Classification of concrete corrosion states by GPR with machine learning

The evaluation of rebar corrosion in reinforced concrete by using ground penetrating radar (GPR) and machine learning (ML) is a complex process. In this paper, a multi-variate method is presented. It uses full-volume data obtained from the amplitude domain in a regular GPR x-y scanning exercise, and the shape of the rebar's reflection to categorise different corrosion phases. This method allows multi-dimensional analysis with quantifiable GPR attributes. GPR data were extracted from the field and laboratory and then labelled according to the ground truths and reference specimens. A classic ML algorithm, logistic regression, was applied. The cross-validation accuracy (sensitivity and specificity) of individual corrosion phases was high (>99%), and the false alarm rate was low (<1%). This work shows that GPR as an evaluation tool can assess unseen data like doing blind tests. Nonetheless, continuous expansion of the training database is suggested to increase its diversity in the future.

Automated summary

This paper presents a method for evaluating rebar corrosion in reinforced concrete using ground penetrating radar (GPR) and machine learning (ML). The method categorises different corrosion phases based on analysis of GPR data and the shape of rebar reflection. The results demonstrate high accuracy and low false alarm rate.