Heritage conservation demands innovative approaches that integrate advanced technologies with traditional principles to protect monuments and historic buildings. This research investigates the potential of Building Information Modeling (BIM) in heritage conservation, with a focus on developing and adapting workflows tailored to Heritage Building Information Modeling (HBIM). Through a systematic analysis of literature, the research highlights the adaptation of scan-to-BIM methodologies for HBIM creation and their significant role in enhancing preservation efforts. Key technologies, including laser scanning, photogrammetry, and machine learning, are discussed for their contributions to generate accurate and information-rich digital models of heritage structures. Furthermore, this work discovers critical specifications and proposes a structured framework for balancing these specifications within HBIM workflows. This framework addresses challenges such as standardization, scalability, and adaptability, which are essential for accurately capturing the complexity of heritage buildings. By examining these issues, the study identifies opportunities to improve HBIM's capability to monitor, document, and manage culturally significant assets. The findings provide a comprehensive understanding of HBIM processes and their potential to support the effective conservation of heritage.

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.

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.

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.

This paper presents an enhanced predictive framework, denoted as the Predictive Scan Feasibility Estimation Model (PSFEM), developed to support reliable data acquisition planning for Historic Building Information Modeling (HBIM) applications. The proposed formulation addresses limitations of Terrestrial Laser Scanning (TLS) in heritage documentation, particularly those associated with restricted access, vegetation-induced occlusions, and unfavorable scanner–structure geometries. An Adjusted Scan Feasibility Index (SFIadj) is introduced to quantify expected scan feasibility under environmental and geometric constraints. The formulation incorporates a structured uncertainty factor together with two environment-dependent modifiers, namely the Vegetation Density Index (VDI) and the Access Factor (AF), enabling representation of macro-occlusions and accessibility constraints through deterministic environmental adjustment. In addition, the geometric model incorporates distance, height, and Field-of-View (FOV) parameters, with improved representation of vertical visibility based on a sinusoidal function. The accuracy and effectiveness of the proposed model are validated through two heritage chapel case studies representing distinct scanning challenges. The first case involves a densely vegetated environment characterized by significant occlusions, while the second examines controlled variations in scanner-to-object distance to evaluate the influence of incidence angles on roof visibility. Multiple TLS surveys are conducted to obtain empirical coverage data for comparison. The results demonstrate that the PSFEM accurately reproduces global and element-level visibility, achieving improved agreement with measured coverage compared to geometric deterministic formulations, while reducing overestimation of feasible coverage under occlusion- and access-constrained conditions and providing a quantitative basis for identifying coverage deficiencies during the planning stage.