Aiming at the lack of accuracy and effectiveness of the current shield machine speed
prediction method, the study proposes to improve the residual network and combine this …

The performance of shield machine is sensitive to geological conditions and its operation
depends on driver experience. Based on D-ResNet network and improved inertia weight …

The tunnelling advance speed (TAS) of a super-large-diameter tunnel boring machine
(TBM) significantly affects project progress and safety. Effective prediction of the TAS of a …

This paper proposes a deep neural network (DNN)-based method for predicting ore
production by truck-haulage systems in open-pit mines. The proposed method utilizes two …

Accurately predicting support resistance in coal mine working faces is a pressing matter in
the field of intelligent mining research, which holds immense importance in enhancing the …

Geological layers excavated using tunnel boring machines are buried deeply and sampled
difficultly, and the geological behavior exhibits high diversity and complexity. Excavating in …

A new method using a deep neural network (DNN) model is proposed to predict the ore
production and crusher utilization of a truck haulage system in an underground mine. An …

During tunneling process, it is of critical importance to dynamically adjust operation
parameters of shield machine due to changes of geological conditions. Cutterhead torque is …

This study establishes a prediction model based on the back propagation (BP) neural
network for controlling the underground directional drilling path in a coal mine. The four …

This study presents a hybrid model based on the particle swarm optimization (PSO)
algorithm and a long short-term memory (LSTM) neural network. PSO can determine the …