AI-Driven Seismic Fragility Assessment of RC Buildings: A Localized Comparison of RVS Methods in Bingol

Rapid assessment of existing reinforced concrete (RC) buildings is essential for effective seismic risk mitigation, particularly in highly active regions such as Bingol, Turkiye. This study evaluates the local performance of three Rapid Visual Screening (RVS) methods—RBTY-2019, FEMA-P154, and IITK-GSDMA—using verified post-earthquake damage data from the 2003 Bingol Earthquake (SERU-2003). To overcome the limitations of traditional RVS approaches, an Artificial Neural Network (ANN) model was developed and trained with the same dataset to predict building damage levels based on structural deficiency parameters. The ANN achieved a regression coefficient above 0.90 and 100% consistency in test predictions, demonstrating superior accuracy and adaptability to local construction characteristics. A Local Scaling Function (LSF) was also proposed to translate RBTY-2019 performance scores into empirical damage states, achieving 100% consistency with observed data. The findings highlight the reliability of locally trained AI models and the importance of adapting national screening regulations to regional seismic experiences. This integrated ANN–RVS framework provides a practical, data-driven tool for local authorities to prioritize urban building stock and strengthen disaster risk management strategies.