Predicting Slurry Pressure Balance with a Long Short‐Term Memory Recurrent Neural Network in Difficult Ground Condition

Abstract

The safety of tunneling with shield tunnel boring machines largely depends on the tunnel face pressure, which is currently decided by human operators empirically. Face pressure control is vulnerable to human misjudgment and human errors can cause severe consequences, especially in difficult ground conditions. From a practical perspective, it is therefore beneficial to have a model capable of predicting the tunnel face pressure given operation and the changing geology. In this paper, we propose such a model based on deep learning. More specifically, a long short-term memory (LSTM) recurrent neural network is employed for tunnel face pressure prediction. To correlate with PLC data, linear interpolation is employed to transform the borehole geological data into sequential geological data according to the shield machine position. The slurry pressure in the excavation chamber (SPE) is taken as the output in the case study of Nanning Metro, which is confronted with the clogging problem due to the mixed ground of mudstone and round gravel. The LSTM-based SPE prediction model achieved an overall MAPE and RMSE of 3.83% and 10.3 kPa, respectively, in mudstone rich ground conditions. Factors that influence the model, including different kinds and length of input data and comparison with the traditional machine learning-based model, are also discussed.

Prediction of Geological Parameters during Tunneling by Time Series Analysis on In Situ Data

“…Hsueh and Yang [ 33 ] used a LSTM network to analyze sequential sensor data and predict the car speed of the next time interval on a freeway. Wang et al [ 34 ] proposed a LSTM-based model that is capable of predicting the tunnel face pressure in a given operation and changing geology.…”

“…The output gate o t is responsible for taking the useful information in the current cell state C t as the output of the current neuron. The current candidate information and the three gates i t , f t , and o t of the LSTM neuron can be calculated using equation ( 1 ) [ 34 , 35 ] with similar forms: …”

“…To further quantitatively evaluate the performance of the LSTM method, statistical indicators including the coefficient of determination ( R 2 ), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used to evaluate the performance of the LSTM model. For a given predicted value , measured value y ={ y 1 , y 2 ,…, y n }, and the average value of , the above-mentioned evaluation indices are calculated by equations ( 5 )–( 7 ) [ 18 , 34 ]: …”

Clogging Risk Early Warning for Slurry Shield Tunneling in Mixed Mudstone–Gravel Ground: A Real-Time Self-Updating Machine Learning Approach

“…However, clogging has made the SPB shield tunneling unsustainable, due to the overflowing slurry which has caused environmental pollution. As a typical clayey soil, mudstone has a high potential to cause clogging [6,7]. Several projects in China have encountered clogging problems in the mudstone-rich area, for example, the Wuhan Sanyang cross-river road tunnel [8], the metro tunnel line 1 and 2 in Nanning city [9], Nanchang Metro Line 1 [10], and the Nanjing Yangze river tunnel [11].…”

“…It is filled with excavated material (usually soil and slurry) to support the tunnel face stability. The second is the working chamber, which uses an air cushion at the top to realize the pressure adjustment [7]. A submerged wall is used to separate these chambers.…”