Seismic Vulnerability of Reinforced Concrete Viaducts with AI and FEM Approaches

Over the past five years, there has been a significant transformation in the methodologies used to assess the health and structural integrity of bridges and viaducts. This shift has been marked by a rapid and exponential growth in the adoption of artificial intelligence technologies, alongside a much more detailed and localized evaluation of structural degradation and damage levels. A wide range of techniques and approaches are now available in the technical and scientific literature, particularly in relation to the development of digital twin models and the assessment of seismic vulnerability using advanced computational tools and analytical methods. Within this evolving context, the primary objective of the present study is to carry out a comparative analysis between the results obtained from state-of-the-art algorithms (specifically, fiber-based models enhanced and guided by artificial intelligence, which explicitly incorporate the current condition of damage and deterioration) and those derived from more conventional finite element modeling techniques. The comparison is conducted using a case study of a reinforced concrete road viaduct located in Italy. The structure comprises three spans, each with an approximate length of 44 meters, resulting in a total bridge length of about 135 meters. The analysis aims to highlight the potential advantages and limitations of next-generation, AI-supported modeling strategies when applied to real-world infrastructure assets.