Prediction of TBM cutterhead speed and penetration rate for high-efficiency excavation of hard rock tunnel using CNN-LSTM model with construction big data

Li, Long; Liu, Zaobao; Zhou, Hongyuan; Zhang, Jing; Shen, Wanqing; Shao, Jian-Fu

doi:10.1007/s12517-022-09542-0

Cited by 20 publications

(3 citation statements)

References 43 publications

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“…The number of CNN convolution kernels is typically determined by the complexity of the objective. A batch normalization layer is added after the convolution layer to enhance the model performance 51 . Overall, CNNs consist of several layers such as the input layer, convolutional layers, nonlinear activation layer, pooling layers, dropout layer, batch normalization layer, one or more completely connected layers, and loss activation layer.…”

Section: Methodsmentioning

confidence: 99%

Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm

Karbasi,

Ali,

Bateni

et al. 2024

Sci Rep

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Electrical conductivity (EC) is widely recognized as one of the most essential water quality metrics for predicting salinity and mineralization. In the current research, the EC of two Australian rivers (Albert River and Barratta Creek) was forecasted for up to 10 days using a novel deep learning algorithm (Convolutional Neural Network combined with Long Short-Term Memory Model, CNN-LSTM). The Boruta-XGBoost feature selection method was used to determine the significant inputs (time series lagged data) to the model. To compare the performance of Boruta-XGB-CNN-LSTM models, three machine learning approaches—multi-layer perceptron neural network (MLP), K-nearest neighbour (KNN), and extreme gradient boosting (XGBoost) were used. Different statistical metrics, such as correlation coefficient (R), root mean square error (RMSE), and mean absolute percentage error, were used to assess the models' performance. From 10 years of data in both rivers, 7 years (2012–2018) were used as a training set, and 3 years (2019–2021) were used for testing the models. Application of the Boruta-XGB-CNN-LSTM model in forecasting one day ahead of EC showed that in both stations, Boruta-XGB-CNN-LSTM can forecast the EC parameter better than other machine learning models for the test dataset (R = 0.9429, RMSE = 45.6896, MAPE = 5.9749 for Albert River, and R = 0.9215, RMSE = 43.8315, MAPE = 7.6029 for Barratta Creek). Considering the better performance of the Boruta-XGB-CNN-LSTM model in both rivers, this model was used to forecast 3–10 days ahead of EC. The results showed that the Boruta-XGB-CNN-LSTM model is very capable of forecasting the EC for the next 10 days. The results showed that by increasing the forecasting horizon from 3 to 10 days, the performance of the Boruta-XGB-CNN-LSTM model slightly decreased. The results of this study show that the Boruta-XGB-CNN-LSTM model can be used as a good soft computing method for accurately predicting how the EC will change in rivers.

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Section: Methodsmentioning

confidence: 99%

Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm

Karbasi,

Ali,

Bateni

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The number of CNN convolution kernels is typically determined by the complexity of the objective. A batch normalization layer is added after the convolution layer to enhance the model performance (Li et al 2022). Overall, CNNs consist of several layers such as the input layer, convolutional layers, nonlinear activation layer, pooling layers, dropout layer, batch normalization layer, one or more completely connected layers, and loss activation layer.…”

Section: Cnn Layermentioning

confidence: 99%

Multistep Ahead Forecasting of Electrical Conductivity in Rivers by Using a Hybrid Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) Model Enhanced by Boruta-XGBoost Feature Selection Algorithm

Karbasi

Ali

Bateni

et al. 2023

Preprint

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Electrical conductivity (EC) is a key water quality metric for predicting the salinity and mineralization. In this study, the 10-day-ahead EC of two Australian rivers, Albert River and Barratta Creek, was forecasted using a novel deep learning algorithm, i.e., the convolutional neural network combined with long short-term memory (CNN-LSTM) model. The Boruta-extreme gradient boosting (XGBoost, XGB) feature selection method was used to determine the significant inputs (time series lagged data) for the model. The performance of the proposed Boruta-XGB-CNN-LSTM model was compared with those of three machine learning approaches: multi-layer perceptron neural network (MLP), K-nearest neighbor (KNN), and XGBoost, considering different statistical metrics such as the correlation coefficient (R), root mean square error (RMSE), and mean absolute percentage error (MAPE). Ten years of data for both rivers were extracted, with data for seven (2012–2018) and three years (2019–2021) used for training and testing the models, respectively. The Boruta-XGB-CNN-LSTM algorithm outperformed the other models in forecasting the 1-day-ahead EC in both stations over the test dataset (R = 0.9429, RMSE = 45.6896, and MAPE = 5.9749 for Albert River; and R = 0.9215, RMSE = 43.8315, and MAPE = 7.6029 for Barratta Creek). In addition, the Boruta-XGB-CNN-LSTM model could effectively forecast the EC for the next 3–10 days. Nevertheless, the performance of the Boruta-XGB-CNN-LSTM model slightly deteriorated as the forecasting horizon increased from 3 to 10 days. Overall, the Boruta-XGB-CNN-LSTM model is an effective soft computing method for accurately predicting the EC fluctuation in rivers.

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“…Fu and Zhang [42] considered spatio-temporal feature fusion in a LSTM model, and conducted a global sensitivity analysis for TBM performance prediction. Li et al [43] combined CNNs and LSTM to establish the prediction model and analyzed the TBM cutter head speed and penetration rate. However, it is difficult to deal with complex high-dimensional tunneling parameters using the maximum likelihood and classic artificial intelligence (AI) techniques, and the prediction accuracy needs to be improved.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Prediction of TBM cutterhead speed and penetration rate for high-efficiency excavation of hard rock tunnel using CNN-LSTM model with construction big data

Cited by 20 publications

References 43 publications

Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm

Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm

Multistep Ahead Forecasting of Electrical Conductivity in Rivers by Using a Hybrid Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) Model Enhanced by Boruta-XGBoost Feature Selection Algorithm

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

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