What does this research mean for the field?

Combining a Scan-to-BIM workflow with DEMATEL-based system analysis provides a highly accurate method for heritage building documentation (average absolute deviation of ~7 mm) and identifies preliminary assessment, LiDAR scanning, and H-BIM modelling as the foundational causal stages for subsequent digital twin development. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop and validate an integrated workflow combining LiDAR-based Scan-to-BIM documentation with DEMATEL system analysis to map causal pathways for heritage building digital twins.

August 22, 2026Applied SciencesOpen Access

Towards a Digital Twin of Heritage Buildings: Scan-to-BIM Documentation and DEMATEL-Based Analysis of LiDAR, IoT and AI Integration Pathways

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Authors

GOGrzegorz Oleniacz ISIzabela Skrzypczak ALAgnieszka Leśniak

Discussion

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Overview

Methodological study demonstrates high geometric accuracy of Scan-to-BIM workflows in historic structures, suggesting foundational causal roles for LiDAR and BIM in digital twin development.

Key Points

To develop and validate an integrated workflow combining LiDAR-based Scan-to-BIM documentation with DEMATEL system analysis to map causal pathways for heritage building digital twins.
Captured high-resolution point clouds of two heritage buildings in Poland using terrestrial laser scanning to generate parametric Historic Building Information Modeling (H-BIM) models.
Assessed geometric accuracy by comparing point-cloud-derived measurements of the Church of St Onuphrius against traditional surveying data.
Applied Decision-Making Trial and Evaluation Laboratory (DEMATEL) analysis to evaluate cause-and-effect relationships among seven digitization stages.
Geometric validation of the Scan-to-BIM model demonstrated an average absolute deviation of approximately 7 mm and a maximum deviation not exceeding 31 mm relative to traditional surveying.
DEMATEL analysis classified preliminary assessment, LiDAR scanning, and H-BIM modeling as causal stages, while identifying validation, IoT integration, AI-based predictive analysis, and digital twin development as dependent effect stages.

Cite This Study

Oleniacz et al. (2026) studied this question.

synapsesocial.com/papers/6a895ffeca7ade938187efc6 https://doi.org/10.3390/app16168305

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August 22, 2026Applied SciencesOpen Access

Towards a Digital Twin of Heritage Buildings: Scan-to-BIM Documentation and DEMATEL-Based Analysis of LiDAR, IoT and AI Integration Pathways

View Full Paper

Ask AI

Bookmark

Authors

GOGrzegorz Oleniacz ISIzabela Skrzypczak ALAgnieszka Leśniak

Discussion

Member takes

Overview

Methodological study demonstrates high geometric accuracy of Scan-to-BIM workflows in historic structures, suggesting foundational causal roles for LiDAR and BIM in digital twin development.

Key Points

To develop and validate an integrated workflow combining LiDAR-based Scan-to-BIM documentation with DEMATEL system analysis to map causal pathways for heritage building digital twins.
Captured high-resolution point clouds of two heritage buildings in Poland using terrestrial laser scanning to generate parametric Historic Building Information Modeling (H-BIM) models.
Assessed geometric accuracy by comparing point-cloud-derived measurements of the Church of St Onuphrius against traditional surveying data.
Applied Decision-Making Trial and Evaluation Laboratory (DEMATEL) analysis to evaluate cause-and-effect relationships among seven digitization stages.
Geometric validation of the Scan-to-BIM model demonstrated an average absolute deviation of approximately 7 mm and a maximum deviation not exceeding 31 mm relative to traditional surveying.
DEMATEL analysis classified preliminary assessment, LiDAR scanning, and H-BIM modeling as causal stages, while identifying validation, IoT integration, AI-based predictive analysis, and digital twin development as dependent effect stages.

Cite This Study

Oleniacz et al. (2026) studied this question.

synapsesocial.com/papers/6a895ffeca7ade938187efc6 https://doi.org/10.3390/app16168305

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