Integrating Computer Vision-based corrosion severity grading into time-dependent seismic risk assessment of corroded RC bridge piers

Bridges are critical infrastructure assets whose seismic performance is increasingly compromised by aging and corrosion of reinforced concrete elements. Existing risk assessment methods are often computationally demanding and neglect degradation effects, underestimating seismic vulnerability. This study presents a novel AI-based framework that leverages computer vision to quantify corrosion severity from inspection images and integrates time-dependent corrosion evolution laws to forecast the Mean Annual Frequency of Exceedance over the remaining service life. A custom convolutional neural network with attention mechanisms translates observed damage into probabilistic degradation parameters for steel and concrete, incorporated into nonlinear seismic analyses and fragility assessment. By considering the expected evolution of the observed corrosion state, the framework identifies the time at which reliability thresholds are exceeded. The approach provides a fast, cost-effective tool for estimating time-dependent seismic performance and residual service life of bridges characterized by corroded piers, supporting life-cycle–oriented maintenance planning and risk-informed decision-making.