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Article analysis:

The article is generally reliable and trustworthy in its reporting of the proposed autonomous concrete crack detection method using deep fully convolutional neural networks (FCNs). The authors provide evidence for their claims by citing relevant research studies and experiments conducted to evaluate the performance of the proposed method. Furthermore, they discuss potential applications of the proposed method in structural health monitoring (SHM) systems and provide detailed descriptions of how it can be used to detect cracks in bridges and other infrastructure.

However, there are some areas where the article could be improved upon. For example, while the authors discuss various AI methods that have been used for SHM applications such as photogrammetry, infrared thermography, and laser imaging, they do not provide any details on how these methods compare to the proposed FCN approach or how they could be combined with it for improved performance. Additionally, while the authors mention that four datasets were analyzed in their study (CEMI, CEMIII, CEMI + GP and CEMIII + GP), they do not provide any details on what these datasets contain or how they were obtained. This lack of information makes it difficult to assess the reliability of their results or draw meaningful conclusions from them.

In addition, while the authors discuss potential applications for their proposed method in SHM systems for bridges and other infrastructure in developing countries, they do not address any potential risks associated with its use or possible ethical considerations that should be taken into account when deploying such systems. Finally, while the authors cite several relevant research studies throughout their paper to support their claims, some of these studies are quite old (e.g., from 2011) which may limit their relevance to current technologies and practices.