Research on Prediction of TBM Performance of Deep-Buried Tunnel Based on Machine Learning
Based on the relevant data in the construction process of the south of the Qinling tunnel of the Hanjiang-to-Weihe River Diversion Project, this article obtains the main influencing factors of the tunnel boring machine (TBM) performance of the deep-buried tunnel. According to the characteristics of...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/13/6599 |
| _version_ | 1797480905951936512 |
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| author | Tianhui Ma Yang Jin Zheng Liu Yadav Kedar Prasad |
| author_facet | Tianhui Ma Yang Jin Zheng Liu Yadav Kedar Prasad |
| author_sort | Tianhui Ma |
| collection | DOAJ |
| description | Based on the relevant data in the construction process of the south of the Qinling tunnel of the Hanjiang-to-Weihe River Diversion Project, this article obtains the main influencing factors of the tunnel boring machine (TBM) performance of the deep-buried tunnel. According to the characteristics of deep-buried tunnel excavation, the random forest algorithm is used to select the features of the factors affecting the TBM penetration rate, and the four factors with large influence weights including total thrust, revolutions per minute, uniaxial compressive strength and volumetric joint count, are used as TBM penetration rate prediction models input parameters, which can improve the prediction accuracy and convergence speed of the model, and enhance the engineering practicality of the prediction model. Three types of TBM penetration rate prediction models are established: multiple regression model (MR), back propagation neural network model (BPNN) and support vector regression model (SVR). The prediction accuracy of the three models is compared and analyzed. The BPNN prediction model exhibits better prediction performance and generalization ability than the multiple regression model and SVR model, which manifest higher prediction accuracy and prediction stability. |
| first_indexed | 2024-03-09T22:06:54Z |
| format | Article |
| id | doaj.art-c492a1ff50504cccb4dea4d67221b2d0 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:06:54Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c492a1ff50504cccb4dea4d67221b2d02023-11-23T19:39:31ZengMDPI AGApplied Sciences2076-34172022-06-011213659910.3390/app12136599Research on Prediction of TBM Performance of Deep-Buried Tunnel Based on Machine LearningTianhui Ma0Yang Jin1Zheng Liu2Yadav Kedar Prasad3State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, ChinaZheJiang Poly Real Estate Development Co., Ltd., Hangzhou 311215, ChinaState Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, ChinaState Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, ChinaBased on the relevant data in the construction process of the south of the Qinling tunnel of the Hanjiang-to-Weihe River Diversion Project, this article obtains the main influencing factors of the tunnel boring machine (TBM) performance of the deep-buried tunnel. According to the characteristics of deep-buried tunnel excavation, the random forest algorithm is used to select the features of the factors affecting the TBM penetration rate, and the four factors with large influence weights including total thrust, revolutions per minute, uniaxial compressive strength and volumetric joint count, are used as TBM penetration rate prediction models input parameters, which can improve the prediction accuracy and convergence speed of the model, and enhance the engineering practicality of the prediction model. Three types of TBM penetration rate prediction models are established: multiple regression model (MR), back propagation neural network model (BPNN) and support vector regression model (SVR). The prediction accuracy of the three models is compared and analyzed. The BPNN prediction model exhibits better prediction performance and generalization ability than the multiple regression model and SVR model, which manifest higher prediction accuracy and prediction stability.https://www.mdpi.com/2076-3417/12/13/6599tunnel boring machinepenetration rate predictionmachine learningdeep-buried tunnelfeature selection |
| spellingShingle | Tianhui Ma Yang Jin Zheng Liu Yadav Kedar Prasad Research on Prediction of TBM Performance of Deep-Buried Tunnel Based on Machine Learning Applied Sciences tunnel boring machine penetration rate prediction machine learning deep-buried tunnel feature selection |
| title | Research on Prediction of TBM Performance of Deep-Buried Tunnel Based on Machine Learning |
| title_full | Research on Prediction of TBM Performance of Deep-Buried Tunnel Based on Machine Learning |
| title_fullStr | Research on Prediction of TBM Performance of Deep-Buried Tunnel Based on Machine Learning |
| title_full_unstemmed | Research on Prediction of TBM Performance of Deep-Buried Tunnel Based on Machine Learning |
| title_short | Research on Prediction of TBM Performance of Deep-Buried Tunnel Based on Machine Learning |
| title_sort | research on prediction of tbm performance of deep buried tunnel based on machine learning |
| topic | tunnel boring machine penetration rate prediction machine learning deep-buried tunnel feature selection |
| url | https://www.mdpi.com/2076-3417/12/13/6599 |
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