This study proposes a hybrid deep learning approach for dynamic attitude and position
prediction of the tunnel boring machine (TBM) with high accuracy. By utilizing the key …

This paper investigates the prediction of geological conditions ahead of tunnel boring
machines (TBM) using a hybrid deep learning approach. By integrating graph convolutional …

The accurate assessment and effective management of deep excavation risk have faced
longstanding challenges due to the highly complicated and uncertain construction process …

In shield tunneling projects, the precise prediction and control of the shield machine's
attitude is critical for quality assurance. Existing prediction methods utilize historical data to …

As a social information product, the privacy and usability of high-dimensional data are the
core issues in the field of privacy protection. Feature selection is a commonly used …

The production systems in process industries are confirmed to be tremendously energy-
consuming, and the trust in promoting their energy efficiency has become a concern, with its …

The traditional cutting trajectory of the axial robotic roadheader (spiral or reciprocating) is too
simple to meet the requirements of adaptive planning. We proposed a method for planning …

In tunnel excavation with boring machines, the tunnel face is supported to avoid collapse
and minimise settlement. This article proposes the use of reinforcement learning, specifically …

This paper established a novel ensemble deep learning method for the identification of the
geological condition encountered during tunnel boring machine (TBM) operation. By …

This research presents a hybrid approach to conduct the prediction and optimization of
tunnel boring machine (TBM) performance, such as cutter wear and cutter power, where the …