Abstract:

 Grease is a major consumable material in the shield tunneling process, and the accurate prediction of grease consumption is significant in controlling the project cost. In this study, a time series prediction model of shield tail sealing grease consumption based on bidirectional long shortterm memory and autoregressive integrated moving average model(BiLSTMARIMA) is constructed to manage and predict the shield tail sealing grease consumption accurately. Moreover, a backpropagation(BP) neural network prediction model of shield tail sealing grease consumption is established, considering shield tunneling parameters and related engineering, geological, and hydrogeological parameters. The prediction model was applied to the Yellow river tunnel in Jinan, China, and the shield tail sealing grease consumption in the eastern section of the tunnel was trained and predicted. The results show that the root mean square error of the applied BiLSTMARIMA model is 13.47 and the mean absolute percentage error is only 3.13 %, indicating better practicability and reliability than the ARIMA time series and the BP neural network models.

Key words:  , superlarge diameter slurry balance shield, grease consumption, time series, neural network, prediction model