This study presents a novel probabilistic methodology for the rapid seismic assessment of reinforced concrete bridge piers affected by end-corrosion. The approach integrates visual inspection supported by computer vision and stochastic structural analysis to capture the degradation effects caused by corrosion on seismic performance. Central to the procedure is an image-based classification system that leverages a customized convolutional neural network (CNN), designed with attention mechanisms and color space preprocessing, to automatically assess corrosion severity from photographs. The classified severity levels are then linked to a probabilistic model of material deterioration, enabling condition-informed adjustments to structural parameters such as steel and confined concrete strength and ductility. The assessment workflow includes geometric characterization, artificial intelligence-driven visual inspection, stochastic modeling of degraded materials, nonlinear analysis, seismic fragility evaluation and loss assessment. Application to a representative case study demonstrates significant impacts of corrosion on the seismic fragility, emphasizing the added value of integrating CV with probabilistic analysis for data-informed risk assessment of aging infrastructure. The results, in terms of expected annual losses, support the use of image-based methods as effective tools for prioritizing maintenance and optimizing resource allocation in bridge management systems.
Nettis et al. (2026) studied this question.