Assessing the Impact of Seismic Parameters on Urban Building Retrofitting Using Spatial Data Approaches

Abstract / Note:
Urban areas face increasing risks due to population growth, building aging, and seismic hazards. Effective retrofitting of vulnerable buildings is essential to reduce potential damages and casualties. This study presents a comprehensive approach for prioritizing retrofitting interventions by integrating spatial data with social vulnerability analysis. GIS-based information, including building locations, structural types, age, and population density, was combined with seismic parameters such as local hazard intensity and historical earthquake performance. Machine learning algorithms were applied to rank buildings according to both physical vulnerability and social importance.

The results indicate that population density, building age, and structural type significantly influence the prioritization of retrofitting projects. Older buildings in densely populated areas, particularly those with non-engineered structures, were identified as the highest priority for retrofitting. Comparison with previous studies and local seismic risk assessments confirmed the reliability of the ranking system. The integration of social vulnerability ensures that interventions not only focus on structural safety but also maximize societal benefits.

This methodology provides a practical decision-making framework for urban planners, civil engineers, and disaster management organizations. By systematically identifying the most vulnerable and socially critical buildings, resources can be allocated more efficiently, improving overall urban resilience. The approach is adaptable and can be applied to other cities with similar seismic and urbanization challenges.

Keywords:
Retrofitting, Spatial Data, Vulnerability Analysis, Urban Buildings, Seismicity