Mapping Architectural Precedents through Visual Similarity Graphs: A SigLIP-Based Analysis of the Serpentine Pavilion Archive
NEXUS NETWORK JOURNAL : ARCHITECTURE AND MATHEMATICS, ss.1-23, 2026 (SCI-Expanded, AHCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s00004-026-00921-8
- Dergi Adı: NEXUS NETWORK JOURNAL : ARCHITECTURE AND MATHEMATICS
- Derginin Tarandığı İndeksler: Art, Design & Architecture Collection (ProQuest), Art, Design & Architecture Collection (ProQuest), Arts Premium Collection (ProQuest), Scopus, Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest), Aerospace Database, Science Citation Index Expanded (SCI-EXPANDED), Arts and Humanities Citation Index (AHCI), ABI/INFORM, Avery, Index Islamicus, zbMATH
- Sayfa Sayıları: ss.1-23
- Bursa Uludağ Üniversitesi Adresli: Evet
Özet
This study presents a computable methodology for examining architectural precedents through machine-derived visual similarity. Using the Serpentine Pavilion archive (2000–2025), the SigLIP vision encoder maps 210 images from 24 pavilion/year groups into a multidimensional latent space. Cosine similarity and graph-theoretical modeling generate two complementary networks: an image-level graph and a pavilion-centroid graph based on averaged, L2-normalized embeddings. The centroid graph provides the primary basis for pavilion-level interpretation by controlling for multiple photographs. Network metrics identify central, bridge, and peripheral cases. Comparison across the two analytical levels shows that network-role assignments are sensitive to image aggregation and the unit of analysis, while bridge roles exhibit greater cross-level consistency. The workflow is reproducible and inspectable without treating AI embeddings as neutral or bias-free representations of architectural morphology.