Aging infrastructure represents an increasing safety risk across Germany and Europe. Among the most common causes of damage to road bridges is chloride-induced reinforcement corrosion, which results from the use of de-icing salts. When chlorides penetrate the concrete through pores or cracks and reach a critical concentration at the reinforcement, pitting corrosion occurs. This process often remains undetected and can lead to a significant reduction of the reinforcement cross-section.

The early detection and prediction of such corrosion processes is therefore of growing importance. In the first funding phase, an adaptive service life prognosis approach for reinforced concrete structures was developed. The objective was the continuous updating of the prediction model by integrating monitoring data. Laboratory and sensor data were used to calibrate model parameters through Bayesian optimization, enabling a spatially resolved estimation of the service life under combined chloride exposure and sustained mechanical loading. [1]

The system shown in Figure 1 illustrates the principle of the adaptive service life prognosis. At the beginning of the service life, the material-dependent resistance to chloride migration is determined as a function of the load level and combined with locally measured strains obtained from distributed fiber optic sensors (DFOS) on the structure. From this, a load-dependent diffusion resistance is derived, which forms the basis for an initial, spatially resolved estimation of the service life. As the structure ages, this prognosis is continuously updated using corrosion wire sensors, which detect the advancement of the corrosion front through wire breakage, allowing a time- and space-resolved model adaptation.

Extension of the research approach

In the second funding phase, the concept will be transferred to real structures and extended to include practically relevant combined actions. In addition to sustained loading, cyclic loading, freeze–thaw cycles with de-icing salt, and intermittent moisture exposure will be considered. These combined actions lead to complex damage mechanisms in the concrete that significantly affect chloride ingress and transport.

The research consortium, led by Lowke (durability and maintenance), Leusmann (large-scale experiments on reinforced concrete elements), and Wessels (AI-based modelling), follows an integrated research approach combining laboratory investigations, sensor applications, and data-driven modelling. The project is structured into four work packages, covering the full range from material-level investigations to practical implementation on real bridge structures.