Bridging Heritage Knowledge and Digital Models: An HBIM Integration Framework

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Publicado en:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences vol. X-M-2-2025 (2025), p. 365-373
Autor Principal: Wang, Xi
Outros autores: Wu, Cong, Zhang, Xiao, Pan, Ruolin
Publicado:
Copernicus GmbH
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Acceso en liña:Citation/Abstract
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Resumo:Architectural heritage conservation demands the integration of precise physical documentation and interpretative design knowledge, yet current HBIM approaches remain fragmented: ‘scan-to-BIM’ prioritizes geometric accuracy at the expense of semantic richness, while “rule-based reconstruction” emphasizes idealized logic over as-built evidence. To bridge this gap, this study introduces the KSQI paradigm (Knowledge-Semantics-Quantities-Image), a novel framework that systematically connects domain expertise with digital modelling to balance spatial accuracy and architectural semantics. The research develops an as-recognized modelling or semantic-driven modelling through (1) a conservation cycle-guided information indexing system for semantic-driven knowledge integration, (2) a data-model decoupling workflow that teams from different disciplines maintain their working habits, handling data and models separately, then recoupling data-model by BIM team, and (3) a pattern book tooling solution including check forms for hierarchical investigation, algorithm modelling generator. By linking physical attributes (quantities/images) with design logic (semantics/knowledge), KSQI enhances information management, supports iterative knowledge updates, and facilitates informed conservation decisions. Case studies demonstrate its effectiveness in encoding both as-built conditions and historical, traditional design/construction principles, reinforcing the ‘H’ (history/heritage knowledge) in HBIM. This framework advances heritage documentation toward the smart metric survey, ensuring models serve as dynamic, semantically rich assets for conservation, research, dissemination, and digital twin applications.
ISSN:2194-9042
2194-9050
DOI:10.5194/isprs-annals-X-M-2-2025-365-2025
Fonte:Engineering Database