Evaluation of thermal cracks on fire exposed concrete structures using Ripplet transform

Crack detection is an important task to monitor the structural health of the infrastructure exposed to fire. The manual inspections are time consuming and the outcome depends on the knowledge of the inspector. In order to overcome the subjective nature of the outcome, a method for detecting thermal cracks using Ripplet transform is proposed. The major components of the proposed method are noise removal, image enhancement, crack detection and detection of crack geometric parameters. Discrete Ripplet Transform (DRT) is used to identify the cracks in concrete subjected to elevated temperature. The bilateral filter is applied to the Ripplet coefficients to remove the noises in the image. Min-max gray level enhancement scheme is used to enhance the contours of the image. The crack pixels and background pixels are detected in the Ripplet domain in order to identify the major and minor thermal cracks completely. The geometric parameters of detected cracks are quantified through length, width, perimeter and area. The novelty of the proposed method depends on the usage of Ripplet transform for thermal crack detection. The results of the proposed thermal crack detection are compared with the other transform domain based state-of-the-art methods. (C) 2020 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

Automated summary

The study proposed a method for thermal crack detection using Ripplet transform, including steps like noise removal, image enhancement, crack detection and detection of crack geometric parameters. The geometric parameters of the cracks were quantified through length, width, perimeter and area to identify the thermal cracks. The results were compared with other transform domain based methods.