An Article Review on Vision-Based Defect Detection Technologies for Reinforced Concrete Bridges

Abstract

Ahmed Mohammed Abdelalim, Yasmin Shalaby, Gamal A Ebrahim and Mohamed Badawy

Bridges are crucial and the most vulnerable element in the infrastructure systems. A major challenge is to maintain bridge structures at a sufficient level of safety. Scheduled inspections in these structures are important to prevent any failure. The requirement of periodic inspection is urgently needed to maintain the bridges in safe operating condition for the public. Visual inspection is currently the main form for the flaw’s inspection. Nevertheless, it is suffering from time consuming and some limitations related to subjectivity and uncertainty. Due to the complexity of bridge structure, automatic defect detection is an urgent requirement for reinforced concrete bridges. In view of this, the creation and utilization of computer vision method has received considerable attention in several applications of civil engineering. Thus, this paper introduces a comprehensive study in computer vision-based defect detection related to concrete bridges. In this study, a detailed survey is undertaken to identify the research problems and the accomplishments to date in this field. Accordingly, 50 studies between peer-reviewed publications and conference papers Scopus found in are reviewed. Through the analysis, the current review divided the image technology into three groups based on: 1) image processing; 2) machine learning; and 3) quantifying the severity of defects by identifying their parameters. This article highlights the difference and the advantages and disadvantages of applying image processing techniques and machine learning. The paper identifies the types of defects detected by image technology in previous studies and their shortcomings in determining some parameters related to those defects. Finally, this research addresses issues related to the efficiency of detection and the main factors to be considered that may help further research in image-based approaches for defect detection effectively in concrete bridges.

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