include inverse modeling, RANSAC-based segmentation, region growing, and semantic modeling. Special strategies are proposed for complex wooden architecture (e.g., semi-automated inference of invisible dimensions for Dougong). - Data exchange and database creation: Addresses interoperability challenges by leveraging OpenBIM standards (IFC, MVD/mvdXML, IDM), semantic web links (IFCOWL, IFC-to-RDF) to connect HBIM geometric models with ontology-based knowledge representations. External databases (e.g., SQL Server, Revit DB Link, custom web/desktop interfaces) are linked via unique IDs for each model entity, enabling multimodal access and data sharing across stakeholders. - Auxiliary management: Expands HBIM toward diagnosis-aided management (DA-HBIMM) by integrating IoT/RFID/Web-GIS for real-time monitoring, preventive conservation planning, structural analysis (Cloud-to-BIM-to-FEM, FE models), masonry/vulnerability assessment, energy performance modeling, and microclimate-informed management. VR/AR enhance context-aware risk management and communication for managers and conservators. Overall, the workflow operationalizes the mapped hotspots—particularly semiautomatic modeling, information systems, VR, and pilot validations—into a coherent route from data acquisition to management, supporting preventive conservation and sustainable heritage stewardship.

The study systematically reviewed 372 HBIM-related papers (2010–2022) using CiteSpace to visualize research status and trends. Key conclusions: (1) HBIM evolved from a conceptual phase (2010–2017) into accelerated growth post-2017, with expanding multidisciplinary integration (e.g., AI). (2) Ten co-citation clusters—semiautomatic 3D modeling, heritage complexes, specific case sites, laser scanners, heritage information systems, pilot studies, VR, national palaces, and fine surveying—constitute HBIM’s knowledge base. (3) High-frequency keywords (cultural heritage, building, model, documentation, point cloud, BIM) mark core themes; keyword bursts reveal a transition from measurement/algorithms to practical applications and preventive conservation with VR since 2018; AI is anticipated as a major next leap. (4) Fourteen keyword clusters reflect hotspots and frontiers; based on them, a systematic HBIM workflow is proposed across modeling, data exchange/database creation, and auxiliary management. Future research directions include deeper integration with artificial intelligence, wider deployment of wireless sensor networks/IoT, and development of mobile applications to enable rapid, scalable inspections and data collection for heritage assets.

Data were sourced solely from the Web of Science Core Collection; relevant studies in other databases (e.g., PubMed, Google Scholar) were not integrated, and current tools (CiteSpace) cannot merge cross-database citation data, potentially biasing results. Sample selection relied on subject-term searches in a field with evolving and non-unified terminology (heritage/historic/cultural buildings), followed by manual screening of titles/abstracts/full texts, introducing subjectivity. The review is not exhaustive of all works in the period. As the field matures and concepts standardize, future studies can refine search strategies and integrate multi-database records.