Random forest-based evaluation technique for internal damage in reinforced concrete featuring multiple nondestructive testing results

The evaluation of internal damage in concrete structures is related to not only its rapid repair and reinforcement but also its safe usage, and is therefore essential for ensuring its longevity. This paper proposes a method to evaluate the extent of internal damage due to rebar corrosion using Random Forest, one of the supervised machine learning methods. In supervised machine learning, appropriate inputs should be identified to obtain accurate results. This research uses air permeability coefficient, electrical resistivity, ultrasonic velocity, and compressive strength which is obtained by nondestructive tests as inputs. In order to acquire a large number of data for the training, the rebar corrosion was promoted by electrolytic corrosion, and the data was frequently acquired. Then, the high accuracy of the model was confirmed by cross-validation. In addition, the proposed method was applied to the inspection of actual bridges, and it can detect internal damage that is otherwise invisible on the exterior surface.