Monthly advance rate estimation of hard rock tunnel boring machine based on rock mass classification and data augmentation

Abstract

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.

Keywords

Tunnel boring machine / Advance rate estimation / Rock mass classification / Data augmentation / Machine learning

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

CRediT authorship contribution statement

Honggan Yu: Writing - review & editing, Writing - original draft, Visualization, Validation, Resources, Methodology, Data curation, Conceptualization. Yin Bo: Writing - review & editing, Visualization, Validation, Data curation. Quansheng Liu: Writing - review & editing, Validation, Supervision, Methodology. Xuhui Yang: Visualization, Validation, Resources. Shuzhan Xu: Writing - review & editing, Validation, Methodology. Xing Huang: Writing - review & editing, Software.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

This work was supported by China Postdoctoral Science Foundation (Grant Nos. 2023M742714 and 2023TQ0251), and National Natural Science Foundation of China (Grant No. 42177140). Thanks to China Railway Engineering Equipment Group Co., Ltd. and China Railway Construction Heavy Industry Co., Ltd. for supporting some TBM cases.

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