An automated framework to characterize crack patterns in existing RC bridges

The paper presents a framework to automatically identify crack patterns and the related features in existing reinforced concrete (RC) bridges. The challenge of this work is to define a tool for detecting the focused defect and highlighting the number and the orientation of cracks, allowing for correct interpretation and driving further evaluations on the residual life of the structure. The study is framed within the increasing interest in monitoring the structural health of existing bridges through automated tools, able to support engineers in the phase of visual assessment and interpretation of structural defects. When dealing with periodic inspection of large bridge portfolios, the support provided by automated tools can be fundamental for planning further strategies aimed at ensuring the structural safety and preventing future disasters. Given a stack of photos of a bridge structural element, an image stitching procedure is proposed to produce a near-complete image of the entire element. On the latter, a pipeline of deep-learning (DL) algorithms is employed to automatically detect and identify cracks (as a combination of object detection and segmentation algorithms). Finally, the proposed tool extracts cracks for counting and defines their orientation (i.e., vertical, horizontal, diagonal), in order to provide near-complete information about the crack pattern for the structural element. A full description of the methodology and the proposed algorithms is reported throughout the manuscript, showing the main pros and cons and assessing the effectiveness of the tool on a real-life case study.