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Article analysis:

This article provides a comprehensive overview of the use of various machine learning algorithms for predicting tunnel boring machine (TBM) operating parameters. The authors provide a detailed description of their methodology, including data preprocessing and selection of the most influential parameters for each model. They also present results from their experiments with five different statistical and ensemble machine learning methods, as well as two different deep neural networks, which suggest that the ensemble methods are most accurate for relatively limited datasets.

The article is generally reliable and trustworthy; however, there are some potential biases that should be noted. For example, the authors do not explore any counterarguments or alternative approaches to predicting TBM operating parameters; they only focus on their own approach using various machine learning algorithms. Additionally, they do not discuss any possible risks associated with using these algorithms or provide evidence for their claims about the accuracy of each method. Furthermore, they do not present both sides equally when discussing the advantages and disadvantages of each algorithm; instead, they focus mainly on highlighting the benefits without providing an equal amount of information about potential drawbacks or limitations. Finally, there is some promotional content in the article that could be seen as biased towards certain algorithms over others.