Abstract
An accurate prediction of the penetration rate (PR) of a tunnel boring machine (TBM) is essential for the schedule and cost estimation of tunnel excavation. To better meet the needs of modern information construction, more computer technologies are being used to integrate the analysis and management of construction data. Herein, an online prediction platform based on a data mining algorithm using ensemble learning (extreme gradient boosting (XGBoost)) is developed for TBM performance prediction. The platform establishes the model and displays the prediction results, while storing a considerable amount of machine data, and providing services for TBMs of multiple projects simultaneously. In establishing the prediction model, users can change the algorithm parameters according to the engineering situation. The prediction capabilities of the platform are demonstrated by 200 field samples obtained from the Songhua River water conveyance project in Jilin. The mean absolute percentage error, coefficient of determination, root mean squared error, variance account for (VAF), and a20-index of the PR are 6.07%, 0.8651, 3.5862, 87.06%, and 0.925, respectively. The results show that the prediction model has a reliable prediction accuracy, which is higher than that of the gradient boosting decision tree, and these results can be displayed on the online platform. It provides effective help for TBM intelligent tunneling.
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source area: a Location of the water conveyance project from Songhua River (Liu et al. 2019). b Position of "Extreme gradient boosting" and study area
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Acknowledgements
The authors wish to thank the Beijing Vibroflotation Engineering Company Limited for sharing their experiences of data gathering in the field.
Funding
This research was supported by the National Natural Science Foundation of China (NSFC) [grant number 51739007], the National Natural Science Foundation of China (NSFC) [grant number 51991391], the National Science Fund for Excellent Young Scientists Fund [grant number 51922067], Taishan Scholars (Youth Expert) Program of Shandong Province [grant number tsqn201909003], and Science & Technology Program of Department of Transport of Shandong Province [2019B47_2].
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Wang, Y., Gao, X., Jiang, P. et al. An extreme gradient boosting technique to estimate TBM penetration rate and prediction platform. Bull Eng Geol Environ 81, 58 (2022). https://doi.org/10.1007/s10064-021-02527-5
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DOI: https://doi.org/10.1007/s10064-021-02527-5