Digital twin assessment and maintenance technology for structural performance

Many large-scale reinforced concrete structures worldwide have been in service for several decades. Over time, their durability has progressively deteriorated due to environmental actions such as temperature variations, moisture ingress, carbonation, and chloride-induced corrosion. As a consequence, the structural reliability of these systems has gradually declined. Critical infrastructure assets, including bridges and tunnels, are particularly affected, as they are continuously exposed to cyclic traffic loading and aggressive environmental conditions, leading to ongoing performance degradation. Conventional maintenance and assessment approaches, however, primarily rely on visual inspections, the evaluation of limited experimental data, and empirical engineering experience. These traditional methodologies are insufficient to comprehensively capture the global structural behavior of such systems. Furthermore, they do not allow for reliable long-term performance prediction nor for the quantitative derivation of appropriate maintenance and strengthening strategies. Accordingly, there is an urgent need for a systematic and quantitatively robust assessment framework to support informed decision-making in the maintenance and rehabilitation of reinforced concrete infrastructure. In parallel, modern safety requirements in structural engineering are becoming increasingly stringent. Consequently, many existing structures no longer comply with current design standards and require comprehensive reassessment as well as suitable retrofitting strategies. Full-scale experimental validation of structural performance is generally infeasible due to economic and technological constraints. Against this background, objective analytical methods based on structural reliability theory and digital twin technology are of critical importance. Reliability-based approaches enable the explicit quantification of uncertainties in structural behavior by integrating experimental observations and structural health monitoring data. Digital twins, in turn, provide high-resolution, continuously updated representations of the current structural state and facilitate the prediction of long-term performance under varying loading and environmental conditions. The combined application of reliability theory and digital twin technology thus constitutes a powerful foundation for condition-based maintenance, risk-informed decision-making, and the sustainable management of reinforced concrete infrastructure systems.