A Novel Machine Learning Framework for Seismic Vulnerability Assessment of Urban Infrastructure

The assessment of seismic vulnerability in urban infrastructure presents a significant challenge in rapidly growing cities, as conventional evaluation methods frequently do not yield reliable and accurate insights for emergency response and planning. Machine learning algorithms demonstrate potential in structural analysis; however, a notable gap exists in methodologies capable of integrating diverse data sources for thorough vulnerability assessment. This paper presents a novel framework integrating Geographic Information Systems (GIS) data, structural analysis, and machine learning techniques to overcome existing limitations. The proposed methodology utilizes ArcGIS Pro for spatial data management, SAP2000 for conducting Nonlinear Static Pushover Analysis (NSPA) to assess structural response to seismic forces, and employs advanced machine learning algorithms to train the ANN model applicable to various urban structures, including residential buildings, bridges, and water tanks. The framework employs artificial neural networks for damage prediction and utilizes Monte Carlo simulation for uncertainty quantification, thereby providing a probabilistic method for vulnerability assessment. The proposed system processes structural and seismic data via an automated pipeline, facilitating rapid risk assessment and decision-making support. This approach addresses existing gaps in probabilistic seismic vulnerability assessment and provides a theoretical foundation for developing resilient urban infrastructure systems. The framework can be applied in urban planning, emergency response, and infrastructure prioritization, providing essential support for decision-makers in disaster management.