MİMARLIKTA YAPAY ZEKÂ VE DİJİTAL MİRASIN KESİŞİMİ: ARAŞTIRMA EĞİLİMLERİNİN KONU MODELLEMESİYLE ANALİZİ (2016-2026)

Öz

Yapay zekâ (YZ) ile dijital mirasın mimarlık araştırmalarındaki kesişimi hızla büyümektedir; ancak bu alanın tematik yapısını ve zamansal gelişimini bütüncül biçimde ele alan kapsamlı bir hesaplamalı analiz bulunmamaktadır. Bu çalışma, söz konusu kesişimde öne çıkan araştırma eğilimlerini belirlemeyi ve bunların zaman içindeki değişimini izlemeyi amaçlamaktadır. Bu amaçla Scopus ve Web of Science veritabanlarında sistematik bir tarama yapılmış, elde edilen veriler Latent Dirichlet Allocation (LDA) konu modellemesiyle çözümlenmiştir. Başlangıçtaki 2.347 kayıttan yinelenenler çıkarılarak 2016-2026 dönemine ait 2.100 özgün makale incelenmiştir. 2026 yılı yalnızca kısmi verilerle (Nisan 2026 başına kadar) temsil edildiğinden tüm büyüme ve eğilim çıkarımlarının dışında tutulmuş, korpusta yalnızca betimsel bütünlük amacıyla yer almıştır. Metin ön işleme ve vektörleştirme sonrasında scikit-learn ile on konulu bir LDA modeli kurulmuştur. Sonuçlara göre en büyük küme %34,9 ile 3B nokta bulutu işlemedir; bunu %17,7 ile HBIM ve dijital dokümantasyon, %16,9 ile anlamsal segmentasyon ve %16,8 ile derin öğrenme sistemleri izlemektedir. Zamansal çözümleme, geleneksel fotogrametrik belgelemeden YZ destekli otomatik iş akışlarına doğru belirgin bir paradigma değişimi ortaya koymaktadır; anlamsal segmentasyon ve derin öğrenme, 2020-2022 ile 2023-2025 dönemleri arasında en yüksek büyümeyi göstermiştir. Bulgular önemli bir araştırma-uygulama boşluğuna da işaret etmektedir: teknik YZ yetkinlikleri hızla gelişirken bunların koruma politikalarına, katılımcı süreçlere ve Küresel Güney bağlamlarına aktarımı sınırlı kalmaktadır. Çalışma, bibliyometrik veri toplama ile hesaplamalı metin madenciliğini birleştiren, yinelenebilir bir yöntemsel çerçeve sunmaktadır.

Anahtar Kelimeler

• Dijital miras
• Yapay zeka
• Konu modellemesi
• Mimari miras
• LDA

MAPPING THE CONVERGENCE OF ARTIFICIAL INTELLIGENCE AND DIGITAL HERITAGE IN ARCHITECTURE: A COMPUTATIONAL TOPIC MODELING ANALYSIS OF EMERGING RESEARCH TRAJECTORIES (2016-2026)

Öz

Background/Aim: The convergence of artificial intelligence (AI) and digital heritage in architectural research has grown exponentially, yet no comprehensive computational analysis has mapped its thematic structure and evolution. This study aims to identify and temporally trace dominant research trajectories at the intersection of AI and digital architectural heritage. Methods: A dual-database systematic search (Scopus and Web of Science) was combined with Latent Dirichlet Allocation (LDA) topic modeling. From 2,347 initial records, 2,100 unique articles (2016-2026) were analyzed after deduplication. Text preprocessing and vectorization were followed by LDA modeling (k = 10 topics) using scikit-learn. Because 2026 is covered only by partial-year data (records indexed through early April 2026), it was retained for descriptive completeness but excluded from all growth and trend inferences. Results: The model identified ten distinct research topics. 3D point cloud processing formed the largest cluster (34.9%), followed by HBIM and digital documentation (17.7%), semantic segmentation (16.9%), and deep learning systems (16.8%). Temporal analysis reveals a paradigm shift from conventional photogrammetric documentation toward AI-driven automated workflows; semantic segmentation and deep learning exhibited the highest growth rates between the 2020-2022 and 2023-2025 periods. Conclusion: The findings identify a critical research-practice gap: while technical AI capabilities advance rapidly, their integration into heritage conservation policy, participatory processes, and Global South contexts remains underexplored. Ethical and epistemological dimensions of automated heritage classification are virtually absent from current discourse. This study contributes a replicable methodological framework combining bibliometric retrieval with computational text mining.

Anahtar Kelimeler

• Digital heritage
• Artificial intelligence
• Topic modeling
• Architectural heritage
• LDA

Kaynakça

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