AI-PRAESIDIUM — agentic AI for Performance-based Robustness AssESsment of Degraded InfrastrUcture across Multiple scales
ProjectA significant portion of transportation infrastructure consists of reinforced and prestressed concrete bridges that are approaching or exceeding their design service life. These structures are affected by multiple interacting degradation processes, including reinforcement corrosion, fatigue, creep and shrinkage, and chemical reactions such as alkali–silica reaction. Environmental exposure and climate change further accelerate these phenomena, increasing uncertainty in structural capacity and demand over time.
In this context, structural robustness—defined as the ability to withstand localized damage without disproportionate consequences—is a key requirement that remains insufficiently addressed in engineering practice, particularly when degradation and uncertainty are considered. This project aims to develop a probabilistic, multi-scale framework for assessing the robustness of concrete bridges under multiple degradation scenarios, explicitly accounting for time-dependent deterioration, uncertainty, and progressive failure mechanisms.
The approach is structured across three scales: element, bridge, and infrastructure network. At the element level, experimental investigations will support the calibration of probabilistic models for material degradation and residual capacity. At the bridge level, system reliability methods will be used to analyze component interactions, redundancy, and progressive collapse. At the network level, the impact of bridge failures on transportation systems will be assessed, considering interdependencies and effects on connectivity and service continuity.
The project’s main contribution lies in integrating these scales into a unified probabilistic framework to support risk-informed decision-making for inspection, maintenance, and retrofit. Expected outcomes include new methodologies, validated experimental data, and computational tools to enhance the safety, resilience, and sustainability of aging bridge infrastructure.
In this context, structural robustness—defined as the ability to withstand localized damage without disproportionate consequences—is a key requirement that remains insufficiently addressed in engineering practice, particularly when degradation and uncertainty are considered. This project aims to develop a probabilistic, multi-scale framework for assessing the robustness of concrete bridges under multiple degradation scenarios, explicitly accounting for time-dependent deterioration, uncertainty, and progressive failure mechanisms.
The approach is structured across three scales: element, bridge, and infrastructure network. At the element level, experimental investigations will support the calibration of probabilistic models for material degradation and residual capacity. At the bridge level, system reliability methods will be used to analyze component interactions, redundancy, and progressive collapse. At the network level, the impact of bridge failures on transportation systems will be assessed, considering interdependencies and effects on connectivity and service continuity.
The project’s main contribution lies in integrating these scales into a unified probabilistic framework to support risk-informed decision-making for inspection, maintenance, and retrofit. Expected outcomes include new methodologies, validated experimental data, and computational tools to enhance the safety, resilience, and sustainability of aging bridge infrastructure.