Historical transportation infrastructures (HTIs) like railways and bridges are essential to our cultural heritage. However, the preservation and enhancement of these structures pose significant challenges due to their complex nature and the need for modern upgrades. Historic building information modeling (HBIM) has emerged as a solution, facilitating the documentation, restoration, and maintenance of historic transportation assets. The purpose of the proposed work is to provide a systematic review of research findings on the application of HBIM in historic transportation infrastructure, highlighting its role in capturing intricate architectural details and supporting decision making for preservation efforts. A series of case studies in which HBIM has been instrumental in preserving historic transportation infrastructure are investigated and analyzed using a comprehensive literature review method. Furthermore, future directions in HBIM research are proposed, identifying potential applications and recommending areas for further investigation. Additionally, this paper suggests HBIM’s potential to balance modernization demands with the conservation needs of historic transportation infrastructure, providing policymakers and stakeholders with insightful strategies for sustainable heritage management.

Incomplete survey data often undermines the reliability of Building Information Models (BIM), particularly for structures with restricted access and complex geometries. This study demonstrates a hybrid Scan-to-BIM workflow that integrates terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) photogrammetry, supported by a predictive feasibility concept, to improve documentation accuracy and completeness. A two-phase strategy was validated on a chapel case study. Phase 1, combining TLS and ground-based photogrammetry, achieved only 54% coverage due to severe occlusions and limited scanner placement. These results led to the formulation of a Predictive Scan Feasibility Estimation Model (PSFEM), designed to generalize site-specific parameters such as scanner range, clearance angle, and building height into a decision-support tool for future surveys. Guided by the recognition of Phase 1 limitations, Phase 2 incorporated UAV photogrammetry and supplemental TLS, increasing coverage to 96%. Comparative analyses confirmed consistency in accuracy and improved geometric completeness. While the PSFEM was developed retrospectively based on the limitations identified in Phase 1, its analytical validation demonstrates the potential value of predictive planning for reducing redundant site visits and enhancing BIM reliability. The proposed framework provides a transferable basis for applying predictive hybrid workflows in both heritage and complex building documentation. This workflow offers a practical and scalable method for Scan-to-BIM documentation, applicable to heritage as well as other complex buildings, enabling high accuracy and completeness while effectively managing time and resources.

The digitization of historic architecture increasingly relies on dense point clouds, yet the conversion of these datasets into structured Historic Building Information Models (HBIM) remains slow, inconsistent, and heavily dependent on manual interpretation. This paper introduces a classification-driven, mesh-based semi-automated workflow designed to close this gap by providing a controlled, repeatable path from raw TLS data to BIM-ready geometry. The method combines three elements strategically integrated into a unified framework: (1) pre-classified point cloud groups that establish a structured starting point, (2) mesh simplification and slice-based geometric reconstruction executed through Rhino and Grasshopper, and (3) direct BIM integration using Rhino.Inside.Revit to generate categorized HBIM components rather than passive mesh imports. The workflow is validated on an irregular exterior stone column from the historic chapel in Sopronhorpács, Hungary, an element characterized by surface erosion, asymmetric profiles, and deviations from verticality. This type of geometry typically challenges both manual modeling and fully automated shape-fitting. The proposed method reconstructed the column as a Revit Structural Column element with a substantial reduction in modeling time compared to a manual Scan-to-BIM workflow. A deviations analysis confirmed that the reconstructed geometry remained within the millimeter-level accuracy required for conservation-grade documentation. The study demonstrates that combining element-based classification, mesh preprocessing, and controlled semi-automation can significantly improve both the speed and reliability of Scan-to-HBIM processes without requiring technical expertise yet delivers results that align with the precision expected in scientific documentation. By formalizing the Pre-Classified Modeling Logic (PCML), the approach provides a foundation for reconstructing a wide range of heritage elements and establishes a practical step forward toward more efficient, interpretable, and accessible digital preservation practices.

The transition toward sustainable practices in the built environment must extend to the digital documentation of cultural heritage. Traditional Historic Building Information Modeling (HBIM) survey workflows frequently operate under reactive paradigms, identifying data gaps only during post-processing. This approach often results in redundant site visits, repeated equipment mobilization, and avoidable resource waste. This study presents a multi-criteria decision-support framework for sustainable and resource-circular HBIM survey planning. Powered by the Predictive Scan Feasibility Estimation Model (PSFEM), the framework embeds predictive feasibility assessment directly into the planning phase. Rather than treating documentation gaps as retrospective metrics, it anticipates occlusions and coverage limitations prior to field deployment, enabling proactive strategy selection between ground-based and hybrid acquisition. The framework was validated across two Hungarian chapel case studies characterized by contrasting environmental constraints. At the vegetation-constrained Sopronhorpács Chapel, predictive assessment identified ground-based limitations in advance, supporting a targeted hybrid UAV–TLS intervention that increased geometric completeness from 54% to 96% within a single consolidated acquisition phase. At the Lucsony Szent Anna Chapel, predictive distance analysis demonstrated that optimized far-range scanning achieved 100% roof coverage, compared to 74.9% under close-range conditions. The results confirm that embedding predictive intelligence into HBIM survey workflows fosters resource circularity, minimizes environmental impact, and provides an accessible foundation for sustainable digital heritage acquisition across diverse architectural typologies

This study presents a sustainable framework for incorporating Heritage Building Information Modeling (HBIM) into heritage preservation based on insights from existing literature and prac- tical applications. It highlights essential components such as stakeholder engagement, data management, modelling standards, and decision support systems. A case study of the timber roof in the Michelangelo Cloister, Rome, demonstrates the framework’s practical use. The find- ings indicate that HBIM integration can significantly improve sustainability practices in heritage conservation, offering valuable insights for future maintenance and restoration efforts. This re- search provides new perspectives on the role of HBIM in heritage preservation, underscoring its significance for upcoming conservation initiatives.