Image Post-Processing Method for Quantification of Cracking in RC Precast Beams under Bending

Abstract

Image processing methods are increasingly used in civil engineering, especially in the maintenance of concrete structures. Current digital cameras and post-processing methods allow verifying qualitatively the state of conservation of wide areas of concrete in dams and bridges. When dealing with building refurbishments and rehabilitation, it is important to verify that existing structural elements fit the requirements of the standards; in the case of structures formed by traditional RC joists, cracking of the bottom-face provides information about the serviceability of these elements. This research proposed and put in practice through experimental tests an image post-processing method for quantification of cracking (five specimens were used and calibrated). Based on a sequence of shots and through a complex step-by-step post-processing, cracks were identified and measured to calibrate this method for real purposes. The method quantifies the crack opening width and spacing by analyzing the bottom-face of the joists through the shots. Measured values of crack spacing are very similar to those predicted by the standards, while the values of crack opening width differ more from theoretical ones due to the scattering of results. However, the proposed method has been proved as suitable and useful for fast inspections of RC elements under bending.

Flexural behavior of fire damaged self-compacting concrete beams strengthened with fiber reinforced polymer (FRP) wrapping

“…The crack geometry is defined by the parameters of circularity (CY), solidity (SY) and area (Albareda-Valls et al, 2018). The crack area (A) is defined by limiting selective saturation of black or white in the greyscale.…”

Image-Based Monitoring of Cracks: Effectiveness Analysis of an Open-Source Machine Learning-Assisted Procedure

“…The proposed method proved its efficiency in generating noise-free segmentation, avoiding the need for manual operations and further post-processing that can affect the quality of the crack detection [22,30]. The error in automated crack detection and measurements is generally caused by a range of different sources, including the illumination of the environment [31] and the presence on the investigated structural surface of other discontinuities (e.g., stains, scratching, etc.)…”

“…Besides crack identification, other critical aspects for the development of a complete monitoring solution must be considered (e.g., automated data acquisition and accurate and precise measurement over time). Although the implementation of more complete workflows (including both crack detection and crack analysis) has been proposed by various authors [6,[29][30][31][32] further investigation is needed to improve remote and automated capabilities for crack monitoring. In this context, modern monitoring schemes can benefit from recent innovation in transmission and communication networks, which offer the opportunity to develop more complete, reliable and intelligent solutions through the adoption of Internet of Things (IoT) [33].…”

Non-Destructive Testing Methods for In Situ Crack Measurements and Morphology Analysis with a Focus on a Novel Approach to the Use of the Acoustic Emission Method

“…An example solution in the case of two-dimensional measurements is the use of a laser rangefinder to determine the distance from which the photo is taken [17] or detecting the laser beam mark in the image [18]. In the literature, you can find equipment and many algorithms enabling such measurements, from simple ones that allow the analysis of individual cracks [19,20] to solutions that analyze entire elements based on one photo [21][22][23] or many photos integrated into one image [24]. However, ready-made solutions are scarce.…”