Resumen
With the aging of bridges, the efficiency of periodic inspections has become a problem. As issues with the continuing close visual inspection of bridges are surfacing, remote imaging systems are expected to become a new inspection method that replaces close visual inspection. The objective of the study is to develop a classification model of countermeasure categories using the results of past periodic inspections of bridges conducted by skilled inspectors. Focusing on concrete slabs, a model was constructed to classify the countermeasure categories based on the characteristics of the damage maps by random forest classification. As a result, it was possible to classify two classes of countermeasure categories with a macro-average precision rate of about 88%. It became clear that the degree of crack development and the number of cracks are the most important factors in the classification of judgment categories.