Extension usage examples:
Here's how our browser extension sees the article:
Article summary:
1. Tunnel boring machines (TBMs) are commonly used in tunnel construction due to their safety and environmental friendliness.
2. Geological hazards, such as rock bursts, tunnel convergence and faults, can cause delays or instrument damages during construction.
3. Machine learning (ML) and deep learning (DL) models can be used to predict geological conditions ahead of the excavation face in order to ensure safe and effective tunneling.
Article analysis:
The article is generally reliable and trustworthy, as it provides a comprehensive overview of the use of ML and DL algorithms for predicting rock mass class ahead of TBM excavation face. The article is well-researched, with references to relevant studies that support its claims. Additionally, the article provides an in-depth discussion on the various ML and DL algorithms that can be used for this purpose, as well as the hyperparameter optimization methods that can be employed to improve prediction performance.
However, there are some potential biases in the article that should be noted. For example, while the article does mention some potential risks associated with using ML and DL algorithms for predicting rock mass class ahead of TBM excavation face (e.g., geological hazards), it does not provide any detailed information on how these risks can be mitigated or avoided. Additionally, while the article does discuss various ML and DL algorithms that can be used for this purpose, it does not explore any potential counterarguments or alternative approaches that could also be employed for this purpose. Furthermore, while the article does provide an overview of hyperparameter optimization methods such as grid search and random search, it does not provide any information on how these methods could potentially affect prediction performance or accuracy. Finally, while the article does provide a comprehensive overview of ML and DL algorithms for predicting rock mass class ahead of TBM excavation face, it fails to mention any potential limitations or drawbacks associated with using these algorithms for this purpose.
Topics for further research:
Mitigating geological hazards associated with ML and DL algorithms
Alternative approaches for predicting rock mass class ahead of TBM excavation face
Impact of hyperparameter optimization on prediction performance
Limitations of ML and DL algorithms for predicting rock mass class
Advantages of using ML and DL algorithms for predicting rock mass class
Best practices for using ML and DL algorithms for predicting rock mass class