An Intelligent Decision-Support Framework Using Linguistic Assessment and Probabilistic Neural Networks for Rapid Prioritization of Urban Building Stocks
Burak Oz, Memduh Karalar
article (Uluslararası, Hakemli) | Yazar(lar): Burak Oz, Memduh Karalar | Yıl: 2026 | Dergi/Kurum: STRUCTURAL CONTROL & HEALTH MONITORING | İndeks: WOS.SCI | DOI: 10.1155/stc/1127824 | Anahtar Kelimeler: building stocks, decision support systems, linguistic modeling, prioritization, seismic vulnerability — Özet: Rapid a
Rapid and reliable tools are essential for urban regeneration and disaster risk mitigation in earthquake-prone regions. This study proposes a hybrid decision-support framework that integrates linguistic expert assessments with a probabilistic neural network (PNN) for rapid prioritization of urban building stocks. Eleven seismic vulnerability parameters were evaluated using a five-level linguistic scale and applied to 194 buildings affected by the 2023 Kahramanmara & scedil; earthquake in T & uuml;rkiye. The model achieved 100% training accuracy and 95% testing accuracy, with misclassifications limited to adjacent damage classes. The proposed framework can support municipalities in rapidly identifying high-risk buildings during urban regeneration planning. PCA-based visualization confirmed clear class separation and consistent generalization. A post hoc sensitivity analysis further quantified decision-boundary uncertainty, showing that near-boundary transitions were concentrated in a small number of influential parameters. Class-level stability indices indicated higher robustness for severe-damage predictions. Overall, the proposed framework offers a practical and operationally efficient tool for first-level seismic vulnerability screening and evidence-based urban regeneration prioritization.