A Heritage DNA (HDNA) Framework for Quantitative Classification of Historic Facades
Buildings, cilt.16, sa.14, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 16 Sayı: 14
- Basım Tarihi: 2026
- Doi Numarası: 10.3390/buildings16142799
- Dergi Adı: Buildings
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Avery, Compendex, INSPEC, Directory of Open Access Journals, Natural Science Collection (ProQuest), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: descriptor-based classification, facade morphology, heritage DNA (HDNA), hierarchical clustering, historic facade classification, material composition, photogrammetric orthophotos, principal component analysis
- Çukurova Üniversitesi Adresli: Evet
Özet
Historic facade classification is generally based on qualitative assessments, limiting reproducibility and quantitative comparison. This study introduces the Heritage DNA (HDNA) framework, a descriptor-based approach integrating Morphological DNA (MDNA) and Material DNA (MaDNA) for the quantitative characterization of historic facades. Thirty-one facades from the Tepebağ Historic District (Adana, Türkiye) were documented using close-range photogrammetry and represented through a nine-dimensional HDNA vector. Principal Component Analysis (PCA) and hierarchical clustering were employed to identify facade patterns and typological relationships. The first two principal components explained 50.38% of the total variance, while the four-cluster solution produced a silhouette coefficient of 0.256. Hierarchical clustering identified four facade clusters, which were subsequently interpreted as four facade families characterized by distinct morphological and material compositions. The results demonstrate that facade identity can be represented through measurable descriptor combinations, providing a reproducible framework for facade classification. The proposed HDNA approach offers a potentially transferable descriptor-based methodology for comparative heritage studies and data-driven heritage management, subject to further validation across different historic contexts.