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• W4224064038 abstract "SHM and NDE/NDT are developed to evaluate the amount of damage to a structure. Since human inspections are prone to misdiagnosis and/or miss-interpretations, the damage assessment and identification methods are shifting from human-based to computer-based. In this article, a computer vision method is used to detect and quantify the cracks on a concrete surface. After processing the crack images, cracks are modeled as graphs for feature extraction. To study the proposed method, concrete surface crack images from a reinforced concrete shell under quasi-linear load at each load step are used. Pearson correlation analysis is implemented to evaluate the relationship between graph measures at the north and south faces of the wall. All correlation values are above 50%, and eight out of ten graph measures demonstrated a correlation above 85%. The average correlation value between both sides of the wall reports 84% compatibility. High correlation values between the two sides of the wall attribute to the fidelity of graph features to the crack patterns on the two sides of the wall. Moreover, the monotonic increases in different graph measures and damage indexes indicate that the extracted features from the converted crack patterns to graph are meaningful information. Results of this study reveal that the proposed crack quantification method has the potential of translating crack patterns for further machine learning applications. This novel idea of using different graph measures introduces more features and can act as a base to study the fundamental and mathematical relation between crack patterns and graph theory." @default.
• W4224064038 created "2022-04-19" @default.
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• W4224064038 date "2022-04-18" @default.
• W4224064038 modified "2023-10-16" @default.
• W4224064038 title "A computer vision-based crack quantification of reinforced concrete shells using graph theory measures" @default.
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• W4224064038 doi "https://doi.org/10.1117/12.2612359" @default.
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