Monthly advance rate estimation of hard rock tunnel boring machine based on rock mass classification and data augmentation
Accurately estimating the monthly advance rate of hard rock tunnel boring machine is of great significance for construction method selection, machine type determination, and project planning. However, current researches mainly focus on estimating the advance rate during construction, and few studies...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-08-01
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| Series: | Underground Space |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2467967425000388 |
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| author | Honggan Yu Yin Bo Quansheng Liu Xuhui Yang Shuzhan Xu Xing Huang |
| author_facet | Honggan Yu Yin Bo Quansheng Liu Xuhui Yang Shuzhan Xu Xing Huang |
| author_sort | Honggan Yu |
| collection | DOAJ |
| description | Accurately estimating the monthly advance rate of hard rock tunnel boring machine is of great significance for construction method selection, machine type determination, and project planning. However, current researches mainly focus on estimating the advance rate during construction, and few studies can estimate the advance rate from the entire tunnel scale. To overcome above shortcomings, a monthly advance rate estimation method based on rock mass classification and data augmentation is proposed. Firstly, 56 cases of tunnel boring machine are collected, and proportions of all rock mass grades in basic quality system of the entire tunnel are selected as main inputs of the model. Then, a two-stage data augmentation method based on synthetic minority over-sampling technique and modified auxiliary classifier generative adversarial network is developed. Finally, monthly advance rate estimation models based on machine learning and augmented datasets are established. The results show that the proposed method can accurately estimate the monthly advance rate and the data augmentation method can significantly augment the dataset. The average accuracy of the models is improved by 44.82% after data augmentation. Extreme gradient boosting model performs the best, with an accuracy of 90.31%. Therefore, the proposed method can accurately estimate the monthly advance rate of tunnel boring machine from the tunnel scale and has essential academic and engineering value. |
| format | Article |
| id | doaj-art-ccd4dc7a47164c839b5ecacc85a189cf |
| institution | Kabale University |
| issn | 2467-9674 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Underground Space |
| spelling | doaj-art-ccd4dc7a47164c839b5ecacc85a189cf2025-08-20T03:32:04ZengKeAi Communications Co., Ltd.Underground Space2467-96742025-08-012317519210.1016/j.undsp.2025.02.002Monthly advance rate estimation of hard rock tunnel boring machine based on rock mass classification and data augmentationHonggan Yu0Yin Bo1Quansheng Liu2Xuhui Yang3Shuzhan Xu4Xing Huang5Key Laboratory of Geotechnical and Structural Engineering Safety of Hubei Province, School of Civil Engineering, Wuhan University, Wuhan 430072, ChinaKey Laboratory of Geotechnical and Structural Engineering Safety of Hubei Province, School of Civil Engineering, Wuhan University, Wuhan 430072, China; Changjiang Institute of Survey, Planning, Design and Research, Wuhan 430014, ChinaKey Laboratory of Geotechnical and Structural Engineering Safety of Hubei Province, School of Civil Engineering, Wuhan University, Wuhan 430072, China; Corresponding author.China South-to-North Water Diversion Jianghan Water Network Construction and Development Co., Ltd., Wuhan 430040, ChinaKey Laboratory of Geotechnical and Structural Engineering Safety of Hubei Province, School of Civil Engineering, Wuhan University, Wuhan 430072, ChinaState Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, ChinaAccurately estimating the monthly advance rate of hard rock tunnel boring machine is of great significance for construction method selection, machine type determination, and project planning. However, current researches mainly focus on estimating the advance rate during construction, and few studies can estimate the advance rate from the entire tunnel scale. To overcome above shortcomings, a monthly advance rate estimation method based on rock mass classification and data augmentation is proposed. Firstly, 56 cases of tunnel boring machine are collected, and proportions of all rock mass grades in basic quality system of the entire tunnel are selected as main inputs of the model. Then, a two-stage data augmentation method based on synthetic minority over-sampling technique and modified auxiliary classifier generative adversarial network is developed. Finally, monthly advance rate estimation models based on machine learning and augmented datasets are established. The results show that the proposed method can accurately estimate the monthly advance rate and the data augmentation method can significantly augment the dataset. The average accuracy of the models is improved by 44.82% after data augmentation. Extreme gradient boosting model performs the best, with an accuracy of 90.31%. Therefore, the proposed method can accurately estimate the monthly advance rate of tunnel boring machine from the tunnel scale and has essential academic and engineering value.http://www.sciencedirect.com/science/article/pii/S2467967425000388Tunnel boring machineAdvance rate estimationRock mass classificationData augmentationMachine learning |
| spellingShingle | Honggan Yu Yin Bo Quansheng Liu Xuhui Yang Shuzhan Xu Xing Huang Monthly advance rate estimation of hard rock tunnel boring machine based on rock mass classification and data augmentation Underground Space Tunnel boring machine Advance rate estimation Rock mass classification Data augmentation Machine learning |
| title | Monthly advance rate estimation of hard rock tunnel boring machine based on rock mass classification and data augmentation |
| title_full | Monthly advance rate estimation of hard rock tunnel boring machine based on rock mass classification and data augmentation |
| title_fullStr | Monthly advance rate estimation of hard rock tunnel boring machine based on rock mass classification and data augmentation |
| title_full_unstemmed | Monthly advance rate estimation of hard rock tunnel boring machine based on rock mass classification and data augmentation |
| title_short | Monthly advance rate estimation of hard rock tunnel boring machine based on rock mass classification and data augmentation |
| title_sort | monthly advance rate estimation of hard rock tunnel boring machine based on rock mass classification and data augmentation |
| topic | Tunnel boring machine Advance rate estimation Rock mass classification Data augmentation Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2467967425000388 |
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