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Article analysis:

The article is a comprehensive review of the available Artificial Intelligence (AI) algorithms used to estimate the hardened performance of Recycled Aggregate Concrete (RAC). The article provides an overview of various AI algorithms such as artificial neural networks, genetic approaches, tree-based models, and other techniques. It also reviews studies that use AI algorithms for sensitivity analysis to determine the impact of input characteristics on the hardened properties of RAC.

The article is well written and provides a thorough overview of AI algorithms used in civil engineering applications. It is clear that the authors have conducted extensive research into this topic and have provided a comprehensive review of existing literature on this subject.

However, there are some potential biases in the article which should be noted. Firstly, it appears that most of the studies reviewed in this paper are from Europe or North America; thus, there may be a bias towards these regions when discussing global trends in construction and demolition waste production or recycled aggregate concrete production. Secondly, while the authors provide an overview of various AI algorithms used to predict RAC's mechanical properties, they do not discuss any potential risks associated with using these technologies or any ethical considerations related to their use in civil engineering applications. Finally, while the authors provide an overview of studies that use AI algorithms for sensitivity analysis to determine the impact of input characteristics on RAC's hardened properties, they do not discuss any potential counterarguments or unexplored perspectives related to this topic.

In conclusion, while this article provides a comprehensive review of existing literature on AI algorithms used to predict RAC's mechanical properties and sensitivity analysis related to this topic, there are some potential biases which should be noted when considering its trustworthiness and reliability.