Computer vision-based seismic assessment of RC simply supported bridges characterized by corroded circular piers

This study proposes a framework for the rapid assessment of seismic fragility and risk of reinforced concrete (RC) circular bridge piers affected by corrosion. The methodology integrates a novel computer vision (CV) algorithm to enhance visual inspections for corrosion level identification, combined with a probabilistic approach to seismic fragility analysis. The aim of the methodology is to quantify the impact of corrosion-induced deterioration on structural performance, expressed as an increment in terms of seismic risk. The first part of the framework consists of defining a custom convolutional neural network able to automatically predict the corrosion severity class starting from a metric-photographic survey. The proposed network incorporates attention mechanisms and color space transformations to ensure robust performance under varying image conditions. The output is used within a probabilistic-based structural modelling and analysis framework, which allows to derive seismic performance of the considered bridge pier typology. On the modelling side, a specific fiber-based approach was employed, in order to account for non-uniform cross-sectional corrosion and current deterioration condition. The results are returned in terms of seismic fragility and risk metrics for quantifying the reduction of seismic performance with respect to the initial conditions. The framework was tested on a real-life case-study exhibiting non-uniform cross-sectional base corrosion, and subsequently, additional scenarios considering full-section base corrosion at varying severity levels were investigated. The outcomes of this study demonstrate the potentialities of artificial intelligence in improving the current practices in the field of seismic assessment of aging RC infrastructures.