DATA MANAGEMENT IN HBIM: CHALLENGES AND STRATEGIES

Francesco Romeo, Università degli Studi di Roma “La Sapienza”


Historic Building Information Modeling (HBIM) plays a key role in understanding and preserving our cultural heritage. However, the complexities inherent in historic building systems presents unique challenges in terms of data management and accessibility. The big volume and variety of data generated during the virtualization process, coupled with the need for accuracy and long-term preservation, require robust data management strategies, that have to address specific challenges.

Challenges:

  • Data Heterogeneity: HBIM projects involve a wide range of data sources, including laser scans, photogrammetry, historical drawings, and archival documents. Integrating and managing this heterogeneous data in a coherent manner is a significant challenge.
  • Data Volume: the level of detail captured in HBIM models often results in massive datasets. Efficient storage, retrieval and processing of this data require reliable processes also in terms of data management security.
  • Data Quality: ensuring the accuracy and reliability of data is crucial for HBIM and CIM. Issues such as incomplete or inaccurate historical documentation, errors in data capture, and inconsistencies between different data sources can compromise the integrity of the model.
  • Data Accessibility: making data accessible to various stakeholders, including researchers, conservators, planners, and the public, is essential for maximizing impact of any intervention. However, the technical complexity of the data and the need for specialized software can hinder accessibility.

Strategies:

  • Standardized data formats: adopting standardized data formats, such as IFC (Industry Foundation Classes), facilitates data exchange and interoperability between different software platforms.
  • Cloud-based storage: cloud storage solutions provide scalable and secure storage for large datasets, enabling remote access and collaboration.
  • Data validation and quality control: implementing rigorous data validation and quality control procedures throughout the BIM process ensures data accuracy and reliability.
  • Dissemination: making the produced data openly available in various forms (video, conferences, events, etc.) via online platforms promotes transparency, collaboration, and public engagement.
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