Detection of Concrete Damage by X-ray CT and Related Non-destructive Elastic Wave Monitoring in Core Test

Abstract

As a detailed inspection of a concrete structure in service, core samples are usually drilled out and then mechanical properties are measured. In this study, damage estimation of freeze-thawed concrete from concrete-core samples is developed, applying X-ray CT with machine learning method. By the authors, the quantitative damage evaluation of concrete has been proposed by applying acoustic emission and damage mechanics in core concrete. In this study, detection of cracking damage distribution in concrete-core by decision tree method. Prior to the machine learning analysis, distribution of micro-cracks in a concrete-core sample is inspected by helical X-ray computer tomography (CT). Two feature values are set as explanatory variables: luminance value and pixel value after DoG (Difference of Gaussian) filter. The evaluation values converged from the sample size of 12,000 or more. This suggests that one cross section can be estimated with about 50% of the X-ray CT image data by using machine learning. The accuracy rate is 0.723 for the threshold processing and 0.793 for the decision tree method, which is higher for the decision tree method. These results suggest that the proposed method is useful for identifying cracks in concrete by X-ray CT image.