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Article analysis:

The article “Prediction of geological conditions for a tunnel boring machine using big operational data” provides an overview of a comprehensive procedure to predict geological conditions (i.e., rock mass types) for a tunneling boring machine (TBM) based on big operational data including four channels: cutterhead speed, cutterhead torque, thrust, and advance rate. The article is written in an organized manner with clear explanations of the methods used and results obtained from the experiments conducted. The authors have provided sufficient evidence to support their claims and have presented both sides of the argument fairly without any bias or partiality towards either side.

The article does not present any unsupported claims or missing points of consideration as all claims are backed up by evidence from experiments conducted by the authors themselves or other researchers in the field. Furthermore, all possible risks associated with using this method are noted in the article such as expensive costs associated with forward prospecting methods and hardware limitations when dealing with streaming data applications.

The only potential issue with this article is that it does not explore counterarguments or alternative methods which could be used to predict geological conditions for TBMs more accurately or efficiently than what has been proposed in this paper. However, this does not detract from the overall quality of the article as it provides a comprehensive overview of a method which can be used to accurately predict geological conditions for TBMs based on big operational data collected from TBMs in real-time.