Real-time analysis and prediction of shield cutterhead torque using optimized gated recurrent unit neural network
An accurate prediction of earth pressure balance (EPB) shield moving performance is important to ensure the safety tunnel excavation. A hybrid model is developed based on the particle swarm optimization (PSO) and gated recurrent unit (GRU) neural network. PSO is utilized to assign the optimal hyperp...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-08-01
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| Series: | Journal of Rock Mechanics and Geotechnical Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674775522001330 |
| _version_ | 1811221373368926208 |
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| author | Song-Shun Lin Shui-Long Shen Annan Zhou |
| author_facet | Song-Shun Lin Shui-Long Shen Annan Zhou |
| author_sort | Song-Shun Lin |
| collection | DOAJ |
| description | An accurate prediction of earth pressure balance (EPB) shield moving performance is important to ensure the safety tunnel excavation. A hybrid model is developed based on the particle swarm optimization (PSO) and gated recurrent unit (GRU) neural network. PSO is utilized to assign the optimal hyperparameters of GRU neural network. There are mainly four steps: data collection and processing, hybrid model establishment, model performance evaluation and correlation analysis. The developed model provides an alternative to tackle with time-series data of tunnel project. Apart from that, a novel framework about model application is performed to provide guidelines in practice. A tunnel project is utilized to evaluate the performance of proposed hybrid model. Results indicate that geological and construction variables are significant to the model performance. Correlation analysis shows that construction variables (main thrust and foam liquid volume) display the highest correlation with the cutterhead torque (CHT). This work provides a feasible and applicable alternative way to estimate the performance of shield tunneling. |
| first_indexed | 2024-04-12T07:58:08Z |
| format | Article |
| id | doaj.art-d73d7d8a2de849299a14ae1c38fd5d2e |
| institution | Directory Open Access Journal |
| issn | 1674-7755 |
| language | English |
| last_indexed | 2024-04-12T07:58:08Z |
| publishDate | 2022-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Rock Mechanics and Geotechnical Engineering |
| spelling | doaj.art-d73d7d8a2de849299a14ae1c38fd5d2e2022-12-22T03:41:25ZengElsevierJournal of Rock Mechanics and Geotechnical Engineering1674-77552022-08-0114412321240Real-time analysis and prediction of shield cutterhead torque using optimized gated recurrent unit neural networkSong-Shun Lin0Shui-Long Shen1Annan Zhou2Department of Civil Engineering, School of Naval Architecture, Ocean, and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Department of Civil and Environmental Engineering, National University of Singapore, 117576, SingaporeKey Laboratory of Intelligent Manufacturing Technology, Department of Civil and Environmental Engineering, College of Engineering, Shantou University, Shantou, 515063, China; Corresponding author.Discipline of Civil and Infrastructure, School of Engineering, Royal Melbourne Institute of Technology (RMIT), Melbourne, Victoria 3001, AustraliaAn accurate prediction of earth pressure balance (EPB) shield moving performance is important to ensure the safety tunnel excavation. A hybrid model is developed based on the particle swarm optimization (PSO) and gated recurrent unit (GRU) neural network. PSO is utilized to assign the optimal hyperparameters of GRU neural network. There are mainly four steps: data collection and processing, hybrid model establishment, model performance evaluation and correlation analysis. The developed model provides an alternative to tackle with time-series data of tunnel project. Apart from that, a novel framework about model application is performed to provide guidelines in practice. A tunnel project is utilized to evaluate the performance of proposed hybrid model. Results indicate that geological and construction variables are significant to the model performance. Correlation analysis shows that construction variables (main thrust and foam liquid volume) display the highest correlation with the cutterhead torque (CHT). This work provides a feasible and applicable alternative way to estimate the performance of shield tunneling.http://www.sciencedirect.com/science/article/pii/S1674775522001330Earth pressure balance (EPB) shield tunnelingCutterhead torque (CHT) predictionParticle swarm optimization (PSO)Gated recurrent unit (GRU) neural network |
| spellingShingle | Song-Shun Lin Shui-Long Shen Annan Zhou Real-time analysis and prediction of shield cutterhead torque using optimized gated recurrent unit neural network Journal of Rock Mechanics and Geotechnical Engineering Earth pressure balance (EPB) shield tunneling Cutterhead torque (CHT) prediction Particle swarm optimization (PSO) Gated recurrent unit (GRU) neural network |
| title | Real-time analysis and prediction of shield cutterhead torque using optimized gated recurrent unit neural network |
| title_full | Real-time analysis and prediction of shield cutterhead torque using optimized gated recurrent unit neural network |
| title_fullStr | Real-time analysis and prediction of shield cutterhead torque using optimized gated recurrent unit neural network |
| title_full_unstemmed | Real-time analysis and prediction of shield cutterhead torque using optimized gated recurrent unit neural network |
| title_short | Real-time analysis and prediction of shield cutterhead torque using optimized gated recurrent unit neural network |
| title_sort | real time analysis and prediction of shield cutterhead torque using optimized gated recurrent unit neural network |
| topic | Earth pressure balance (EPB) shield tunneling Cutterhead torque (CHT) prediction Particle swarm optimization (PSO) Gated recurrent unit (GRU) neural network |
| url | http://www.sciencedirect.com/science/article/pii/S1674775522001330 |
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