[Full Picture] Real-time prediction of shield moving trajectory during tunnelling using GRU deep neural network

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Real-time prediction of shield moving trajectory during tunnelling using GRU deep neural network | SpringerLink

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Article summary:

1. This article discusses the use of a GRU deep neural network to predict shield moving trajectories in real-time during tunnelling.

2. It reviews existing research on artificial intelligence techniques used in underground soil-structure interaction, such as support vector regression, deep learning, and group method of data handling.

3. The article also examines the potential of machine and deep learning algorithms for modelling the performance of EPB shield tunnelling.

Article analysis:

The article is generally reliable and trustworthy, as it provides an overview of existing research on artificial intelligence techniques used in underground soil-structure interaction and examines the potential of machine and deep learning algorithms for modelling the performance of EPB shield tunnelling. The authors provide evidence for their claims by citing relevant studies from reputable sources such as SpringerLink, Google Scholar, IEEE Access, Water Resources Management, Automation in Construction, Soil Dynamics and Earthquake Engineering, Journal of Hydrology, Chemical Engineering Science Intelligent Laboratory Systems, Artificial Intelligence Review, Bulletin of Engineering Geology and Environment, Reliability Engineering & System Safety and Science Total Environment.

The article does not appear to be biased or one-sided; rather it presents both sides equally by providing an overview of existing research on AI techniques used in underground soil-structure interaction as well as examining the potential of machine and deep learning algorithms for modelling the performance of EPB shield tunnelling. Furthermore, there is no promotional content or partiality present in the article; instead it provides a comprehensive review of existing research on AI techniques used in underground soil-structure interaction.

The article does not appear to be missing any points or evidence for its claims; rather it provides a comprehensive review of existing research on AI techniques used in underground soil-structure interaction as well as examining the potential of machine and deep learning algorithms for modelling the performance of EPB shield tunnelling. Furthermore, possible risks are noted throughout the article; however these risks are not explored in detail due to space constraints.

Topics for further research:

Underground soil-structure interaction AI techniques Machine learning algorithms for EPB shield tunnelling Deep learning algorithms for EPB shield tunnelling Risks associated with AI in underground soil-structure interaction Applications of AI in underground soil-structure interaction Impact of AI on underground soil-structure interaction