Application of Artificial Intelligence in Seismic Retrofit Prioritization of Urban Buildings Using Spatial Data and Social Vulnerability Analysis

Seismic risk management in Iranian cities faces several challenges, including widespread building deterioration, limited financial resources, and complex socio-economic conditions. One of the critical issues in this field is prioritizing seismic retrofit projects to maximize the reduction of human and economic losses under resource constraints. This short note introduces a novel approach based on artificial intelligence and spatial data. By integrating machine learning algorithms (such as neural networks and random forests) with Geographic Information Systems (GIS), the proposed method can process diverse datasets, including structural attributes, population density, income levels, and social vulnerability indices.

This data-driven integration enables more accurate and faster identification of high-risk buildings and generates a citywide retrofit prioritization map. Preliminary findings suggest that this approach can serve as an effective tool for urban managers and related organizations, enabling more efficient and targeted allocation of resources. Moreover, the combination of AI and spatial data contributes to evidence-based decision-making and facilitates the implementation of preventive strategies.

In conclusion, this note emphasizes that the adoption of advanced technologies in seismic risk management is not only necessary but also highly effective in enhancing urban resilience. This approach is particularly relevant for Iran, where large-scale deteriorating urban areas require systematic and data-driven retrofit prioritization.