Mimarlıkta Yapay Sinir Ağlarının Kullanımı: Kat Planlarından Derinlik Farkındalığına Sahip ve Açıklanabilir Bina Cephelerinin Oluşturulması
BUILDINGS (BASEL), cilt.16, sa.21, ss.1-31, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 16 Sayı: 21
- Basım Tarihi: 2026
- Doi Numarası: 10.3390/buildings16214279
- Dergi Adı: BUILDINGS (BASEL)
- Derginin Tarandığı İndeksler: Applied Science & Technology Source, Natural Science Collection (ProQuest), Scopus, Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest), Science Citation Index Expanded (SCI-EXPANDED), Avery, Compendex, INSPEC, Directory of Open Access Journals
- Sayfa Sayıları: ss.1-31
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- İstanbul Gelişim Üniversitesi Adresli: Evet
Özet
This study aims to position the use of artificial intelligence in architectural design, not merely
as a tool for visual generation, but as an exploratory decision-support framework in which
selected design relationships can be represented in an interpretable and transparent manner.
To this end, the study developed ArchAI 2.0, a five-stage hierarchical system that models
semantic and spatial relationships between two-dimensional floor plans and generated
building facades. The objective was to conditionally generate exterior building image alternatives
using floor plan-derived structural information. Following a pair-integrity screening,
657 valid floor plan–exterior photograph project pairs sourced from the ArchDaily platform
were retained and partitioned at the project level into training (n = 459), validation (n = 99),
and independent test (n = 99) sets. Plan-derived geometric information was extracted
using a deterministic computer-vision parsing pipeline and subsequently processed into
structural tokens and a depth-aware parametric facade skeleton. The resulting skeleton
representation was used as conditioning guidance for a ControlNet integrated with Stable
Diffusion 1.5. A Plan-Edge ControlNet trained and evaluated under the same protocol was
used as the primary conditioned baseline, while an unconditional text-only Stable Diffusion
1.5 model provided a secondary reference. On the independent test set, the Skeleton and
Plan-Edge models showed comparable performance, with no statistically significant differences
in Chamfer Distance, Edge IoU, SSIM, or CLIP cosine similarity after Holm correction.
Relative to the text-only baseline, Skeleton conditioning produced a modest but statistically
significant improvement in Edge IoU (0.0611 vs. 0.0574; Holm-adjusted p = 0.031), whereas
the text-only model achieved significantly higher CLIP similarity (0.2749 vs. 0.2647; Holmadjusted
p < 0.001). FID was treated as a descriptive dataset-level measure because of the
limited test set size. A bounded human conditioning adherence evaluation further showed
that 58 of 98 evaluable cases reproduced the requested two-story organization (59.18%;
58.59% across the full 99-case test set), with a mean absolute story error of 0.4184 stories.
Opening-column correspondence was more limited: across 87 evaluable cases, bipartite
matching at the primary threshold of τ = 0.08 normalized facade width yielded a micro
F1-score of 0.2970. An independent second-rater assessment on a predefined 25-case subset
demonstrated high human annotation reliability, with 96.00% exact agreement and linearly
weighted Cohen’s κw = 0.9351 for story-count ratings and an inter-rater opening-column
matching F1-score of 0.7692. Rather than demonstrating uniform superiority or guaranteed
realization of user-specified constraints, the findings indicate that the proposed depthaware
representation provides an alternative structured conditioning mechanism with a performance comparable to direct plan-edge guidance, while human evaluation reveals a clear distinction between representational editability and actual conditioning adherence. The deterministic rule engine complements this generative pipeline as an interpretable rule-based decision-support