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  • An ANN to Predict Ground Condition ahead of Tunnel Face using . . .
    This paper presents an artificial neural network (ANN) model that predicts ground conditions ahead of a tunnel face by using shield tunnel boring machine (TBM) data obtained during the tunneling operation The primary advantage of the proposed technique is that, by using TBM data, no additional data acquisition device is required
  • An integrated parameter prediction framework for intelligent . . .
    In this study, an integrated parameter prediction framework is proposed based on ML for predicting the thrust (F), cutterhead torque (T) and net advance rate (NAR) by using the TBM operational data along with the pre-construction geological investigation information It provides a prediction-based control method that operators can rely on to
  • Data-Driven Prediction of Stability of Rock Tunnel Heading . . .
    In this paper, Artificial Neural Networks (ANN) have been utilized to predict the stability of a planar tunnel heading in rock mass based on the well-defined Hoek-Brown (HB) yield criterion The HB model was developed to capture the failure criterion of rock masses
  • (PDF) Application of Artificial Neural Network in Tunnel . . .
    This paper systematically reviews the application of ANNs from different aspects of tunnel engineering It reveals that the backpropagation algorithm (BPA) and Levenberg-Marquardt algorithm (LMA)
  • Data-driven multi-output prediction for TBM performance . . .
    Experimental results reveal that the att-GCN model can exhibit a powerful capability in simultaneously predicting two targets named penetration rate (y1) and energy consumption (y2), reaching the mean absolute percentage error (MAPE) value at 15 475% and 15 173%, respectively
  • Optimized ANN model for predicting rock mass quality ahead of . . .
    The purpose of this research is to introduce hybrid ANN technologies, including GA-ANN, PSO-ANN, and ICA-ANN, to predict the RQS (a rock mass quality index used in Japan) using the MWD data obtained from the new Nagasaki tunnel (east) of the West Kyushu line of the high-speed railway project in Japan
  • Resampling and ensemble techniques for improving ANN-based . . .
    In this research, we systematically evaluate and compare three resampling methods, random undersampling (RUS), random oversampling (ROS), and the synthetic minority oversampling technique for regression (SMOTER), and four ensemble techniques, randomised weights and biases, bagging, adaptive boosting (AdaBoost), and least-squares boosting





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