Real-Time Dynamic Earth-Pressure Regulation Model for Shield Tunneling by Integrating GRU Deep Learning Method With GA Optimization

This paper proposes an intelligent framework to predict and automatically regulate earth pressure using a deep learning technique during earth pressure balance shield tunneling. A prediction model was proposed by integrating a new cost function (relative mean square error) with a gated recurrent uni...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.64310-64323
Main Authors: Gao, Min-Yu, Zhang, Ning, Shen, Shui-Long, Zhou, Annan
Format: Article
Language:English
Subjects:
Automatic regulation
Cost function
Deep learning
Earth
Earth pressure
gated recurrent unit
genetic algorithm
Genetic algorithms
Logic gates
Machine learning
Mathematical models
Model accuracy
Neural networks
Noise reduction
Optimization
Parameters
Prediction models
Predictions
Predictive models
Real time
Regulation
shield tunneling
Subway construction
Subway tunnels
Tunnel construction
Tunneling
Tunneling shields
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Summary:This paper proposes an intelligent framework to predict and automatically regulate earth pressure using a deep learning technique during earth pressure balance shield tunneling. A prediction model was proposed by integrating a new cost function (relative mean square error) with a gated recurrent unit (GRU). The moving average smoothing method was also incorporated into the GRU model to reduce the noise of the dataset and improve the accuracy of the proposed model. A real-time dynamic regulation model for adjusting the operational parameters was proposed by integrating the GRU model into a genetic algorithm-based optimizer. By adjusting the operational parameters, the dynamic regulation model regulates the excessive predicted earth pressure within a suggested range. The proposed prediction and regulation models were applied to a metro tunnel construction in Luoyang, China. The results show that the proposed models provide good guidance for automated tunnel construction.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2984515