Historic building information modelling: history research hotspots and emerging trends
Year: 2025
Type: article
Abstract: The rapid adoption of Historic Building Information Modelling (HBIM) in cultural heritage preservation necessitates a comprehensive understanding of its global research trajectories, technological focus areas, and collaborative dynamics. This study conducts a systematic bibliometric analysis of 491 publications from the Web of Science Core Collection (2013–2025), mapping the evolution of HBIM-related research. The results reveal a triphasic growth pattern: initial emergence (2013–2016), consolidation (2017–2019), and exponential expansion (2020–2024), with cumulative publications increasing 117-fold. Geospatial analysis highlights significant regional disparities: European countries lead in productivity, citation impact, and collaborative networks, driven by the density of UNESCO heritage sites and supportive policy frameworks. In contrast, China demonstrates a productivity-impact paradox despite rapid output growth. Keyword co-occurrence and clustering analyses identify key thematic areas, including 3D modeling, interoperability challenges, conservation management, and multidisciplinary methodologies. While emerging areas of technological interest, such as domain-specific AI, semantic segmentation, immersive media, and digital twins, are identified, these findings reflect trends in the published literature rather than in-depth evaluations of practical implementation. The study provides an overview of HBIM’s disciplinary development and offers a foundation for future research, particularly in exploring the real-world application and integration of advanced technologies within cultural heritage preservation.
Authors: Lu Xu, Qiunan Zhang, Yihang Li, Yanhong Li, Jinhan Ren, Suoyi Kong, Ding Ding, Kun Sang, Guiye Lin, Andrea Giordano
Institutions: Xihua University, Xiamen University Malaysia, University of Padua
Language: English
Subfield: Geology
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,165
Funder: Xihua University
OpenAlex ID: W4415569871
