Multi-step intelligent prediction of shield machine position attitude on the basis of BWO-CNN-LSTM-GRU
Abstract Realizing automatic control of shield machine tunneling attitude is a challenging problem. Realizing multi-step intelligent prediction for attitude and position is an important prerequisite for solving this problem in the tunneling process with complex and varied geological environments. In...
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crioppubl:10.1088/1361-6501/ad6176 2024-09-15T17:59:03+00:00 Multi-step intelligent prediction of shield machine position attitude on the basis of BWO-CNN-LSTM-GRU Liu, Xuanyu Zhang, Wenshuai Mengting, Jiang Wang, Yudong Ma, Lili The Basic Scientific Research Program of The Educational Department of Liaoning Province of China—General Program Scientific Research Fund Program of The Educational Department of Liaoning Province of China 2024 http://dx.doi.org/10.1088/1361-6501/ad6176 https://iopscience.iop.org/article/10.1088/1361-6501/ad6176 https://iopscience.iop.org/article/10.1088/1361-6501/ad6176/pdf unknown IOP Publishing https://iopscience.iop.org/page/copyright https://iopscience.iop.org/info/page/text-and-data-mining Measurement Science and Technology volume 35, issue 10, page 106205 ISSN 0957-0233 1361-6501 journal-article 2024 crioppubl https://doi.org/10.1088/1361-6501/ad6176 2024-07-29T04:15:33Z Abstract Realizing automatic control of shield machine tunneling attitude is a challenging problem. Realizing multi-step intelligent prediction for attitude and position is an important prerequisite for solving this problem in the tunneling process with complex and varied geological environments. In this paper, a multi-step intelligent predictive scheme based on beluga whale optimization-convolutional neural network-Long Short-term memory-gated recurrent unit (BWO-CNN-LSTM-GRU) is proposed for shield machine position attitude. First, Pearson correlation analysis is utilized to determine the input feature variables from the construction data and temporalize the input features. Subsequently, CNN-LSTM-GRU predictive models are established for the six positional parameters, separately. Among them, CNN performs feature extraction on the input variables, and LSTM-GRU realizes the predictions for the target positional parameters. In the end, the optimization of the convolutional layer dimension, the number of convolutional layers, iterations, the learning rate, the number of neurons in the LSTM layer and GRU layer of each position predictive model is performed on the basis of BWO, separately, and the best hyperparameters found are built into a BWO-CNN-LSTM-GRU position predictive model, which realizes the multi-step intelligent predictions for the shield machine’s position. The proposed approach is examined by utilizing the Beijing Metro Line 10. The results show that the predictive deviation of the position predictive model is within 3 mm, and the positional trajectory points obtained on the basis of the predicted values and the 3D coordinate system are highly coincident with the actual trajectory points. Therefore, the approach provides a more accurate predictive result for shield attitude and position and can provide a decision-making scheme for further realizing the coordinated autonomous control of shield machine. Article in Journal/Newspaper Beluga Beluga whale Beluga* IOP Publishing Measurement Science and Technology 35 10 106205 |
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Abstract Realizing automatic control of shield machine tunneling attitude is a challenging problem. Realizing multi-step intelligent prediction for attitude and position is an important prerequisite for solving this problem in the tunneling process with complex and varied geological environments. In this paper, a multi-step intelligent predictive scheme based on beluga whale optimization-convolutional neural network-Long Short-term memory-gated recurrent unit (BWO-CNN-LSTM-GRU) is proposed for shield machine position attitude. First, Pearson correlation analysis is utilized to determine the input feature variables from the construction data and temporalize the input features. Subsequently, CNN-LSTM-GRU predictive models are established for the six positional parameters, separately. Among them, CNN performs feature extraction on the input variables, and LSTM-GRU realizes the predictions for the target positional parameters. In the end, the optimization of the convolutional layer dimension, the number of convolutional layers, iterations, the learning rate, the number of neurons in the LSTM layer and GRU layer of each position predictive model is performed on the basis of BWO, separately, and the best hyperparameters found are built into a BWO-CNN-LSTM-GRU position predictive model, which realizes the multi-step intelligent predictions for the shield machine’s position. The proposed approach is examined by utilizing the Beijing Metro Line 10. The results show that the predictive deviation of the position predictive model is within 3 mm, and the positional trajectory points obtained on the basis of the predicted values and the 3D coordinate system are highly coincident with the actual trajectory points. Therefore, the approach provides a more accurate predictive result for shield attitude and position and can provide a decision-making scheme for further realizing the coordinated autonomous control of shield machine. |
author2 |
The Basic Scientific Research Program of The Educational Department of Liaoning Province of China—General Program Scientific Research Fund Program of The Educational Department of Liaoning Province of China |
format |
Article in Journal/Newspaper |
author |
Liu, Xuanyu Zhang, Wenshuai Mengting, Jiang Wang, Yudong Ma, Lili |
spellingShingle |
Liu, Xuanyu Zhang, Wenshuai Mengting, Jiang Wang, Yudong Ma, Lili Multi-step intelligent prediction of shield machine position attitude on the basis of BWO-CNN-LSTM-GRU |
author_facet |
Liu, Xuanyu Zhang, Wenshuai Mengting, Jiang Wang, Yudong Ma, Lili |
author_sort |
Liu, Xuanyu |
title |
Multi-step intelligent prediction of shield machine position attitude on the basis of BWO-CNN-LSTM-GRU |
title_short |
Multi-step intelligent prediction of shield machine position attitude on the basis of BWO-CNN-LSTM-GRU |
title_full |
Multi-step intelligent prediction of shield machine position attitude on the basis of BWO-CNN-LSTM-GRU |
title_fullStr |
Multi-step intelligent prediction of shield machine position attitude on the basis of BWO-CNN-LSTM-GRU |
title_full_unstemmed |
Multi-step intelligent prediction of shield machine position attitude on the basis of BWO-CNN-LSTM-GRU |
title_sort |
multi-step intelligent prediction of shield machine position attitude on the basis of bwo-cnn-lstm-gru |
publisher |
IOP Publishing |
publishDate |
2024 |
url |
http://dx.doi.org/10.1088/1361-6501/ad6176 https://iopscience.iop.org/article/10.1088/1361-6501/ad6176 https://iopscience.iop.org/article/10.1088/1361-6501/ad6176/pdf |
genre |
Beluga Beluga whale Beluga* |
genre_facet |
Beluga Beluga whale Beluga* |
op_source |
Measurement Science and Technology volume 35, issue 10, page 106205 ISSN 0957-0233 1361-6501 |
op_rights |
https://iopscience.iop.org/page/copyright https://iopscience.iop.org/info/page/text-and-data-mining |
op_doi |
https://doi.org/10.1088/1361-6501/ad6176 |
container_title |
Measurement Science and Technology |
container_volume |
35 |
container_issue |
10 |
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106205 |
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1810435997595336704 |