A defect-based framework for predictive life-cycle assessment of corroded RC bridges

The structural vulnerability assessment of reinforced concrete (RC) bridges affected by corrosion phenomena represents a crucial topic within civil engineering, particularly in the context of managing aging infrastructure. According to current regulations and guidelines, the degradation state of bridges is first evaluated through visual inspections and classified in terms of defect indices. However, although defect classification forms are available for the identification and categorization of damage, a direct and quantitative correlation between these qualitative descriptors and the reduction in structural capacity is still missing. Therefore, this study proposes a methodological framework to address this gap by integrating defect-based classification tools with mechanical degradation models. The aim is to quantify the impact of observed deterioration mechanisms on the structural performance of bridges through a simplified, yet mechanically-based, assessment procedure. Moreover, the proposed methodology allows for the development of predictive life-cycle models to inform both ordinary and extraordinary maintenance planning, thereby contributing to a more informed and efficient management of existing infrastructure assets. The effectiveness of the proposed approach is demonstrated for a case-study RC bridge. Several defect-index-based degradation scenarios are considered. A fiber-based section analysis is carried out to evaluate the load-carrying capacity of each structural component. This capacity is then locally and progressively reduced over time using deterioration models available in the literature. The significant uncertainty associated with the visual assessment of corrosion processes is also evaluated and considered for the structural safety evaluation over time. Results highlight the potential of the framework as a decision-support tool for proactive infrastructure asset management.