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This work presents machine learning-inspired data fusion approaches to improve the non-destructive testing of reinforced concrete. The principal effects that are used for data fusion are shown theoretically. Their effectiveness is tested in case studies carried out on largescale concrete specimens with built-in chloride-induced rebar corrosion. The dataset consists of half-cell potential mapping, Wenner resistivity, microwave moisture and ground penetrating radar measurements. Data fusion is based on the logistic Regression algorithm.
It learns an optimal linear decision boundary from multivariate labeled training data, to separate intact and defect areas. The training data are generated in an experiment that simulates the entire life cycle of chloride-exposed concrete building parts. The unique possibility to monitor the deterioration, and targeted corrosion initiation, allows data labeling.
The results exhibit an improved sensitivity of the data fusion with logistic regression compared to the best individual method half-cell potential.
Electrochemical potential mapping according to guideline B3 of DGZfP (German Society for Nondestructive Testing) is a recognized technique for the localization of corroding reinforcing steels. In reinforced concrete structures the measured potentials are not necessarily directly linked to the corrosion likelihood of the reinforcing steel. The measured values may be significantly affected, different from, e.g., stress measurement, by different influences on the potential formation at the phase boundary metal/concrete itself as well as the acquisition procedure. Due to the complexity of influencing factors there is a risk that the results are misinterpreted. Therefore, in a training concept firstly the theoretical basics of the test method should be imparted. Then, frequently occurring practical situations of various influencing factors will be made accessible to the participants by a model object specially designed for this purpose. The aim is to impart profound knowledge concerning the characteristics of potential mapping for detecting corrosion of reinforcing steel in order to apply this technique in practice as reliable and economical test method.