Prediction of Jacking Force in Vertical Tunneling Projects Based on Neuro-Genetic Models
The vertical tunneling method is an emerging technique to build sewage inlets or outlets in constructed horizontal tunnels. The jacking force used to drive the standpipes upward is an essential factor during the construction process. This study aims to predict the jacking forces during the vertical...
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MDPI AG
2021-01-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/9/1/71 |
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| author | Xin-Jiang Wei Xiao Wang Gang Wei Cheng-Wei Zhu Yu Shi |
| author_facet | Xin-Jiang Wei Xiao Wang Gang Wei Cheng-Wei Zhu Yu Shi |
| author_sort | Xin-Jiang Wei |
| collection | DOAJ |
| description | The vertical tunneling method is an emerging technique to build sewage inlets or outlets in constructed horizontal tunnels. The jacking force used to drive the standpipes upward is an essential factor during the construction process. This study aims to predict the jacking forces during the vertical tunneling construction process through two intelligence systems, namely, artificial neural networks (ANNs) and hybrid genetic algorithm optimized ANNs (GA-ANNs). In this paper, the Beihai hydraulic tunnel constructed by the vertical tunneling method in China is introduced, and the direct shear tests have been conducted. A database composed of 546 datasets with ten inputs and one output was prepared. The effective parameters are classified into three categories, including tunnel geometry factors, the geological factor, and jacking operation factors. These factors are considered as input parameters. The tunnel geometry factors include the jacking distance, the thickness of overlaying soil, and the height of overlaying water; the geological factor refers to the geological conditions; and the jacking operation factors consist of the dead weight of standpipes, effective overburden soil pressure, effective lateral soil pressure, average jacking speed, construction hours, and soil weakening measure. The output parameter, on the other hand, refers to the jacking force. Performance indices, including the coefficient of determination (R<sup>2</sup>), root mean square error (RMSE), and the absolute value of relative error (RE), are computed to compare the performance of the ANN models and the GA-ANN models. Comparison results show that the GA-ANN models perform better than the ANN model, especially on the RMSE values. Finally, parametric sensitivity analysis between the input parameters and output parameter is conducted, reaching the result that the height of overlaying water, the average jacking speed, and the geological condition are the most effective input parameters on the jacking force in this study. |
| first_indexed | 2024-03-09T05:05:04Z |
| format | Article |
| id | doaj.art-6b1f6a3a97c845069bdaa20d3fe67969 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-09T05:05:04Z |
| publishDate | 2021-01-01 |
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| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-6b1f6a3a97c845069bdaa20d3fe679692023-12-03T12:56:07ZengMDPI AGJournal of Marine Science and Engineering2077-13122021-01-01917110.3390/jmse9010071Prediction of Jacking Force in Vertical Tunneling Projects Based on Neuro-Genetic ModelsXin-Jiang Wei0Xiao Wang1Gang Wei2Cheng-Wei Zhu3Yu Shi4Department of Civil Engineering, Zhejiang University City College, Hangzhou 310015, ChinaCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, ChinaDepartment of Civil Engineering, Zhejiang University City College, Hangzhou 310015, ChinaInstitut für Geotechnik, Universität für Bodenkultur Wien, Feistmantelstraße 4, A-1180 Vienna, AustriaShanghai Foundation Engineering Group Co., Ltd., Shanghai 200002, ChinaThe vertical tunneling method is an emerging technique to build sewage inlets or outlets in constructed horizontal tunnels. The jacking force used to drive the standpipes upward is an essential factor during the construction process. This study aims to predict the jacking forces during the vertical tunneling construction process through two intelligence systems, namely, artificial neural networks (ANNs) and hybrid genetic algorithm optimized ANNs (GA-ANNs). In this paper, the Beihai hydraulic tunnel constructed by the vertical tunneling method in China is introduced, and the direct shear tests have been conducted. A database composed of 546 datasets with ten inputs and one output was prepared. The effective parameters are classified into three categories, including tunnel geometry factors, the geological factor, and jacking operation factors. These factors are considered as input parameters. The tunnel geometry factors include the jacking distance, the thickness of overlaying soil, and the height of overlaying water; the geological factor refers to the geological conditions; and the jacking operation factors consist of the dead weight of standpipes, effective overburden soil pressure, effective lateral soil pressure, average jacking speed, construction hours, and soil weakening measure. The output parameter, on the other hand, refers to the jacking force. Performance indices, including the coefficient of determination (R<sup>2</sup>), root mean square error (RMSE), and the absolute value of relative error (RE), are computed to compare the performance of the ANN models and the GA-ANN models. Comparison results show that the GA-ANN models perform better than the ANN model, especially on the RMSE values. Finally, parametric sensitivity analysis between the input parameters and output parameter is conducted, reaching the result that the height of overlaying water, the average jacking speed, and the geological condition are the most effective input parameters on the jacking force in this study.https://www.mdpi.com/2077-1312/9/1/71jacking forcevertical tunneling methodartificial neural networkgenetic algorithm |
| spellingShingle | Xin-Jiang Wei Xiao Wang Gang Wei Cheng-Wei Zhu Yu Shi Prediction of Jacking Force in Vertical Tunneling Projects Based on Neuro-Genetic Models Journal of Marine Science and Engineering jacking force vertical tunneling method artificial neural network genetic algorithm |
| title | Prediction of Jacking Force in Vertical Tunneling Projects Based on Neuro-Genetic Models |
| title_full | Prediction of Jacking Force in Vertical Tunneling Projects Based on Neuro-Genetic Models |
| title_fullStr | Prediction of Jacking Force in Vertical Tunneling Projects Based on Neuro-Genetic Models |
| title_full_unstemmed | Prediction of Jacking Force in Vertical Tunneling Projects Based on Neuro-Genetic Models |
| title_short | Prediction of Jacking Force in Vertical Tunneling Projects Based on Neuro-Genetic Models |
| title_sort | prediction of jacking force in vertical tunneling projects based on neuro genetic models |
| topic | jacking force vertical tunneling method artificial neural network genetic algorithm |
| url | https://www.mdpi.com/2077-1312/9/1/71 |
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