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Article analysis:

The article is written by experienced researchers in the field of civil engineering and computer science, which adds to its trustworthiness and reliability. The authors provide detailed descriptions of their proposed method, which makes it easy to understand the concept behind it. Furthermore, they provide evidence for their claims by testing their method on real-world sensor data from a bridge monitoring system, which shows that it can accurately detect structural damage with high accuracy.

However, there are some points of consideration that are missing from the article. For example, the authors do not discuss any potential risks associated with using their proposed method or any possible limitations of their approach. Additionally, they do not explore any counterarguments or alternative approaches to structural damage detection that could be used instead of their proposed method. Furthermore, there is no discussion about how this approach could be applied in practice or what kind of impact it could have on civil engineering projects such as bridge health monitoring and early warning systems for structural damage.

In conclusion, while the article provides an interesting approach to structural damage detection based on deep convolutional neural networks and continuous wavelet transform, there are some points of consideration that are missing from the article which should be addressed in order to make it more reliable and trustworthy.