Abstract: The authors present a tunneling attitude prediction method, based on the long short-term memory(LSTM) algorithm, to enhance tunneling safety and efficiency for large-diameter slurry balance shields. Shield tunneling parameters are analyzed using the Pearson correlation coefficient to identify the primary factors that affect the tunneling attitude. These factors are then used to establish a prediction dataset. An LSTM neural network model is developed for shield attitude prediction, with the Adam algorithm applied to optimize the LSTM′s performance. The key prediction parameters for shield attitude include horizontal deviation of the shield head, vertical deviation of the shield head, horizontal deviation of the shield tail, vertical deviation of the shield tail, pitch angle, and roll angle. The identified primary factors that influence the shield attitude are the shield operational parameters and geological conditions, with hydraulic cylinder pressure and the average compressive/shear strength of the strata showing the most significant effect. The optimized Adam-LSTM neutral network demonstrates a superior shield-attitude prediction performance, achieving a mean square error of <0.1 and an average prediction error of <5%, with more than 80% of the output results.

Key words: large-diameter shield tunnel, slurry balance shield, attitude prediction, long short-term memory algorithm