Big Data-Based Performance Analysis of Tunnel Boring Machine Tunneling Using Deep Learning

Abstract

In tunnel boring machine (TBM) construction, the advance rate is a crucial parameter that affects the TBM driving efficiency, project schedule, and construction cost. During the operation process, various types of indicators that are monitored in real-time can help to control the advance rate of TBM. Although some studies have already been carried out in advance rate prediction, the research is almost all based on statistical methods and shallow machine learning algorithms, thereby having difficulties in dealing with a very large amount of monitored data and in modeling the time-dependent characteristics of the parameters. To solve this problem, a deep learning model is proposed based on the CNN architecture, bidirectional Long Short-Term Memory module, and the attention mechanism, which is called the CNN-Bi-LSTM-Attention model. In the first step, the monitored data is processed, and the CNN architecture is adopted to extract features from the data sequence. Then the Bi-LSTM module is adopted to obtain the time-dependent indicators. The significant features can be addressed by the added attention mechanism. In the model training process, the rotation speed of the cutter head (N), thrust (F), torque (T), penetration rate (P), and chamber earth pressure (Soil_P) are adopted to predict the advance rate. The influence of the training periods on the model performance is also discussed. The result shows that not only the data amount, but also the data periods have an influence on the prediction. The long-term data may lead to a failure of the advance rate of TBM. The model evaluation result on the test data shows that the proposed model cannot predict the monitored data in the starting stage, which denotes that the working state of TBM in the starting stage is not stable. Especially when the TBM starts to work, the prediction error is big. The proposed model is also compared with several traditional machine methods, and the result shows the excellent performance of the proposed model.

Analysis and Enlightenment on the Relationships between Two Kinds of Cutter Life Evaluation Indexes and Installation Radius: A Case Study

“…TBMs (tunnel boring machines) have been widely used to construct long-distance tunnels due to their high efficiency, good stability control of surrounding rock, and low labor intensity [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Inevitably, the rock-machine interaction during TBM tunneling leads to the continuous wear of cutter rings.…”

Reinforcement learning for the face support pressure of tunnel boring machines

“…Some promising research domains for machine learning in tunnelling are the geological prognosis ahead of the face, the interpretation of monitoring results, automation and maintenance [32]. At present, however, research appears to be focussed on the following topics: prediction of TBM operational parameters [34,[39][40][41][42][43][44][45][46], penetration rate [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63], porewater pressure [64], ground settlement [65][66][67], disc cutter replacement [68][69][70], jamming risk [71,72] and geological classification [73][74][75][76]). Few authors estimated the face support pressure of TBMs with machine learning [35,52].…”

Four-Dimensional (4D) Millimeter Wave-Based Sensing and Its Potential Applications in Digital Construction: A Review

“…The construction industry is a multidisciplinary field where sensing technology plays a crucial role in enhancing the productivity and safety management of modern construction projects [1,2]. In recent years, advancements in smart construction, the Internet of Things (IoT) for construction, and digital twin in construction have increased the demands for sensing technologies [3].…”