During crises such as natural disasters or emergencies, bridges serve as vital connection lines, guaranteeing the rapid movement of emergency services, supplies, and evacuees, making their structural integrity paramount to perform an effective response capable of ensuring public safety. A deep, fundamental, and comprehensive understanding of a bridge structural response, coupled with advanced predictive analyses techniques, is a necessary building block to make the early detection of potential failures possible, enhance its resilience, and support informed decision-making to maintain critical connectivity when it is needed most. If possible, the topic is gaining even more importance in the current background of aging infrastructures, increasing traffic volume and loads, and the escalating impacts of extreme weather events triggered by climate change. Given this context, sensitivity analysis is a well suited tool to provide quantitative insights on the structural behavior of a bridge. In the proposed work, generalized Polynomial Chaos Expansion (gPCE) is employed to derive a surrogate model serving as a building block for a digital shadow of a real structure located at the University of the Bundeswehr Munich, designed to provide researchers with a benchmark for extensive monitoring studies. A sensitivity analysis study is performed to evaluate the structural response under various mechanical and loading conditions, together with different damage scenarios including the settlement of the foundations, this latter being one of the most common cause of failure, triggered by foundations erosion, for constructions located in river or coastal areas. The obtained results will highlight how the damages reflect in the measured outputs, and their statistical relevance will be investigated in the context of structural reliability, considering the long-term goal of conceptualizing a model-driven decision making framework that empowers practitioners with data-driven insights for prioritizing maintenance and optimize sensors’ location, thus ensuring safety and improving resource allocation in infrastructure management.