Brief
Heritage Building Information Modeling (HBIM) is a process for managing and conserving historical structures. Despite its potential and the European Directive 2014/24/EU, HBIM remains underutilized due to complex processes.
AI-based technology can streamline HBIM processes at reduced costs, making heritage conservation more feasible. Technologies like LIDAR, TLS, photogrammetry (including UAVs), and SLAM, initially tested on heritage sites, are now used in new construction. AI optimizes HBIM processes, enhancing preservation efforts.
This research explores how AI and related technologies can transform HBIM, making heritage conservation more efficient. It highlights AI’s role in HBIM, such as semi-automatic generation of virtual heritage experiences and improved efficiency in preserving structures. By examining cases like those by Fiorenza et al. (2025) and Intrigila et al. (2024), and highlighting the potential outlined by Cotella (2023), the research underscores HBIM’s crucial role in technology and cultural preservation.
First Inteview: Profa. Mylene Melo
Adjunct professor @ Federal University of Ceará (UFC); researcher @ GPREB
Inteview
Q: Can you introduce yourself and your research?
A: Professor Milene Melo is an adjunct professor at the Federal University of Ceará and a researcher in structural design, construction, laser scanning, and HBIM (Historic Building Information Modeling). Her latest research focuses on semi-automatic scan-to-BIM procedures and HBIM implementation for architectural documentation.
Q: Why is cultural heritage important, and why should it be modeled in HBIM?
A: Cultural heritage is essential as it represents the history and values of a community. Unlike modern buildings, heritage structures carry historical significance and community connections. HBIM is vital for documenting, preserving, and understanding these structures for future generations.
Q: How does working with HBIM differ from standard BIM?
A: Transitioning from BIM to HBIM requires a change in perspective. Modern constructions follow predefined models, while heritage buildings need tailored approaches to capture their unique characteristics. HBIM involves analyzing historical data, irregular geometries, and cultural significance, making it more complex than standard BIM.
Q: What are the challenges and advancements in HBIM?
A: One key challenge is adapting BIM tools to the irregularities of historical structures. Recent advancements include semi-automatic scan-to-BIM techniques and AI-assisted modeling, which improve efficiency in processing complex historical data.
Conclusions
Based on the discussion with Professor Milene Melo, several key conclusions align with the research brief on HBIM and AI:
1. The Need for HBIM in Heritage Conservation
The interview reinforces the idea that HBIM is crucial for documenting and preserving historic structures, as it helps maintain cultural identity. Professor Melo emphasizes that heritage buildings require a different approach than modern structures, aligning with the research brief’s argument that HBIM remains underutilized despite its potential.
2. AI’s Role in Streamlining HBIM Processes
Professor Melo highlights semi-automatic scan-to-BIM procedures, which align with the research brief’s assertion that AI can optimize HBIM workflows. By leveraging AI and automation, HBIM can become more efficient and cost-effective, addressing the complexity that currently limits its widespread use.
3. The Importance of Advanced Technologies
The interview touches on laser scanning and AI-driven documentation, supporting the research brief’s focus on LIDAR, TLS, photogrammetry, and SLAM as transformative tools in HBIM. These technologies bridge the gap between manual documentation and automated modeling, making heritage conservation more accessible and accurate.
4. The Shift from Standard BIM to HBIM
Professor Melo describes how HBIM requires a mindset shift, as historical buildings demand tailored methodologies. This aligns with the research’s discussion on adapting AI to heritage conservation and how new AI-assisted modeling approaches can enhance HBIM’s effectiveness.
Final Thought
The interview confirms that AI and HBIM are deeply interconnected, and integrating AI-driven automation can significantly reduce costs and improve the feasibility of heritage preservation. These findings reinforce the research brief’s argument that AI is a game-changer for HBIM, making historic preservation more efficient, scalable, and technologically advanced.
Second Inteview: Prof. Vincenzo Donato
BIM/CDE Manager; Adjunct professor @UNIFI (Italy)
Interview
Q: What is your experience with BIM and HBIM methodologies?
A: Vincenzo Donato is a construction engineer, BIM manager, and lecturer with expertise in architectural design, digital drawing, and BIM technologies, particularly for building heritage and transformation. He explains that BIM was originally created for new construction, so adapting it to HBIM for existing buildings requires methodological adjustments.
Q: What are the main challenges of applying BIM to historical buildings?
A: Unlike new constructions, historical buildings have unique constraints such as structural displacements, irregular alignments, and non-standardized elements (e.g., windows and walls not following uniform patterns). Three key challenges include:
- Structural irregularities that complicate digital modeling.
- Data collection and processing difficulties due to the lack of standardized methodologies for historical elements.
- Preservation concerns, ensuring interventions respect heritage value.
Q: How do Digital Twins help in HBIM?
A: Digital Twins replicate real-world structures, allowing detailed analysis and simulation. In HBIM, this concept is crucial for:
- Monitoring structural changes over time.
- Supporting restoration and conservation efforts.
- Enhancing decision-making through real-time data integration.
Q: How does transformability apply to HBIM?
A: Heritage buildings evolve over time due to renovations, restorations, and adaptive reuse. HBIM must incorporate historical transformations while allowing future modifications that balance preservation with usability.
Q: What role does AI play in HBIM processes?
A: AI can:
- Automate data acquisition from sources like LIDAR and photogrammetry.
- Enhance model accuracy by processing complex structures.
- Optimize workflows, reducing time and costs.
However, data consistency and reliability remain challenges.
Conclusions
- AI and Digital Twins improve HBIM processes
- Donato’s insights align with the research brief’s claim that AI can streamline HBIM workflows, particularly in automating complex tasks and enhancing heritage preservation efforts.
- Lack of standardization is a major limitation
- The research brief emphasizes HBIM’s underutilization due to complex processes and lack of standardization. Donato confirms this, highlighting the difficulty of adapting BIM standards to historical structures.
- Transformability is key to HBIM’s success
- The research brief discusses heritage conservation efficiency, which aligns with Donato’s emphasis on integrating past, present, and future transformations into HBIM models.
- AI facilitates but does not replace expert knowledge
- Donato underscores that AI can support, but not replace, human expertise in heritage conservation. This supports the research brief’s cautious approach to AI implementation in HBIM.
Final Thought
The interview reinforces the potential of AI in HBIM, while also acknowledging key challenges in adaptation, standardization, and data reliability. AI’s role in optimizing HBIM processes and ensuring long-term preservation is clear, but human expertise remains essential.
Third Inteview: Prof. Marinos Ioannides
Director of DHR Lab @CUT in Limassol; Project Coordinator – UNESCO Chair holder
Interview
Q: How did a computer scientist become involved in built heritage?
A: Dr. Ioannides started in computer science with an interest in how computers generate 3D models. His experience at Hewlett-Packard in CAD and CAM systems led him to reverse engineering, which was key to early 3D digitization. Over time, he applied these skills to cultural heritage when he returned to Cyprus, a country rich in historical sites.
Q: How has software evolved from early CAD to BIM and HBIM?
A: Over 40 years, CAD systems evolved into BIM, allowing for 3D modeling with embedded data. HBIM expands on BIM to document historic structures with more complexity.
Q: What is a “Memory Twin,” and how does it differ from a Digital Twin?
A: A Digital Twin is an exact virtual replica of a structure, primarily used for new constructions. A Memory Twin, in contrast, incorporates historical and cultural significance, capturing the accumulated memory of a heritage site rather than just its geometry. This approach ensures that monuments like the Parthenon are recognized not just as buildings but as symbols of human history and values.
Q: How do AI and data complexity impact HBIM and Memory Twins?
A: HBIM requires multidisciplinary collaboration, including historians, engineers, chemists, and even theologians for religious monuments. AI can assist in data acquisition, metadata management, and knowledge reconstruction, but also introduces ethical risks such as misinformation and data manipulation.
Q: What are the biggest challenges in creating Memory Twins?
A: The biggest challenge is maintaining identity and authenticity in documentation. High-quality data acquisition and standardization are essential to ensure accuracy, but lack of clear standards in HBIM makes this difficult.
Q: How will AI impact the future of HBIM?
A: AI will enhance heritage preservation by making artifacts and monuments more interactive, potentially allowing them to “talk” and share their histories. However, ethical concerns regarding data accuracy and misuse must be addressed.
Conclusions
- HBIM and Memory Twins align with AI’s role in optimizing heritage conservation
- The interview supports the idea that AI can improve HBIM workflows by automating data acquisition and enhancing historical accuracy.
- Dr. Ioannides introduces the concept of Memory Twins, which goes beyond standard HBIM by incorporating cultural memory and historical narratives.
- AI improves efficiency but poses risks
- AI simplifies complex HBIM processes, making them more accessible. However, Dr. Ioannides warns about potential misinformation, reinforcing the research brief’s emphasis on data integrity and standardization.
- Multidisciplinary collaboration is necessary
- Memory Twins require input from various fields, from engineering to history, reflecting the research brief’s focus on integrating different technologies like LIDAR, TLS, and photogrammetry.
- The need for standards in HBIM and AI applications
- Dr. Ioannides highlights the lack of clear standards in HBIM and cultural heritage documentation, echoing the research brief’s concern that standardization is necessary for widespread adoption.
- Future of AI in HBIM and cultural heritage
- AI-driven virtual heritage experiences could make history more interactive, which aligns with the brief’s idea that AI will revolutionize HBIM and preservation efforts.
Final Thought
The interview validates the research brief’s core arguments: AI is essential for HBIM’s future but must be implemented responsibly. The concept of Memory Twins represents a significant step forward, ensuring that heritage sites are preserved not just physically, but with their full historical and cultural context.
