Durability is one of the main uncertainties of reinforced concrete structures. Moreover, one of the main causes of the reduction of this durability is the corrosion of reinforcement. In the literature, several authors provide mechanisms and models that allow predicting the behaviour of structures.
However, due to the very heterogeneity of the structures and the dispersion of the parameters taken into account in the models, it is necessary to calibrate or validate these models with direct measurements on the structures or, more precisely, the monitoring of durability-related parameters. Additionally, monitoring structures allow decisions to be taken at early stages and even before any pathology occurs.
Since 1995, a mockup has been monitored at El Cabril, Córdoba (Spain), where sensors for temperature, deformation, corrosion potential, resistivity, oxygen availability and corrosion rate have been installed. The data have been analyzed and filtered using machine learning algorithms.
The analysis of the data obtained during these years allows us to appreciate the evolution of the different parameters and their daily and seasonal variation. The sensors that were successfully installed are still active. To date, no corrosion problems have been reported in the structure.
