Machine learning-based evaluation of the damage caused by cracks on concrete structures.

Identification through image processing has been used in various fields, including product examination and more recently infrastructure inspection, such as identifying cracks in structures. The conventional method, in which a human inspector uses a crack gauge or determines the size through a visual evaluation, results in a subjective evaluation of the extent of concrete life. Currently, an operator inspects the concrete structure by visually examining the concrete surface. However, this method presents numerous problems, including when the inspector has to work in dangerously high places. Therefore, we used machine learning to extract the features of cracks in image processing. We identified that non-arbitrary features, such as color-related features, are also important. Because concrete appears monochromatic, it is difficult for humans to analyze this color-related feature. We compared these newly obtained machine learning features with the previously used arbitrary features and confirmed that the machine learning features were more accurate in terms of detection. We also compared the generation of discriminators based on these features with a fixed threshold for discrimination and the utilization of support vector machine (SVM) and other machine learning methods. In this paper, two issues are discussed: an analysis of the effectiveness of machine learning and SVM-based discriminant generation in detecting cracks, and the classification results based on crack width. Crack width classification using machine learning is useful when sufficient image resolution is not available. The final detection accuracy of the new method was 11.7% better than that of the method using arbitrary features; moreover, the false detection rate was also higher in the proposed method. We further attempted to classify these cracks in accordance with their damage levels. To evaluate the degree of damage, we focused on the difference in the width of the cracks and extracted different features in three classes on the basis of the different crack widths, and were able to classify these clacks. • We used machine learning to extract the features of cracks. We identified that non-arbitrary features, such as color-related features, are also important. Because concrete is apparently monochromatic, it is difficult for humans to analyze this color-related feature. We compared these newly obtained machine learning features with the previously used arbitrary features and confirmed that the machine learning features were more accurate in detection.. • We compared the generation of discriminators based on these features with a fixed threshold for discrimination and the utilization of support vector machine (SVM). The final detection accuracy of the new method was 11.7% better than that of the method using arbitrary features; moreover, the false-positive rate was also higher in the proposed method.. • We attempted to classify these cracks according to their damage levels. To evaluate the degree of damage, we focused on the difference in the width of the cracks and extracted different features in three classes based on the different crack widths. In this paper, the following two issues are discussed: the first is an analysis of the effectiveness of machine learning and SVM-based discriminant generation in detecting cracks, and the second is the classification results based on crack width. Finally, we evaluated the degree of damage by classifying the cracks based on crack width using machine learning.. [ABSTRACT FROM AUTHOR]