Concrete has been one of the most essential building materials for decades, valued for its durability, cost efficiency, and wide availability of required components. Over time, the number of concrete bridges has been drastically increasing, highlighting the need for timely structural health monitoring (SHM) to ensure their safety and long-term durability. Therefore, a narrative review was conducted to examine the use of Artificial Intelligence (AI)-integrated techniques in the SHM of concrete bridges for more effective monitoring. Moreover, this review also examined significant damage observed in various types of concrete bridges, with particular emphasis on concrete cracking, detection methods, and identification accuracy. Evidence points to the fact that the conventional SHM of concrete bridges relies on manual inspections that are time-consuming, error-prone, and require frequent checks, while AI-driven SHM methods have emerged as promising alternatives, especially through Machine Learning- and Deep Learning-based solutions. In addition, it was noticeable that integrating multimodal AI approaches improved the accuracy and reliability of concrete bridge assessments. Furthermore, this review is essential as it also addresses critical gaps in SHM approaches and suggests developing more accurate detection techniques, providing enhanced spatial resolution for monitoring concrete bridges.

In order to avoid possible failures and prevent damage in civil infrastructures, such as tunnels and bridges, inspection should be done on a regular basis. Cracks are one of the earliest indications of degradation, hence, their detection allows preventive measures to be taken to avoid further damage. In this paper, we demonstrate that Mask R-CNN can be used to localize cracks on concrete surfaces and obtain their corresponding masks to aid extract other properties that are useful for inspection. Such a tool can help mitigate the drawbacks of manual inspection by automating crack detection, lowering time consumption in executing this task, reducing costs and increasing the safety of the personnel. To train Mask R-CNN for crack detection we built a groundtruth database of masks on images from a subset of a standard crack dataset. Tests on the trained model achieved a precision value of 93.94 % and a recall of 77.5 %.

Current trends in wireless communications provide fast and location-independent access to multimedia services. Due to its high compression efficiency, H.264/AVC is expected to become the dominant underlying technology in the delivery of future wireless video applications. The error resilient mechanisms adopted by this standard alleviate the problem of spatio-temporal propagation of visual artifacts caused by transmission errors by dropping and concealing all macroblocks (MBs) contained within corrupted segments, including uncorrupted MBs. Concealing these uncorrupted MBs generally causes a reduction in quality of the reconstructed video sequence.