RADAR: A Framework for Risk Assessment and Degradation Analysis for Cultural Heritage Buildings Through CFD Modeling
Journal article, 2026
Cultural heritage buildings constitute an irreplaceable record of historical, social, and architectural identity, and their preservation is essential for cultural continuity and sustainable development. However, their conservation is inherently challenging due to material aging, complex construction techniques, limited documentation, and strict intervention constraints that restrict invasive monitoring or retrofitting solutions. Environmental degradation and microclimatic effects further accelerate deterioration, often in ways that are difficult to quantify or predict. This paper presents RADAR, a non-invasive, data-driven framework for assessing environmental and structural risk in cultural heritage buildings. The proposed approach integrates high-resolution geometric acquisition, computational fluid dynamics (CFD) modeling, and environmental monitoring to analyze airflow patterns, temperature distribution, and moisture-related decay mechanisms. By combining measured data with numerical simulations, RADAR enables the identification of high-risk zones and deterioration drivers without altering the building fabric. The framework is demonstrated through a real-world case study, illustrating its applicability as a decision-support tool for preventive conservation and heritage management.
3D visualization
digital twins
computational fluid dynamics (CFD)
environmental degradation
risk assessment
cultural heritage preservation
Author
Asimina Dimara
University of the Aegean
Mariya Pantusheva
Big Data for Smart Society Institute (GATE)
Nikolaos Alexios Stefanis
Panepistimio Dytikis Attikis
Orfeas Eleftheriou
University of Gothenburg
Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
Radostin Mitkov
Big Data for Smart Society Institute (GATE)
Vasilis Alexandros Naserentin
Aristotle University of Thessaloniki
Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
University of Gothenburg
Dessislava Petrova-Antonova
Big Data for Smart Society Institute (GATE)
Anders Logg
Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
University of Gothenburg
Christos Nikolaos Anagnostopoulos
University of the Aegean
Heritage
25719408 (eISSN)
Vol. 9 3 112Subject Categories (SSIF 2025)
Building Technologies
DOI
10.3390/heritage9030112