Bridges are critical components of transportation infrastructure, whose safety and functionality are essential for economic development, public safety, and emergency response. in the face of increasing structural failures due to extreme events, data-driven tools have become increasingly valuable for supporting risk assessment and maintenance prioritization, particularly in contexts with limited resources.

This study extends a previously developed framework based on Artificial Neural Networks (ANNs), originally designed to rapidly estimate structural degradation and risk levels of existing bridges in accordance with the new Italian Guidelines for risk classification. the enhanced framework introduces the capability to assess seismic risk, enabling the rapid estimation of the seismic vulnerability of existing bridges using only a limited set of easily accessible parameters.

This study builds upon a previously developed framework based on Artificial Neural Networks (ANNs) designed to rapidly estimate structural degradation and structural risk levels of existing bridges, in accordance with the new Italian Guidelines for Risk Classification. The framework is extended to manage seismic risk, allowing to develop an ANN able to estimate the seismic risk of existing bridges, using only a reduced set of easily available data. Several methodological improvements are introduced within the framework, including Exploratory Data Analyses (EDA), cross-sensitivity analyses, and evaluation of model stability to ensure robustness and unbiased generalization. The extended framework is then applied to a case study involving 95 bridges along the Italian state highway, predicting seismic risk for each structure. Finally, the results are visualized through GIS-based geospatial analyses to explore the spatial distribution of risk, identify clusters of high-risk bridges, and highlight territorial patterns that can guide strategic planning.

The findings provide a practical and scalable tool to support inspection prioritization and the definition of risk mitigation strategies within large-scale Bridge Management Systems (BMS), promoting more efficient and informed infrastructure management under seismic risk.