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Prediction of tunnel boring machine operating parameters using various machine learning algorithms
Highlights TBM parameters were predicted using seven different machine learning methods. The 30 most influential TBM operating parameters were selected for each model. The calculation times and prediction accuracy of all methods were determined. The random forest model provides the best balance of calculation accuracy and time.
Abstract The operating parameters of a tunnel boring machine (TBM) reflect its geological conditions and working status and are accordingly critical data for ensuring safe and efficient tunnel construction. The accurate prediction of the advance rate, rotation speed, thrust, and torque indicators based on the operating parameters can guide the control and application of a TBM. In this study, we analyzed the relationships between the TBM operating parameters and daily collected TBM data. We used the smoothing method and outlier detection to process this data, and determined the stable values of four different TBM indicators in the ascending phase of a complete TBM operational segment. Then, we evaluated the application of five different statistical and ensemble machine learning methods (Bayesian ridge regression (BR), nearest neighbors regression, random forests, gradient tree boosting (GTB), and support vector machine) and two different deep neural networks (a convolutional neural network (CNN) and long short-term memory network (LSTM)) to establish prediction models. The GTB method provided the best prediction accuracy and the BR method provided the least calculation time of the five different statistical and ensemble machine learning methods evaluated. The LSTM method provided a higher prediction accuracy than the CNN model. The ensemble machine learning methods were found to be the most accurate for the relatively limited data sets used in this study, suggesting that sufficient data must be present before the advantages of deep neural networks can be truly realized. The successful application of statistical, ensemble, and deep neural network machine learning methods to predict TBM indicators in this study suggests the promise of machine learning in this application.
Prediction of tunnel boring machine operating parameters using various machine learning algorithms
Highlights TBM parameters were predicted using seven different machine learning methods. The 30 most influential TBM operating parameters were selected for each model. The calculation times and prediction accuracy of all methods were determined. The random forest model provides the best balance of calculation accuracy and time.
Abstract The operating parameters of a tunnel boring machine (TBM) reflect its geological conditions and working status and are accordingly critical data for ensuring safe and efficient tunnel construction. The accurate prediction of the advance rate, rotation speed, thrust, and torque indicators based on the operating parameters can guide the control and application of a TBM. In this study, we analyzed the relationships between the TBM operating parameters and daily collected TBM data. We used the smoothing method and outlier detection to process this data, and determined the stable values of four different TBM indicators in the ascending phase of a complete TBM operational segment. Then, we evaluated the application of five different statistical and ensemble machine learning methods (Bayesian ridge regression (BR), nearest neighbors regression, random forests, gradient tree boosting (GTB), and support vector machine) and two different deep neural networks (a convolutional neural network (CNN) and long short-term memory network (LSTM)) to establish prediction models. The GTB method provided the best prediction accuracy and the BR method provided the least calculation time of the five different statistical and ensemble machine learning methods evaluated. The LSTM method provided a higher prediction accuracy than the CNN model. The ensemble machine learning methods were found to be the most accurate for the relatively limited data sets used in this study, suggesting that sufficient data must be present before the advantages of deep neural networks can be truly realized. The successful application of statistical, ensemble, and deep neural network machine learning methods to predict TBM indicators in this study suggests the promise of machine learning in this application.
Prediction of tunnel boring machine operating parameters using various machine learning algorithms
Xu, Chen (author) / Liu, Xiaoli (author) / Wang, Enzhi (author) / Wang, Sijing (author)
2020-10-27
Article (Journal)
Electronic Resource
English
Mechanical Tunnel Boring Prediction and Machine Design
| NTIS | 1977
Mechanical Tunnel Boring Prediction and Machine Design
| NTIS | 1979
Tunnel Boring Machine Development
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