Data-Driven Prioritization of Seismic Retrofit for Urban Buildings Considering Social Vulnerability
13 مهر 1404 - خواندن 3 دقیقه - 68 بازدید
- Urban areas around the world are increasingly exposed to seismic hazards, and the consequences of earthquakes can be devastating, both in terms of human casualties and economic losses. Effective risk mitigation requires more than general awareness; it demands targeted interventions that prioritize buildings and infrastructures most at risk. This study proposes a holistic, data-driven approach to guide seismic retrofitting decisions by combining spatial information with an assessment of social vulnerability. Unlike traditional methods that focus primarily on structural characteristics, this approach recognizes that the impact of an earthquake is also profoundly influenced by the social and economic conditions of the affected communities.
- The methodology begins with comprehensive data collection, encompassing building-specific information such as structural type, age, number of floors, occupancy type, and the current state of retrofit measures. Simultaneously, social data are gathered, including population density, economic status, access to emergency services, and other indicators of community vulnerability. By integrating these datasets, artificial intelligence techniques—including ranking algorithms, clustering models, and risk scoring systems—are applied to identify buildings and neighborhoods where retrofitting interventions can yield the greatest benefits.
- The outcome is a set of prioritized maps and actionable recommendations that allow urban planners, engineers, and policymakers to allocate limited resources effectively. For instance, older buildings in densely populated areas with high social vulnerability emerge as critical targets for retrofit programs. Beyond immediate safety improvements, this approach supports long-term urban resilience by providing a clear, evidence-based framework for planning, resource allocation, and policy-making.
- Preliminary applications of this method in urban case studies indicate that integrating social vulnerability with structural data not only enhances the accuracy of risk assessments but also improves the efficiency of retrofit interventions. By considering both human and physical factors, cities can better prepare for seismic events, minimizing casualties and economic disruption. This research contributes to a growing body of knowledge advocating for data-informed, socially conscious approaches to disaster risk management, emphasizing that effective earthquake preparedness is not solely a matter of engineering, but also of understanding and responding to the needs of the communities at risk.